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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 07:20:05 -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/t1351941621ez6c478al0mn12e.htm/, Retrieved Sun, 03 Jul 2022 14:21:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185708, Retrieved Sun, 03 Jul 2022 14:21:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
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]
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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'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185708&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185708&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185708&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 17.0817345691088 + 0.432448214035159Separate[t] + 0.150459183351842Learning[t] -0.0360703314238801Software[t] + 0.0356641953949802Happiness[t] -0.0600916055960086Depression[t] -0.0727511401051175Belonging[t] + 0.141751549253917Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  17.0817345691088 +  0.432448214035159Separate[t] +  0.150459183351842Learning[t] -0.0360703314238801Software[t] +  0.0356641953949802Happiness[t] -0.0600916055960086Depression[t] -0.0727511401051175Belonging[t] +  0.141751549253917Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185708&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  17.0817345691088 +  0.432448214035159Separate[t] +  0.150459183351842Learning[t] -0.0360703314238801Software[t] +  0.0356641953949802Happiness[t] -0.0600916055960086Depression[t] -0.0727511401051175Belonging[t] +  0.141751549253917Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185708&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185708&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
Connected[t] = + 17.0817345691088 + 0.432448214035159Separate[t] + 0.150459183351842Learning[t] -0.0360703314238801Software[t] + 0.0356641953949802Happiness[t] -0.0600916055960086Depression[t] -0.0727511401051175Belonging[t] + 0.141751549253917Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.08173456910883.2902055.191700
Separate0.4324482140351590.0580067.455300
Learning0.1504591833518420.1114971.34950.1783840.089192
Software-0.03607033142388010.115018-0.31360.7540750.377037
Happiness0.03566419539498020.1043310.34180.7327540.366377
Depression-0.06009160559600860.076224-0.78840.4312180.215609
Belonging-0.07275114010511750.06762-1.07590.2829910.141495
Belonging_Final0.1417515492539170.1006851.40790.1603820.080191

\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) & 17.0817345691088 & 3.290205 & 5.1917 & 0 & 0 \tabularnewline
Separate & 0.432448214035159 & 0.058006 & 7.4553 & 0 & 0 \tabularnewline
Learning & 0.150459183351842 & 0.111497 & 1.3495 & 0.178384 & 0.089192 \tabularnewline
Software & -0.0360703314238801 & 0.115018 & -0.3136 & 0.754075 & 0.377037 \tabularnewline
Happiness & 0.0356641953949802 & 0.104331 & 0.3418 & 0.732754 & 0.366377 \tabularnewline
Depression & -0.0600916055960086 & 0.076224 & -0.7884 & 0.431218 & 0.215609 \tabularnewline
Belonging & -0.0727511401051175 & 0.06762 & -1.0759 & 0.282991 & 0.141495 \tabularnewline
Belonging_Final & 0.141751549253917 & 0.100685 & 1.4079 & 0.160382 & 0.080191 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185708&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]17.0817345691088[/C][C]3.290205[/C][C]5.1917[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Separate[/C][C]0.432448214035159[/C][C]0.058006[/C][C]7.4553[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Learning[/C][C]0.150459183351842[/C][C]0.111497[/C][C]1.3495[/C][C]0.178384[/C][C]0.089192[/C][/ROW]
[ROW][C]Software[/C][C]-0.0360703314238801[/C][C]0.115018[/C][C]-0.3136[/C][C]0.754075[/C][C]0.377037[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0356641953949802[/C][C]0.104331[/C][C]0.3418[/C][C]0.732754[/C][C]0.366377[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0600916055960086[/C][C]0.076224[/C][C]-0.7884[/C][C]0.431218[/C][C]0.215609[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.0727511401051175[/C][C]0.06762[/C][C]-1.0759[/C][C]0.282991[/C][C]0.141495[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.141751549253917[/C][C]0.100685[/C][C]1.4079[/C][C]0.160382[/C][C]0.080191[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185708&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185708&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)17.08173456910883.2902055.191700
Separate0.4324482140351590.0580067.455300
Learning0.1504591833518420.1114971.34950.1783840.089192
Software-0.03607033142388010.115018-0.31360.7540750.377037
Happiness0.03566419539498020.1043310.34180.7327540.366377
Depression-0.06009160559600860.076224-0.78840.4312180.215609
Belonging-0.07275114010511750.06762-1.07590.2829910.141495
Belonging_Final0.1417515492539170.1006851.40790.1603820.080191







Multiple Linear Regression - Regression Statistics
Multiple R0.480972591576232
R-squared0.231334633847557
Adjusted R-squared0.210316440241826
F-TEST (value)11.0063994169547
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value3.75210973402318e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.37371188530587
Sum Squared Residuals2913.77456257384

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.480972591576232 \tabularnewline
R-squared & 0.231334633847557 \tabularnewline
Adjusted R-squared & 0.210316440241826 \tabularnewline
F-TEST (value) & 11.0063994169547 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 3.75210973402318e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.37371188530587 \tabularnewline
Sum Squared Residuals & 2913.77456257384 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185708&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.480972591576232[/C][/ROW]
[ROW][C]R-squared[/C][C]0.231334633847557[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.210316440241826[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.0063994169547[/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]3.75210973402318e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.37371188530587[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2913.77456257384[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185708&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185708&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 R0.480972591576232
R-squared0.231334633847557
Adjusted R-squared0.210316440241826
F-TEST (value)11.0063994169547
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value3.75210973402318e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.37371188530587
Sum Squared Residuals2913.77456257384







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.4963307278645.503669272136
23934.10258294497934.89741705502073
33035.2381050653936-5.23810506539361
43134.0373496039588-3.03734960395881
53435.0200207733198-1.02002077331982
63532.12658898215612.87341101784394
73933.47979002003845.52020997996156
83435.365799163724-1.36579916372402
93634.82028887379081.17971112620919
103736.33402969502930.665970304970658
113833.73813393448174.26186606551834
123634.70560323028661.29439676971338
133834.72643156851363.27356843148638
143936.00392419809012.99607580190985
153336.3235920164948-3.32359201649476
163233.71807530969-1.71807530968995
173633.39676232000082.6032376799992
183837.28887338301490.711126616985102
193936.88038158936812.11961841063194
203233.6402574154977-1.64025741549772
213234.4444224566563-2.44442245665627
223133.3031464717667-2.30314647176669
233936.43201590572.56798409430003
243736.71521954735080.284780452649183
253934.10480573045084.89519426954918
264133.63135414142027.36864585857977
273634.73991511442321.26008488557684
283335.6493857502619-2.64938575026186
293333.7249398296197-0.724939829619672
303434.0721079416822-0.0721079416822105
313133.467210358378-2.46721035837797
322732.7485249829625-5.74852498296251
333733.16871539309543.83128460690459
343436.4742646052029-2.47426460520288
353432.01685414367031.98314585632974
363233.766256647081-1.76625664708096
372931.4482191973094-2.44821919730944
383633.7699595563362.23004044366401
392933.5311327070353-4.53113270703526
403534.19562413724580.804375862754187
413733.95609486683283.04390513316721
423433.53618752937240.463812470627597
433834.22733995516713.77266004483291
443533.2961774927571.70382250724297
453831.74370148556036.25629851443972
463733.77372881972853.22627118027154
473836.94536016654181.0546398334582
483334.5832789780842-1.58327897808416
493635.8002379290720.199762070927981
503833.31877309519424.68122690480575
513236.3858261461035-4.38582614610351
523232.25465808315-0.254658083149998
533232.6973345997938-0.697334599793776
543437.0498877731224-3.04988777312244
553232.3805550409834-0.380555040983375
563734.90429945544972.09570054455035
573934.54414840506634.45585159493371
582934.8328539650294-5.83285396502942
593735.1215220580141.87847794198597
603534.36095053705470.639049462945252
613030.507023044142-0.507023044142014
623834.2789940041623.721005995838
633434.7749743920058-0.774974392005842
643134.1963319801934-3.19633198019343
653432.96514377862121.03485622137882
663535.9925792943068-0.992579294306827
673634.94344139667761.05655860332237
683031.2771793720533-1.27717937205328
693936.30933845685382.69066154314623
703535.6842382989446-0.684238298944616
713834.9467490994133.05325090058698
723135.6429142257904-4.64291422579045
733436.815499650729-2.81549965072896
743837.59967218281740.400327817182622
753431.51144787704572.48855212295434
763932.70031866777816.29968133222187
773735.84858837305551.1514116269445
783433.15751923708740.842480762912613
792832.8250116957028-4.82501169570279
803731.40438895407465.59561104592538
813335.271559697662-2.27155969766197
823536.2644486944762-1.26444869447617
833733.81058065487263.18941934512742
843234.4579376942957-2.45793769429567
853333.3128657132111-0.312865713211093
863836.40526676730991.59473323269006
873334.4714126726362-1.47141267263617
882933.2060803906744-4.20608039067436
893333.106940260673-0.106940260673026
903135.1655859787549-4.16558597875489
913633.24832857975462.75167142024544
923537.459119836856-2.45911983685599
933231.45073386244230.549266137557676
942932.1330274733603-3.13302747336032
953935.44836496505353.55163503494649
963734.69784729132742.30215270867262
973533.2546165664681.74538343353204
983734.61599247867922.38400752132079
993235.3211270197174-3.32112701971741
1003835.40734090226162.59265909773838
1013734.76248649226252.2375135077375
1023636.5872086955246-0.587208695524629
