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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 03 Nov 2012 07:22:14 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/03/t1351941745jjvtiyihc98cmzo.htm/, Retrieved Sun, 03 Jul 2022 15:27:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185709, Retrieved Sun, 03 Jul 2022 15:27:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R       [Multiple Regression] [] [2012-11-03 11:22:14] [54fc54845b7758f1604e617c5518fca2] [Current]
- R P       [Multiple Regression] [Paper_4.3_Model1] [2012-12-21 17:45:06] [be6dd99035eed41c2358246baf91f928]
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	83	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	9	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	9.5	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	12	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	31	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	10	7	16	8	71	44
29	31	10	9	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	15	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	35	14	6	15	14.5	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	80	48
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	12.5	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	85	52
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	67	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
36	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	85	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	11	9	13	12	69	41
32	34	16	8	15	10	54	33
30	37	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
38	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	38	12	8	12	18	84	50
34	36	13	8	13	16	69	46
27	26	13	10	10	24	66	43
31	26	12	8	14	14	81	47
38	33	17	14	16	20	82	50
34	39	15	10	10	18	72	43
24	30	10	8	11	23	54	33
30	33	14	11	14	12	78	48
26	25	11	12	12	14	74	44
34	38	13	12	9	16	82	50
27	37	16	12	9	18	73	41
37	31	12	5	11	20	55	34
36	37	16	12	16	12	72	44
41	35	12	10	9	12	78	47
29	25	9	7	13	17	59	35
36	28	12	12	16	13	72	44
32	35	15	11	13	9	78	44
37	33	12	8	9	16	68	43
30	30	12	9	12	18	69	41
31	31	14	10	16	10	67	41
38	37	12	9	11	14	74	42
36	36	16	12	14	11	54	33
35	30	11	6	13	9	67	41
31	36	19	15	15	11	70	44
38	32	15	12	14	10	80	48
22	28	8	12	16	11	89	55
32	36	16	12	13	19	76	44
36	34	17	11	14	14	74	43
39	31	12	7	15	12	87	52
28	28	11	7	13	14	54	30
32	36	11	5	11	21	61	39
32	36	14	12	11	13	38	11
38	40	16	12	14	10	75	44
32	33	12	3	15	15	69	42
35	37	16	11	11	16	62	41
32	32	13	10	15	14	72	44
37	38	15	12	12	12	70	44
34	31	16	9	14	19	79	48
33	37	16	12	14	15	87	53
33	33	14	9	8	19	62	37
26	32	16	12	13	13	77	44
30	30	16	12	9	17	69	44
24	30	14	10	15	12	69	40
34	31	11	9	17	11	75	42
34	32	12	12	13	14	54	35
33	34	15	8	15	11	72	43
34	36	15	11	15	13	74	45
35	37	16	11	14	12	85	55
35	36	16	12	16	15	52	31
36	33	11	10	13	14	70	44
34	33	15	10	16	12	84	50
34	33	12	12	9	17	64	40
41	44	12	12	16	11	84	53
32	39	15	11	11	18	87	54
30	32	15	8	10	13	79	49
35	35	16	12	11	17	67	40
28	25	14	10	15	13	65	41
33	35	17	11	17	11	85	52
39	34	14	10	14	12	83	52
36	35	13	8	8	22	61	36
36	39	15	12	15	14	82	52
35	33	13	12	11	12	76	46
38	36	14	10	16	12	58	31
33	32	15	12	10	17	72	44
31	32	12	9	15	9	72	44
34	36	13	9	9	21	38	11
32	36	8	6	16	10	78	46
31	32	14	10	19	11	54	33
33	34	14	9	12	12	63	34
34	33	11	9	8	23	66	42
34	35	12	9	11	13	70	43
34	30	13	6	14	12	71	43
33	38	10	10	9	16	67	44
32	34	16	6	15	9	58	36
41	33	18	14	13	17	72	46
34	32	13	10	16	9	72	44
36	31	11	10	11	14	70	43
37	30	4	6	12	17	76	50
36	27	13	12	13	13	50	33
29	31	16	12	10	11	72	43
37	30	10	7	11	12	72	44
27	32	12	8	12	10	88	53
35	35	12	11	8	19	53	34
28	28	10	3	12	16	58	35
35	33	13	6	12	16	66	40
37	31	15	10	15	14	82	53
29	35	12	8	11	20	69	42
32	35	14	9	13	15	68	43
36	32	10	9	14	23	44	29
19	21	12	8	10	20	56	36
21	20	12	9	12	16	53	30
31	34	11	7	15	14	70	42
33	32	10	7	13	17	78	47
36	34	12	6	13	11	71	44
33	32	16	9	13	13	72	45
37	33	12	10	12	17	68	44
34	33	14	11	12	15	67	43
35	37	16	12	9	21	75	43
31	32	14	8	9	18	62	40
37	34	13	11	15	15	67	41
35	30	4	3	10	8	83	52
27	30	15	11	14	12	64	38
34	38	11	12	15	12	68	41
40	36	11	7	7	22	62	39
29	32	14	9	14	12	72	43




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 14 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185709&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185709&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185709&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.77956658273096 + 0.0469437389403787Connected[t] + 0.0419978310517143Separate[t] + 0.607405158110716Software[t] + 0.0986310792586457Happiness[t] -0.0393791266630167Depression[t] + 0.0158660592613708Belonging[t] -0.0194154193631069Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  3.77956658273096 +  0.0469437389403787Connected[t] +  0.0419978310517143Separate[t] +  0.607405158110716Software[t] +  0.0986310792586457Happiness[t] -0.0393791266630167Depression[t] +  0.0158660592613708Belonging[t] -0.0194154193631069Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185709&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  3.77956658273096 +  0.0469437389403787Connected[t] +  0.0419978310517143Separate[t] +  0.607405158110716Software[t] +  0.0986310792586457Happiness[t] -0.0393791266630167Depression[t] +  0.0158660592613708Belonging[t] -0.0194154193631069Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185709&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185709&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.77956658273096 + 0.0469437389403787Connected[t] + 0.0419978310517143Separate[t] + 0.607405158110716Software[t] + 0.0986310792586457Happiness[t] -0.0393791266630167Depression[t] + 0.0158660592613708Belonging[t] -0.0194154193631069Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.779566582730961.9176521.97090.0498090.024904
Connected0.04694373894037870.0347871.34950.1783840.089192
Separate0.04199783105171430.0356481.17810.2398470.119923
Software0.6074051581107160.05184511.715800
Happiness0.09863107925864570.0579631.70160.090040.04502
Depression-0.03937912666301670.042557-0.92530.355670.177835
Belonging0.01586605926137080.0378430.41930.6753760.337688
Belonging_Final-0.01941541936310690.056444-0.3440.7311470.365573

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3.77956658273096 & 1.917652 & 1.9709 & 0.049809 & 0.024904 \tabularnewline
Connected & 0.0469437389403787 & 0.034787 & 1.3495 & 0.178384 & 0.089192 \tabularnewline
Separate & 0.0419978310517143 & 0.035648 & 1.1781 & 0.239847 & 0.119923 \tabularnewline
Software & 0.607405158110716 & 0.051845 & 11.7158 & 0 & 0 \tabularnewline
Happiness & 0.0986310792586457 & 0.057963 & 1.7016 & 0.09004 & 0.04502 \tabularnewline
Depression & -0.0393791266630167 & 0.042557 & -0.9253 & 0.35567 & 0.177835 \tabularnewline
Belonging & 0.0158660592613708 & 0.037843 & 0.4193 & 0.675376 & 0.337688 \tabularnewline
Belonging_Final & -0.0194154193631069 & 0.056444 & -0.344 & 0.731147 & 0.365573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185709&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]3.77956658273096[/C][C]1.917652[/C][C]1.9709[/C][C]0.049809[/C][C]0.024904[/C][/ROW]
[ROW][C]Connected[/C][C]0.0469437389403787[/C][C]0.034787[/C][C]1.3495[/C][C]0.178384[/C][C]0.089192[/C][/ROW]
[ROW][C]Separate[/C][C]0.0419978310517143[/C][C]0.035648[/C][C]1.1781[/C][C]0.239847[/C][C]0.119923[/C][/ROW]
[ROW][C]Software[/C][C]0.607405158110716[/C][C]0.051845[/C][C]11.7158[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0986310792586457[/C][C]0.057963[/C][C]1.7016[/C][C]0.09004[/C][C]0.04502[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0393791266630167[/C][C]0.042557[/C][C]-0.9253[/C][C]0.35567[/C][C]0.177835[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0158660592613708[/C][C]0.037843[/C][C]0.4193[/C][C]0.675376[/C][C]0.337688[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0194154193631069[/C][C]0.056444[/C][C]-0.344[/C][C]0.731147[/C][C]0.365573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185709&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185709&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.779566582730961.9176521.97090.0498090.024904
Connected0.04694373894037870.0347871.34950.1783840.089192
Separate0.04199783105171430.0356481.17810.2398470.119923
Software0.6074051581107160.05184511.715800
Happiness0.09863107925864570.0579631.70160.090040.04502
Depression-0.03937912666301670.042557-0.92530.355670.177835
Belonging0.01586605926137080.0378430.41930.6753760.337688
Belonging_Final-0.01941541936310690.056444-0.3440.7311470.365573







Multiple Linear Regression - Regression Statistics
Multiple R0.653394258005352
R-squared0.426924056394364
Adjusted R-squared0.411254011061397
F-TEST (value)27.244596127377
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.88446241278469
Sum Squared Residuals909.106837810759

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.653394258005352 \tabularnewline
R-squared & 0.426924056394364 \tabularnewline
Adjusted R-squared & 0.411254011061397 \tabularnewline
F-TEST (value) & 27.244596127377 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.88446241278469 \tabularnewline
Sum Squared Residuals & 909.106837810759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185709&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.653394258005352[/C][/ROW]
[ROW][C]R-squared[/C][C]0.426924056394364[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.411254011061397[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.244596127377[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]256[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.88446241278469[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]909.106837810759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185709&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185709&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.653394258005352
R-squared0.426924056394364
Adjusted R-squared0.411254011061397
F-TEST (value)27.244596127377
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.88446241278469
Sum Squared Residuals909.106837810759







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.7169326674783-2.71693266747828
21615.30464529881620.69535470118377
31916.53422660597592.46577339402408
41511.24319907102563.75680092897442
51415.8896873324328-1.88968733243282
61314.3424239462714-1.34242394627141
71914.83819200878984.16180799121016
81516.6568417465609-1.65684174656086
91415.6143966049743-1.61439660497434
101513.83988226254521.16011773745481
111614.52441321979851.47558678020148
121615.96134519141310.0386548085868939
131614.96541689650651.03458310349349
141615.13059457657630.869405423423655
151717.7266051778577-0.726605177857681
161515.0073602272847-0.00736022728471113
171514.03331126894840.966688731051579
182016.03954666325573.96045333674426
191815.094186162422.90581383758003
201615.18613762215190.813862377848066
211615.00153683346760.998463166532416
221614.58386482617381.41613517382618
231916.22690098760652.77309901239348
241614.6382510537831.36174894621697
251715.84160213608931.15839786391069
261715.8344851325851.16551486741504
271614.52940119859751.47059880140251
281516.4169297769606-1.41692977696059
291615.29551745644320.70448254355681
301413.61739445627860.382605543721402
311515.367231470103-0.367231470103047
321212.1577948842104-0.157794884210414
331414.4670335229458-0.46703352294582
341615.54904748087970.450952519120262
351415.1519977706766-1.15199777067663
361012.4548067707195-2.45480677071949
371012.2908025947004-2.29080259470042
381415.442875350487-1.44287535048698
391614.11256764964031.88743235035968
401614.05030069097431.94969930902573
411614.35991566120121.64008433879884
421415.4129095162212-1.41290951622121
432017.49755924374662.50244075625335
441413.73799971061440.262000289385567
451414.1390257843911-0.139025784391113
461115.1505593881384-4.15055938813839
471416.4190959972096-2.41909599720956
481514.7812339122260.218766087773982
491615.19159772331030.808402276689707
501415.361515363781-1.36151536378102
511616.8537398386884-0.853739838688409
521413.7316892738080.268310726191954
531214.6138653598005-2.61386535980045
541615.59068990343190.409310096568121
55910.7137318072366-1.7137318072366
561411.83260460506342.16739539493663
571615.69372369001340.306276309986645
581615.23035557126270.769644428737286
591514.82300455854460.17699544145538
601613.79322244935052.20677755064948
611210.83672794920741.1632720507926
621615.43786401774420.562135982255831
631616.2989869704333-0.2989869704333
641414.4388877335844-0.438887733584355
651615.15004427002750.849955729972461
661715.74385384140771.25614615859234
671816.09034650151841.90965349848158
681814.07649049189673.92350950810328
691215.8084608807317-3.80846088073166
701615.47284798326780.527152016732213
711013.0122458267498-3.01224582674976
721414.7156655383903-0.7156655383903
731816.83798954427311.16201045572691
741817.19282818589550.807171814104507
751615.00946868929770.990531310702269
761713.09025249257753.90974750742254
771616.4072325443378-0.407232544337833
781614.21228172267391.78771827732615
791314.9614853283133-1.9614853283133
801615.08313851077660.916861489223406
811615.53609896948560.463901030514354
821615.64322947701630.356770522983708
831515.6201822872087-0.620182287208743
841514.68614370756470.313856292435276
851613.88276262753512.11723737246488
861413.95178861200210.0482113879978756
871615.23309418629930.766905813700725
881614.6813029945341.31869700546596
891514.10333817370510.896661826294862
901213.6433035668958-1.64330356689577
911716.83212464144880.167875358551165
921615.72948523077620.2705147692238
931514.90579233527870.0942076647212599
941314.8364568188003-1.83645681880029
951614.69803280606341.3019671939366
961615.69607804669080.303921953309245
971613.48328467925572.51671532074433
981615.83004170145760.169958298542448
991414.2661258632027-0.26612586320274
1001617.1940865071683-1.19408650716827
1011614.50590112878861.49409887121141
1022017.57909928367592.42090071632414
1031514.02264601404090.977353985959138
1041614.83837625185191.16162374814812
1051314.7124447739459-1.71244477394589
1061715.76043731524731.23956268475275
1071615.77206452484710.227935475152911
1081614.23260426438521.76739573561482
1091212.0024802659182-0.0024802659181661
1101615.17183854996160.828161450038385
1111615.93848803340390.0615119665961118
1121714.86253282826022.13746717173979
1131314.5286402413612-1.52864024136123
1141214.614568460888-2.614568460888
1151816.27645244436051.72354755563947
1161415.9504759627416-1.95047596274164
1171412.99246219519511.0075378048049
1181314.7858242392293-1.78582423922932
1191615.51445795588390.485542044116056
1201314.3730308681848-1.37303086818476
1211615.40726953321840.592730466781632
1221315.9001483950515-2.90014839505154
1231617.0738036864139-1.07380368641385
1241515.9985652529538-0.998565252953809
1251616.9217558949015-0.921755894901496
1261514.75014717881310.249852821186913
1271715.76721026047391.23278973952609
1281514.00423199516910.995768004830927
1291214.7455979666711-2.74559796667113
1301613.90182246601292.09817753398714
1311013.490018922728-3.49001892272798
1321613.48472934881742.51527065118258
1331214.1318955712898-2.13189557128976
1341415.6891601643807-1.68916016438072
1351515.1349525324793-0.13495253247934
1361311.87987029892291.12012970107706
1371514.54067598928640.459324010713607
1381113.3639577865016-2.3639577865016
1391212.9948717800782-0.99487178007819
1401113.2797734052425-2.27977340524254
1411612.87066703284813.12933296715189
1421513.5777970081451.42220299185501
1431717.0126296693111-0.0126296693111288
1441614.21772328463611.78227671536387
1451013.3076482519713-3.30764825197125
1461815.7108182646392.28918173536104
1471315.057515844885-2.05751584488498
1481614.91980860046531.08019139953472
1491312.63776380440320.362236195596834
1501012.8783257394112-2.87832573941121
1511516.155043271253-1.15504327125299
1521613.99236085150662.00763914849345
1531611.70404505131494.29595494868509
1541412.24996754626091.75003245373915
1551012.3106299805489-2.3106299805489
1561716.83212464144880.167875358551165
1571311.56953931916061.4304606808394
1581514.00423199516910.995768004830927
1591614.67139552040851.32860447959149
1601212.5736517546432-0.573651754643204
1611312.60060369153710.399396308462923
1621312.46655135388390.533448646116147
1631212.3881607885568-0.38816078855682
1641716.5737899268770.426210073122991
1651513.67260044568511.32739955431488
1661011.4206728334644-1.42067283346442
1671414.4691619974894-0.469161997489404
1681114.2909865799885-3.29098657998851
1691314.8482927619942-1.84829276199418
1701614.43087474594941.56912525405059
1711210.36531181629621.63468818370377
1721615.70594839385070.294051606149256
1731213.9883936528962-1.98839365289616
174911.3120335908731-2.31203359087306
1751215.2885887877223-3.2885887877223
1761514.74421311565640.255786884343578
1771212.2632972969964-0.263297296996437
1781212.6879346718068-0.687934671806849
1791414.0621066117256-0.0621066117256325
1801213.476269705029-1.47626970502905
1811615.43404807723430.565951922765744
1821111.5217489928796-0.521748992879648
1831917.16046327131091.83953672868909
1841515.5206096079202-0.520609607920211
185814.7662880903322-6.76628809033216
1861614.96809271966591.03190728033406
1871714.74767686862742.25232313137259
1881212.5418032885651-0.541803288565075
1891111.5269675255856-0.526967525585557
1901110.29932240894260.700677591057422
1911415.0449039081772-1.04490390817719
1921615.85492363747950.145076362520501
193129.657999892580332.34200010741967
1941614.300513259331.69948674067003
1951313.9159846340245-0.915984634024546
1961515.3686335282855-0.368633528285513
1971613.09834314754632.90165685245369
1981615.31296975317590.687030246824134
1991412.48745520070841.51254479929164
2001614.77057978105631.22942021894371
2011614.19538977693681.80461022306324
2021413.56526081339240.434739186607563
2031113.7622976777595-2.76229767775954
2041214.9165699771727-2.91656997717273
2051512.96966651819912.03033348180092
2061514.83696442004550.163035579954472
2071614.8470264956861.152973504314
2081615.43394871036260.566051289637426
2091113.9167631437977-2.91676314379774
2101514.30315947049950.696840529500495
2111214.507489606999-2.50748960699899
2121216.2896849694461-4.28968496944614
2131514.30917048110010.690829518899894
2141512.16749588830622.83250411169379
2151614.88328934434421.1167106556558
2161413.42078783082380.579212169176227
2171715.06266197823011.93733802176991
2181414.3279169397482-0.327916939748237
2191311.99028890163491.00971109836511
2201515.6158919610783-0.615891961078346
2211315.0224913327295-2.02249133272955
2221414.5733033465196-0.573303346519553
2231514.56644591290410.433554087095921
2241213.4585313702885-1.45853137028853
2251312.80428073990440.195719260095626
226811.9668684285413-3.96686842854126
2271414.3096831383973-0.309683138397302
2281413.27374355278650.726256447213534
2291112.3432695732266-1.34326957322662
2301213.1609985574185-1.16099855741853
2311311.47993235152811.52006764847187
2321013.4650403340904-3.46504033409045
2331611.70045382224594.29954617775414
2341816.455866370751.54413362925002
2351314.305398824479-1.30539882447903
2361113.6549207425401-2.65492074254013
237411.170028137282-7.170028137282
2381314.8152140281387-1.81521402813867
2391614.59236330543211.40763669456794
2401011.9287261285823-1.92872612858231
2411212.4071970658913-0.407197065891321
2421213.795600381651-1.79560038165096
243108.886344700787961.11365529921204
2441311.27700688023681.72299311976324
2451514.09262731602050.907372683979529
2461212.0467701780665-0.0467701780665387
2471413.15388286620630.846117133793712
2481012.8902928435778-2.89029284357777
2491210.80096582046091.19903417953907
2501211.88393362896460.116066371035426
2511112.1379198030589-1.13791980305892
2521011.8622634576053-1.86226345760534
2531211.6631437816570.336856218342988
2541613.17822476363682.82177523636317
2551213.7152063049677-1.71520630496768
2561414.264087859685-0.264087859685033
2571614.68118855727991.31881144272009
2581411.82392868149752.17607131850249
2591314.78164098406-1.78164098406003
26049.48330674262234-5.48330674262234
2611514.1743666514850.825633348514997
2621115.5502096888069-4.55020968880689
2631111.4716452522508-0.471645252250812
2641413.16729085252320.832709147476811

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.7169326674783 & -2.71693266747828 \tabularnewline
2 & 16 & 15.3046452988162 & 0.69535470118377 \tabularnewline
3 & 19 & 16.5342266059759 & 2.46577339402408 \tabularnewline
4 & 15 & 11.2431990710256 & 3.75680092897442 \tabularnewline
5 & 14 & 15.8896873324328 & -1.88968733243282 \tabularnewline
6 & 13 & 14.3424239462714 & -1.34242394627141 \tabularnewline
7 & 19 & 14.8381920087898 & 4.16180799121016 \tabularnewline
8 & 15 & 16.6568417465609 & -1.65684174656086 \tabularnewline
9 & 14 & 15.6143966049743 & -1.61439660497434 \tabularnewline
10 & 15 & 13.8398822625452 & 1.16011773745481 \tabularnewline
11 & 16 & 14.5244132197985 & 1.47558678020148 \tabularnewline
12 & 16 & 15.9613451914131 & 0.0386548085868939 \tabularnewline
13 & 16 & 14.9654168965065 & 1.03458310349349 \tabularnewline
14 & 16 & 15.1305945765763 & 0.869405423423655 \tabularnewline
15 & 17 & 17.7266051778577 & -0.726605177857681 \tabularnewline
16 & 15 & 15.0073602272847 & -0.00736022728471113 \tabularnewline
17 & 15 & 14.0333112689484 & 0.966688731051579 \tabularnewline
18 & 20 & 16.0395466632557 & 3.96045333674426 \tabularnewline
19 & 18 & 15.09418616242 & 2.90581383758003 \tabularnewline
20 & 16 & 15.1861376221519 & 0.813862377848066 \tabularnewline
21 & 16 & 15.0015368334676 & 0.998463166532416 \tabularnewline
22 & 16 & 14.5838648261738 & 1.41613517382618 \tabularnewline
23 & 19 & 16.2269009876065 & 2.77309901239348 \tabularnewline
24 & 16 & 14.638251053783 & 1.36174894621697 \tabularnewline
25 & 17 & 15.8416021360893 & 1.15839786391069 \tabularnewline
26 & 17 & 15.834485132585 & 1.16551486741504 \tabularnewline
27 & 16 & 14.5294011985975 & 1.47059880140251 \tabularnewline
28 & 15 & 16.4169297769606 & -1.41692977696059 \tabularnewline
29 & 16 & 15.2955174564432 & 0.70448254355681 \tabularnewline
30 & 14 & 13.6173944562786 & 0.382605543721402 \tabularnewline
31 & 15 & 15.367231470103 & -0.367231470103047 \tabularnewline
32 & 12 & 12.1577948842104 & -0.157794884210414 \tabularnewline
33 & 14 & 14.4670335229458 & -0.46703352294582 \tabularnewline
34 & 16 & 15.5490474808797 & 0.450952519120262 \tabularnewline
35 & 14 & 15.1519977706766 & -1.15199777067663 \tabularnewline
36 & 10 & 12.4548067707195 & -2.45480677071949 \tabularnewline
37 & 10 & 12.2908025947004 & -2.29080259470042 \tabularnewline
38 & 14 & 15.442875350487 & -1.44287535048698 \tabularnewline
39 & 16 & 14.1125676496403 & 1.88743235035968 \tabularnewline
40 & 16 & 14.0503006909743 & 1.94969930902573 \tabularnewline
41 & 16 & 14.3599156612012 & 1.64008433879884 \tabularnewline
42 & 14 & 15.4129095162212 & -1.41290951622121 \tabularnewline
43 & 20 & 17.4975592437466 & 2.50244075625335 \tabularnewline
44 & 14 & 13.7379997106144 & 0.262000289385567 \tabularnewline
45 & 14 & 14.1390257843911 & -0.139025784391113 \tabularnewline
46 & 11 & 15.1505593881384 & -4.15055938813839 \tabularnewline
47 & 14 & 16.4190959972096 & -2.41909599720956 \tabularnewline
48 & 15 & 14.781233912226 & 0.218766087773982 \tabularnewline
49 & 16 & 15.1915977233103 & 0.808402276689707 \tabularnewline
50 & 14 & 15.361515363781 & -1.36151536378102 \tabularnewline
51 & 16 & 16.8537398386884 & -0.853739838688409 \tabularnewline
52 & 14 & 13.731689273808 & 0.268310726191954 \tabularnewline
53 & 12 & 14.6138653598005 & -2.61386535980045 \tabularnewline
54 & 16 & 15.5906899034319 & 0.409310096568121 \tabularnewline
55 & 9 & 10.7137318072366 & -1.7137318072366 \tabularnewline
56 & 14 & 11.8326046050634 & 2.16739539493663 \tabularnewline
57 & 16 & 15.6937236900134 & 0.306276309986645 \tabularnewline
58 & 16 & 15.2303555712627 & 0.769644428737286 \tabularnewline
59 & 15 & 14.8230045585446 & 0.17699544145538 \tabularnewline
60 & 16 & 13.7932224493505 & 2.20677755064948 \tabularnewline
61 & 12 & 10.8367279492074 & 1.1632720507926 \tabularnewline
62 & 16 & 15.4378640177442 & 0.562135982255831 \tabularnewline
63 & 16 & 16.2989869704333 & -0.2989869704333 \tabularnewline
64 & 14 & 14.4388877335844 & -0.438887733584355 \tabularnewline
65 & 16 & 15.1500442700275 & 0.849955729972461 \tabularnewline
66 & 17 & 15.7438538414077 & 1.25614615859234 \tabularnewline
67 & 18 & 16.0903465015184 & 1.90965349848158 \tabularnewline
68 & 18 & 14.0764904918967 & 3.92350950810328 \tabularnewline
69 & 12 & 15.8084608807317 & -3.80846088073166 \tabularnewline
70 & 16 & 15.4728479832678 & 0.527152016732213 \tabularnewline
71 & 10 & 13.0122458267498 & -3.01224582674976 \tabularnewline
72 & 14 & 14.7156655383903 & -0.7156655383903 \tabularnewline
73 & 18 & 16.8379895442731 & 1.16201045572691 \tabularnewline
74 & 18 & 17.1928281858955 & 0.807171814104507 \tabularnewline
75 & 16 & 15.0094686892977 & 0.990531310702269 \tabularnewline
76 & 17 & 13.0902524925775 & 3.90974750742254 \tabularnewline
77 & 16 & 16.4072325443378 & -0.407232544337833 \tabularnewline
78 & 16 & 14.2122817226739 & 1.78771827732615 \tabularnewline
79 & 13 & 14.9614853283133 & -1.9614853283133 \tabularnewline
80 & 16 & 15.0831385107766 & 0.916861489223406 \tabularnewline
81 & 16 & 15.5360989694856 & 0.463901030514354 \tabularnewline
82 & 16 & 15.6432294770163 & 0.356770522983708 \tabularnewline
83 & 15 & 15.6201822872087 & -0.620182287208743 \tabularnewline
84 & 15 & 14.6861437075647 & 0.313856292435276 \tabularnewline
85 & 16 & 13.8827626275351 & 2.11723737246488 \tabularnewline
86 & 14 & 13.9517886120021 & 0.0482113879978756 \tabularnewline
87 & 16 & 15.2330941862993 & 0.766905813700725 \tabularnewline
88 & 16 & 14.681302994534 & 1.31869700546596 \tabularnewline
89 & 15 & 14.1033381737051 & 0.896661826294862 \tabularnewline
90 & 12 & 13.6433035668958 & -1.64330356689577 \tabularnewline
91 & 17 & 16.8321246414488 & 0.167875358551165 \tabularnewline
92 & 16 & 15.7294852307762 & 0.2705147692238 \tabularnewline
93 & 15 & 14.9057923352787 & 0.0942076647212599 \tabularnewline
94 & 13 & 14.8364568188003 & -1.83645681880029 \tabularnewline
95 & 16 & 14.6980328060634 & 1.3019671939366 \tabularnewline
96 & 16 & 15.6960780466908 & 0.303921953309245 \tabularnewline
97 & 16 & 13.4832846792557 & 2.51671532074433 \tabularnewline
98 & 16 & 15.8300417014576 & 0.169958298542448 \tabularnewline
99 & 14 & 14.2661258632027 & -0.26612586320274 \tabularnewline
100 & 16 & 17.1940865071683 & -1.19408650716827 \tabularnewline
101 & 16 & 14.5059011287886 & 1.49409887121141 \tabularnewline
102 & 20 & 17.5790992836759 & 2.42090071632414 \tabularnewline
103 & 15 & 14.0226460140409 & 0.977353985959138 \tabularnewline
104 & 16 & 14.8383762518519 & 1.16162374814812 \tabularnewline
105 & 13 & 14.7124447739459 & -1.71244477394589 \tabularnewline
106 & 17 & 15.7604373152473 & 1.23956268475275 \tabularnewline
107 & 16 & 15.7720645248471 & 0.227935475152911 \tabularnewline
108 & 16 & 14.2326042643852 & 1.76739573561482 \tabularnewline
109 & 12 & 12.0024802659182 & -0.0024802659181661 \tabularnewline
110 & 16 & 15.1718385499616 & 0.828161450038385 \tabularnewline
111 & 16 & 15.9384880334039 & 0.0615119665961118 \tabularnewline
112 & 17 & 14.8625328282602 & 2.13746717173979 \tabularnewline
113 & 13 & 14.5286402413612 & -1.52864024136123 \tabularnewline
114 & 12 & 14.614568460888 & -2.614568460888 \tabularnewline
115 & 18 & 16.2764524443605 & 1.72354755563947 \tabularnewline
116 & 14 & 15.9504759627416 & -1.95047596274164 \tabularnewline
117 & 14 & 12.9924621951951 & 1.0075378048049 \tabularnewline
118 & 13 & 14.7858242392293 & -1.78582423922932 \tabularnewline
119 & 16 & 15.5144579558839 & 0.485542044116056 \tabularnewline
120 & 13 & 14.3730308681848 & -1.37303086818476 \tabularnewline
121 & 16 & 15.4072695332184 & 0.592730466781632 \tabularnewline
122 & 13 & 15.9001483950515 & -2.90014839505154 \tabularnewline
123 & 16 & 17.0738036864139 & -1.07380368641385 \tabularnewline
124 & 15 & 15.9985652529538 & -0.998565252953809 \tabularnewline
125 & 16 & 16.9217558949015 & -0.921755894901496 \tabularnewline
126 & 15 & 14.7501471788131 & 0.249852821186913 \tabularnewline
127 & 17 & 15.7672102604739 & 1.23278973952609 \tabularnewline
128 & 15 & 14.0042319951691 & 0.995768004830927 \tabularnewline
129 & 12 & 14.7455979666711 & -2.74559796667113 \tabularnewline
130 & 16 & 13.9018224660129 & 2.09817753398714 \tabularnewline
131 & 10 & 13.490018922728 & -3.49001892272798 \tabularnewline
132 & 16 & 13.4847293488174 & 2.51527065118258 \tabularnewline
133 & 12 & 14.1318955712898 & -2.13189557128976 \tabularnewline
134 & 14 & 15.6891601643807 & -1.68916016438072 \tabularnewline
135 & 15 & 15.1349525324793 & -0.13495253247934 \tabularnewline
136 & 13 & 11.8798702989229 & 1.12012970107706 \tabularnewline
137 & 15 & 14.5406759892864 & 0.459324010713607 \tabularnewline
138 & 11 & 13.3639577865016 & -2.3639577865016 \tabularnewline
139 & 12 & 12.9948717800782 & -0.99487178007819 \tabularnewline
140 & 11 & 13.2797734052425 & -2.27977340524254 \tabularnewline
141 & 16 & 12.8706670328481 & 3.12933296715189 \tabularnewline
142 & 15 & 13.577797008145 & 1.42220299185501 \tabularnewline
143 & 17 & 17.0126296693111 & -0.0126296693111288 \tabularnewline
144 & 16 & 14.2177232846361 & 1.78227671536387 \tabularnewline
145 & 10 & 13.3076482519713 & -3.30764825197125 \tabularnewline
146 & 18 & 15.710818264639 & 2.28918173536104 \tabularnewline
147 & 13 & 15.057515844885 & -2.05751584488498 \tabularnewline
148 & 16 & 14.9198086004653 & 1.08019139953472 \tabularnewline
149 & 13 & 12.6377638044032 & 0.362236195596834 \tabularnewline
150 & 10 & 12.8783257394112 & -2.87832573941121 \tabularnewline
151 & 15 & 16.155043271253 & -1.15504327125299 \tabularnewline
152 & 16 & 13.9923608515066 & 2.00763914849345 \tabularnewline
153 & 16 & 11.7040450513149 & 4.29595494868509 \tabularnewline
154 & 14 & 12.2499675462609 & 1.75003245373915 \tabularnewline
155 & 10 & 12.3106299805489 & -2.3106299805489 \tabularnewline
156 & 17 & 16.8321246414488 & 0.167875358551165 \tabularnewline
157 & 13 & 11.5695393191606 & 1.4304606808394 \tabularnewline
158 & 15 & 14.0042319951691 & 0.995768004830927 \tabularnewline
159 & 16 & 14.6713955204085 & 1.32860447959149 \tabularnewline
160 & 12 & 12.5736517546432 & -0.573651754643204 \tabularnewline
161 & 13 & 12.6006036915371 & 0.399396308462923 \tabularnewline
162 & 13 & 12.4665513538839 & 0.533448646116147 \tabularnewline
163 & 12 & 12.3881607885568 & -0.38816078855682 \tabularnewline
164 & 17 & 16.573789926877 & 0.426210073122991 \tabularnewline
165 & 15 & 13.6726004456851 & 1.32739955431488 \tabularnewline
166 & 10 & 11.4206728334644 & -1.42067283346442 \tabularnewline
167 & 14 & 14.4691619974894 & -0.469161997489404 \tabularnewline
168 & 11 & 14.2909865799885 & -3.29098657998851 \tabularnewline
169 & 13 & 14.8482927619942 & -1.84829276199418 \tabularnewline
170 & 16 & 14.4308747459494 & 1.56912525405059 \tabularnewline
171 & 12 & 10.3653118162962 & 1.63468818370377 \tabularnewline
172 & 16 & 15.7059483938507 & 0.294051606149256 \tabularnewline
173 & 12 & 13.9883936528962 & -1.98839365289616 \tabularnewline
174 & 9 & 11.3120335908731 & -2.31203359087306 \tabularnewline
175 & 12 & 15.2885887877223 & -3.2885887877223 \tabularnewline
176 & 15 & 14.7442131156564 & 0.255786884343578 \tabularnewline
177 & 12 & 12.2632972969964 & -0.263297296996437 \tabularnewline
178 & 12 & 12.6879346718068 & -0.687934671806849 \tabularnewline
179 & 14 & 14.0621066117256 & -0.0621066117256325 \tabularnewline
180 & 12 & 13.476269705029 & -1.47626970502905 \tabularnewline
181 & 16 & 15.4340480772343 & 0.565951922765744 \tabularnewline
182 & 11 & 11.5217489928796 & -0.521748992879648 \tabularnewline
183 & 19 & 17.1604632713109 & 1.83953672868909 \tabularnewline
184 & 15 & 15.5206096079202 & -0.520609607920211 \tabularnewline
185 & 8 & 14.7662880903322 & -6.76628809033216 \tabularnewline
186 & 16 & 14.9680927196659 & 1.03190728033406 \tabularnewline
187 & 17 & 14.7476768686274 & 2.25232313137259 \tabularnewline
188 & 12 & 12.5418032885651 & -0.541803288565075 \tabularnewline
189 & 11 & 11.5269675255856 & -0.526967525585557 \tabularnewline
190 & 11 & 10.2993224089426 & 0.700677591057422 \tabularnewline
191 & 14 & 15.0449039081772 & -1.04490390817719 \tabularnewline
192 & 16 & 15.8549236374795 & 0.145076362520501 \tabularnewline
193 & 12 & 9.65799989258033 & 2.34200010741967 \tabularnewline
194 & 16 & 14.30051325933 & 1.69948674067003 \tabularnewline
195 & 13 & 13.9159846340245 & -0.915984634024546 \tabularnewline
196 & 15 & 15.3686335282855 & -0.368633528285513 \tabularnewline