1033231.93368697622370.0663130237763221
1043336.4462681924652-3.44626819246521
1054032.48717606921467.51282393078543
1063835.38523983194292.61476016805709
1074136.42520078486664.5747992151334
1083634.43250277801881.56749722198122
1094336.61801489126826.38198510873184
1103034.6403909076964-4.64039090769643
1113133.5993037907625-2.59930379076254
1123238.1700055741627-6.17000557416273
1133231.30436973956860.695630260431432
1143734.28387314919022.71612685080975
1153734.38537354183372.61462645816627
1163336.4244332058191-3.42443320581906
1173437.278378688961-3.27837868896103
1183334.6688284848277-1.66882848482771
1193835.47103048201192.52896951798808
1203334.9173500468235-1.91735004682353
1213131.345769848054-0.345769848054008
1223836.52402778269211.47597221730792
1233736.60459524924180.395404750758173
1243633.21754440408722.78245559591276
1253133.9364272197488-2.93642721974881
1263934.19055293072424.8094470692758
1274436.96710645294697.0328935470531
1283336.2187268547289-3.21872685472887
1293533.5140203246091.48597967539102
1303233.9900302514396-1.99003025143957
1312832.0155279759432-4.01552797594323
1324036.05388312319893.9461168768011
1332732.0387442550316-5.03874425503164
1343736.13920185395820.860798146041814
1353232.5051063988177-0.505106398817684
1362827.98637045898090.0136295410191456
1373435.1057956641821-1.10579566418206
1383033.9753528809903-3.97535288099025
1393534.18986566793310.810134332066931
1403134.0823602195349-3.08236021953487
1413234.5870445632102-2.5870445632102
1423035.8621511469679-5.86215114696788
1433035.2686067357576-5.26860673575762
1443129.7712139212431.22878607875699
1454032.93476159069977.0652384093003
1463231.97995136599360.0200486340064136
1473634.80560142280041.19439857719959
1483233.2933306017217-1.29333060172171
1493532.94038910923022.05961089076979
1503836.37782749072151.62217250927852
1514235.22735192180936.77264807819068
1523436.2737659011623-2.27376590116229
1533536.8439970187578-1.84399701875777
1543833.8575649647354.14243503526503
1553332.64038410948330.359615890516675
1563633.24832857975462.75167142024544
1573235.5553700912913-3.55537009129131
1583336.2187268547289-3.21872685472887
1593433.97169403909130.0283059609087293
1603235.3545173891535-3.35451738915352
1613435.3201884555831-1.32018845558314
1622730.1288389939844-3.12883899398438
1633130.26983240645920.730167593540791
1643833.89612609779184.1038739022082
1653435.9756269360905-1.97562693609048
1662431.0306489526305-7.03064895263048
1673033.9698654578987-3.96986545789865
1682629.5553186255608-3.5553186255608
1693435.5193881520272-1.51938815202716
1702734.7971305945163-7.79713059451631
1713732.12150175357784.87849824642221
1723635.90533538372410.0946646162759061
1734134.24984132446036.75015867553965
1742929.105644452875-0.105644452874981
1753631.35137311840434.64862688159572
1763234.5628254936982-2.56282549369819
1773733.37722434088933.62277565911073
1783031.6743645037399-1.67436450373988
1793133.1405526596131-2.14055265961307
1803834.68420007770343.31579992229662
1813635.2119038645940.788096135405986
1823532.3541072406292.64589275937103
1833135.9859834158841-4.98598341588409
1843833.62648702677324.37351297322676
1852231.192217256195-9.19221725619502
1863234.6542487839115-2.65424878391147
1873634.31575482494821.68424517505179
1883932.89624212028486.10375787971524
1892830.5391802327282-2.53918023272817
1903234.3454429404446-2.34544294044462
1913232.7292938586091-0.729293858609085
1923837.03328142591630.966718574083724
1933233.6171500866155-1.61715008661552
1943535.8249750690785-0.824975069078463
1953233.2080100197535-1.20801001975353
1963736.19016991303770.809830086962276
1973432.98463568010261.0153643198974
1983335.8382290178546-2.8382290178546
1993333.0121309090933-0.0121309090932692
2002633.2122544212418-7.21225442124176
2013032.5463439100484-2.54634391004843
2022432.2650032095268-8.26500320952676
2033432.26056045919341.73943954080663
2043432.94783836137081.05216163862916
2053334.4844887449094-1.48448874490941
2063435.2589917858137-1.25899178581367
2073536.4835795447846-1.48357954478457
2083534.90486501470230.0951349852976578
2093633.41371375650532.58628624349472
2103434.0747196213415-0.0747196213414655
2113433.03860132225650.961398677743543
2124138.79347801618272.20652198381731
2133236.4432207402779-4.4432207402779
2143033.6623394434598-3.66233944345985
2153534.35840945420880.641590545791248
2162830.4754266434293-2.47542664342934
2173335.5109718440853-2.51097184408529
2183934.64163449984784.35836550015223
2193633.51336325929882.4866367407012
2203636.8704262148357-0.870426214835731
2213533.53834253864041.46165746135961
2223834.41985528700373.58014471299626
2233333.0781919298465-0.0781919298465318
2243133.3940791958056-2.39407919580561
2253432.13498443397091.86501556602915
2263234.4528151604866-2.45281516048662
2273133.4316542815724-2.43165428157235
2283333.5098713560135-0.509871356013542
2293432.73814012250281.26185987749718
2303434.3121513649035-0.312151364903456
2313432.5029135240271.49708647597303
2323335.3809090708727-2.38090907087265
2333234.8535269189953-2.85352691899525
2344134.28037271578226.71962728421777
2353433.54413224312860.455867756871443
2363632.33573738839113.66426261160891
2373731.40549959934225.59450040065778
2383631.00364954205554.99635045794447
2392933.0150009834854-4.01500098348535
2403731.97747346551145.02252653448857
2412733.3748110370519-6.37481103705191
2423533.73347392079641.26652607920355
2432830.794908154334-2.79490815433397
2443533.42706440572231.57293559427771
2453733.62473271465613.37526728534387
2462933.8585800480065-4.85858004800648
2473234.7097171914153-2.70971719141532
2483632.12697283949743.87302716050263
2491927.8638963819623-8.8638963819623
2502127.0748167744691-6.0748167744691
2513133.7421982570942-2.74219825709421
2523332.60198806352270.398011936477294
2533634.24842615627071.75157384372929
2543333.8259726652929-0.825972665292906
2553733.49373620788443.50626379211565
2563433.80976704520740.190232954792623
2573534.75485659602590.245143403974114
2583133.1367634752347-2.13676347523473
2593733.91524556356783.0847544364322
2603531.757451770963.24254822904001
2612732.4239804680775-5.4239804680775
2623435.4155733982638-1.41557339826378
2634033.99780275031586.00219724968419
2642933.3373070010724-4.33730700107238

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.496330727864 & 5.503669272136 \tabularnewline
2 & 39 & 34.1025829449793 & 4.89741705502073 \tabularnewline
3 & 30 & 35.2381050653936 & -5.23810506539361 \tabularnewline
4 & 31 & 34.0373496039588 & -3.03734960395881 \tabularnewline
5 & 34 & 35.0200207733198 & -1.02002077331982 \tabularnewline
6 & 35 & 32.1265889821561 & 2.87341101784394 \tabularnewline
7 & 39 & 33.4797900200384 & 5.52020997996156 \tabularnewline
8 & 34 & 35.365799163724 & -1.36579916372402 \tabularnewline
9 & 36 & 34.8202888737908 & 1.17971112620919 \tabularnewline
10 & 37 & 36.3340296950293 & 0.665970304970658 \tabularnewline
11 & 38 & 33.7381339344817 & 4.26186606551834 \tabularnewline
12 & 36 & 34.7056032302866 & 1.29439676971338 \tabularnewline
13 & 38 & 34.7264315685136 & 3.27356843148638 \tabularnewline
14 & 39 & 36.0039241980901 & 2.99607580190985 \tabularnewline
15 & 33 & 36.3235920164948 & -3.32359201649476 \tabularnewline
16 & 32 & 33.71807530969 & -1.71807530968995 \tabularnewline
17 & 36 & 33.3967623200008 & 2.6032376799992 \tabularnewline
18 & 38 & 37.2888733830149 & 0.711126616985102 \tabularnewline
19 & 39 & 36.8803815893681 & 2.11961841063194 \tabularnewline
20 & 32 & 33.6402574154977 & -1.64025741549772 \tabularnewline
21 & 32 & 34.4444224566563 & -2.44442245665627 \tabularnewline
22 & 31 & 33.3031464717667 & -2.30314647176669 \tabularnewline
23 & 39 & 36.4320159057 & 2.56798409430003 \tabularnewline
24 & 37 & 36.7152195473508 & 0.284780452649183 \tabularnewline
25 & 39 & 34.1048057304508 & 4.89519426954918 \tabularnewline
26 & 41 & 33.6313541414202 & 7.36864585857977 \tabularnewline
27 & 36 & 34.7399151144232 & 1.26008488557684 \tabularnewline
28 & 33 & 35.6493857502619 & -2.64938575026186 \tabularnewline
29 & 33 & 33.7249398296197 & -0.724939829619672 \tabularnewline
30 & 34 & 34.0721079416822 & -0.0721079416822105 \tabularnewline
31 & 31 & 33.467210358378 & -2.46721035837797 \tabularnewline
32 & 27 & 32.7485249829625 & -5.74852498296251 \tabularnewline
33 & 37 & 33.1687153930954 & 3.83128460690459 \tabularnewline
34 & 34 & 36.4742646052029 & -2.47426460520288 \tabularnewline
35 & 34 & 32.0168541436703 & 1.98314585632974 \tabularnewline
36 & 32 & 33.766256647081 & -1.76625664708096 \tabularnewline
37 & 29 & 31.4482191973094 & -2.44821919730944 \tabularnewline
38 & 36 & 33.769959556336 & 2.23004044366401 \tabularnewline
39 & 29 & 33.5311327070353 & -4.53113270703526 \tabularnewline
40 & 35 & 34.1956241372458 & 0.804375862754187 \tabularnewline
41 & 37 & 33.9560948668328 & 3.04390513316721 \tabularnewline
42 & 34 & 33.5361875293724 & 0.463812470627597 \tabularnewline
43 & 38 & 34.2273399551671 & 3.77266004483291 \tabularnewline
44 & 35 & 33.296177492757 & 1.70382250724297 \tabularnewline
45 & 38 & 31.7437014855603 & 6.25629851443972 \tabularnewline
46 & 37 & 33.7737288197285 & 3.22627118027154 \tabularnewline
47 & 38 & 36.9453601665418 & 1.0546398334582 \tabularnewline
48 & 33 & 34.5832789780842 & -1.58327897808416 \tabularnewline
49 & 36 & 35.800237929072 & 0.199762070927981 \tabularnewline
50 & 38 & 33.3187730951942 & 4.68122690480575 \tabularnewline
51 & 32 & 36.3858261461035 & -4.38582614610351 \tabularnewline
52 & 32 & 32.25465808315 & -0.254658083149998 \tabularnewline
53 & 32 & 32.6973345997938 & -0.697334599793776 \tabularnewline
54 & 34 & 37.0498877731224 & -3.04988777312244 \tabularnewline
55 & 32 & 32.3805550409834 & -0.380555040983375 \tabularnewline
56 & 37 & 34.9042994554497 & 2.09570054455035 \tabularnewline
57 & 39 & 34.5441484050663 & 4.45585159493371 \tabularnewline
58 & 29 & 34.8328539650294 & -5.83285396502942 \tabularnewline
59 & 37 & 35.121522058014 & 1.87847794198597 \tabularnewline
60 & 35 & 34.3609505370547 & 0.639049462945252 \tabularnewline
61 & 30 & 30.507023044142 & -0.507023044142014 \tabularnewline
62 & 38 & 34.278994004162 & 3.721005995838 \tabularnewline
63 & 34 & 34.7749743920058 & -0.774974392005842 \tabularnewline
64 & 31 & 34.1963319801934 & -3.19633198019343 \tabularnewline
65 & 34 & 32.9651437786212 & 1.03485622137882 \tabularnewline
66 & 35 & 35.9925792943068 & -0.992579294306827 \tabularnewline
67 & 36 & 34.9434413966776 & 1.05655860332237 \tabularnewline
68 & 30 & 31.2771793720533 & -1.27717937205328 \tabularnewline