197 & 16 & 13.0983431475463 & 2.90165685245369 \tabularnewline
198 & 16 & 15.3129697531759 & 0.687030246824134 \tabularnewline
199 & 14 & 12.4874552007084 & 1.51254479929164 \tabularnewline
200 & 16 & 14.7705797810563 & 1.22942021894371 \tabularnewline
201 & 16 & 14.1953897769368 & 1.80461022306324 \tabularnewline
202 & 14 & 13.5652608133924 & 0.434739186607563 \tabularnewline
203 & 11 & 13.7622976777595 & -2.76229767775954 \tabularnewline
204 & 12 & 14.9165699771727 & -2.91656997717273 \tabularnewline
205 & 15 & 12.9696665181991 & 2.03033348180092 \tabularnewline
206 & 15 & 14.8369644200455 & 0.163035579954472 \tabularnewline
207 & 16 & 14.847026495686 & 1.152973504314 \tabularnewline
208 & 16 & 15.4339487103626 & 0.566051289637426 \tabularnewline
209 & 11 & 13.9167631437977 & -2.91676314379774 \tabularnewline
210 & 15 & 14.3031594704995 & 0.696840529500495 \tabularnewline
211 & 12 & 14.507489606999 & -2.50748960699899 \tabularnewline
212 & 12 & 16.2896849694461 & -4.28968496944614 \tabularnewline
213 & 15 & 14.3091704811001 & 0.690829518899894 \tabularnewline
214 & 15 & 12.1674958883062 & 2.83250411169379 \tabularnewline
215 & 16 & 14.8832893443442 & 1.1167106556558 \tabularnewline
216 & 14 & 13.4207878308238 & 0.579212169176227 \tabularnewline
217 & 17 & 15.0626619782301 & 1.93733802176991 \tabularnewline
218 & 14 & 14.3279169397482 & -0.327916939748237 \tabularnewline
219 & 13 & 11.9902889016349 & 1.00971109836511 \tabularnewline
220 & 15 & 15.6158919610783 & -0.615891961078346 \tabularnewline
221 & 13 & 15.0224913327295 & -2.02249133272955 \tabularnewline
222 & 14 & 14.5733033465196 & -0.573303346519553 \tabularnewline
223 & 15 & 14.5664459129041 & 0.433554087095921 \tabularnewline
224 & 12 & 13.4585313702885 & -1.45853137028853 \tabularnewline
225 & 13 & 12.8042807399044 & 0.195719260095626 \tabularnewline
226 & 8 & 11.9668684285413 & -3.96686842854126 \tabularnewline
227 & 14 & 14.3096831383973 & -0.309683138397302 \tabularnewline
228 & 14 & 13.2737435527865 & 0.726256447213534 \tabularnewline
229 & 11 & 12.3432695732266 & -1.34326957322662 \tabularnewline
230 & 12 & 13.1609985574185 & -1.16099855741853 \tabularnewline
231 & 13 & 11.4799323515281 & 1.52006764847187 \tabularnewline
232 & 10 & 13.4650403340904 & -3.46504033409045 \tabularnewline
233 & 16 & 11.7004538222459 & 4.29954617775414 \tabularnewline
234 & 18 & 16.45586637075 & 1.54413362925002 \tabularnewline
235 & 13 & 14.305398824479 & -1.30539882447903 \tabularnewline
236 & 11 & 13.6549207425401 & -2.65492074254013 \tabularnewline
237 & 4 & 11.170028137282 & -7.170028137282 \tabularnewline
238 & 13 & 14.8152140281387 & -1.81521402813867 \tabularnewline
239 & 16 & 14.5923633054321 & 1.40763669456794 \tabularnewline
240 & 10 & 11.9287261285823 & -1.92872612858231 \tabularnewline
241 & 12 & 12.4071970658913 & -0.407197065891321 \tabularnewline
242 & 12 & 13.795600381651 & -1.79560038165096 \tabularnewline
243 & 10 & 8.88634470078796 & 1.11365529921204 \tabularnewline
244 & 13 & 11.2770068802368 & 1.72299311976324 \tabularnewline
245 & 15 & 14.0926273160205 & 0.907372683979529 \tabularnewline
246 & 12 & 12.0467701780665 & -0.0467701780665387 \tabularnewline
247 & 14 & 13.1538828662063 & 0.846117133793712 \tabularnewline
248 & 10 & 12.8902928435778 & -2.89029284357777 \tabularnewline
249 & 12 & 10.8009658204609 & 1.19903417953907 \tabularnewline
250 & 12 & 11.8839336289646 & 0.116066371035426 \tabularnewline
251 & 11 & 12.1379198030589 & -1.13791980305892 \tabularnewline
252 & 10 & 11.8622634576053 & -1.86226345760534 \tabularnewline
253 & 12 & 11.663143781657 & 0.336856218342988 \tabularnewline
254 & 16 & 13.1782247636368 & 2.82177523636317 \tabularnewline
255 & 12 & 13.7152063049677 & -1.71520630496768 \tabularnewline
256 & 14 & 14.264087859685 & -0.264087859685033 \tabularnewline
257 & 16 & 14.6811885572799 & 1.31881144272009 \tabularnewline
258 & 14 & 11.8239286814975 & 2.17607131850249 \tabularnewline
259 & 13 & 14.78164098406 & -1.78164098406003 \tabularnewline
260 & 4 & 9.48330674262234 & -5.48330674262234 \tabularnewline
261 & 15 & 14.174366651485 & 0.825633348514997 \tabularnewline
262 & 11 & 15.5502096888069 & -4.55020968880689 \tabularnewline
263 & 11 & 11.4716452522508 & -0.471645252250812 \tabularnewline
264 & 14 & 13.1672908525232 & 0.832709147476811 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185709&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]15.7169326674783[/C][C]-2.71693266747828[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.3046452988162[/C][C]0.69535470118377[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5342266059759[/C][C]2.46577339402408[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.2431990710256[/C][C]3.75680092897442[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.8896873324328[/C][C]-1.88968733243282[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.3424239462714[/C][C]-1.34242394627141[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.8381920087898[/C][C]4.16180799121016[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6568417465609[/C][C]-1.65684174656086[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.6143966049743[/C][C]-1.61439660497434[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]13.8398822625452[/C][C]1.16011773745481[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]14.5244132197985[/C][C]1.47558678020148[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.9613451914131[/C][C]0.0386548085868939[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]14.9654168965065[/C][C]1.03458310349349[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.1305945765763[/C][C]0.869405423423655[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.7266051778577[/C][C]-0.726605177857681[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0073602272847[/C][C]-0.00736022728471113[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.0333112689484[/C][C]0.966688731051579[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.0395466632557[/C][C]3.96045333674426[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.09418616242[/C][C]2.90581383758003[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.1861376221519[/C][C]0.813862377848066[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.0015368334676[/C][C]0.998463166532416[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.5838648261738[/C][C]1.41613517382618[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.2269009876065[/C][C]2.77309901239348[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.638251053783[/C][C]1.36174894621697[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.8416021360893[/C][C]1.15839786391069[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]15.834485132585[/C][C]1.16551486741504[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.5294011985975[/C][C]1.47059880140251[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.4169297769606[/C][C]-1.41692977696059[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.2955174564432[/C][C]0.70448254355681[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.6173944562786[/C][C]0.382605543721402[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.367231470103[/C][C]-0.367231470103047[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.1577948842104[/C][C]-0.157794884210414[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.4670335229458[/C][C]-0.46703352294582[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5490474808797[/C][C]0.450952519120262[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.1519977706766[/C][C]-1.15199777067663[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]12.4548067707195[/C][C]-2.45480677071949[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.2908025947004[/C][C]-2.29080259470042[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.442875350487[/C][C]-1.44287535048698[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1125676496403[/C][C]1.88743235035968[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.0503006909743[/C][C]1.94969930902573[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.3599156612012[/C][C]1.64008433879884[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4129095162212[/C][C]-1.41290951622121[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.4975592437466[/C][C]2.50244075625335[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.7379997106144[/C][C]0.262000289385567[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.1390257843911[/C][C]-0.139025784391113[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.1505593881384[/C][C]-4.15055938813839[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4190959972096[/C][C]-2.41909599720956[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.781233912226[/C][C]0.218766087773982[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1915977233103[/C][C]0.808402276689707[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.361515363781[/C][C]-1.36151536378102[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.8537398386884[/C][C]-0.853739838688409[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.731689273808[/C][C]0.268310726191954[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6138653598005[/C][C]-2.61386535980045[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.5906899034319[/C][C]0.409310096568121[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]10.7137318072366[/C][C]-1.7137318072366[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]11.8326046050634[/C][C]2.16739539493663[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.6937236900134[/C][C]0.306276309986645[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2303555712627[/C][C]0.769644428737286[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.8230045585446[/C][C]0.17699544145538[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]13.7932224493505[/C][C]2.20677755064948[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]10.8367279492074[/C][C]1.1632720507926[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.4378640177442[/C][C]0.562135982255831[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.2989869704333[/C][C]-0.2989869704333[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4388877335844[/C][C]-0.438887733584355[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.1500442700275[/C][C]0.849955729972461[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.7438538414077[/C][C]1.25614615859234[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.0903465015184[/C][C]1.90965349848158[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.0764904918967[/C][C]3.92350950810328[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8084608807317[/C][C]-3.80846088073166[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4728479832678[/C][C]0.527152016732213[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.0122458267498[/C][C]-3.01224582674976[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.7156655383903[/C][C]-0.7156655383903[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.8379895442731[/C][C]1.16201045572691[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1928281858955[/C][C]0.807171814104507[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.0094686892977[/C][C]0.990531310702269[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.0902524925775[/C][C]3.90974750742254[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4072325443378[/C][C]-0.407232544337833[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.2122817226739[/C][C]1.78771827732615[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.9614853283133[/C][C]-1.9614853283133[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.0831385107766[/C][C]0.916861489223406[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5360989694856[/C][C]0.463901030514354[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.6432294770163[/C][C]0.356770522983708[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6201822872087[/C][C]-0.620182287208743[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.6861437075647[/C][C]0.313856292435276[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]13.8827626275351[/C][C]2.11723737246488[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.9517886120021[/C][C]0.0482113879978756[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.2330941862993[/C][C]0.766905813700725[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.681302994534[/C][C]1.31869700546596[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.1033381737051[/C][C]0.896661826294862[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6433035668958[/C][C]-1.64330356689577[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.8321246414488[/C][C]0.167875358551165[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.7294852307762[/C][C]0.2705147692238[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.9057923352787[/C][C]0.0942076647212599[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.8364568188003[/C][C]-1.83645681880029[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.6980328060634[/C][C]1.3019671939366[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.6960780466908[/C][C]0.303921953309245[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.4832846792557[/C][C]2.51671532074433[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.8300417014576[/C][C]0.169958298542448[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.2661258632027[/C][C]-0.26612586320274[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.1940865071683[/C][C]-1.19408650716827[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.5059011287886[/C][C]1.49409887121141[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.5790992836759[/C][C]2.42090071632414[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.0226460140409[/C][C]0.977353985959138[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.8383762518519[/C][C]1.16162374814812[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.7124447739459[/C][C]-1.71244477394589[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.7604373152473[/C][C]1.23956268475275[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.7720645248471[/C][C]0.227935475152911[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.2326042643852[/C][C]1.76739573561482[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.0024802659182[/C][C]-0.0024802659181661[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.1718385499616[/C][C]0.828161450038385[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.9384880334039[/C][C]0.0615119665961118[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.8625328282602[/C][C]2.13746717173979[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.5286402413612[/C][C]-1.52864024136123[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.614568460888[/C][C]-2.614568460888[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.2764524443605[/C][C]1.72354755563947[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.9504759627416[/C][C]-1.95047596274164[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.9924621951951[/C][C]1.0075378048049[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.7858242392293[/C][C]-1.78582423922932[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.5144579558839[/C][C]0.485542044116056[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.3730308681848[/C][C]-1.37303086818476[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.4072695332184[/C][C]0.592730466781632[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.9001483950515[/C][C]-2.90014839505154[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.0738036864139[/C][C]-1.07380368641385[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.9985652529538[/C][C]-0.998565252953809[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.9217558949015[/C][C]-0.921755894901496[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.7501471788131[/C][C]0.249852821186913[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.7672102604739[/C][C]1.23278973952609[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]14.0042319951691[/C][C]0.995768004830927[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.7455979666711[/C][C]-2.74559796667113[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.9018224660129[/C][C]2.09817753398714[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.490018922728[/C][C]-3.49001892272798[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.4847293488174[/C][C]2.51527065118258[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.1318955712898[/C][C]-2.13189557128976[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.6891601643807[/C][C]-1.68916016438072[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.1349525324793[/C][C]-0.13495253247934[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]11.8798702989229[/C][C]1.12012970107706[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.5406759892864[/C][C]0.459324010713607[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.3639577865016[/C][C]-2.3639577865016[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.9948717800782[/C][C]-0.99487178007819[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2797734052425[/C][C]-2.27977340524254[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8706670328481[/C][C]3.12933296715189[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.577797008145[/C][C]1.42220299185501[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]17.0126296693111[/C][C]-0.0126296693111288[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.2177232846361[/C][C]1.78227671536387[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.3076482519713[/C][C]-3.30764825197125[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.710818264639[/C][C]2.28918173536104[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]15.057515844885[/C][C]-2.05751584488498[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.9198086004653[/C][C]1.08019139953472[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.6377638044032[/C][C]0.362236195596834[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.8783257394112[/C][C]-2.87832573941121[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.155043271253[/C][C]-1.15504327125299[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.9923608515066[/C][C]2.00763914849345[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.7040450513149[/C][C]4.29595494868509[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.2499675462609[/C][C]1.75003245373915[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.3106299805489[/C][C]-2.3106299805489[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.8321246414488[/C][C]0.167875358551165[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.5695393191606[/C][C]1.4304606808394[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]14.0042319951691[/C][C]0.995768004830927[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.6713955204085[/C][C]1.32860447959149[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.5736517546432[/C][C]-0.573651754643204[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6006036915371[/C][C]0.399396308462923[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.4665513538839[/C][C]0.533448646116147[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.3881607885568[/C][C]-0.38816078855682[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.573789926877[/C][C]0.426210073122991[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.6726004456851[/C][C]1.32739955431488[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.4206728334644[/C][C]-1.42067283346442[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.4691619974894[/C][C]-0.469161997489404[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.2909865799885[/C][C]-3.29098657998851[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.8482927619942[/C][C]-1.84829276199418[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.4308747459494[/C][C]1.56912525405059[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.3653118162962[/C][C]1.63468818370377[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.7059483938507[/C][C]0.294051606149256[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.9883936528962[/C][C]-1.98839365289616[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.3120335908731[/C][C]-2.31203359087306[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]15.2885887877223[/C][C]-3.2885887877223[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.7442131156564[/C][C]0.255786884343578[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.2632972969964[/C][C]-0.263297296996437[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.6879346718068[/C][C]-0.687934671806849[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]14.0621066117256[/C][C]-0.0621066117256325[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.476269705029[/C][C]-1.47626970502905[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.4340480772343[/C][C]0.565951922765744[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.5217489928796[/C][C]-0.521748992879648[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]17.1604632713109[/C][C]1.83953672868909[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.5206096079202[/C][C]-0.520609607920211[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.7662880903322[/C][C]-6.76628809033216[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.9680927196659[/C][C]1.03190728033406[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.7476768686274[/C][C]2.25232313137259[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.5418032885651[/C][C]-0.541803288565075[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.5269675255856[/C][C]-0.526967525585557[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.2993224089426[/C][C]0.700677591057422[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]15.0449039081772[/C][C]-1.04490390817719[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.8549236374795[/C][C]0.145076362520501[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.65799989258033[/C][C]2.34200010741967[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.30051325933[/C][C]1.69948674067003[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.9159846340245[/C][C]-0.915984634024546[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]15.3686335282855[/C][C]-0.368633528285513[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]13.0983431475463[/C][C]2.90165685245369[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.3129697531759[/C][C]0.687030246824134[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4874552007084[/C][C]1.51254479929164[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.7705797810563[/C][C]1.22942021894371[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]14.1953897769368[/C][C]1.80461022306324[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.5652608133924[/C][C]0.434739186607563[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.7622976777595[/C][C]-2.76229767775954[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.9165699771727[/C][C]-2.91656997717273[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.9696665181991[/C][C]2.03033348180092[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.8369644200455[/C][C]0.163035579954472[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.847026495686[/C][C]1.152973504314[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]15.4339487103626[/C][C]0.566051289637426[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.9167631437977[/C][C]-2.91676314379774[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]14.3031594704995[/C][C]0.696840529500495[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.507489606999[/C][C]-2.50748960699899[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]16.2896849694461[/C][C]-4.28968496944614[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.3091704811001[/C][C]0.690829518899894[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]12.1674958883062[/C][C]2.83250411169379[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.8832893443442[/C][C]1.1167106556558[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.4207878308238[/C][C]0.579212169176227[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]15.0626619782301[/C][C]1.93733802176991[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]14.3279169397482[/C][C]-0.327916939748237[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.9902889016349[/C][C]1.00971109836511[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.6158919610783[/C][C]-0.615891961078346[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]15.0224913327295[/C][C]-2.02249133272955[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]14.5733033465196[/C][C]-0.573303346519553[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.5664459129041[/C][C]0.433554087095921[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]13.4585313702885[/C][C]-1.45853137028853[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.8042807399044[/C][C]0.195719260095626[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.9668684285413[/C][C]-3.96686842854126[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]14.3096831383973[/C][C]-0.309683138397302[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]13.2737435527865[/C][C]0.726256447213534[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.3432695732266[/C][C]-1.34326957322662[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]13.1609985574185[/C][C]-1.16099855741853[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.4799323515281[/C][C]1.52006764847187[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.4650403340904[/C][C]-3.46504033409045[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.7004538222459[/C][C]4.29954617775414[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]16.45586637075[/C][C]1.54413362925002[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]14.305398824479[/C][C]-1.30539882447903[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.6549207425401[/C][C]-2.65492074254013[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]11.170028137282[/C][C]-7.170028137282[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.8152140281387[/C][C]-1.81521402813867[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.5923633054321[/C][C]1.40763669456794[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.9287261285823[/C][C]-1.92872612858231[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.4071970658913[/C][C]-0.407197065891321[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.795600381651[/C][C]-1.79560038165096[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.88634470078796[/C][C]1.11365529921204[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]11.2770068802368[/C][C]1.72299311976324[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]14.0926273160205[/C][C]0.907372683979529[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]12.0467701780665[/C][C]-0.0467701780665387[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]13.1538828662063[/C][C]0.846117133793712[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.8902928435778[/C][C]-2.89029284357777[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.8009658204609[/C][C]1.19903417953907[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.8839336289646[/C][C]0.116066371035426[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]12.1379198030589[/C][C]-1.13791980305892[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.8622634576053[/C][C]-1.86226345760534[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.663143781657[/C][C]0.336856218342988[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]13.1782247636368[/C][C]2.82177523636317[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.7152063049677[/C][C]-1.71520630496768[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]14.264087859685[/C][C]-0.264087859685033[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.6811885572799[/C][C]1.31881144272009[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.8239286814975[/C][C]2.17607131850249[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.78164098406[/C][C]-1.78164098406003[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.48330674262234[/C][C]-5.48330674262234[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]14.174366651485[/C][C]0.825633348514997[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]15.5502096888069[/C][C]-4.55020968880689[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.4716452522508[/C][C]-0.471645252250812[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]13.1672908525232[/C][C]0.832709147476811[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185709&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185709&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.7169326674783-2.71693266747828
21615.30464529881620.69535470118377
31916.53422660597592.46577339402408
41511.24319907102563.75680092897442
51415.8896873324328-1.88968733243282
61314.3424239462714-1.34242394627141
71914.83819200878984.16180799121016
81516.6568417465609-1.65684174656086
91415.6143966049743-1.61439660497434
101513.83988226254521.16011773745481
111614.52441321979851.47558678020148
121615.96134519141310.0386548085868939
131614.96541689650651.03458310349349
141615.13059457657630.869405423423655
151717.7266051778577-0.726605177857681
161515.0073602272847-0.00736022728471113
171514.03331126894840.966688731051579
182016.03954666325573.96045333674426
191815.094186162422.90581383758003
201615.18613762215190.813862377848066
211615.00153683346760.998463166532416
221614.58386482617381.41613517382618
231916.22690098760652.77309901239348
241614.6382510537831.36174894621697
251715.84160213608931.15839786391069
261715.8344851325851.16551486741504
271614.52940119859751.47059880140251
281516.4169297769606-1.41692977696059
291615.29551745644320.70448254355681
301413.61739445627860.382605543721402
311515.367231470103-0.367231470103047
321212.1577948842104-0.157794884210414
331414.4670335229458-0.46703352294582
341615.54904748087970.450952519120262
351415.1519977706766-1.15199777067663
361012.4548067707195-2.45480677071949
371012.2908025947004-2.29080259470042
381415.442875350487-1.44287535048698
391614.11256764964031.88743235035968
401614.05030069097431.94969930902573
411614.35991566120121.64008433879884
421415.4129095162212-1.41290951622121
432017.49755924374662.50244075625335
441413.73799971061440.262000289385567
451414.1390257843911-0.139025784391113
461115.1505593881384-4.15055938813839
471416.4190959972096-2.41909599720956
481514.7812339122260.218766087773982
491615.19159772331030.808402276689707
501415.361515363781-1.36151536378102
511616.8537398386884-0.853739838688409
521413.7316892738080.268310726191954
531214.6138653598005-2.61386535980045
541615.59068990343190.409310096568121
55910.7137318072366-1.7137318072366
561411.83260460506342.16739539493663
571615.69372369001340.306276309986645
581615.23035557126270.769644428737286
591514.82300455854460.17699544145538
601613.79322244935052.20677755064948
611210.83672794920741.1632720507926
621615.43786401774420.562135982255831
631616.2989869704333-0.2989869704333
641414.4388877335844-0.438887733584355
651615.15004427002750.849955729972461
661715.74385384140771.25614615859234
671816.09034650151841.90965349848158
681814.07649049189673.92350950810328
691215.8084608807317-3.80846088073166
701615.47284798326780.527152016732213
711013.0122458267498-3.01224582674976
721414.7156655383903-0.7156655383903
731816.83798954427311.16201045572691
741817.19282818589550.807171814104507
751615.00946868929770.990531310702269
761713.09025249257753.90974750742254
771616.4072325443378-0.407232544337833
781614.21228172267391.78771827732615
791314.9614853283133-1.9614853283133
801615.08313851077660.916861489223406
811615.53609896948560.463901030514354
821615.64322947701630.356770522983708
831515.6201822872087-0.620182287208743
841514.68614370756470.313856292435276
851613.88276262753512.11723737246488
861413.95178861200210.0482113879978756
871615.23309418629930.766905813700725
881614.6813029945341.31869700546596
891514.10333817370510.896661826294862
901213.6433035668958-1.64330356689577
911716.83212464144880.167875358551165
921615.72948523077620.2705147692238
931514.90579233527870.0942076647212599
941314.8364568188003-1.83645681880029
951614.69803280606341.3019671939366
961615.69607804669080.303921953309245
971613.48328467925572.51671532074433
981615.83004170145760.169958298542448
991414.2661258632027-0.26612586320274
1001617.1940865071683-1.19408650716827