69 & 39 & 36.3093384568538 & 2.69066154314623 \tabularnewline
70 & 35 & 35.6842382989446 & -0.684238298944616 \tabularnewline
71 & 38 & 34.946749099413 & 3.05325090058698 \tabularnewline
72 & 31 & 35.6429142257904 & -4.64291422579045 \tabularnewline
73 & 34 & 36.815499650729 & -2.81549965072896 \tabularnewline
74 & 38 & 37.5996721828174 & 0.400327817182622 \tabularnewline
75 & 34 & 31.5114478770457 & 2.48855212295434 \tabularnewline
76 & 39 & 32.7003186677781 & 6.29968133222187 \tabularnewline
77 & 37 & 35.8485883730555 & 1.1514116269445 \tabularnewline
78 & 34 & 33.1575192370874 & 0.842480762912613 \tabularnewline
79 & 28 & 32.8250116957028 & -4.82501169570279 \tabularnewline
80 & 37 & 31.4043889540746 & 5.59561104592538 \tabularnewline
81 & 33 & 35.271559697662 & -2.27155969766197 \tabularnewline
82 & 35 & 36.2644486944762 & -1.26444869447617 \tabularnewline
83 & 37 & 33.8105806548726 & 3.18941934512742 \tabularnewline
84 & 32 & 34.4579376942957 & -2.45793769429567 \tabularnewline
85 & 33 & 33.3128657132111 & -0.312865713211093 \tabularnewline
86 & 38 & 36.4052667673099 & 1.59473323269006 \tabularnewline
87 & 33 & 34.4714126726362 & -1.47141267263617 \tabularnewline
88 & 29 & 33.2060803906744 & -4.20608039067436 \tabularnewline
89 & 33 & 33.106940260673 & -0.106940260673026 \tabularnewline
90 & 31 & 35.1655859787549 & -4.16558597875489 \tabularnewline
91 & 36 & 33.2483285797546 & 2.75167142024544 \tabularnewline
92 & 35 & 37.459119836856 & -2.45911983685599 \tabularnewline
93 & 32 & 31.4507338624423 & 0.549266137557676 \tabularnewline
94 & 29 & 32.1330274733603 & -3.13302747336032 \tabularnewline
95 & 39 & 35.4483649650535 & 3.55163503494649 \tabularnewline
96 & 37 & 34.6978472913274 & 2.30215270867262 \tabularnewline
97 & 35 & 33.254616566468 & 1.74538343353204 \tabularnewline
98 & 37 & 34.6159924786792 & 2.38400752132079 \tabularnewline
99 & 32 & 35.3211270197174 & -3.32112701971741 \tabularnewline
100 & 38 & 35.4073409022616 & 2.59265909773838 \tabularnewline
101 & 37 & 34.7624864922625 & 2.2375135077375 \tabularnewline
102 & 36 & 36.5872086955246 & -0.587208695524629 \tabularnewline
103 & 32 & 31.9336869762237 & 0.0663130237763221 \tabularnewline
104 & 33 & 36.4462681924652 & -3.44626819246521 \tabularnewline
105 & 40 & 32.4871760692146 & 7.51282393078543 \tabularnewline
106 & 38 & 35.3852398319429 & 2.61476016805709 \tabularnewline
107 & 41 & 36.4252007848666 & 4.5747992151334 \tabularnewline
108 & 36 & 34.4325027780188 & 1.56749722198122 \tabularnewline
109 & 43 & 36.6180148912682 & 6.38198510873184 \tabularnewline
110 & 30 & 34.6403909076964 & -4.64039090769643 \tabularnewline
111 & 31 & 33.5993037907625 & -2.59930379076254 \tabularnewline
112 & 32 & 38.1700055741627 & -6.17000557416273 \tabularnewline
113 & 32 & 31.3043697395686 & 0.695630260431432 \tabularnewline
114 & 37 & 34.2838731491902 & 2.71612685080975 \tabularnewline
115 & 37 & 34.3853735418337 & 2.61462645816627 \tabularnewline
116 & 33 & 36.4244332058191 & -3.42443320581906 \tabularnewline
117 & 34 & 37.278378688961 & -3.27837868896103 \tabularnewline
118 & 33 & 34.6688284848277 & -1.66882848482771 \tabularnewline
119 & 38 & 35.4710304820119 & 2.52896951798808 \tabularnewline
120 & 33 & 34.9173500468235 & -1.91735004682353 \tabularnewline
121 & 31 & 31.345769848054 & -0.345769848054008 \tabularnewline
122 & 38 & 36.5240277826921 & 1.47597221730792 \tabularnewline
123 & 37 & 36.6045952492418 & 0.395404750758173 \tabularnewline
124 & 36 & 33.2175444040872 & 2.78245559591276 \tabularnewline
125 & 31 & 33.9364272197488 & -2.93642721974881 \tabularnewline
126 & 39 & 34.1905529307242 & 4.8094470692758 \tabularnewline
127 & 44 & 36.9671064529469 & 7.0328935470531 \tabularnewline
128 & 33 & 36.2187268547289 & -3.21872685472887 \tabularnewline
129 & 35 & 33.514020324609 & 1.48597967539102 \tabularnewline
130 & 32 & 33.9900302514396 & -1.99003025143957 \tabularnewline
131 & 28 & 32.0155279759432 & -4.01552797594323 \tabularnewline
132 & 40 & 36.0538831231989 & 3.9461168768011 \tabularnewline
133 & 27 & 32.0387442550316 & -5.03874425503164 \tabularnewline
134 & 37 & 36.1392018539582 & 0.860798146041814 \tabularnewline
135 & 32 & 32.5051063988177 & -0.505106398817684 \tabularnewline
136 & 28 & 27.9863704589809 & 0.0136295410191456 \tabularnewline
137 & 34 & 35.1057956641821 & -1.10579566418206 \tabularnewline
138 & 30 & 33.9753528809903 & -3.97535288099025 \tabularnewline
139 & 35 & 34.1898656679331 & 0.810134332066931 \tabularnewline
140 & 31 & 34.0823602195349 & -3.08236021953487 \tabularnewline
141 & 32 & 34.5870445632102 & -2.5870445632102 \tabularnewline
142 & 30 & 35.8621511469679 & -5.86215114696788 \tabularnewline
143 & 30 & 35.2686067357576 & -5.26860673575762 \tabularnewline
144 & 31 & 29.771213921243 & 1.22878607875699 \tabularnewline
145 & 40 & 32.9347615906997 & 7.0652384093003 \tabularnewline
146 & 32 & 31.9799513659936 & 0.0200486340064136 \tabularnewline
147 & 36 & 34.8056014228004 & 1.19439857719959 \tabularnewline
148 & 32 & 33.2933306017217 & -1.29333060172171 \tabularnewline
149 & 35 & 32.9403891092302 & 2.05961089076979 \tabularnewline
150 & 38 & 36.3778274907215 & 1.62217250927852 \tabularnewline
151 & 42 & 35.2273519218093 & 6.77264807819068 \tabularnewline
152 & 34 & 36.2737659011623 & -2.27376590116229 \tabularnewline
153 & 35 & 36.8439970187578 & -1.84399701875777 \tabularnewline
154 & 38 & 33.857564964735 & 4.14243503526503 \tabularnewline
155 & 33 & 32.6403841094833 & 0.359615890516675 \tabularnewline
156 & 36 & 33.2483285797546 & 2.75167142024544 \tabularnewline
157 & 32 & 35.5553700912913 & -3.55537009129131 \tabularnewline
158 & 33 & 36.2187268547289 & -3.21872685472887 \tabularnewline
159 & 34 & 33.9716940390913 & 0.0283059609087293 \tabularnewline
160 & 32 & 35.3545173891535 & -3.35451738915352 \tabularnewline
161 & 34 & 35.3201884555831 & -1.32018845558314 \tabularnewline
162 & 27 & 30.1288389939844 & -3.12883899398438 \tabularnewline
163 & 31 & 30.2698324064592 & 0.730167593540791 \tabularnewline
164 & 38 & 33.8961260977918 & 4.1038739022082 \tabularnewline
165 & 34 & 35.9756269360905 & -1.97562693609048 \tabularnewline
166 & 24 & 31.0306489526305 & -7.03064895263048 \tabularnewline
167 & 30 & 33.9698654578987 & -3.96986545789865 \tabularnewline
168 & 26 & 29.5553186255608 & -3.5553186255608 \tabularnewline
169 & 34 & 35.5193881520272 & -1.51938815202716 \tabularnewline
170 & 27 & 34.7971305945163 & -7.79713059451631 \tabularnewline
171 & 37 & 32.1215017535778 & 4.87849824642221 \tabularnewline
172 & 36 & 35.9053353837241 & 0.0946646162759061 \tabularnewline
173 & 41 & 34.2498413244603 & 6.75015867553965 \tabularnewline
174 & 29 & 29.105644452875 & -0.105644452874981 \tabularnewline
175 & 36 & 31.3513731184043 & 4.64862688159572 \tabularnewline
176 & 32 & 34.5628254936982 & -2.56282549369819 \tabularnewline
177 & 37 & 33.3772243408893 & 3.62277565911073 \tabularnewline
178 & 30 & 31.6743645037399 & -1.67436450373988 \tabularnewline
179 & 31 & 33.1405526596131 & -2.14055265961307 \tabularnewline
180 & 38 & 34.6842000777034 & 3.31579992229662 \tabularnewline
181 & 36 & 35.211903864594 & 0.788096135405986 \tabularnewline
182 & 35 & 32.354107240629 & 2.64589275937103 \tabularnewline
183 & 31 & 35.9859834158841 & -4.98598341588409 \tabularnewline
184 & 38 & 33.6264870267732 & 4.37351297322676 \tabularnewline
185 & 22 & 31.192217256195 & -9.19221725619502 \tabularnewline
186 & 32 & 34.6542487839115 & -2.65424878391147 \tabularnewline
187 & 36 & 34.3157548249482 & 1.68424517505179 \tabularnewline
188 & 39 & 32.8962421202848 & 6.10375787971524 \tabularnewline
189 & 28 & 30.5391802327282 & -2.53918023272817 \tabularnewline
190 & 32 & 34.3454429404446 & -2.34544294044462 \tabularnewline
191 & 32 & 32.7292938586091 & -0.729293858609085 \tabularnewline
192 & 38 & 37.0332814259163 & 0.966718574083724 \tabularnewline
193 & 32 & 33.6171500866155 & -1.61715008661552 \tabularnewline
194 & 35 & 35.8249750690785 & -0.824975069078463 \tabularnewline
195 & 32 & 33.2080100197535 & -1.20801001975353 \tabularnewline
196 & 37 & 36.1901699130377 & 0.809830086962276 \tabularnewline
197 & 34 & 32.9846356801026 & 1.0153643198974 \tabularnewline
198 & 33 & 35.8382290178546 & -2.8382290178546 \tabularnewline
199 & 33 & 33.0121309090933 & -0.0121309090932692 \tabularnewline
200 & 26 & 33.2122544212418 & -7.21225442124176 \tabularnewline
201 & 30 & 32.5463439100484 & -2.54634391004843 \tabularnewline
202 & 24 & 32.2650032095268 & -8.26500320952676 \tabularnewline
203 & 34 & 32.2605604591934 & 1.73943954080663 \tabularnewline
204 & 34 & 32.9478383613708 & 1.05216163862916 \tabularnewline
205 & 33 & 34.4844887449094 & -1.48448874490941 \tabularnewline
206 & 34 & 35.2589917858137 & -1.25899178581367 \tabularnewline
207 & 35 & 36.4835795447846 & -1.48357954478457 \tabularnewline
208 & 35 & 34.9048650147023 & 0.0951349852976578 \tabularnewline
209 & 36 & 33.4137137565053 & 2.58628624349472 \tabularnewline
210 & 34 & 34.0747196213415 & -0.0747196213414655 \tabularnewline
211 & 34 & 33.0386013222565 & 0.961398677743543 \tabularnewline
212 & 41 & 38.7934780161827 & 2.20652198381731 \tabularnewline
213 & 32 & 36.4432207402779 & -4.4432207402779 \tabularnewline
214 & 30 & 33.6623394434598 & -3.66233944345985 \tabularnewline
215 & 35 & 34.3584094542088 & 0.641590545791248 \tabularnewline
216 & 28 & 30.4754266434293 & -2.47542664342934 \tabularnewline
217 & 33 & 35.5109718440853 & -2.51097184408529 \tabularnewline
218 & 39 & 34.6416344998478 & 4.35836550015223 \tabularnewline
219 & 36 & 33.5133632592988 & 2.4866367407012 \tabularnewline
220 & 36 & 36.8704262148357 & -0.870426214835731 \tabularnewline
221 & 35 & 33.5383425386404 & 1.46165746135961 \tabularnewline
222 & 38 & 34.4198552870037 & 3.58014471299626 \tabularnewline
223 & 33 & 33.0781919298465 & -0.0781919298465318 \tabularnewline
224 & 31 & 33.3940791958056 & -2.39407919580561 \tabularnewline
225 & 34 & 32.1349844339709 & 1.86501556602915 \tabularnewline
226 & 32 & 34.4528151604866 & -2.45281516048662 \tabularnewline
227 & 31 & 33.4316542815724 & -2.43165428157235 \tabularnewline
228 & 33 & 33.5098713560135 & -0.509871356013542 \tabularnewline
229 & 34 & 32.7381401225028 & 1.26185987749718 \tabularnewline
230 & 34 & 34.3121513649035 & -0.312151364903456 \tabularnewline
231 & 34 & 32.502913524027 & 1.49708647597303 \tabularnewline
232 & 33 & 35.3809090708727 & -2.38090907087265 \tabularnewline
233 & 32 & 34.8535269189953 & -2.85352691899525 \tabularnewline
234 & 41 & 34.2803727157822 & 6.71962728421777 \tabularnewline
235 & 34 & 33.5441322431286 & 0.455867756871443 \tabularnewline