1011614.50590112878861.49409887121141
1022017.57909928367592.42090071632414
1031514.02264601404090.977353985959138
1041614.83837625185191.16162374814812
1051314.7124447739459-1.71244477394589
1061715.76043731524731.23956268475275
1071615.77206452484710.227935475152911
1081614.23260426438521.76739573561482
1091212.0024802659182-0.0024802659181661
1101615.17183854996160.828161450038385
1111615.93848803340390.0615119665961118
1121714.86253282826022.13746717173979
1131314.5286402413612-1.52864024136123
1141214.614568460888-2.614568460888
1151816.27645244436051.72354755563947
1161415.9504759627416-1.95047596274164
1171412.99246219519511.0075378048049
1181314.7858242392293-1.78582423922932
1191615.51445795588390.485542044116056
1201314.3730308681848-1.37303086818476
1211615.40726953321840.592730466781632
1221315.9001483950515-2.90014839505154
1231617.0738036864139-1.07380368641385
1241515.9985652529538-0.998565252953809
1251616.9217558949015-0.921755894901496
1261514.75014717881310.249852821186913
1271715.76721026047391.23278973952609
1281514.00423199516910.995768004830927
1291214.7455979666711-2.74559796667113
1301613.90182246601292.09817753398714
1311013.490018922728-3.49001892272798
1321613.48472934881742.51527065118258
1331214.1318955712898-2.13189557128976
1341415.6891601643807-1.68916016438072
1351515.1349525324793-0.13495253247934
1361311.87987029892291.12012970107706
1371514.54067598928640.459324010713607
1381113.3639577865016-2.3639577865016
1391212.9948717800782-0.99487178007819
1401113.2797734052425-2.27977340524254
1411612.87066703284813.12933296715189
1421513.5777970081451.42220299185501
1431717.0126296693111-0.0126296693111288
1441614.21772328463611.78227671536387
1451013.3076482519713-3.30764825197125
1461815.7108182646392.28918173536104
1471315.057515844885-2.05751584488498
1481614.91980860046531.08019139953472
1491312.63776380440320.362236195596834
1501012.8783257394112-2.87832573941121
1511516.155043271253-1.15504327125299
1521613.99236085150662.00763914849345
1531611.70404505131494.29595494868509
1541412.24996754626091.75003245373915
1551012.3106299805489-2.3106299805489
1561716.83212464144880.167875358551165
1571311.56953931916061.4304606808394
1581514.00423199516910.995768004830927
1591614.67139552040851.32860447959149
1601212.5736517546432-0.573651754643204
1611312.60060369153710.399396308462923
1621312.46655135388390.533448646116147
1631212.3881607885568-0.38816078855682
1641716.5737899268770.426210073122991
1651513.67260044568511.32739955431488
1661011.4206728334644-1.42067283346442
1671414.4691619974894-0.469161997489404
1681114.2909865799885-3.29098657998851
1691314.8482927619942-1.84829276199418
1701614.43087474594941.56912525405059
1711210.36531181629621.63468818370377
1721615.70594839385070.294051606149256
1731213.9883936528962-1.98839365289616
174911.3120335908731-2.31203359087306
1751215.2885887877223-3.2885887877223
1761514.74421311565640.255786884343578
1771212.2632972969964-0.263297296996437
1781212.6879346718068-0.687934671806849
1791414.0621066117256-0.0621066117256325
1801213.476269705029-1.47626970502905
1811615.43404807723430.565951922765744
1821111.5217489928796-0.521748992879648
1831917.16046327131091.83953672868909
1841515.5206096079202-0.520609607920211
185814.7662880903322-6.76628809033216
1861614.96809271966591.03190728033406
1871714.74767686862742.25232313137259
1881212.5418032885651-0.541803288565075
1891111.5269675255856-0.526967525585557
1901110.29932240894260.700677591057422
1911415.0449039081772-1.04490390817719
1921615.85492363747950.145076362520501
193129.657999892580332.34200010741967
1941614.300513259331.69948674067003
1951313.9159846340245-0.915984634024546
1961515.3686335282855-0.368633528285513
1971613.09834314754632.90165685245369
1981615.31296975317590.687030246824134
1991412.48745520070841.51254479929164
2001614.77057978105631.22942021894371
2011614.19538977693681.80461022306324
2021413.56526081339240.434739186607563
2031113.7622976777595-2.76229767775954
2041214.9165699771727-2.91656997717273
2051512.96966651819912.03033348180092
2061514.83696442004550.163035579954472
2071614.8470264956861.152973504314
2081615.43394871036260.566051289637426
2091113.9167631437977-2.91676314379774
2101514.30315947049950.696840529500495
2111214.507489606999-2.50748960699899
2121216.2896849694461-4.28968496944614
2131514.30917048110010.690829518899894
2141512.16749588830622.83250411169379
2151614.88328934434421.1167106556558
2161413.42078783082380.579212169176227
2171715.06266197823011.93733802176991
2181414.3279169397482-0.327916939748237
2191311.99028890163491.00971109836511
2201515.6158919610783-0.615891961078346
2211315.0224913327295-2.02249133272955
2221414.5733033465196-0.573303346519553
2231514.56644591290410.433554087095921
2241213.4585313702885-1.45853137028853
2251312.80428073990440.195719260095626
226811.9668684285413-3.96686842854126
2271414.3096831383973-0.309683138397302
2281413.27374355278650.726256447213534
2291112.3432695732266-1.34326957322662
2301213.1609985574185-1.16099855741853
2311311.47993235152811.52006764847187
2321013.4650403340904-3.46504033409045
2331611.70045382224594.29954617775414
2341816.455866370751.54413362925002
2351314.305398824479-1.30539882447903
2361113.6549207425401-2.65492074254013
237411.170028137282-7.170028137282
2381314.8152140281387-1.81521402813867
2391614.59236330543211.40763669456794
2401011.9287261285823-1.92872612858231
2411212.4071970658913-0.407197065891321
2421213.795600381651-1.79560038165096
243108.886344700787961.11365529921204
2441311.27700688023681.72299311976324
2451514.09262731602050.907372683979529
2461212.0467701780665-0.0467701780665387
2471413.15388286620630.846117133793712
2481012.8902928435778-2.89029284357777
2491210.80096582046091.19903417953907
2501211.88393362896460.116066371035426
2511112.1379198030589-1.13791980305892
2521011.8622634576053-1.86226345760534
2531211.6631437816570.336856218342988
2541613.17822476363682.82177523636317
2551213.7152063049677-1.71520630496768
2561414.264087859685-0.264087859685033
2571614.68118855727991.31881144272009
2581411.82392868149752.17607131850249
2591314.78164098406-1.78164098406003
26049.48330674262234-5.48330674262234
2611514.1743666514850.825633348514997
2621115.5502096888069-4.55020968880689
2631111.4716452522508-0.471645252250812
2641413.16729085252320.832709147476811







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2344495241033230.4688990482066470.765550475896677
120.1202935178405560.2405870356811130.879706482159444
130.08155415734578130.1631083146915630.918445842654219
140.1028747764979160.2057495529958330.897125223502084
150.05875197565154840.1175039513030970.941248024348452
160.03916866759316350.07833733518632690.960831332406836
170.0630191184441720.1260382368883440.936980881555828
180.2558643279353050.5117286558706090.744135672064695
190.1942166329001490.3884332658002990.805783367099851
200.137764883334640.2755297666692810.86223511666536
210.09860626155137140.1972125231027430.901393738448629
220.09685339904276960.1937067980855390.90314660095723
230.2570543779111420.5141087558222840.742945622088858
240.3214954184239460.6429908368478930.678504581576054
250.2948695385950260.5897390771900510.705130461404974
260.2774478577119780.5548957154239550.722552142288022
270.3520988888725530.7041977777451050.647901111127447
280.3638496488593340.7276992977186680.636150351140666
290.3467855921475840.6935711842951690.653214407852416
300.3808983862722690.7617967725445380.619101613727731
310.3286758031111580.6573516062223170.671324196888842
320.2894928679699310.5789857359398630.710507132030069
330.2624907774068950.5249815548137910.737509222593105
340.2263044170330430.4526088340660860.773695582966957
350.187880541814590.375761083629180.81211945818541
360.3049298148882140.6098596297764280.695070185111786
370.3349710741512010.6699421483024020.665028925848799
380.3259636225842460.6519272451684910.674036377415754
390.3560227398554310.7120454797108620.643977260144569
400.3342094491346410.6684188982692830.665790550865359
410.2980013652937390.5960027305874790.701998634706261
420.2638357288282250.5276714576564510.736164271171775
430.2782742652114350.556548530422870.721725734788565
440.2365365213239230.4730730426478460.763463478676077
450.2144422801981570.4288845603963140.785557719801843
460.3926897177725630.7853794355451270.607310282227437
470.5455190334856560.9089619330286870.454480966514344
480.4965487764023340.9930975528046680.503451223597666
490.483216160724750.96643232144950.51678383927525
500.4683902306446540.9367804612893080.531609769355346
510.4236717203499130.8473434406998250.576328279650087
520.3781691975725320.7563383951450640.621830802427468
530.3866036095760940.7732072191521880.613396390423906
540.3697095512826040.7394191025652080.630290448717396
550.3609375571723210.7218751143446420.639062442827679
560.3393852385237350.678770477047470.660614761476265
570.2997777542626510.5995555085253020.700222245737349
580.2711577202394340.5423154404788680.728842279760566
590.2366942697965370.4733885395930740.763305730203463
600.2424586147505540.4849172295011070.757541385249446
610.2186501591049540.4373003182099080.781349840895046
620.1917356708049250.383471341609850.808264329195075
630.1637320578437610.3274641156875220.836267942156239
640.1379788888949110.2759577777898220.862021111105089
650.1190738662615420.2381477325230850.880926133738458
660.1102288514213880.2204577028427760.889771148578612
670.1059861502412970.2119723004825940.894013849758703
680.223058679356040.446117358712080.77694132064396
690.3130388526634780.6260777053269560.686961147336522
700.2821425098269240.5642850196538480.717857490173076
710.3979023773212270.7958047546424530.602097622678773
720.3598978352378180.7197956704756360.640102164762182
730.3515646464888880.7031292929777750.648435353511112
740.3361970271152260.6723940542304510.663802972884775
750.3040241548740880.6080483097481750.695975845125912
760.369544725558950.7390894511178990.63045527444105
770.3338177811756470.6676355623512940.666182218824353
780.3129770526536390.6259541053072780.687022947346361
790.3260565689197160.6521131378394330.673943431080284
800.2984783473115490.5969566946230990.701521652688451
810.2703419068908390.5406838137816790.729658093109161
820.2394460425874980.4788920851749960.760553957412502
830.2151396628227070.4302793256454140.784860337177293
840.1879137227384080.3758274454768150.812086277261592
850.1846071916740780.3692143833481560.815392808325922
860.1595725120415980.3191450240831960.840427487958402
870.1403225373613220.2806450747226450.859677462638678
880.1307388017647960.2614776035295910.869261198235204
890.1129764378758930.2259528757517860.887023562124107
900.1102031142192710.2204062284385420.889796885780729
910.09429193694486590.1885838738897320.905708063055134
920.08142409060866170.1628481812173230.918575909391338
930.06916220468658760.1383244093731750.930837795313412
940.07105056196411460.1421011239282290.928949438035885
950.06420477509780920.1284095501956180.935795224902191
960.05379758650354130.1075951730070830.946202413496459
970.05943151463996930.1188630292799390.940568485360031
980.04922705113043850.0984541022608770.950772948869562
990.04036379712394110.08072759424788230.959636202876059
1000.03531497706865580.07062995413731160.964685022931344
1010.03133001394681790.06266002789363580.968669986053182
1020.03688207108464640.07376414216929280.963117928915354
1030.03165139025063740.06330278050127480.968348609749363
1040.02858591411915050.05717182823830090.97141408588085
1050.03381713581376670.06763427162753340.966182864186233
1060.02971708786873120.05943417573746240.970282912131269
1070.02413307862849560.04826615725699110.975866921371504
1080.02400791135008510.04801582270017020.975992088649915
1090.01945150490639090.03890300981278170.980548495093609
1100.01591366614214850.03182733228429690.984086333857851
1110.01261465227192230.02522930454384470.987385347728078
1120.01303812775868250.02607625551736510.986961872241317
1130.01164521464788010.02329042929576020.98835478535212
1140.01672065608724870.03344131217449730.983279343912751
1150.01597102344518570.03194204689037130.984028976554814
1160.0152811978016850.030562395603370.984718802198315
1170.01263902994539410.02527805989078830.987360970054606
1180.01262195624937510.02524391249875020.987378043750625
1190.01026120162714510.02052240325429030.989738798372855
1200.009038025678921090.01807605135784220.990961974321079
1210.007353567803015730.01470713560603150.992646432196984
1220.01131955121710580.02263910243421170.988680448782894
1230.009538495994803650.01907699198960730.990461504005196
1240.008473116391724750.01694623278344950.991526883608275
1250.006935559729241880.01387111945848380.993064440270758
1260.005650421037164460.01130084207432890.994349578962835
1270.00508716401279390.01017432802558780.994912835987206
1280.004098793520274620.008197587040549240.995901206479725
1290.006048869827586880.01209773965517380.993951130172413
1300.006553238028811910.01310647605762380.993446761971188
1310.01354029438996480.02708058877992970.986459705610035
1320.01629872750422860.03259745500845720.983701272495771
1330.01758236498167170.03516472996334340.982417635018328
1340.01698334499195070.03396668998390140.983016655008049
1350.0138803744734780.0277607489469560.986119625526522
1360.01171233869970170.02342467739940330.988287661300298
1370.00937464333820910.01874928667641820.990625356661791
1380.01141690215884620.02283380431769240.988583097841154
1390.009937139879518260.01987427975903650.990062860120482
1400.01129281549976360.02258563099952720.988707184500236
1410.01751656482001360.03503312964002710.982483435179986
1420.01592895790096320.03185791580192640.984071042099037
1430.01268316359323410.02536632718646820.987316836406766
1440.01344583330598330.02689166661196660.986554166694017
1450.02564031348952370.05128062697904730.974359686510476
1460.03141841095330010.06283682190660010.9685815890467
1470.03297957803633510.06595915607267020.967020421963665
1480.02977342271015450.05954684542030910.970226577289845
1490.02481272874093870.04962545748187750.975187271259061
1500.03443623206436750.06887246412873510.965563767935632
1510.02971241893936210.05942483787872420.970287581060638
1520.03128065816490130.06256131632980260.968719341835099
1530.06388043297827720.1277608659565540.936119567021723
1540.06276143572712580.1255228714542520.937238564272874
1550.07217477719129810.1443495543825960.927825222808702
1560.06174785743390260.1234957148678050.938252142566097
1570.05516287941602720.1103257588320540.944837120583973
1580.04827041228233410.09654082456466830.951729587717666
1590.04722610868202220.09445221736404450.952773891317978
1600.04049302819331040.08098605638662090.95950697180669
1610.03341243056971730.06682486113943450.966587569430283
1620.02746679527600250.05493359055200510.972533204723998
1630.02287565330103760.04575130660207520.977124346698962
1640.01929641328181750.03859282656363490.980703586718183
1650.01693535633760470.03387071267520940.983064643662395
1660.01718430506346220.03436861012692430.982815694936538
1670.01380762438724070.02761524877448140.986192375612759
1680.02037358612615640.04074717225231280.979626413873844
1690.02019288594577440.04038577189154880.979807114054226
1700.01885801717267590.03771603434535170.981141982827324
1710.01813496534063030.03626993068126060.98186503465937
1720.01469419658669620.02938839317339240.985305803413304
1730.01474229074869420.02948458149738840.985257709251306
1740.01642261616407160.03284523232814310.983577383835928
1750.02194945967986790.04389891935973590.978050540320132
1760.01765315232126220.03530630464252440.982346847678738
1770.01438269225106910.02876538450213820.985617307748931
1780.0117427428384760.02348548567695210.988257257161524
1790.009192159794017650.01838431958803530.990807840205982
1800.008005361728639290.01601072345727860.991994638271361
1810.006603604186685780.01320720837337160.993396395813314
1820.005345217149551930.01069043429910390.994654782850448
1830.005609366225755050.01121873245151010.994390633774245
1840.004436472950334990.008872945900669970.995563527049665
1850.0940865399009820.1881730798019640.905913460099018
1860.08168458117219110.1633691623443820.918315418827809
1870.09284398026744250.1856879605348850.907156019732557
1880.08021271356467330.1604254271293470.919787286435327
1890.06795963394561670.1359192678912330.932040366054383