236 & 36 & 32.3357373883911 & 3.66426261160891 \tabularnewline
237 & 37 & 31.4054995993422 & 5.59450040065778 \tabularnewline
238 & 36 & 31.0036495420555 & 4.99635045794447 \tabularnewline
239 & 29 & 33.0150009834854 & -4.01500098348535 \tabularnewline
240 & 37 & 31.9774734655114 & 5.02252653448857 \tabularnewline
241 & 27 & 33.3748110370519 & -6.37481103705191 \tabularnewline
242 & 35 & 33.7334739207964 & 1.26652607920355 \tabularnewline
243 & 28 & 30.794908154334 & -2.79490815433397 \tabularnewline
244 & 35 & 33.4270644057223 & 1.57293559427771 \tabularnewline
245 & 37 & 33.6247327146561 & 3.37526728534387 \tabularnewline
246 & 29 & 33.8585800480065 & -4.85858004800648 \tabularnewline
247 & 32 & 34.7097171914153 & -2.70971719141532 \tabularnewline
248 & 36 & 32.1269728394974 & 3.87302716050263 \tabularnewline
249 & 19 & 27.8638963819623 & -8.8638963819623 \tabularnewline
250 & 21 & 27.0748167744691 & -6.0748167744691 \tabularnewline
251 & 31 & 33.7421982570942 & -2.74219825709421 \tabularnewline
252 & 33 & 32.6019880635227 & 0.398011936477294 \tabularnewline
253 & 36 & 34.2484261562707 & 1.75157384372929 \tabularnewline
254 & 33 & 33.8259726652929 & -0.825972665292906 \tabularnewline
255 & 37 & 33.4937362078844 & 3.50626379211565 \tabularnewline
256 & 34 & 33.8097670452074 & 0.190232954792623 \tabularnewline
257 & 35 & 34.7548565960259 & 0.245143403974114 \tabularnewline
258 & 31 & 33.1367634752347 & -2.13676347523473 \tabularnewline
259 & 37 & 33.9152455635678 & 3.0847544364322 \tabularnewline
260 & 35 & 31.75745177096 & 3.24254822904001 \tabularnewline
261 & 27 & 32.4239804680775 & -5.4239804680775 \tabularnewline
262 & 34 & 35.4155733982638 & -1.41557339826378 \tabularnewline
263 & 40 & 33.9978027503158 & 6.00219724968419 \tabularnewline
264 & 29 & 33.3373070010724 & -4.33730700107238 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185708&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]41[/C][C]35.496330727864[/C][C]5.503669272136[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.1025829449793[/C][C]4.89741705502073[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.2381050653936[/C][C]-5.23810506539361[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.0373496039588[/C][C]-3.03734960395881[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.0200207733198[/C][C]-1.02002077331982[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.1265889821561[/C][C]2.87341101784394[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]33.4797900200384[/C][C]5.52020997996156[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.365799163724[/C][C]-1.36579916372402[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.8202888737908[/C][C]1.17971112620919[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.3340296950293[/C][C]0.665970304970658[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]33.7381339344817[/C][C]4.26186606551834[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.7056032302866[/C][C]1.29439676971338[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.7264315685136[/C][C]3.27356843148638[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.0039241980901[/C][C]2.99607580190985[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.3235920164948[/C][C]-3.32359201649476[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]33.71807530969[/C][C]-1.71807530968995[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]33.3967623200008[/C][C]2.6032376799992[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]37.2888733830149[/C][C]0.711126616985102[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.8803815893681[/C][C]2.11961841063194[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.6402574154977[/C][C]-1.64025741549772[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.4444224566563[/C][C]-2.44442245665627[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.3031464717667[/C][C]-2.30314647176669[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.4320159057[/C][C]2.56798409430003[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.7152195473508[/C][C]0.284780452649183[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]34.1048057304508[/C][C]4.89519426954918[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]33.6313541414202[/C][C]7.36864585857977[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]34.7399151144232[/C][C]1.26008488557684[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.6493857502619[/C][C]-2.64938575026186[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]33.7249398296197[/C][C]-0.724939829619672[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.0721079416822[/C][C]-0.0721079416822105[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]33.467210358378[/C][C]-2.46721035837797[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]32.7485249829625[/C][C]-5.74852498296251[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.1687153930954[/C][C]3.83128460690459[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]36.4742646052029[/C][C]-2.47426460520288[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.0168541436703[/C][C]1.98314585632974[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]33.766256647081[/C][C]-1.76625664708096[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]31.4482191973094[/C][C]-2.44821919730944[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.769959556336[/C][C]2.23004044366401[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]33.5311327070353[/C][C]-4.53113270703526[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.1956241372458[/C][C]0.804375862754187[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]33.9560948668328[/C][C]3.04390513316721[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.5361875293724[/C][C]0.463812470627597[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.2273399551671[/C][C]3.77266004483291[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.296177492757[/C][C]1.70382250724297[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]31.7437014855603[/C][C]6.25629851443972[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.7737288197285[/C][C]3.22627118027154[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]36.9453601665418[/C][C]1.0546398334582[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.5832789780842[/C][C]-1.58327897808416[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]35.800237929072[/C][C]0.199762070927981[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.3187730951942[/C][C]4.68122690480575[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.3858261461035[/C][C]-4.38582614610351[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]32.25465808315[/C][C]-0.254658083149998[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.6973345997938[/C][C]-0.697334599793776[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]37.0498877731224[/C][C]-3.04988777312244[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.3805550409834[/C][C]-0.380555040983375[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.9042994554497[/C][C]2.09570054455035[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.5441484050663[/C][C]4.45585159493371[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.8328539650294[/C][C]-5.83285396502942[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.121522058014[/C][C]1.87847794198597[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.3609505370547[/C][C]0.639049462945252[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]30.507023044142[/C][C]-0.507023044142014[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.278994004162[/C][C]3.721005995838[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.7749743920058[/C][C]-0.774974392005842[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.1963319801934[/C][C]-3.19633198019343[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]32.9651437786212[/C][C]1.03485622137882[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.9925792943068[/C][C]-0.992579294306827[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.9434413966776[/C][C]1.05655860332237[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]31.2771793720533[/C][C]-1.27717937205328[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]36.3093384568538[/C][C]2.69066154314623[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.6842382989446[/C][C]-0.684238298944616[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.946749099413[/C][C]3.05325090058698[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.6429142257904[/C][C]-4.64291422579045[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.815499650729[/C][C]-2.81549965072896[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.5996721828174[/C][C]0.400327817182622[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]31.5114478770457[/C][C]2.48855212295434[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]32.7003186677781[/C][C]6.29968133222187[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.8485883730555[/C][C]1.1514116269445[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.1575192370874[/C][C]0.842480762912613[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.8250116957028[/C][C]-4.82501169570279[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]31.4043889540746[/C][C]5.59561104592538[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.271559697662[/C][C]-2.27155969766197[/C][/ROW]
[ROW][C]82[/C][C]35[/C][C]36.2644486944762[/C][C]-1.26444869447617[/C][/ROW]
[ROW][C]83[/C][C]37[/C][C]33.8105806548726[/C][C]3.18941934512742[/C][/ROW]
[ROW][C]84[/C][C]32[/C][C]34.4579376942957[/C][C]-2.45793769429567[/C][/ROW]
[ROW][C]85[/C][C]33[/C][C]33.3128657132111[/C][C]-0.312865713211093[/C][/ROW]
[ROW][C]86[/C][C]38[/C][C]36.4052667673099[/C][C]1.59473323269006[/C][/ROW]
[ROW][C]87[/C][C]33[/C][C]34.4714126726362[/C][C]-1.47141267263617[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]33.2060803906744[/C][C]-4.20608039067436[/C][/ROW]
[ROW][C]89[/C][C]33[/C][C]33.106940260673[/C][C]-0.106940260673026[/C][/ROW]
[ROW][C]90[/C][C]31[/C][C]35.1655859787549[/C][C]-4.16558597875489[/C][/ROW]
[ROW][C]91[/C][C]36[/C][C]33.2483285797546[/C][C]2.75167142024544[/C][/ROW]
[ROW][C]92[/C][C]35[/C][C]37.459119836856[/C][C]-2.45911983685599[/C][/ROW]
[ROW][C]93[/C][C]32[/C][C]31.4507338624423[/C][C]0.549266137557676[/C][/ROW]
[ROW][C]94[/C][C]29[/C][C]32.1330274733603[/C][C]-3.13302747336032[/C][/ROW]
[ROW][C]95[/C][C]39[/C][C]35.4483649650535[/C][C]3.55163503494649[/C][/ROW]
[ROW][C]96[/C][C]37[/C][C]34.6978472913274[/C][C]2.30215270867262[/C][/ROW]