1900.05635614350327380.1127122870065480.943643856496726
1910.04971116188416230.09942232376832470.950288838115838
1920.04196119331522220.08392238663044450.958038806684778
1930.04871552798395550.0974310559679110.951284472016044
1940.05228906663103810.1045781332620760.947710933368962
1950.04438117382695730.08876234765391450.955618826173043
1960.0370263282172370.07405265643447390.962973671782763
1970.04831570611723950.09663141223447890.951684293882761
1980.03958629462185280.07917258924370570.960413705378147
1990.03670115629514750.0734023125902950.963298843704853
2000.03103986761341980.06207973522683970.96896013238658
2010.02922197249448330.05844394498896650.970778027505517
2020.02438057070672770.04876114141345540.975619429293272
2030.03130536910663020.06261073821326040.96869463089337
2040.03568031777393910.07136063554787830.964319682226061
2050.03890633637672130.07781267275344250.961093663623279
2060.0308791545420790.06175830908415790.969120845457921
2070.03034222282480850.0606844456496170.969657777175192
2080.02494987273114620.04989974546229240.975050127268854
2090.02786814120547050.0557362824109410.97213185879453
2100.02254174262108910.04508348524217830.977458257378911
2110.02497753329313890.04995506658627780.975022466706861
2120.03997350874230030.07994701748460070.9600264912577
2130.03158710624416730.06317421248833450.968412893755833
2140.04415894259893950.08831788519787890.955841057401061
2150.03805930928693740.07611861857387480.961940690713063
2160.02975082592105630.05950165184211260.970249174078944
2170.03219553127290540.06439106254581090.967804468727095
2180.02752949098226520.05505898196453040.972470509017735
2190.02474180700445380.04948361400890750.975258192995546
2200.01896337737057050.0379267547411410.981036622629429
2210.01689473392096760.03378946784193510.983105266079032
2220.01242064858491530.02484129716983060.987579351415085
2230.009311741862016570.01862348372403310.990688258137983
2240.00750211854397710.01500423708795420.992497881456023
2250.006547056161290890.01309411232258180.993452943838709
2260.01499519031446630.02999038062893250.985004809685534
2270.01154161843724660.02308323687449310.988458381562753
2280.008350318923407940.01670063784681590.991649681076592
2290.006096834243210920.01219366848642180.993903165756789
2300.004410362517329940.008820725034659890.99558963748267
2310.004394614579941070.008789229159882130.995605385420059
2320.008144979571466480.0162899591429330.991855020428533
2330.03775654421187850.0755130884237570.962243455788122
2340.05429422005691110.1085884401138220.945705779943089
2350.04050658575470140.08101317150940270.959493414245299
2360.03611011259517660.07222022519035320.963889887404823
2370.2465057005702010.4930114011404030.753494299429799
2380.2007821309639770.4015642619279530.799217869036023
2390.180044405468040.3600888109360790.81995559453196
2400.1421285353331370.2842570706662740.857871464666863
2410.1137073431033520.2274146862067040.886292656896648
2420.09453010324072960.1890602064814590.90546989675927
2430.08790231871979390.1758046374395880.912097681280206
2440.1535504342222450.3071008684444910.846449565777755
2450.1376303397172550.275260679434510.862369660282745
2460.1101093305386860.2202186610773710.889890669461314
2470.07777060082222360.1555412016444470.922229399177776
2480.06669484702562430.1333896940512490.933305152974376
2490.06248882703230030.1249776540646010.9375111729677
2500.0773809930539610.1547619861079220.922619006946039
2510.04605497640822620.09210995281645230.953945023591774
2520.1122856641622770.2245713283245540.887714335837723
2530.2349985110953930.4699970221907850.765001488904607

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.234449524103323 & 0.468899048206647 & 0.765550475896677 \tabularnewline
12 & 0.120293517840556 & 0.240587035681113 & 0.879706482159444 \tabularnewline
13 & 0.0815541573457813 & 0.163108314691563 & 0.918445842654219 \tabularnewline
14 & 0.102874776497916 & 0.205749552995833 & 0.897125223502084 \tabularnewline
15 & 0.0587519756515484 & 0.117503951303097 & 0.941248024348452 \tabularnewline
16 & 0.0391686675931635 & 0.0783373351863269 & 0.960831332406836 \tabularnewline
17 & 0.063019118444172 & 0.126038236888344 & 0.936980881555828 \tabularnewline
18 & 0.255864327935305 & 0.511728655870609 & 0.744135672064695 \tabularnewline
19 & 0.194216632900149 & 0.388433265800299 & 0.805783367099851 \tabularnewline
20 & 0.13776488333464 & 0.275529766669281 & 0.86223511666536 \tabularnewline
21 & 0.0986062615513714 & 0.197212523102743 & 0.901393738448629 \tabularnewline
22 & 0.0968533990427696 & 0.193706798085539 & 0.90314660095723 \tabularnewline
23 & 0.257054377911142 & 0.514108755822284 & 0.742945622088858 \tabularnewline
24 & 0.321495418423946 & 0.642990836847893 & 0.678504581576054 \tabularnewline
25 & 0.294869538595026 & 0.589739077190051 & 0.705130461404974 \tabularnewline
26 & 0.277447857711978 & 0.554895715423955 & 0.722552142288022 \tabularnewline
27 & 0.352098888872553 & 0.704197777745105 & 0.647901111127447 \tabularnewline
28 & 0.363849648859334 & 0.727699297718668 & 0.636150351140666 \tabularnewline
29 & 0.346785592147584 & 0.693571184295169 & 0.653214407852416 \tabularnewline
30 & 0.380898386272269 & 0.761796772544538 & 0.619101613727731 \tabularnewline
31 & 0.328675803111158 & 0.657351606222317 & 0.671324196888842 \tabularnewline
32 & 0.289492867969931 & 0.578985735939863 & 0.710507132030069 \tabularnewline
33 & 0.262490777406895 & 0.524981554813791 & 0.737509222593105 \tabularnewline
34 & 0.226304417033043 & 0.452608834066086 & 0.773695582966957 \tabularnewline
35 & 0.18788054181459 & 0.37576108362918 & 0.81211945818541 \tabularnewline
36 & 0.304929814888214 & 0.609859629776428 & 0.695070185111786 \tabularnewline
37 & 0.334971074151201 & 0.669942148302402 & 0.665028925848799 \tabularnewline
38 & 0.325963622584246 & 0.651927245168491 & 0.674036377415754 \tabularnewline
39 & 0.356022739855431 & 0.712045479710862 & 0.643977260144569 \tabularnewline
40 & 0.334209449134641 & 0.668418898269283 & 0.665790550865359 \tabularnewline
41 & 0.298001365293739 & 0.596002730587479 & 0.701998634706261 \tabularnewline
42 & 0.263835728828225 & 0.527671457656451 & 0.736164271171775 \tabularnewline
43 & 0.278274265211435 & 0.55654853042287 & 0.721725734788565 \tabularnewline
44 & 0.236536521323923 & 0.473073042647846 & 0.763463478676077 \tabularnewline
45 & 0.214442280198157 & 0.428884560396314 & 0.785557719801843 \tabularnewline
46 & 0.392689717772563 & 0.785379435545127 & 0.607310282227437 \tabularnewline
47 & 0.545519033485656 & 0.908961933028687 & 0.454480966514344 \tabularnewline
48 & 0.496548776402334 & 0.993097552804668 & 0.503451223597666 \tabularnewline
49 & 0.48321616072475 & 0.9664323214495 & 0.51678383927525 \tabularnewline
50 & 0.468390230644654 & 0.936780461289308 & 0.531609769355346 \tabularnewline
51 & 0.423671720349913 & 0.847343440699825 & 0.576328279650087 \tabularnewline
52 & 0.378169197572532 & 0.756338395145064 & 0.621830802427468 \tabularnewline
53 & 0.386603609576094 & 0.773207219152188 & 0.613396390423906 \tabularnewline
54 & 0.369709551282604 & 0.739419102565208 & 0.630290448717396 \tabularnewline
55 & 0.360937557172321 & 0.721875114344642 & 0.639062442827679 \tabularnewline
56 & 0.339385238523735 & 0.67877047704747 & 0.660614761476265 \tabularnewline
57 & 0.299777754262651 & 0.599555508525302 & 0.700222245737349 \tabularnewline
58 & 0.271157720239434 & 0.542315440478868 & 0.728842279760566 \tabularnewline
59 & 0.236694269796537 & 0.473388539593074 & 0.763305730203463 \tabularnewline
60 & 0.242458614750554 & 0.484917229501107 & 0.757541385249446 \tabularnewline
61 & 0.218650159104954 & 0.437300318209908 & 0.781349840895046 \tabularnewline
62 & 0.191735670804925 & 0.38347134160985 & 0.808264329195075 \tabularnewline
63 & 0.163732057843761 & 0.327464115687522 & 0.836267942156239 \tabularnewline
64 & 0.137978888894911 & 0.275957777789822 & 0.862021111105089 \tabularnewline
65 & 0.119073866261542 & 0.238147732523085 & 0.880926133738458 \tabularnewline
66 & 0.110228851421388 & 0.220457702842776 & 0.889771148578612 \tabularnewline
67 & 0.105986150241297 & 0.211972300482594 & 0.894013849758703 \tabularnewline
68 & 0.22305867935604 & 0.44611735871208 & 0.77694132064396 \tabularnewline
69 & 0.313038852663478 & 0.626077705326956 & 0.686961147336522 \tabularnewline
70 & 0.282142509826924 & 0.564285019653848 & 0.717857490173076 \tabularnewline
71 & 0.397902377321227 & 0.795804754642453 & 0.602097622678773 \tabularnewline
72 & 0.359897835237818 & 0.719795670475636 & 0.640102164762182 \tabularnewline
73 & 0.351564646488888 & 0.703129292977775 & 0.648435353511112 \tabularnewline
74 & 0.336197027115226 & 0.672394054230451 & 0.663802972884775 \tabularnewline
75 & 0.304024154874088 & 0.608048309748175 & 0.695975845125912 \tabularnewline
76 & 0.36954472555895 & 0.739089451117899 & 0.63045527444105 \tabularnewline
77 & 0.333817781175647 & 0.667635562351294 & 0.666182218824353 \tabularnewline
78 & 0.312977052653639 & 0.625954105307278 & 0.687022947346361 \tabularnewline
79 & 0.326056568919716 & 0.652113137839433 & 0.673943431080284 \tabularnewline
80 & 0.298478347311549 & 0.596956694623099 & 0.701521652688451 \tabularnewline
81 & 0.270341906890839 & 0.540683813781679 & 0.729658093109161 \tabularnewline
82 & 0.239446042587498 & 0.478892085174996 & 0.760553957412502 \tabularnewline
83 & 0.215139662822707 & 0.430279325645414 & 0.784860337177293 \tabularnewline
84 & 0.187913722738408 & 0.375827445476815 & 0.812086277261592 \tabularnewline
85 & 0.184607191674078 & 0.369214383348156 & 0.815392808325922 \tabularnewline
86 & 0.159572512041598 & 0.319145024083196 & 0.840427487958402 \tabularnewline
87 & 0.140322537361322 & 0.280645074722645 & 0.859677462638678 \tabularnewline
88 & 0.130738801764796 & 0.261477603529591 & 0.869261198235204 \tabularnewline
89 & 0.112976437875893 & 0.225952875751786 & 0.887023562124107 \tabularnewline
90 & 0.110203114219271 & 0.220406228438542 & 0.889796885780729 \tabularnewline
91 & 0.0942919369448659 & 0.188583873889732 & 0.905708063055134 \tabularnewline
92 & 0.0814240906086617 & 0.162848181217323 & 0.918575909391338 \tabularnewline
93 & 0.0691622046865876 & 0.138324409373175 & 0.930837795313412 \tabularnewline
94 & 0.0710505619641146 & 0.142101123928229 & 0.928949438035885 \tabularnewline
95 & 0.0642047750978092 & 0.128409550195618 & 0.935795224902191 \tabularnewline
96 & 0.0537975865035413 & 0.107595173007083 & 0.946202413496459 \tabularnewline
97 & 0.0594315146399693 & 0.118863029279939 & 0.940568485360031 \tabularnewline
98 & 0.0492270511304385 & 0.098454102260877 & 0.950772948869562 \tabularnewline
99 & 0.0403637971239411 & 0.0807275942478823 & 0.959636202876059 \tabularnewline
100 & 0.0353149770686558 & 0.0706299541373116 & 0.964685022931344 \tabularnewline
101 & 0.0313300139468179 & 0.0626600278936358 & 0.968669986053182 \tabularnewline
102 & 0.0368820710846464 & 0.0737641421692928 & 0.963117928915354 \tabularnewline
103 & 0.0316513902506374 & 0.0633027805012748 & 0.968348609749363 \tabularnewline
104 & 0.0285859141191505 & 0.0571718282383009 & 0.97141408588085 \tabularnewline
105 & 0.0338171358137667 & 0.0676342716275334 & 0.966182864186233 \tabularnewline
106 & 0.0297170878687312 & 0.0594341757374624 & 0.970282912131269 \tabularnewline
107 & 0.0241330786284956 & 0.0482661572569911 & 0.975866921371504 \tabularnewline
108 & 0.0240079113500851 & 0.0480158227001702 & 0.975992088649915 \tabularnewline
109 & 0.0194515049063909 & 0.0389030098127817 & 0.980548495093609 \tabularnewline
110 & 0.0159136661421485 & 0.0318273322842969 & 0.984086333857851 \tabularnewline
111 & 0.0126146522719223 & 0.0252293045438447 & 0.987385347728078 \tabularnewline
112 & 0.0130381277586825 & 0.0260762555173651 & 0.986961872241317 \tabularnewline
113 & 0.0116452146478801 & 0.0232904292957602 & 0.98835478535212 \tabularnewline
114 & 0.0167206560872487 & 0.0334413121744973 & 0.983279343912751 \tabularnewline
115 & 0.0159710234451857 & 0.0319420468903713 & 0.984028976554814 \tabularnewline
116 & 0.015281197801685 & 0.03056239560337 & 0.984718802198315 \tabularnewline
117 & 0.0126390299453941 & 0.0252780598907883 & 0.987360970054606 \tabularnewline
118 & 0.0126219562493751 & 0.0252439124987502 & 0.987378043750625 \tabularnewline
119 & 0.0102612016271451 & 0.0205224032542903 & 0.989738798372855 \tabularnewline
120 & 0.00903802567892109 & 0.0180760513578422 & 0.990961974321079 \tabularnewline
121 & 0.00735356780301573 & 0.0147071356060315 & 0.992646432196984 \tabularnewline
122 & 0.0113195512171058 & 0.0226391024342117 & 0.988680448782894 \tabularnewline
123 & 0.00953849599480365 & 0.0190769919896073 & 0.990461504005196 \tabularnewline
124 & 0.00847311639172475 & 0.0169462327834495 & 0.991526883608275 \tabularnewline
125 & 0.00693555972924188 & 0.0138711194584838 & 0.993064440270758 \tabularnewline
126 & 0.00565042103716446 & 0.0113008420743289 & 0.994349578962835 \tabularnewline
127 & 0.0050871640127939 & 0.0101743280255878 & 0.994912835987206 \tabularnewline
128 & 0.00409879352027462 & 0.00819758704054924 & 0.995901206479725 \tabularnewline
129 & 0.00604886982758688 & 0.0120977396551738 & 0.993951130172413 \tabularnewline
130 & 0.00655323802881191 & 0.0131064760576238 & 0.993446761971188 \tabularnewline
131 & 0.0135402943899648 & 0.0270805887799297 & 0.986459705610035 \tabularnewline
132 & 0.0162987275042286 & 0.0325974550084572 & 0.983701272495771 \tabularnewline
133 & 0.0175823649816717 & 0.0351647299633434 & 0.982417635018328 \tabularnewline
134 & 0.0169833449919507 & 0.0339666899839014 & 0.983016655008049 \tabularnewline
135 & 0.013880374473478 & 0.027760748946956 & 0.986119625526522 \tabularnewline
136 & 0.0117123386997017 & 0.0234246773994033 & 0.988287661300298 \tabularnewline
137 & 0.0093746433382091 & 0.0187492866764182 & 0.990625356661791 \tabularnewline
138 & 0.0114169021588462 & 0.0228338043176924 & 0.988583097841154 \tabularnewline
139 & 0.00993713987951826 & 0.0198742797590365 & 0.990062860120482 \tabularnewline
140 & 0.0112928154997636 & 0.0225856309995272 & 0.988707184500236 \tabularnewline
141 & 0.0175165648200136 & 0.0350331296400271 & 0.982483435179986 \tabularnewline
142 & 0.0159289579009632 & 0.0318579158019264 & 0.984071042099037 \tabularnewline
143 & 0.0126831635932341 & 0.0253663271864682 & 0.987316836406766 \tabularnewline
144 & 0.0134458333059833 & 0.0268916666119666 & 0.986554166694017 \tabularnewline
145 & 0.0256403134895237 & 0.0512806269790473 & 0.974359686510476 \tabularnewline
146 & 0.0314184109533001 & 0.0628368219066001 & 0.9685815890467 \tabularnewline
147 & 0.0329795780363351 & 0.0659591560726702 & 0.967020421963665 \tabularnewline
148 & 0.0297734227101545 & 0.0595468454203091 & 0.970226577289845 \tabularnewline
149 & 0.0248127287409387 & 0.0496254574818775 & 0.975187271259061 \tabularnewline
150 & 0.0344362320643675 & 0.0688724641287351 & 0.965563767935632 \tabularnewline
151 & 0.0297124189393621 & 0.0594248378787242 & 0.970287581060638 \tabularnewline
152 & 0.0312806581649013 & 0.0625613163298026 & 0.968719341835099 \tabularnewline
153 & 0.0638804329782772 & 0.127760865956554 & 0.936119567021723 \tabularnewline
154 & 0.0627614357271258 & 0.125522871454252 & 0.937238564272874 \tabularnewline
155 & 0.0721747771912981 & 0.144349554382596 & 0.927825222808702 \tabularnewline
156 & 0.0617478574339026 & 0.123495714867805 & 0.938252142566097 \tabularnewline
157 & 0.0551628794160272 & 0.110325758832054 & 0.944837120583973 \tabularnewline
158 & 0.0482704122823341 & 0.0965408245646683 & 0.951729587717666 \tabularnewline
159 & 0.0472261086820222 & 0.0944522173640445 & 0.952773891317978 \tabularnewline
160 & 0.0404930281933104 & 0.0809860563866209 & 0.95950697180669 \tabularnewline
161 & 0.0334124305697173 & 0.0668248611394345 & 0.966587569430283 \tabularnewline
162 & 0.0274667952760025 & 0.0549335905520051 & 0.972533204723998 \tabularnewline
163 & 0.0228756533010376 & 0.0457513066020752 & 0.977124346698962 \tabularnewline
164 & 0.0192964132818175 & 0.0385928265636349 & 0.980703586718183 \tabularnewline
165 & 0.0169353563376047 & 0.0338707126752094 & 0.983064643662395 \tabularnewline
166 & 0.0171843050634622 & 0.0343686101269243 & 0.982815694936538 \tabularnewline
167 & 0.0138076243872407 & 0.0276152487744814 & 0.986192375612759 \tabularnewline
168 & 0.0203735861261564 & 0.0407471722523128 & 0.979626413873844 \tabularnewline