[ROW][C]97[/C][C]35[/C][C]33.254616566468[/C][C]1.74538343353204[/C][/ROW]
[ROW][C]98[/C][C]37[/C][C]34.6159924786792[/C][C]2.38400752132079[/C][/ROW]
[ROW][C]99[/C][C]32[/C][C]35.3211270197174[/C][C]-3.32112701971741[/C][/ROW]
[ROW][C]100[/C][C]38[/C][C]35.4073409022616[/C][C]2.59265909773838[/C][/ROW]
[ROW][C]101[/C][C]37[/C][C]34.7624864922625[/C][C]2.2375135077375[/C][/ROW]
[ROW][C]102[/C][C]36[/C][C]36.5872086955246[/C][C]-0.587208695524629[/C][/ROW]
[ROW][C]103[/C][C]32[/C][C]31.9336869762237[/C][C]0.0663130237763221[/C][/ROW]
[ROW][C]104[/C][C]33[/C][C]36.4462681924652[/C][C]-3.44626819246521[/C][/ROW]
[ROW][C]105[/C][C]40[/C][C]32.4871760692146[/C][C]7.51282393078543[/C][/ROW]
[ROW][C]106[/C][C]38[/C][C]35.3852398319429[/C][C]2.61476016805709[/C][/ROW]
[ROW][C]107[/C][C]41[/C][C]36.4252007848666[/C][C]4.5747992151334[/C][/ROW]
[ROW][C]108[/C][C]36[/C][C]34.4325027780188[/C][C]1.56749722198122[/C][/ROW]
[ROW][C]109[/C][C]43[/C][C]36.6180148912682[/C][C]6.38198510873184[/C][/ROW]
[ROW][C]110[/C][C]30[/C][C]34.6403909076964[/C][C]-4.64039090769643[/C][/ROW]
[ROW][C]111[/C][C]31[/C][C]33.5993037907625[/C][C]-2.59930379076254[/C][/ROW]
[ROW][C]112[/C][C]32[/C][C]38.1700055741627[/C][C]-6.17000557416273[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]31.3043697395686[/C][C]0.695630260431432[/C][/ROW]
[ROW][C]114[/C][C]37[/C][C]34.2838731491902[/C][C]2.71612685080975[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.3853735418337[/C][C]2.61462645816627[/C][/ROW]
[ROW][C]116[/C][C]33[/C][C]36.4244332058191[/C][C]-3.42443320581906[/C][/ROW]
[ROW][C]117[/C][C]34[/C][C]37.278378688961[/C][C]-3.27837868896103[/C][/ROW]
[ROW][C]118[/C][C]33[/C][C]34.6688284848277[/C][C]-1.66882848482771[/C][/ROW]
[ROW][C]119[/C][C]38[/C][C]35.4710304820119[/C][C]2.52896951798808[/C][/ROW]
[ROW][C]120[/C][C]33[/C][C]34.9173500468235[/C][C]-1.91735004682353[/C][/ROW]
[ROW][C]121[/C][C]31[/C][C]31.345769848054[/C][C]-0.345769848054008[/C][/ROW]
[ROW][C]122[/C][C]38[/C][C]36.5240277826921[/C][C]1.47597221730792[/C][/ROW]
[ROW][C]123[/C][C]37[/C][C]36.6045952492418[/C][C]0.395404750758173[/C][/ROW]
[ROW][C]124[/C][C]36[/C][C]33.2175444040872[/C][C]2.78245559591276[/C][/ROW]
[ROW][C]125[/C][C]31[/C][C]33.9364272197488[/C][C]-2.93642721974881[/C][/ROW]
[ROW][C]126[/C][C]39[/C][C]34.1905529307242[/C][C]4.8094470692758[/C][/ROW]
[ROW][C]127[/C][C]44[/C][C]36.9671064529469[/C][C]7.0328935470531[/C][/ROW]
[ROW][C]128[/C][C]33[/C][C]36.2187268547289[/C][C]-3.21872685472887[/C][/ROW]
[ROW][C]129[/C][C]35[/C][C]33.514020324609[/C][C]1.48597967539102[/C][/ROW]
[ROW][C]130[/C][C]32[/C][C]33.9900302514396[/C][C]-1.99003025143957[/C][/ROW]
[ROW][C]131[/C][C]28[/C][C]32.0155279759432[/C][C]-4.01552797594323[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]36.0538831231989[/C][C]3.9461168768011[/C][/ROW]
[ROW][C]133[/C][C]27[/C][C]32.0387442550316[/C][C]-5.03874425503164[/C][/ROW]
[ROW][C]134[/C][C]37[/C][C]36.1392018539582[/C][C]0.860798146041814[/C][/ROW]
[ROW][C]135[/C][C]32[/C][C]32.5051063988177[/C][C]-0.505106398817684[/C][/ROW]
[ROW][C]136[/C][C]28[/C][C]27.9863704589809[/C][C]0.0136295410191456[/C][/ROW]
[ROW][C]137[/C][C]34[/C][C]35.1057956641821[/C][C]-1.10579566418206[/C][/ROW]
[ROW][C]138[/C][C]30[/C][C]33.9753528809903[/C][C]-3.97535288099025[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]34.1898656679331[/C][C]0.810134332066931[/C][/ROW]
[ROW][C]140[/C][C]31[/C][C]34.0823602195349[/C][C]-3.08236021953487[/C][/ROW]
[ROW][C]141[/C][C]32[/C][C]34.5870445632102[/C][C]-2.5870445632102[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]35.8621511469679[/C][C]-5.86215114696788[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]35.2686067357576[/C][C]-5.26860673575762[/C][/ROW]
[ROW][C]144[/C][C]31[/C][C]29.771213921243[/C][C]1.22878607875699[/C][/ROW]
[ROW][C]145[/C][C]40[/C][C]32.9347615906997[/C][C]7.0652384093003[/C][/ROW]
[ROW][C]146[/C][C]32[/C][C]31.9799513659936[/C][C]0.0200486340064136[/C][/ROW]
[ROW][C]147[/C][C]36[/C][C]34.8056014228004[/C][C]1.19439857719959[/C][/ROW]
[ROW][C]148[/C][C]32[/C][C]33.2933306017217[/C][C]-1.29333060172171[/C][/ROW]
[ROW][C]149[/C][C]35[/C][C]32.9403891092302[/C][C]2.05961089076979[/C][/ROW]
[ROW][C]150[/C][C]38[/C][C]36.3778274907215[/C][C]1.62217250927852[/C][/ROW]
[ROW][C]151[/C][C]42[/C][C]35.2273519218093[/C][C]6.77264807819068[/C][/ROW]
[ROW][C]152[/C][C]34[/C][C]36.2737659011623[/C][C]-2.27376590116229[/C][/ROW]
[ROW][C]153[/C][C]35[/C][C]36.8439970187578[/C][C]-1.84399701875777[/C][/ROW]
[ROW][C]154[/C][C]38[/C][C]33.857564964735[/C][C]4.14243503526503[/C][/ROW]
[ROW][C]155[/C][C]33[/C][C]32.6403841094833[/C][C]0.359615890516675[/C][/ROW]
[ROW][C]156[/C][C]36[/C][C]33.2483285797546[/C][C]2.75167142024544[/C][/ROW]
[ROW][C]157[/C][C]32[/C][C]35.5553700912913[/C][C]-3.55537009129131[/C][/ROW]
[ROW][C]158[/C][C]33[/C][C]36.2187268547289[/C][C]-3.21872685472887[/C][/ROW]
[ROW][C]159[/C][C]34[/C][C]33.9716940390913[/C][C]0.0283059609087293[/C][/ROW]
[ROW][C]160[/C][C]32[/C][C]35.3545173891535[/C][C]-3.35451738915352[/C][/ROW]
[ROW][C]161[/C][C]34[/C][C]35.3201884555831[/C][C]-1.32018845558314[/C][/ROW]
[ROW][C]162[/C][C]27[/C][C]30.1288389939844[/C][C]-3.12883899398438[/C][/ROW]
[ROW][C]163[/C][C]31[/C][C]30.2698324064592[/C][C]0.730167593540791[/C][/ROW]
[ROW][C]164[/C][C]38[/C][C]33.8961260977918[/C][C]4.1038739022082[/C][/ROW]
[ROW][C]165[/C][C]34[/C][C]35.9756269360905[/C][C]-1.97562693609048[/C][/ROW]
[ROW][C]166[/C][C]24[/C][C]31.0306489526305[/C][C]-7.03064895263048[/C][/ROW]
[ROW][C]167[/C][C]30[/C][C]33.9698654578987[/C][C]-3.96986545789865[/C][/ROW]
[ROW][C]168[/C][C]26[/C][C]29.5553186255608[/C][C]-3.5553186255608[/C][/ROW]
[ROW][C]169[/C][C]34[/C][C]35.5193881520272[/C][C]-1.51938815202716[/C][/ROW]
[ROW][C]170[/C][C]27[/C][C]34.7971305945163[/C][C]-7.79713059451631[/C][/ROW]
[ROW][C]171[/C][C]37[/C][C]32.1215017535778[/C][C]4.87849824642221[/C][/ROW]
[ROW][C]172[/C][C]36[/C][C]35.9053353837241[/C][C]0.0946646162759061[/C][/ROW]
[ROW][C]173[/C][C]41[/C][C]34.2498413244603[/C][C]6.75015867553965[/C][/ROW]
[ROW][C]174[/C][C]29[/C][C]29.105644452875[/C][C]-0.105644452874981[/C][/ROW]
[ROW][C]175[/C][C]36[/C][C]31.3513731184043[/C][C]4.64862688159572[/C][/ROW]
[ROW][C]176[/C][C]32[/C][C]34.5628254936982[/C][C]-2.56282549369819[/C][/ROW]
[ROW][C]177[/C][C]37[/C][C]33.3772243408893[/C][C]3.62277565911073[/C][/ROW]
[ROW][C]178[/C][C]30[/C][C]31.6743645037399[/C][C]-1.67436450373988[/C][/ROW]
[ROW][C]179[/C][C]31[/C][C]33.1405526596131[/C][C]-2.14055265961307[/C][/ROW]
[ROW][C]180[/C][C]38[/C][C]34.6842000777034[/C][C]3.31579992229662[/C][/ROW]
[ROW][C]181[/C][C]36[/C][C]35.211903864594[/C][C]0.788096135405986[/C][/ROW]
[ROW][C]182[/C][C]35[/C][C]32.354107240629[/C][C]2.64589275937103[/C][/ROW]
[ROW][C]183[/C][C]31[/C][C]35.9859834158841[/C][C]-4.98598341588409[/C][/ROW]
[ROW][C]184[/C][C]38[/C][C]33.6264870267732[/C][C]4.37351297322676[/C][/ROW]
[ROW][C]185[/C][C]22[/C][C]31.192217256195[/C][C]-9.19221725619502[/C][/ROW]
[ROW][C]186[/C][C]32[/C][C]34.6542487839115[/C][C]-2.65424878391147[/C][/ROW]
[ROW][C]187[/C][C]36[/C][C]34.3157548249482[/C][C]1.68424517505179[/C][/ROW]
[ROW][C]188[/C][C]39[/C][C]32.8962421202848[/C][C]6.10375787971524[/C][/ROW]
[ROW][C]189[/C][C]28[/C][C]30.5391802327282[/C][C]-2.53918023272817[/C][/ROW]
[ROW][C]190[/C][C]32[/C][C]34.3454429404446[/C][C]-2.34544294044462[/C][/ROW]
[ROW][C]191[/C][C]32[/C][C]32.7292938586091[/C][C]-0.729293858609085[/C][/ROW]
[ROW][C]192[/C][C]38[/C][C]37.0332814259163[/C][C]0.966718574083724[/C][/ROW]
[ROW][C]193[/C][C]32[/C][C]33.6171500866155[/C][C]-1.61715008661552[/C][/ROW]
[ROW][C]194[/C][C]35[/C][C]35.8249750690785[/C][C]-0.824975069078463[/C][/ROW]
[ROW][C]195[/C][C]32[/C][C]33.2080100197535[/C][C]-1.20801001975353[/C][/ROW]
[ROW][C]196[/C][C]37[/C][C]36.1901699130377[/C][C]0.809830086962276[/C][/ROW]
[ROW][C]197[/C][C]34[/C][C]32.9846356801026[/C][C]1.0153643198974[/C][/ROW]
[ROW][C]198[/C][C]33[/C][C]35.8382290178546[/C][C]-2.8382290178546[/C][/ROW]
[ROW][C]199[/C][C]33[/C][C]33.0121309090933[/C][C]-0.0121309090932692[/C][/ROW]
[ROW][C]200[/C][C]26[/C][C]33.2122544212418[/C][C]-7.21225442124176[/C][/ROW]
[ROW][C]201[/C][C]30[/C][C]32.5463439100484[/C][C]-2.54634391004843[/C][/ROW]
[ROW][C]202[/C][C]24[/C][C]32.2650032095268[/C][C]-8.26500320952676[/C][/ROW]
[ROW][C]203[/C][C]34[/C][C]32.2605604591934[/C][C]1.73943954080663[/C][/ROW]
[ROW][C]204[/C][C]34[/C][C]32.9478383613708[/C][C]1.05216163862916[/C][/ROW]
[ROW][C]205[/C][C]33[/C][C]34.4844887449094[/C][C]-1.48448874490941[/C][/ROW]
[ROW][C]206[/C][C]34[/C][C]35.2589917858137[/C][C]-1.25899178581367[/C][/ROW]
[ROW][C]207[/C][C]35[/C][C]36.4835795447846[/C][C]-1.48357954478457[/C][/ROW]
[ROW][C]208[/C][C]35[/C][C]34.9048650147023[/C][C]0.0951349852976578[/C][/ROW]
[ROW][C]209[/C][C]36[/C][C]33.4137137565053[/C][C]2.58628624349472[/C][/ROW]
[ROW][C]210[/C][C]34[/C][C]34.0747196213415[/C][C]-0.0747196213414655[/C][/ROW]
[ROW][C]211[/C][C]34[/C][C]33.0386013222565[/C][C]0.961398677743543[/C][/ROW]
[ROW][C]212[/C][C]41[/C][C]38.7934780161827[/C][C]2.20652198381731[/C][/ROW]
[ROW][C]213[/C][C]32[/C][C]36.4432207402779[/C][C]-4.4432207402779[/C][/ROW]
[ROW][C]214[/C][C]30[/C][C]33.6623394434598[/C][C]-3.66233944345985[/C][/ROW]
[ROW][C]215[/C][C]35[/C][C]34.3584094542088[/C][C]0.641590545791248[/C][/ROW]
[ROW][C]216[/C][C]28[/C][C]30.4754266434293[/C][C]-2.47542664342934[/C][/ROW]
[ROW][C]217[/C][C]33[/C][C]35.5109718440853[/C][C]-2.51097184408529[/C][/ROW]
[ROW][C]218[/C][C]39[/C][C]34.6416344998478[/C][C]4.35836550015223[/C][/ROW]
[ROW][C]219[/C][C]36[/C][C]33.5133632592988[/C][C]2.4866367407012[/C][/ROW]
[ROW][C]220[/C][C]36[/C][C]36.8704262148357[/C][C]-0.870426214835731[/C][/ROW]
[ROW][C]221[/C][C]35[/C][C]33.5383425386404[/C][C]1.46165746135961[/C][/ROW]
[ROW][C]222[/C][C]38[/C][C]34.4198552870037[/C][C]3.58014471299626[/C][/ROW]
[ROW][C]223[/C][C]33[/C][C]33.0781919298465[/C][C]-0.0781919298465318[/C][/ROW]
[ROW][C]224[/C][C]31[/C][C]33.3940791958056[/C][C]-2.39407919580561[/C][/ROW]
[ROW][C]225[/C][C]34[/C][C]32.1349844339709[/C][C]1.86501556602915[/C][/ROW]
[ROW][C]226[/C][C]32[/C][C]34.4528151604866[/C][C]-2.45281516048662[/C][/ROW]
[ROW][C]227[/C][C]31[/C][C]33.4316542815724[/C][C]-2.43165428157235[/C][/ROW]
[ROW][C]228[/C][C]33[/C][C]33.5098713560135[/C][C]-0.509871356013542[/C][/ROW]
[ROW][C]229[/C][C]34[/C][C]32.7381401225028[/C][C]1.26185987749718[/C][/ROW]
[ROW][C]230[/C][C]34[/C][C]34.3121513649035[/C][C]-0.312151364903456[/C][/ROW]