169 & 0.0201928859457744 & 0.0403857718915488 & 0.979807114054226 \tabularnewline
170 & 0.0188580171726759 & 0.0377160343453517 & 0.981141982827324 \tabularnewline
171 & 0.0181349653406303 & 0.0362699306812606 & 0.98186503465937 \tabularnewline
172 & 0.0146941965866962 & 0.0293883931733924 & 0.985305803413304 \tabularnewline
173 & 0.0147422907486942 & 0.0294845814973884 & 0.985257709251306 \tabularnewline
174 & 0.0164226161640716 & 0.0328452323281431 & 0.983577383835928 \tabularnewline
175 & 0.0219494596798679 & 0.0438989193597359 & 0.978050540320132 \tabularnewline
176 & 0.0176531523212622 & 0.0353063046425244 & 0.982346847678738 \tabularnewline
177 & 0.0143826922510691 & 0.0287653845021382 & 0.985617307748931 \tabularnewline
178 & 0.011742742838476 & 0.0234854856769521 & 0.988257257161524 \tabularnewline
179 & 0.00919215979401765 & 0.0183843195880353 & 0.990807840205982 \tabularnewline
180 & 0.00800536172863929 & 0.0160107234572786 & 0.991994638271361 \tabularnewline
181 & 0.00660360418668578 & 0.0132072083733716 & 0.993396395813314 \tabularnewline
182 & 0.00534521714955193 & 0.0106904342991039 & 0.994654782850448 \tabularnewline
183 & 0.00560936622575505 & 0.0112187324515101 & 0.994390633774245 \tabularnewline
184 & 0.00443647295033499 & 0.00887294590066997 & 0.995563527049665 \tabularnewline
185 & 0.094086539900982 & 0.188173079801964 & 0.905913460099018 \tabularnewline
186 & 0.0816845811721911 & 0.163369162344382 & 0.918315418827809 \tabularnewline
187 & 0.0928439802674425 & 0.185687960534885 & 0.907156019732557 \tabularnewline
188 & 0.0802127135646733 & 0.160425427129347 & 0.919787286435327 \tabularnewline
189 & 0.0679596339456167 & 0.135919267891233 & 0.932040366054383 \tabularnewline
190 & 0.0563561435032738 & 0.112712287006548 & 0.943643856496726 \tabularnewline
191 & 0.0497111618841623 & 0.0994223237683247 & 0.950288838115838 \tabularnewline
192 & 0.0419611933152222 & 0.0839223866304445 & 0.958038806684778 \tabularnewline
193 & 0.0487155279839555 & 0.097431055967911 & 0.951284472016044 \tabularnewline
194 & 0.0522890666310381 & 0.104578133262076 & 0.947710933368962 \tabularnewline
195 & 0.0443811738269573 & 0.0887623476539145 & 0.955618826173043 \tabularnewline
196 & 0.037026328217237 & 0.0740526564344739 & 0.962973671782763 \tabularnewline
197 & 0.0483157061172395 & 0.0966314122344789 & 0.951684293882761 \tabularnewline
198 & 0.0395862946218528 & 0.0791725892437057 & 0.960413705378147 \tabularnewline
199 & 0.0367011562951475 & 0.073402312590295 & 0.963298843704853 \tabularnewline
200 & 0.0310398676134198 & 0.0620797352268397 & 0.96896013238658 \tabularnewline
201 & 0.0292219724944833 & 0.0584439449889665 & 0.970778027505517 \tabularnewline
202 & 0.0243805707067277 & 0.0487611414134554 & 0.975619429293272 \tabularnewline
203 & 0.0313053691066302 & 0.0626107382132604 & 0.96869463089337 \tabularnewline
204 & 0.0356803177739391 & 0.0713606355478783 & 0.964319682226061 \tabularnewline
205 & 0.0389063363767213 & 0.0778126727534425 & 0.961093663623279 \tabularnewline
206 & 0.030879154542079 & 0.0617583090841579 & 0.969120845457921 \tabularnewline
207 & 0.0303422228248085 & 0.060684445649617 & 0.969657777175192 \tabularnewline
208 & 0.0249498727311462 & 0.0498997454622924 & 0.975050127268854 \tabularnewline
209 & 0.0278681412054705 & 0.055736282410941 & 0.97213185879453 \tabularnewline
210 & 0.0225417426210891 & 0.0450834852421783 & 0.977458257378911 \tabularnewline
211 & 0.0249775332931389 & 0.0499550665862778 & 0.975022466706861 \tabularnewline
212 & 0.0399735087423003 & 0.0799470174846007 & 0.9600264912577 \tabularnewline
213 & 0.0315871062441673 & 0.0631742124883345 & 0.968412893755833 \tabularnewline
214 & 0.0441589425989395 & 0.0883178851978789 & 0.955841057401061 \tabularnewline
215 & 0.0380593092869374 & 0.0761186185738748 & 0.961940690713063 \tabularnewline
216 & 0.0297508259210563 & 0.0595016518421126 & 0.970249174078944 \tabularnewline
217 & 0.0321955312729054 & 0.0643910625458109 & 0.967804468727095 \tabularnewline
218 & 0.0275294909822652 & 0.0550589819645304 & 0.972470509017735 \tabularnewline
219 & 0.0247418070044538 & 0.0494836140089075 & 0.975258192995546 \tabularnewline
220 & 0.0189633773705705 & 0.037926754741141 & 0.981036622629429 \tabularnewline
221 & 0.0168947339209676 & 0.0337894678419351 & 0.983105266079032 \tabularnewline
222 & 0.0124206485849153 & 0.0248412971698306 & 0.987579351415085 \tabularnewline
223 & 0.00931174186201657 & 0.0186234837240331 & 0.990688258137983 \tabularnewline
224 & 0.0075021185439771 & 0.0150042370879542 & 0.992497881456023 \tabularnewline
225 & 0.00654705616129089 & 0.0130941123225818 & 0.993452943838709 \tabularnewline
226 & 0.0149951903144663 & 0.0299903806289325 & 0.985004809685534 \tabularnewline
227 & 0.0115416184372466 & 0.0230832368744931 & 0.988458381562753 \tabularnewline
228 & 0.00835031892340794 & 0.0167006378468159 & 0.991649681076592 \tabularnewline
229 & 0.00609683424321092 & 0.0121936684864218 & 0.993903165756789 \tabularnewline
230 & 0.00441036251732994 & 0.00882072503465989 & 0.99558963748267 \tabularnewline
231 & 0.00439461457994107 & 0.00878922915988213 & 0.995605385420059 \tabularnewline
232 & 0.00814497957146648 & 0.016289959142933 & 0.991855020428533 \tabularnewline
233 & 0.0377565442118785 & 0.075513088423757 & 0.962243455788122 \tabularnewline
234 & 0.0542942200569111 & 0.108588440113822 & 0.945705779943089 \tabularnewline
235 & 0.0405065857547014 & 0.0810131715094027 & 0.959493414245299 \tabularnewline
236 & 0.0361101125951766 & 0.0722202251903532 & 0.963889887404823 \tabularnewline
237 & 0.246505700570201 & 0.493011401140403 & 0.753494299429799 \tabularnewline
238 & 0.200782130963977 & 0.401564261927953 & 0.799217869036023 \tabularnewline
239 & 0.18004440546804 & 0.360088810936079 & 0.81995559453196 \tabularnewline
240 & 0.142128535333137 & 0.284257070666274 & 0.857871464666863 \tabularnewline
241 & 0.113707343103352 & 0.227414686206704 & 0.886292656896648 \tabularnewline
242 & 0.0945301032407296 & 0.189060206481459 & 0.90546989675927 \tabularnewline
243 & 0.0879023187197939 & 0.175804637439588 & 0.912097681280206 \tabularnewline
244 & 0.153550434222245 & 0.307100868444491 & 0.846449565777755 \tabularnewline
245 & 0.137630339717255 & 0.27526067943451 & 0.862369660282745 \tabularnewline
246 & 0.110109330538686 & 0.220218661077371 & 0.889890669461314 \tabularnewline
247 & 0.0777706008222236 & 0.155541201644447 & 0.922229399177776 \tabularnewline
248 & 0.0666948470256243 & 0.133389694051249 & 0.933305152974376 \tabularnewline
249 & 0.0624888270323003 & 0.124977654064601 & 0.9375111729677 \tabularnewline
250 & 0.077380993053961 & 0.154761986107922 & 0.922619006946039 \tabularnewline
251 & 0.0460549764082262 & 0.0921099528164523 & 0.953945023591774 \tabularnewline
252 & 0.112285664162277 & 0.224571328324554 & 0.887714335837723 \tabularnewline
253 & 0.234998511095393 & 0.469997022190785 & 0.765001488904607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185709&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.234449524103323[/C][C]0.468899048206647[/C][C]0.765550475896677[/C][/ROW]
[ROW][C]12[/C][C]0.120293517840556[/C][C]0.240587035681113[/C][C]0.879706482159444[/C][/ROW]
[ROW][C]13[/C][C]0.0815541573457813[/C][C]0.163108314691563[/C][C]0.918445842654219[/C][/ROW]
[ROW][C]14[/C][C]0.102874776497916[/C][C]0.205749552995833[/C][C]0.897125223502084[/C][/ROW]
[ROW][C]15[/C][C]0.0587519756515484[/C][C]0.117503951303097[/C][C]0.941248024348452[/C][/ROW]
[ROW][C]16[/C][C]0.0391686675931635[/C][C]0.0783373351863269[/C][C]0.960831332406836[/C][/ROW]
[ROW][C]17[/C][C]0.063019118444172[/C][C]0.126038236888344[/C][C]0.936980881555828[/C][/ROW]
[ROW][C]18[/C][C]0.255864327935305[/C][C]0.511728655870609[/C][C]0.744135672064695[/C][/ROW]
[ROW][C]19[/C][C]0.194216632900149[/C][C]0.388433265800299[/C][C]0.805783367099851[/C][/ROW]
[ROW][C]20[/C][C]0.13776488333464[/C][C]0.275529766669281[/C][C]0.86223511666536[/C][/ROW]
[ROW][C]21[/C][C]0.0986062615513714[/C][C]0.197212523102743[/C][C]0.901393738448629[/C][/ROW]
[ROW][C]22[/C][C]0.0968533990427696[/C][C]0.193706798085539[/C][C]0.90314660095723[/C][/ROW]
[ROW][C]23[/C][C]0.257054377911142[/C][C]0.514108755822284[/C][C]0.742945622088858[/C][/ROW]
[ROW][C]24[/C][C]0.321495418423946[/C][C]0.642990836847893[/C][C]0.678504581576054[/C][/ROW]
[ROW][C]25[/C][C]0.294869538595026[/C][C]0.589739077190051[/C][C]0.705130461404974[/C][/ROW]
[ROW][C]26[/C][C]0.277447857711978[/C][C]0.554895715423955[/C][C]0.722552142288022[/C][/ROW]
[ROW][C]27[/C][C]0.352098888872553[/C][C]0.704197777745105[/C][C]0.647901111127447[/C][/ROW]
[ROW][C]28[/C][C]0.363849648859334[/C][C]0.727699297718668[/C][C]0.636150351140666[/C][/ROW]
[ROW][C]29[/C][C]0.346785592147584[/C][C]0.693571184295169[/C][C]0.653214407852416[/C][/ROW]
[ROW][C]30[/C][C]0.380898386272269[/C][C]0.761796772544538[/C][C]0.619101613727731[/C][/ROW]
[ROW][C]31[/C][C]0.328675803111158[/C][C]0.657351606222317[/C][C]0.671324196888842[/C][/ROW]
[ROW][C]32[/C][C]0.289492867969931[/C][C]0.578985735939863[/C][C]0.710507132030069[/C][/ROW]
[ROW][C]33[/C][C]0.262490777406895[/C][C]0.524981554813791[/C][C]0.737509222593105[/C][/ROW]
[ROW][C]34[/C][C]0.226304417033043[/C][C]0.452608834066086[/C][C]0.773695582966957[/C][/ROW]
[ROW][C]35[/C][C]0.18788054181459[/C][C]0.37576108362918[/C][C]0.81211945818541[/C][/ROW]
[ROW][C]36[/C][C]0.304929814888214[/C][C]0.609859629776428[/C][C]0.695070185111786[/C][/ROW]
[ROW][C]37[/C][C]0.334971074151201[/C][C]0.669942148302402[/C][C]0.665028925848799[/C][/ROW]
[ROW][C]38[/C][C]0.325963622584246[/C][C]0.651927245168491[/C][C]0.674036377415754[/C][/ROW]
[ROW][C]39[/C][C]0.356022739855431[/C][C]0.712045479710862[/C][C]0.643977260144569[/C][/ROW]
[ROW][C]40[/C][C]0.334209449134641[/C][C]0.668418898269283[/C][C]0.665790550865359[/C][/ROW]
[ROW][C]41[/C][C]0.298001365293739[/C][C]0.596002730587479[/C][C]0.701998634706261[/C][/ROW]
[ROW][C]42[/C][C]0.263835728828225[/C][C]0.527671457656451[/C][C]0.736164271171775[/C][/ROW]
[ROW][C]43[/C][C]0.278274265211435[/C][C]0.55654853042287[/C][C]0.721725734788565[/C][/ROW]
[ROW][C]44[/C][C]0.236536521323923[/C][C]0.473073042647846[/C][C]0.763463478676077[/C][/ROW]
[ROW][C]45[/C][C]0.214442280198157[/C][C]0.428884560396314[/C][C]0.785557719801843[/C][/ROW]
[ROW][C]46[/C][C]0.392689717772563[/C][C]0.785379435545127[/C][C]0.607310282227437[/C][/ROW]
[ROW][C]47[/C][C]0.545519033485656[/C][C]0.908961933028687[/C][C]0.454480966514344[/C][/ROW]
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[ROW][C]125[/C][C]0.00693555972924188[/C][C]0.0138711194584838[/C][C]0.993064440270758[/C][/ROW]
[ROW][C]126[/C][C]0.00565042103716446[/C][C]0.0113008420743289[/C][C]0.994349578962835[/C][/ROW]
[ROW][C]127[/C][C]0.0050871640127939[/C][C]0.0101743280255878[/C][C]0.994912835987206[/C][/ROW]
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[ROW][C]130[/C][C]0.00655323802881191[/C][C]0.0131064760576238[/C][C]0.993446761971188[/C][/ROW]
[ROW][C]131[/C][C]0.0135402943899648[/C][C]0.0270805887799297[/C][C]0.986459705610035[/C][/ROW]
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[ROW][C]179[/C][C]0.00919215979401765[/C][C]0.0183843195880353[/C][C]0.990807840205982[/C][/ROW]
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[ROW][C]184[/C][C]0.00443647295033499[/C][C]0.00887294590066997[/C][C]0.995563527049665[/C][/ROW]
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[ROW][C]186[/C][C]0.0816845811721911[/C][C]0.163369162344382[/C][C]0.918315418827809[/C][/ROW]
[ROW][C]187[/C][C]0.0928439802674425[/C][C]0.185687960534885[/C][C]0.907156019732557[/C][/ROW]
[ROW][C]188[/C][C]0.0802127135646733[/C][C]0.160425427129347[/C][C]0.919787286435327[/C][/ROW]
[ROW][C]189[/C][C]0.0679596339456167[/C][C]0.135919267891233[/C][C]0.932040366054383[/C][/ROW]
[ROW][C]190[/C][C]0.0563561435032738[/C][C]0.112712287006548[/C][C]0.943643856496726[/C][/ROW]
[ROW][C]191[/C][C]0.0497111618841623[/C][C]0.0994223237683247[/C][C]0.950288838115838[/C][/ROW]
[ROW][C]192[/C][C]0.0419611933152222[/C][C]0.0839223866304445[/C][C]0.958038806684778[/C][/ROW]
[ROW][C]193[/C][C]0.0487155279839555[/C][C]0.097431055967911[/C][C]0.951284472016044[/C][/ROW]
[ROW][C]194[/C][C]0.0522890666310381[/C][C]0.104578133262076[/C][C]0.947710933368962[/C][/ROW]
[ROW][C]195[/C][C]0.0443811738269573[/C][C]0.0887623476539145[/C][C]0.955618826173043[/C][/ROW]
[ROW][C]196[/C][C]0.037026328217237[/C][C]0.0740526564344739[/C][C]0.962973671782763[/C][/ROW]
[ROW][C]197[/C][C]0.0483157061172395[/C][C]0.0966314122344789[/C][C]0.951684293882761[/C][/ROW]
[ROW][C]198[/C][C]0.0395862946218528[/C][C]0.0791725892437057[/C][C]0.960413705378147[/C][/ROW]
[ROW][C]199[/C][C]0.0367011562951475[/C][C]0.073402312590295[/C][C]0.963298843704853[/C][/ROW]
[ROW][C]200[/C][C]0.0310398676134198[/C][C]0.0620797352268397[/C][C]0.96896013238658[/C][/ROW]
[ROW][C]201[/C][C]0.0292219724944833[/C][C]0.0584439449889665[/C][C]0.970778027505517[/C][/ROW]
[ROW][C]202[/C][C]0.0243805707067277[/C][C]0.0487611414134554[/C][C]0.975619429293272[/C][/ROW]
[ROW][C]203[/C][C]0.0313053691066302[/C][C]0.0626107382132604[/C][C]0.96869463089337[/C][/ROW]
[ROW][C]204[/C][C]0.0356803177739391[/C][C]0.0713606355478783[/C][C]0.964319682226061[/C][/ROW]
[ROW][C]205[/C][C]0.0389063363767213[/C][C]0.0778126727534425[/C][C]0.961093663623279[/C][/ROW]
[ROW][C]206[/C][C]0.030879154542079[/C][C]0.0617583090841579[/C][C]0.969120845457921[/C][/ROW]
[ROW][C]207[/C][C]0.0303422228248085[/C][C]0.060684445649617[/C][C]0.969657777175192[/C][/ROW]
[ROW][C]208[/C][C]0.0249498727311462[/C][C]0.0498997454622924[/C][C]0.975050127268854[/C][/ROW]
[ROW][C]209[/C][C]0.0278681412054705[/C][C]0.055736282410941[/C][C]0.97213185879453[/C][/ROW]
[ROW][C]210[/C][C]0.0225417426210891[/C][C]0.0450834852421783[/C][C]0.977458257378911[/C][/ROW]
[ROW][C]211[/C][C]0.0249775332931389[/C][C]0.0499550665862778[/C][C]0.975022466706861[/C][/ROW]
[ROW][C]212[/C][C]0.0399735087423003[/C][C]0.0799470174846007[/C][C]0.9600264912577[/C][/ROW]
[ROW][C]213[/C][C]0.0315871062441673[/C][C]0.0631742124883345[/C][C]0.968412893755833[/C][/ROW]
[ROW][C]214[/C][C]0.0441589425989395[/C][C]0.0883178851978789[/C][C]0.955841057401061[/C][/ROW]
[ROW][C]215[/C][C]0.0380593092869374[/C][C]0.0761186185738748[/C][C]0.961940690713063[/C][/ROW]
[ROW][C]216[/C][C]0.0297508259210563[/C][C]0.0595016518421126[/C][C]0.970249174078944[/C][/ROW]
[ROW][C]217[/C][C]0.0321955312729054[/C][C]0.0643910625458109[/C][C]0.967804468727095[/C][/ROW]
[ROW][C]218[/C][C]0.0275294909822652[/C][C]0.0550589819645304[/C][C]0.972470509017735[/C][/ROW]
[ROW][C]219[/C][C]0.0247418070044538[/C][C]0.0494836140089075[/C][C]0.975258192995546[/C][/ROW]
[ROW][C]220[/C][C]0.0189633773705705[/C][C]0.037926754741141[/C][C]0.981036622629429[/C][/ROW]
[ROW][C]221[/C][C]0.0168947339209676[/C][C]0.0337894678419351[/C][C]0.983105266079032[/C][/ROW]
[ROW][C]222[/C][C]0.0124206485849153[/C][C]0.0248412971698306[/C][C]0.987579351415085[/C][/ROW]
[ROW][C]223[/C][C]0.00931174186201657[/C][C]0.0186234837240331[/C][C]0.990688258137983[/C][/ROW]
[ROW][C]224[/C][C]0.0075021185439771[/C][C]0.0150042370879542[/C][C]0.992497881456023[/C][/ROW]
[ROW][C]225[/C][C]0.00654705616129089[/C][C]0.0130941123225818[/C][C]0.993452943838709[/C][/ROW]
[ROW][C]226[/C][C]0.0149951903144663[/C][C]0.0299903806289325[/C][C]0.985004809685534[/C][/ROW]
[ROW][C]227[/C][C]0.0115416184372466[/C][C]0.0230832368744931[/C][C]0.988458381562753[/C][/ROW]
[ROW][C]228[/C][C]0.00835031892340794[/C][C]0.0167006378468159[/C][C]0.991649681076592[/C][/ROW]
[ROW][C]229[/C][C]0.00609683424321092[/C][C]0.0121936684864218[/C][C]0.993903165756789[/C][/ROW]
[ROW][C]230[/C][C]0.00441036251732994[/C][C]0.00882072503465989[/C][C]0.99558963748267[/C][/ROW]
[ROW][C]231[/C][C]0.00439461457994107[/C][C]0.00878922915988213[/C][C]0.995605385420059[/C][/ROW]
[ROW][C]232[/C][C]0.00814497957146648[/C][C]0.016289959142933[/C][C]0.991855020428533[/C][/ROW]
[ROW][C]233[/C][C]0.0377565442118785[/C][C]0.075513088423757[/C][C]0.962243455788122[/C][/ROW]
[ROW][C]234[/C][C]0.0542942200569111[/C][C]0.108588440113822[/C][C]0.945705779943089[/C][/ROW]
[ROW][C]235[/C][C]0.0405065857547014[/C][C]0.0810131715094027[/C][C]0.959493414245299[/C][/ROW]
[ROW][C]236[/C][C]0.0361101125951766[/C][C]0.0722202251903532[/C][C]0.963889887404823[/C][/ROW]