[ROW][C]231[/C][C]34[/C][C]32.502913524027[/C][C]1.49708647597303[/C][/ROW]
[ROW][C]232[/C][C]33[/C][C]35.3809090708727[/C][C]-2.38090907087265[/C][/ROW]
[ROW][C]233[/C][C]32[/C][C]34.8535269189953[/C][C]-2.85352691899525[/C][/ROW]
[ROW][C]234[/C][C]41[/C][C]34.2803727157822[/C][C]6.71962728421777[/C][/ROW]
[ROW][C]235[/C][C]34[/C][C]33.5441322431286[/C][C]0.455867756871443[/C][/ROW]
[ROW][C]236[/C][C]36[/C][C]32.3357373883911[/C][C]3.66426261160891[/C][/ROW]
[ROW][C]237[/C][C]37[/C][C]31.4054995993422[/C][C]5.59450040065778[/C][/ROW]
[ROW][C]238[/C][C]36[/C][C]31.0036495420555[/C][C]4.99635045794447[/C][/ROW]
[ROW][C]239[/C][C]29[/C][C]33.0150009834854[/C][C]-4.01500098348535[/C][/ROW]
[ROW][C]240[/C][C]37[/C][C]31.9774734655114[/C][C]5.02252653448857[/C][/ROW]
[ROW][C]241[/C][C]27[/C][C]33.3748110370519[/C][C]-6.37481103705191[/C][/ROW]
[ROW][C]242[/C][C]35[/C][C]33.7334739207964[/C][C]1.26652607920355[/C][/ROW]
[ROW][C]243[/C][C]28[/C][C]30.794908154334[/C][C]-2.79490815433397[/C][/ROW]
[ROW][C]244[/C][C]35[/C][C]33.4270644057223[/C][C]1.57293559427771[/C][/ROW]
[ROW][C]245[/C][C]37[/C][C]33.6247327146561[/C][C]3.37526728534387[/C][/ROW]
[ROW][C]246[/C][C]29[/C][C]33.8585800480065[/C][C]-4.85858004800648[/C][/ROW]
[ROW][C]247[/C][C]32[/C][C]34.7097171914153[/C][C]-2.70971719141532[/C][/ROW]
[ROW][C]248[/C][C]36[/C][C]32.1269728394974[/C][C]3.87302716050263[/C][/ROW]
[ROW][C]249[/C][C]19[/C][C]27.8638963819623[/C][C]-8.8638963819623[/C][/ROW]
[ROW][C]250[/C][C]21[/C][C]27.0748167744691[/C][C]-6.0748167744691[/C][/ROW]
[ROW][C]251[/C][C]31[/C][C]33.7421982570942[/C][C]-2.74219825709421[/C][/ROW]
[ROW][C]252[/C][C]33[/C][C]32.6019880635227[/C][C]0.398011936477294[/C][/ROW]
[ROW][C]253[/C][C]36[/C][C]34.2484261562707[/C][C]1.75157384372929[/C][/ROW]
[ROW][C]254[/C][C]33[/C][C]33.8259726652929[/C][C]-0.825972665292906[/C][/ROW]
[ROW][C]255[/C][C]37[/C][C]33.4937362078844[/C][C]3.50626379211565[/C][/ROW]
[ROW][C]256[/C][C]34[/C][C]33.8097670452074[/C][C]0.190232954792623[/C][/ROW]
[ROW][C]257[/C][C]35[/C][C]34.7548565960259[/C][C]0.245143403974114[/C][/ROW]
[ROW][C]258[/C][C]31[/C][C]33.1367634752347[/C][C]-2.13676347523473[/C][/ROW]
[ROW][C]259[/C][C]37[/C][C]33.9152455635678[/C][C]3.0847544364322[/C][/ROW]
[ROW][C]260[/C][C]35[/C][C]31.75745177096[/C][C]3.24254822904001[/C][/ROW]
[ROW][C]261[/C][C]27[/C][C]32.4239804680775[/C][C]-5.4239804680775[/C][/ROW]
[ROW][C]262[/C][C]34[/C][C]35.4155733982638[/C][C]-1.41557339826378[/C][/ROW]
[ROW][C]263[/C][C]40[/C][C]33.9978027503158[/C][C]6.00219724968419[/C][/ROW]
[ROW][C]264[/C][C]29[/C][C]33.3373070010724[/C][C]-4.33730700107238[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185708&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185708&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
14135.4963307278645.503669272136
23934.10258294497934.89741705502073
33035.2381050653936-5.23810506539361
43134.0373496039588-3.03734960395881
53435.0200207733198-1.02002077331982
63532.12658898215612.87341101784394
73933.47979002003845.52020997996156
83435.365799163724-1.36579916372402
93634.82028887379081.17971112620919
103736.33402969502930.665970304970658
113833.73813393448174.26186606551834
123634.70560323028661.29439676971338
133834.72643156851363.27356843148638
143936.00392419809012.99607580190985
153336.3235920164948-3.32359201649476
163233.71807530969-1.71807530968995
173633.39676232000082.6032376799992
183837.28887338301490.711126616985102
193936.88038158936812.11961841063194
203233.6402574154977-1.64025741549772
213234.4444224566563-2.44442245665627
223133.3031464717667-2.30314647176669
233936.43201590572.56798409430003
243736.71521954735080.284780452649183
253934.10480573045084.89519426954918
264133.63135414142027.36864585857977
273634.73991511442321.26008488557684
283335.6493857502619-2.64938575026186
293333.7249398296197-0.724939829619672
303434.0721079416822-0.0721079416822105
313133.467210358378-2.46721035837797
322732.7485249829625-5.74852498296251
333733.16871539309543.83128460690459
343436.4742646052029-2.47426460520288
353432.01685414367031.98314585632974
363233.766256647081-1.76625664708096
372931.4482191973094-2.44821919730944
383633.7699595563362.23004044366401
392933.5311327070353-4.53113270703526
403534.19562413724580.804375862754187
413733.95609486683283.04390513316721
423433.53618752937240.463812470627597
433834.22733995516713.77266004483291
443533.2961774927571.70382250724297
453831.74370148556036.25629851443972
463733.77372881972853.22627118027154
473836.94536016654181.0546398334582
483334.5832789780842-1.58327897808416
493635.8002379290720.199762070927981
503833.31877309519424.68122690480575
513236.3858261461035-4.38582614610351
523232.25465808315-0.254658083149998
533232.6973345997938-0.697334599793776
543437.0498877731224-3.04988777312244
553232.3805550409834-0.380555040983375
563734.90429945544972.09570054455035
573934.54414840506634.45585159493371
582934.8328539650294-5.83285396502942
593735.1215220580141.87847794198597
603534.36095053705470.639049462945252
613030.507023044142-0.507023044142014
623834.2789940041623.721005995838
633434.7749743920058-0.774974392005842
643134.1963319801934-3.19633198019343
653432.96514377862121.03485622137882
663535.9925792943068-0.992579294306827
673634.94344139667761.05655860332237
683031.2771793720533-1.27717937205328
693936.30933845685382.69066154314623
703535.6842382989446-0.684238298944616
713834.9467490994133.05325090058698
723135.6429142257904-4.64291422579045
733436.815499650729-2.81549965072896
743837.59967218281740.400327817182622
753431.51144787704572.48855212295434
763932.70031866777816.29968133222187
773735.84858837305551.1514116269445
783433.15751923708740.842480762912613
792832.8250116957028-4.82501169570279
803731.40438895407465.59561104592538
813335.271559697662-2.27155969766197
823536.2644486944762-1.26444869447617
833733.81058065487263.18941934512742
843234.4579376942957-2.45793769429567
853333.3128657132111-0.312865713211093
863836.40526676730991.59473323269006
873334.4714126726362-1.47141267263617
882933.2060803906744-4.20608039067436
893333.106940260673-0.106940260673026
903135.1655859787549-4.16558597875489
913633.24832857975462.75167142024544
923537.459119836856-2.45911983685599
933231.45073386244230.549266137557676
942932.1330274733603-3.13302747336032
953935.44836496505353.55163503494649
963734.69784729132742.30215270867262
973533.2546165664681.74538343353204
983734.61599247867922.38400752132079
993235.3211270197174-3.32112701971741
1003835.40734090226162.59265909773838
1013734.76248649226252.2375135077375
1023636.5872086955246-0.587208695524629
1033231.93368697622370.0663130237763221
1043336.4462681924652-3.44626819246521
1054032.48717606921467.51282393078543
1063835.38523983194292.61476016805709
1074136.42520078486664.5747992151334
1083634.43250277801881.56749722198122
1094336.61801489126826.38198510873184
1103034.6403909076964-4.64039090769643
1113133.5993037907625-2.59930379076254
1123238.1700055741627-6.17000557416273
1133231.30436973956860.695630260431432
1143734.28387314919022.71612685080975
1153734.38537354183372.61462645816627
1163336.4244332058191-3.42443320581906
1173437.278378688961-3.27837868896103
1183334.6688284848277-1.66882848482771
1193835.47103048201192.52896951798808
1203334.9173500468235-1.91735004682353
1213131.345769848054-0.345769848054008
1223836.52402778269211.47597221730792
1233736.60459524924180.395404750758173
1243633.21754440408722.78245559591276
1253133.9364272197488-2.93642721974881
1263934.19055293072424.8094470692758
1274436.96710645294697.0328935470531
1283336.2187268547289-3.21872685472887
1293533.5140203246091.48597967539102
1303233.9900302514396-1.99003025143957
1312832.0155279759432-4.01552797594323
1324036.05388312319893.9461168768011
1332732.0387442550316-5.03874425503164
1343736.13920185395820.860798146041814
1353232.5051063988177-0.505106398817684
1362827.98637045898090.0136295410191456
1373435.1057956641821-1.10579566418206
1383033.9753528809903-3.97535288099025
1393534.18986566793310.810134332066931
1403134.0823602195349-3.08236021953487
1413234.5870445632102-2.5870445632102
1423035.8621511469679-5.86215114696788
1433035.2686067357576-5.26860673575762
1443129.7712139212431.22878607875699
1454032.93476159069977.0652384093003
1463231.97995136599360.0200486340064136
1473634.80560142280041.19439857719959
1483233.2933306017217-1.29333060172171
1493532.94038910923022.05961089076979
1503836.37782749072151.62217250927852
1514235.22735192180936.77264807819068
1523436.2737659011623-2.27376590116229
1533536.8439970187578-1.84399701875777
1543833.8575649647354.14243503526503
1553332.64038410948330.359615890516675
1563633.24832857975462.75167142024544
1573235.5553700912913-3.55537009129131
1583336.2187268547289-3.21872685472887
1593433.97169403909130.0283059609087293
1603235.3545173891535-3.35451738915352
1613435.3201884555831-1.32018845558314
1622730.1288389939844-3.12883899398438
1633130.26983240645920.730167593540791
1643833.89612609779184.1038739022082
1653435.9756269360905-1.97562693609048
1662431.0306489526305-7.03064895263048
1673033.9698654578987-3.96986545789865
1682629.5553186255608-3.5553186255608
1693435.5193881520272-1.51938815202716
1702734.7971305945163-7.79713059451631
1713732.12150175357784.87849824642221
1723635.90533538372410.0946646162759061
1734134.24984132446036.75015867553965
1742929.105644452875-0.105644452874981
1753631.35137311840434.64862688159572
1763234.5628254936982-2.56282549369819
1773733.37722434088933.62277565911073
1783031.6743645037399-1.67436450373988
1793133.1405526596131-2.14055265961307
1803834.68420007770343.31579992229662
1813635.2119038645940.788096135405986
1823532.3541072406292.64589275937103
1833135.9859834158841-4.98598341588409
1843833.62648702677324.37351297322676
1852231.192217256195-9.19221725619502
1863234.6542487839115-2.65424878391147
1873634.31575482494821.68424517505179
1883932.89624212028486.10375787971524
1892830.5391802327282-2.53918023272817
1903234.3454429404446-2.34544294044462
1913232.7292938586091-0.729293858609085
1923837.03328142591630.966718574083724
1933233.6171500866155-1.61715008661552
1943535.8249750690785-0.824975069078463
1953233.2080100197535-1.20801001975353
1963736.19016991303770.809830086962276
1973432.98463568010261.0153643198974
1983335.8382290178546-2.8382290178546
1993333.0121309090933-0.0121309090932692
2002633.2122544212418-7.21225442124176
2013032.5463439100484-2.54634391004843
2022432.2650032095268-8.26500320952676
2033432.26056045919341.73943954080663
2043432.94783836137081.05216163862916
2053334.4844887449094-1.48448874490941
2063435.2589917858137-1.25899178581367
2073536.4835795447846-1.48357954478457
2083534.90486501470230.0951349852976578
2093633.41371375650532.58628624349472
2103434.0747196213415-0.0747196213414655
2113433.03860132225650.961398677743543
2124138.79347801618272.20652198381731
2133236.4432207402779-4.4432207402779
2143033.6623394434598-3.66233944345985
2153534.35840945420880.641590545791248
2162830.4754266434293-2.47542664342934
2173335.5109718440853-2.51097184408529
2183934.64163449984784.35836550015223
2193633.51336325929882.4866367407012
2203636.8704262148357-0.870426214835731
2213533.53834253864041.46165746135961
2223834.41985528700373.58014471299626
2233333.0781919298465-0.0781919298465318
2243133.3940791958056-2.39407919580561
2253432.13498443397091.86501556602915
2263234.4528151604866-2.45281516048662
2273133.4316542815724-2.43165428157235
2283333.5098713560135-0.509871356013542
2293432.73814012250281.26185987749718
2303434.3121513649035-0.312151364903456
2313432.5029135240271.49708647597303
2323335.3809090708727-2.38090907087265
2333234.8535269189953-2.85352691899525
2344134.28037271578226.71962728421777
2353433.54413224312860.455867756871443
2363632.33573738839113.66426261160891
2373731.40549959934225.59450040065778
2383631.00364954205554.99635045794447
2392933.0150009834854-4.01500098348535
2403731.97747346551145.02252653448857
2412733.3748110370519-6.37481103705191
2423533.73347392079641.26652607920355
2432830.794908154334-2.79490815433397
2443533.42706440572231.57293559427771
2453733.62473271465613.37526728534387
2462933.8585800480065-4.85858004800648
2473234.7097171914153-2.70971719141532
2483632.12697283949743.87302716050263
2491927.8638963819623-8.8638963819623
2502127.0748167744691-6.0748167744691
2513133.7421982570942-2.74219825709421
2523332.60198806352270.398011936477294
2533634.24842615627071.75157384372929
2543333.8259726652929-0.825972665292906
2553733.49373620788443.50626379211565
2563433.80976704520740.190232954792623
2573534.75485659602590.245143403974114
2583133.1367634752347-2.13676347523473
2593733.91524556356783.0847544364322
2603531.757451770963.24254822904001
2612732.4239804680775-5.4239804680775
2623435.4155733982638-1.41557339826378
2634033.99780275031586.00219724968419
2642933.3373070010724-4.33730700107238







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.04628218396004230.09256436792008450.953717816039958
120.3880483599071230.7760967198142470.611951640092877
130.5616376484903750.8767247030192490.438362351509625
140.5581952879175150.883609424164970.441804712082485
150.4581856363212240.9163712726424480.541814363678776
160.4935519160842380.9871038321684760.506448083915762
170.5618927336747850.8762145326504290.438107266325215
180.5629875273386520.8740249453226960.437012472661348
190.4749333064462460.9498666128924920.525066693553754
200.5064498298588870.9871003402822250.493550170141113
210.5690613481444020.8618773037111950.430938651855598
220.5384605648780250.9230788702439510.461539435121975
230.5267562241934850.946487551613030.473243775806515
240.4603308553299190.9206617106598380.539669144670081
250.5145327119946420.9709345760107160.485467288005358
260.6892367914524950.621526417095010.310763208547505
270.6616753324093540.6766493351812920.338324667590646
280.6154952858748250.769009428250350.384504714125175
290.6060146070157850.7879707859684290.393985392984215
300.5433241369034990.9133517261930020.456675863096501
310.5338981476777290.9322037046445420.466101852322271
320.6798670037378820.6402659925242350.320132996262118
330.669150854550280.661698290899440.33084914544972
340.6280117428940910.7439765142118190.371988257105909
350.5741831692384790.8516336615230420.425816830761521
360.5195780427794620.9608439144410760.480421957220538
370.4762088050357350.952417610071470.523791194964265
380.4419409965040530.8838819930081060.558059003495947
390.5578680931674780.8842638136650440.442131906832522
400.5046987608923950.9906024782152090.495301239107605
410.4713539085383910.9427078170767810.528646091461609
420.4189745093413660.8379490186827330.581025490658634
430.3806677914034790.7613355828069570.619332208596521
440.3365349833551320.6730699667102630.663465016644868
450.4084748726463090.8169497452926180.591525127353691
460.4182313018259540.8364626036519090.581768698174046
470.3895081091585660.7790162183171310.610491890841434
480.3596675298058190.7193350596116390.64033247019418
490.3158572451215660.6317144902431320.684142754878434
500.3282310963576110.6564621927152220.671768903642389
510.3724372100576350.744874420115270.627562789942365
520.3375065293381980.6750130586763960.662493470661802
530.298381164255930.596762328511860.70161883574407
540.2649390958509570.5298781917019150.735060904149043
550.229250937870170.458501875740340.77074906212983
560.2165739497433820.4331478994867650.783426050256617
570.2306672507497330.4613345014994660.769332749250267
580.3370831283512080.6741662567024150.662916871648792
590.3025067249598810.6050134499197620.697493275040119
600.2650034196990480.5300068393980960.734996580300952
610.2361450362311140.4722900724622290.763854963768886
620.2234708066997850.4469416133995710.776529193300215
630.1985093567458890.3970187134917780.801490643254111
640.2026618535960580.4053237071921170.797338146403942
650.1744547941080330.3489095882160660.825545205891967
660.1527008559699150.3054017119398310.847299144030085
670.1298278072693250.2596556145386510.870172192730675
680.147076434816250.2941528696324990.85292356518375
690.1459428186731370.2918856373462740.854057181326863
700.1249953853011240.2499907706022490.875004614698876
710.1340376469883640.2680752939767270.865962353011636
720.1563968086488580.3127936172977160.843603191351142
730.1524211487690380.3048422975380770.847578851230962
740.1294475742377430.2588951484754850.870552425762257
750.1135670860892870.2271341721785750.886432913910713
760.1524405965359580.3048811930719160.847559403464042
770.1321606100637170.2643212201274330.867839389936284
780.1122119312871920.2244238625743850.887788068712808
790.1435989897000420.2871979794000840.856401010299958
800.1676782132036150.335356426407230.832321786796385
810.1592247725890310.3184495451780610.840775227410969
820.1389307119503850.277861423900770.861069288049615
830.1326019748750890.2652039497501780.867398025124911
840.1263694667624470.2527389335248950.873630533237553
850.1103736340801420.2207472681602840.889626365919858
860.09818490765408410.1963698153081680.901815092345916
870.08712398377832340.1742479675566470.912876016221677
880.1039488008562040.2078976017124080.896051199143796
890.08735756298944150.1747151259788830.912642437010558
900.09293602888144460.1858720577628890.907063971118555
910.08610819603962770.1722163920792550.913891803960372
920.07728667196093640.1545733439218730.922713328039064
930.0655331075763940.1310662151527880.934466892423606
940.06720472016602420.1344094403320480.932795279833976
950.0668548998957370.1337097997914740.933145100104263
960.06149586911222860.1229917382244570.938504130887771
970.05298890019462910.1059778003892580.947011099805371
980.04737208635671870.09474417271343740.952627913643281
990.04739096854237230.09478193708474470.952609031457628
1000.04387233797249410.08774467594498820.956127662027506
1010.03926932383707430.07853864767414850.960730676162926
1020.03271516866578230.06543033733156470.967284831334218
1030.02697965914327910.05395931828655810.973020340856721
1040.02712045342748060.05424090685496120.972879546572519
1050.0629485324226390.1258970648452780.937051467577361
1060.05886399061801080.1177279812360220.941136009381989
1070.07184311078455130.1436862215691030.928156889215449
1080.06221889572571210.1244377914514240.937781104274288
1090.09942827409903290.1988565481980660.900571725900967
1100.1161716491875030.2323432983750070.883828350812497
1110.111684756855910.223369513711820.88831524314409
1120.1475361163055750.295072232611150.852463883694425
1130.1378806855803010.2757613711606020.862119314419699
1140.13293634723220.26587269446440.8670636527678
1150.1266470289192360.2532940578384720.873352971080764
1160.1244104646459760.2488209292919520.875589535354024
1170.1216882507357780.2433765014715550.878311749264223
1180.107218593102930.2144371862058590.89278140689707
1190.1014335087725110.2028670175450230.898566491227489
1200.09091012519289610.1818202503857920.909089874807104
1210.08011003737934950.1602200747586990.91988996262065
1220.07294698064471830.1458939612894370.927053019355282
1230.06218404219590340.1243680843918070.937815957804097
1240.0594087592439060.1188175184878120.940591240756094
1250.05613093546460450.1122618709292090.943869064535395
1260.06932556662807230.1386511332561450.930674433371928
1270.125259049280850.2505180985616990.87474095071915
1280.1230995644127860.2461991288255730.876900435587214
1290.1095253895148280.2190507790296570.890474610485172
1300.1013584814542080.2027169629084160.898641518545792
1310.1096268709613140.2192537419226290.890373129038686
1320.1177121441473090.2354242882946180.882287855852691
1330.1419041223353450.283808244670690.858095877664655
1340.1252818972454890.2505637944909780.874718102754511
1350.1086169258589960.2172338517179920.891383074141004
1360.09520133845130350.1904026769026070.904798661548696
1370.08183847236515440.1636769447303090.918161527634846
1380.08892454430806960.1778490886161390.91107545569193
1390.07602042060542340.1520408412108470.923979579394577
1400.07427005599677120.1485401119935420.925729944003229
1410.07240153807044590.1448030761408920.927598461929554
1420.1005418314964590.2010836629929170.899458168503541
1430.1198779709782070.2397559419564130.880122029021793
1440.1137099536784070.2274199073568140.886290046321593
1450.1960219172888550.3920438345777110.803978082711145
1460.1806506273957940.3613012547915880.819349372604206
1470.1619564491921880.3239128983843770.838043550807812
1480.1436541920555690.2873083841111370.856345807944431
1490.1316079393613560.2632158787227120.868392060638644
1500.1170691245401040.2341382490802080.882930875459896
1510.187382405002280.3747648100045610.81261759499772
1520.175283044853990.350566089707980.82471695514601
1530.1607220436782690.3214440873565380.839277956321731
1540.1718087810090720.3436175620181430.828191218990928
1550.1497242357397740.2994484714795490.850275764260226
1560.1464203147049070.2928406294098140.853579685295093
1570.1527157479489160.3054314958978320.847284252051084
1580.1486891723673170.2973783447346330.851310827632683
1590.1315938711291870.2631877422583740.868406128870813
1600.1340202160435720.2680404320871450.865979783956428
1610.1205720188677990.2411440377355970.879427981132201