[ROW][C]237[/C][C]0.246505700570201[/C][C]0.493011401140403[/C][C]0.753494299429799[/C][/ROW]
[ROW][C]238[/C][C]0.200782130963977[/C][C]0.401564261927953[/C][C]0.799217869036023[/C][/ROW]
[ROW][C]239[/C][C]0.18004440546804[/C][C]0.360088810936079[/C][C]0.81995559453196[/C][/ROW]
[ROW][C]240[/C][C]0.142128535333137[/C][C]0.284257070666274[/C][C]0.857871464666863[/C][/ROW]
[ROW][C]241[/C][C]0.113707343103352[/C][C]0.227414686206704[/C][C]0.886292656896648[/C][/ROW]
[ROW][C]242[/C][C]0.0945301032407296[/C][C]0.189060206481459[/C][C]0.90546989675927[/C][/ROW]
[ROW][C]243[/C][C]0.0879023187197939[/C][C]0.175804637439588[/C][C]0.912097681280206[/C][/ROW]
[ROW][C]244[/C][C]0.153550434222245[/C][C]0.307100868444491[/C][C]0.846449565777755[/C][/ROW]
[ROW][C]245[/C][C]0.137630339717255[/C][C]0.27526067943451[/C][C]0.862369660282745[/C][/ROW]
[ROW][C]246[/C][C]0.110109330538686[/C][C]0.220218661077371[/C][C]0.889890669461314[/C][/ROW]
[ROW][C]247[/C][C]0.0777706008222236[/C][C]0.155541201644447[/C][C]0.922229399177776[/C][/ROW]
[ROW][C]248[/C][C]0.0666948470256243[/C][C]0.133389694051249[/C][C]0.933305152974376[/C][/ROW]
[ROW][C]249[/C][C]0.0624888270323003[/C][C]0.124977654064601[/C][C]0.9375111729677[/C][/ROW]
[ROW][C]250[/C][C]0.077380993053961[/C][C]0.154761986107922[/C][C]0.922619006946039[/C][/ROW]
[ROW][C]251[/C][C]0.0460549764082262[/C][C]0.0921099528164523[/C][C]0.953945023591774[/C][/ROW]
[ROW][C]252[/C][C]0.112285664162277[/C][C]0.224571328324554[/C][C]0.887714335837723[/C][/ROW]
[ROW][C]253[/C][C]0.234998511095393[/C][C]0.469997022190785[/C][C]0.765001488904607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185709&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185709&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2344495241033230.4688990482066470.765550475896677
120.1202935178405560.2405870356811130.879706482159444
130.08155415734578130.1631083146915630.918445842654219
140.1028747764979160.2057495529958330.897125223502084
150.05875197565154840.1175039513030970.941248024348452
160.03916866759316350.07833733518632690.960831332406836
170.0630191184441720.1260382368883440.936980881555828
180.2558643279353050.5117286558706090.744135672064695
190.1942166329001490.3884332658002990.805783367099851
200.137764883334640.2755297666692810.86223511666536
210.09860626155137140.1972125231027430.901393738448629
220.09685339904276960.1937067980855390.90314660095723
230.2570543779111420.5141087558222840.742945622088858
240.3214954184239460.6429908368478930.678504581576054
250.2948695385950260.5897390771900510.705130461404974
260.2774478577119780.5548957154239550.722552142288022
270.3520988888725530.7041977777451050.647901111127447
280.3638496488593340.7276992977186680.636150351140666
290.3467855921475840.6935711842951690.653214407852416
300.3808983862722690.7617967725445380.619101613727731
310.3286758031111580.6573516062223170.671324196888842
320.2894928679699310.5789857359398630.710507132030069
330.2624907774068950.5249815548137910.737509222593105
340.2263044170330430.4526088340660860.773695582966957
350.187880541814590.375761083629180.81211945818541
360.3049298148882140.6098596297764280.695070185111786
370.3349710741512010.6699421483024020.665028925848799
380.3259636225842460.6519272451684910.674036377415754
390.3560227398554310.7120454797108620.643977260144569
400.3342094491346410.6684188982692830.665790550865359
410.2980013652937390.5960027305874790.701998634706261
420.2638357288282250.5276714576564510.736164271171775
430.2782742652114350.556548530422870.721725734788565
440.2365365213239230.4730730426478460.763463478676077
450.2144422801981570.4288845603963140.785557719801843
460.3926897177725630.7853794355451270.607310282227437
470.5455190334856560.9089619330286870.454480966514344
480.4965487764023340.9930975528046680.503451223597666
490.483216160724750.96643232144950.51678383927525
500.4683902306446540.9367804612893080.531609769355346
510.4236717203499130.8473434406998250.576328279650087
520.3781691975725320.7563383951450640.621830802427468
530.3866036095760940.7732072191521880.613396390423906
540.3697095512826040.7394191025652080.630290448717396
550.3609375571723210.7218751143446420.639062442827679
560.3393852385237350.678770477047470.660614761476265
570.2997777542626510.5995555085253020.700222245737349
580.2711577202394340.5423154404788680.728842279760566
590.2366942697965370.4733885395930740.763305730203463
600.2424586147505540.4849172295011070.757541385249446
610.2186501591049540.4373003182099080.781349840895046
620.1917356708049250.383471341609850.808264329195075
630.1637320578437610.3274641156875220.836267942156239
640.1379788888949110.2759577777898220.862021111105089
650.1190738662615420.2381477325230850.880926133738458
660.1102288514213880.2204577028427760.889771148578612
670.1059861502412970.2119723004825940.894013849758703
680.223058679356040.446117358712080.77694132064396
690.3130388526634780.6260777053269560.686961147336522
700.2821425098269240.5642850196538480.717857490173076
710.3979023773212270.7958047546424530.602097622678773
720.3598978352378180.7197956704756360.640102164762182
730.3515646464888880.7031292929777750.648435353511112
740.3361970271152260.6723940542304510.663802972884775
750.3040241548740880.6080483097481750.695975845125912
760.369544725558950.7390894511178990.63045527444105
770.3338177811756470.6676355623512940.666182218824353
780.3129770526536390.6259541053072780.687022947346361
790.3260565689197160.6521131378394330.673943431080284
800.2984783473115490.5969566946230990.701521652688451
810.2703419068908390.5406838137816790.729658093109161
820.2394460425874980.4788920851749960.760553957412502
830.2151396628227070.4302793256454140.784860337177293
840.1879137227384080.3758274454768150.812086277261592
850.1846071916740780.3692143833481560.815392808325922
860.1595725120415980.3191450240831960.840427487958402
870.1403225373613220.2806450747226450.859677462638678
880.1307388017647960.2614776035295910.869261198235204
890.1129764378758930.2259528757517860.887023562124107
900.1102031142192710.2204062284385420.889796885780729
910.09429193694486590.1885838738897320.905708063055134
920.08142409060866170.1628481812173230.918575909391338
930.06916220468658760.1383244093731750.930837795313412
940.07105056196411460.1421011239282290.928949438035885
950.06420477509780920.1284095501956180.935795224902191
960.05379758650354130.1075951730070830.946202413496459
970.05943151463996930.1188630292799390.940568485360031
980.04922705113043850.0984541022608770.950772948869562
990.04036379712394110.08072759424788230.959636202876059
1000.03531497706865580.07062995413731160.964685022931344
1010.03133001394681790.06266002789363580.968669986053182
1020.03688207108464640.07376414216929280.963117928915354
1030.03165139025063740.06330278050127480.968348609749363
1040.02858591411915050.05717182823830090.97141408588085
1050.03381713581376670.06763427162753340.966182864186233
1060.02971708786873120.05943417573746240.970282912131269
1070.02413307862849560.04826615725699110.975866921371504
1080.02400791135008510.04801582270017020.975992088649915
1090.01945150490639090.03890300981278170.980548495093609
1100.01591366614214850.03182733228429690.984086333857851
1110.01261465227192230.02522930454384470.987385347728078
1120.01303812775868250.02607625551736510.986961872241317
1130.01164521464788010.02329042929576020.98835478535212
1140.01672065608724870.03344131217449730.983279343912751
1150.01597102344518570.03194204689037130.984028976554814
1160.0152811978016850.030562395603370.984718802198315
1170.01263902994539410.02527805989078830.987360970054606
1180.01262195624937510.02524391249875020.987378043750625
1190.01026120162714510.02052240325429030.989738798372855
1200.009038025678921090.01807605135784220.990961974321079
1210.007353567803015730.01470713560603150.992646432196984
1220.01131955121710580.02263910243421170.988680448782894
1230.009538495994803650.01907699198960730.990461504005196
1240.008473116391724750.01694623278344950.991526883608275
1250.006935559729241880.01387111945848380.993064440270758
1260.005650421037164460.01130084207432890.994349578962835
1270.00508716401279390.01017432802558780.994912835987206
1280.004098793520274620.008197587040549240.995901206479725
1290.006048869827586880.01209773965517380.993951130172413
1300.006553238028811910.01310647605762380.993446761971188
1310.01354029438996480.02708058877992970.986459705610035
1320.01629872750422860.03259745500845720.983701272495771
1330.01758236498167170.03516472996334340.982417635018328
1340.01698334499195070.03396668998390140.983016655008049
1350.0138803744734780.0277607489469560.986119625526522
1360.01171233869970170.02342467739940330.988287661300298
1370.00937464333820910.01874928667641820.990625356661791
1380.01141690215884620.02283380431769240.988583097841154
1390.009937139879518260.01987427975903650.990062860120482
1400.01129281549976360.02258563099952720.988707184500236
1410.01751656482001360.03503312964002710.982483435179986
1420.01592895790096320.03185791580192640.984071042099037
1430.01268316359323410.02536632718646820.987316836406766
1440.01344583330598330.02689166661196660.986554166694017
1450.02564031348952370.05128062697904730.974359686510476
1460.03141841095330010.06283682190660010.9685815890467
1470.03297957803633510.06595915607267020.967020421963665
1480.02977342271015450.05954684542030910.970226577289845
1490.02481272874093870.04962545748187750.975187271259061
1500.03443623206436750.06887246412873510.965563767935632
1510.02971241893936210.05942483787872420.970287581060638
1520.03128065816490130.06256131632980260.968719341835099
1530.06388043297827720.1277608659565540.936119567021723
1540.06276143572712580.1255228714542520.937238564272874
1550.07217477719129810.1443495543825960.927825222808702
1560.06174785743390260.1234957148678050.938252142566097
1570.05516287941602720.1103257588320540.944837120583973
1580.04827041228233410.09654082456466830.951729587717666
1590.04722610868202220.09445221736404450.952773891317978
1600.04049302819331040.08098605638662090.95950697180669
1610.03341243056971730.06682486113943450.966587569430283
1620.02746679527600250.05493359055200510.972533204723998
1630.02287565330103760.04575130660207520.977124346698962
1640.01929641328181750.03859282656363490.980703586718183
1650.01693535633760470.03387071267520940.983064643662395
1660.01718430506346220.03436861012692430.982815694936538
1670.01380762438724070.02761524877448140.986192375612759
1680.02037358612615640.04074717225231280.979626413873844
1690.02019288594577440.04038577189154880.979807114054226
1700.01885801717267590.03771603434535170.981141982827324
1710.01813496534063030.03626993068126060.98186503465937
1720.01469419658669620.02938839317339240.985305803413304
1730.01474229074869420.02948458149738840.985257709251306
1740.01642261616407160.03284523232814310.983577383835928
1750.02194945967986790.04389891935973590.978050540320132
1760.01765315232126220.03530630464252440.982346847678738
1770.01438269225106910.02876538450213820.985617307748931
1780.0117427428384760.02348548567695210.988257257161524
1790.009192159794017650.01838431958803530.990807840205982
1800.008005361728639290.01601072345727860.991994638271361
1810.006603604186685780.01320720837337160.993396395813314
1820.005345217149551930.01069043429910390.994654782850448
1830.005609366225755050.01121873245151010.994390633774245
1840.004436472950334990.008872945900669970.995563527049665
1850.0940865399009820.1881730798019640.905913460099018
1860.08168458117219110.1633691623443820.918315418827809
1870.09284398026744250.1856879605348850.907156019732557
1880.08021271356467330.1604254271293470.919787286435327
1890.06795963394561670.1359192678912330.932040366054383
1900.05635614350327380.1127122870065480.943643856496726
1910.04971116188416230.09942232376832470.950288838115838
1920.04196119331522220.08392238663044450.958038806684778
1930.04871552798395550.0974310559679110.951284472016044
1940.05228906663103810.1045781332620760.947710933368962
1950.04438117382695730.08876234765391450.955618826173043
1960.0370263282172370.07405265643447390.962973671782763
1970.04831570611723950.09663141223447890.951684293882761
1980.03958629462185280.07917258924370570.960413705378147
1990.03670115629514750.0734023125902950.963298843704853
2000.03103986761341980.06207973522683970.96896013238658
2010.02922197249448330.05844394498896650.970778027505517
2020.02438057070672770.04876114141345540.975619429293272
2030.03130536910663020.06261073821326040.96869463089337
2040.03568031777393910.07136063554787830.964319682226061
2050.03890633637672130.07781267275344250.961093663623279
2060.0308791545420790.06175830908415790.969120845457921
2070.03034222282480850.0606844456496170.969657777175192
2080.02494987273114620.04989974546229240.975050127268854
2090.02786814120547050.0557362824109410.97213185879453
2100.02254174262108910.04508348524217830.977458257378911
2110.02497753329313890.04995506658627780.975022466706861
2120.03997350874230030.07994701748460070.9600264912577
2130.03158710624416730.06317421248833450.968412893755833
2140.04415894259893950.08831788519787890.955841057401061
2150.03805930928693740.07611861857387480.961940690713063
2160.02975082592105630.05950165184211260.970249174078944
2170.03219553127290540.06439106254581090.967804468727095
2180.02752949098226520.05505898196453040.972470509017735
2190.02474180700445380.04948361400890750.975258192995546
2200.01896337737057050.0379267547411410.981036622629429
2210.01689473392096760.03378946784193510.983105266079032
2220.01242064858491530.02484129716983060.987579351415085
2230.009311741862016570.01862348372403310.990688258137983
2240.00750211854397710.01500423708795420.992497881456023
2250.006547056161290890.01309411232258180.993452943838709
2260.01499519031446630.02999038062893250.985004809685534
2270.01154161843724660.02308323687449310.988458381562753
2280.008350318923407940.01670063784681590.991649681076592
2290.006096834243210920.01219366848642180.993903165756789
2300.004410362517329940.008820725034659890.99558963748267
2310.004394614579941070.008789229159882130.995605385420059
2320.008144979571466480.0162899591429330.991855020428533
2330.03775654421187850.0755130884237570.962243455788122
2340.05429422005691110.1085884401138220.945705779943089
2350.04050658575470140.08101317150940270.959493414245299
2360.03611011259517660.07222022519035320.963889887404823
2370.2465057005702010.4930114011404030.753494299429799
2380.2007821309639770.4015642619279530.799217869036023
2390.180044405468040.3600888109360790.81995559453196
2400.1421285353331370.2842570706662740.857871464666863
2410.1137073431033520.2274146862067040.886292656896648
2420.09453010324072960.1890602064814590.90546989675927
2430.08790231871979390.1758046374395880.912097681280206
2440.1535504342222450.3071008684444910.846449565777755
2450.1376303397172550.275260679434510.862369660282745
2460.1101093305386860.2202186610773710.889890669461314
2470.07777060082222360.1555412016444470.922229399177776
2480.06669484702562430.1333896940512490.933305152974376
2490.06248882703230030.1249776540646010.9375111729677
2500.0773809930539610.1547619861079220.922619006946039
2510.04605497640822620.09210995281645230.953945023591774
2520.1122856641622770.2245713283245540.887714335837723
2530.2349985110953930.4699970221907850.765001488904607







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0164609053497942NOK
5% type I error level790.325102880658436NOK
10% type I error level1280.526748971193416NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 4 & 0.0164609053497942 & NOK \tabularnewline
5% type I error level & 79 & 0.325102880658436 & NOK \tabularnewline
10% type I error level & 128 & 0.526748971193416 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185709&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]4[/C][C]0.0164609053497942[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]79[/C][C]0.325102880658436[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]128[/C][C]0.526748971193416[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185709&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185709&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0164609053497942NOK
5% type I error level790.325102880658436NOK
10% type I error level1280.526748971193416NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}