1620.1158226628952660.2316453257905310.884177337104734
1630.1067563233855610.2135126467711220.893243676614439
1640.135645795424410.2712915908488210.86435420457559
1650.1237804738376840.2475609476753670.876219526162316
1660.2111709266135920.4223418532271840.788829073386408
1670.2133083566667530.4266167133335060.786691643333247
1680.2038325278209570.4076650556419150.796167472179043
1690.184856961166510.369713922333020.81514303883349
1700.3020010398164690.6040020796329390.697998960183531
1710.3347447498488370.6694894996976750.665255250151163
1720.3015384683476530.6030769366953060.698461531652347
1730.405100011875140.8102000237502790.59489998812486
1740.3694168436392270.7388336872784540.630583156360773
1750.4350710486242010.8701420972484030.564928951375799
1760.4116364042745160.8232728085490330.588363595725484
1770.4121114107319050.8242228214638090.587888589268095
1780.3805396252357890.7610792504715780.619460374764211
1790.3533859976624690.7067719953249380.646614002337531
1800.348044106774990.696088213549980.65195589322501
1810.3142609886479060.6285219772958110.685739011352094
1820.3075080383150010.6150160766300030.692491961684999
1830.3314178726892530.6628357453785060.668582127310747
1840.3987073274281470.7974146548562950.601292672571853
1850.6428855222478140.7142289555043730.357114477752186
1860.6264911707242220.7470176585515560.373508829275778
1870.621780652731160.756438694537680.37821934726884
1880.7606209513411840.4787580973176310.239379048658816
1890.7416426083468220.5167147833063550.258357391653178
1900.7474427239198180.5051145521603650.252557276080182
1910.7154235533036860.5691528933926290.284576446696314
1920.6863016043259820.6273967913480350.313698395674018
1930.6555240106547880.6889519786904240.344475989345212
1940.623086904031690.7538261919366210.37691309596831
1950.5854398288607620.8291203422784770.414560171139238
1960.5457886145849920.9084227708300160.454211385415008
1970.5384719182812990.9230561634374020.461528081718701
1980.5123332339306840.9753335321386310.487666766069316
1990.4706926776670620.9413853553341250.529307322332938
2000.550453669065260.8990926618694790.44954633093474
2010.5191171635858020.9617656728283970.480882836414198
2020.6745522562060.6508954875880.325447743794
2030.653984360793240.692031278413520.34601563920676
2040.6161375782478620.7677248435042770.383862421752138
2050.5766989443657530.8466021112684950.423301055634247
2060.536748887576610.9265022248467790.46325111242339
2070.4975600318330150.995120063666030.502439968166985
2080.4534135797799060.9068271595598120.546586420220094
2090.4234052486067790.8468104972135580.576594751393221
2100.3897241491761610.7794482983523230.610275850823839
2110.349332783991420.698665567982840.65066721600858
2120.3182670279413310.6365340558826620.681732972058669
2130.3723109074637180.7446218149274360.627689092536282
2140.3517178807166830.7034357614333660.648282119283317
2150.3092047865349710.6184095730699420.690795213465029
2160.2737702538877840.5475405077755680.726229746112216
2170.2440101605634350.488020321126870.755989839436565
2180.2748457521196960.5496915042393930.725154247880303
2190.2471124724523330.4942249449046660.752887527547667
2200.2249295140264260.4498590280528520.775070485973574
2210.1943539137966590.3887078275933170.805646086203341
2220.2074074939055540.4148149878111080.792592506094446
2230.1724990659947560.3449981319895120.827500934005244
2240.148810725263580.297621450527160.85118927473642
2250.2161934412271290.4323868824542580.783806558772871
2260.1990393813405090.3980787626810180.800960618659491
2270.1767252036758690.3534504073517380.823274796324131
2280.1865707428388050.3731414856776090.813429257161195
2290.1530016565290070.3060033130580150.846998343470992
2300.1222709033388290.2445418066776590.877729096661171
2310.1217777189444270.2435554378888540.878222281055573
2320.2681014741483190.5362029482966380.731898525851681
2330.2269222802923210.4538445605846420.773077719707679
2340.347495435133970.6949908702679410.652504564866029
2350.297616655273980.5952333105479590.70238334472602
2360.3022531206984330.6045062413968650.697746879301567
2370.263905228364460.5278104567289210.73609477163554
2380.3545051612791840.7090103225583670.645494838720816
2390.3004483490483480.6008966980966950.699551650951652
2400.4743330866823470.9486661733646940.525666913317653
2410.5481502940963550.9036994118072910.451849705903645
2420.4705932169698260.9411864339396520.529406783030174
2430.3957264706790020.7914529413580030.604273529320998
2440.3840881537824520.7681763075649040.615911846217548
2450.410767228422030.8215344568440610.589232771577969
2460.6336158280895490.7327683438209030.366384171910451
2470.632497945108830.7350041097823390.36750205489117
2480.5703893036101230.8592213927797550.429610696389877
2490.7792388586346960.4415222827306080.220761141365304
2500.7280635364699910.5438729270600180.271936463530009
2510.7072693093955530.5854613812088930.292730690604447
2520.8146080976471060.3707838047057890.185391902352894
2530.7233806687638240.5532386624723530.276619331236176

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0462821839600423 & 0.0925643679200845 & 0.953717816039958 \tabularnewline
12 & 0.388048359907123 & 0.776096719814247 & 0.611951640092877 \tabularnewline
13 & 0.561637648490375 & 0.876724703019249 & 0.438362351509625 \tabularnewline
14 & 0.558195287917515 & 0.88360942416497 & 0.441804712082485 \tabularnewline
15 & 0.458185636321224 & 0.916371272642448 & 0.541814363678776 \tabularnewline
16 & 0.493551916084238 & 0.987103832168476 & 0.506448083915762 \tabularnewline
17 & 0.561892733674785 & 0.876214532650429 & 0.438107266325215 \tabularnewline
18 & 0.562987527338652 & 0.874024945322696 & 0.437012472661348 \tabularnewline
19 & 0.474933306446246 & 0.949866612892492 & 0.525066693553754 \tabularnewline
20 & 0.506449829858887 & 0.987100340282225 & 0.493550170141113 \tabularnewline
21 & 0.569061348144402 & 0.861877303711195 & 0.430938651855598 \tabularnewline
22 & 0.538460564878025 & 0.923078870243951 & 0.461539435121975 \tabularnewline
23 & 0.526756224193485 & 0.94648755161303 & 0.473243775806515 \tabularnewline
24 & 0.460330855329919 & 0.920661710659838 & 0.539669144670081 \tabularnewline
25 & 0.514532711994642 & 0.970934576010716 & 0.485467288005358 \tabularnewline
26 & 0.689236791452495 & 0.62152641709501 & 0.310763208547505 \tabularnewline
27 & 0.661675332409354 & 0.676649335181292 & 0.338324667590646 \tabularnewline
28 & 0.615495285874825 & 0.76900942825035 & 0.384504714125175 \tabularnewline
29 & 0.606014607015785 & 0.787970785968429 & 0.393985392984215 \tabularnewline
30 & 0.543324136903499 & 0.913351726193002 & 0.456675863096501 \tabularnewline
31 & 0.533898147677729 & 0.932203704644542 & 0.466101852322271 \tabularnewline
32 & 0.679867003737882 & 0.640265992524235 & 0.320132996262118 \tabularnewline
33 & 0.66915085455028 & 0.66169829089944 & 0.33084914544972 \tabularnewline
34 & 0.628011742894091 & 0.743976514211819 & 0.371988257105909 \tabularnewline
35 & 0.574183169238479 & 0.851633661523042 & 0.425816830761521 \tabularnewline
36 & 0.519578042779462 & 0.960843914441076 & 0.480421957220538 \tabularnewline
37 & 0.476208805035735 & 0.95241761007147 & 0.523791194964265 \tabularnewline
38 & 0.441940996504053 & 0.883881993008106 & 0.558059003495947 \tabularnewline
39 & 0.557868093167478 & 0.884263813665044 & 0.442131906832522 \tabularnewline
40 & 0.504698760892395 & 0.990602478215209 & 0.495301239107605 \tabularnewline
41 & 0.471353908538391 & 0.942707817076781 & 0.528646091461609 \tabularnewline
42 & 0.418974509341366 & 0.837949018682733 & 0.581025490658634 \tabularnewline
43 & 0.380667791403479 & 0.761335582806957 & 0.619332208596521 \tabularnewline
44 & 0.336534983355132 & 0.673069966710263 & 0.663465016644868 \tabularnewline
45 & 0.408474872646309 & 0.816949745292618 & 0.591525127353691 \tabularnewline
46 & 0.418231301825954 & 0.836462603651909 & 0.581768698174046 \tabularnewline
47 & 0.389508109158566 & 0.779016218317131 & 0.610491890841434 \tabularnewline
48 & 0.359667529805819 & 0.719335059611639 & 0.64033247019418 \tabularnewline
49 & 0.315857245121566 & 0.631714490243132 & 0.684142754878434 \tabularnewline
50 & 0.328231096357611 & 0.656462192715222 & 0.671768903642389 \tabularnewline
51 & 0.372437210057635 & 0.74487442011527 & 0.627562789942365 \tabularnewline
52 & 0.337506529338198 & 0.675013058676396 & 0.662493470661802 \tabularnewline
53 & 0.29838116425593 & 0.59676232851186 & 0.70161883574407 \tabularnewline
54 & 0.264939095850957 & 0.529878191701915 & 0.735060904149043 \tabularnewline
55 & 0.22925093787017 & 0.45850187574034 & 0.77074906212983 \tabularnewline
56 & 0.216573949743382 & 0.433147899486765 & 0.783426050256617 \tabularnewline
57 & 0.230667250749733 & 0.461334501499466 & 0.769332749250267 \tabularnewline
58 & 0.337083128351208 & 0.674166256702415 & 0.662916871648792 \tabularnewline
59 & 0.302506724959881 & 0.605013449919762 & 0.697493275040119 \tabularnewline
60 & 0.265003419699048 & 0.530006839398096 & 0.734996580300952 \tabularnewline
61 & 0.236145036231114 & 0.472290072462229 & 0.763854963768886 \tabularnewline
62 & 0.223470806699785 & 0.446941613399571 & 0.776529193300215 \tabularnewline
63 & 0.198509356745889 & 0.397018713491778 & 0.801490643254111 \tabularnewline
64 & 0.202661853596058 & 0.405323707192117 & 0.797338146403942 \tabularnewline
65 & 0.174454794108033 & 0.348909588216066 & 0.825545205891967 \tabularnewline
66 & 0.152700855969915 & 0.305401711939831 & 0.847299144030085 \tabularnewline
67 & 0.129827807269325 & 0.259655614538651 & 0.870172192730675 \tabularnewline
68 & 0.14707643481625 & 0.294152869632499 & 0.85292356518375 \tabularnewline
69 & 0.145942818673137 & 0.291885637346274 & 0.854057181326863 \tabularnewline
70 & 0.124995385301124 & 0.249990770602249 & 0.875004614698876 \tabularnewline
71 & 0.134037646988364 & 0.268075293976727 & 0.865962353011636 \tabularnewline
72 & 0.156396808648858 & 0.312793617297716 & 0.843603191351142 \tabularnewline
73 & 0.152421148769038 & 0.304842297538077 & 0.847578851230962 \tabularnewline
74 & 0.129447574237743 & 0.258895148475485 & 0.870552425762257 \tabularnewline
75 & 0.113567086089287 & 0.227134172178575 & 0.886432913910713 \tabularnewline
76 & 0.152440596535958 & 0.304881193071916 & 0.847559403464042 \tabularnewline
77 & 0.132160610063717 & 0.264321220127433 & 0.867839389936284 \tabularnewline
78 & 0.112211931287192 & 0.224423862574385 & 0.887788068712808 \tabularnewline
79 & 0.143598989700042 & 0.287197979400084 & 0.856401010299958 \tabularnewline
80 & 0.167678213203615 & 0.33535642640723 & 0.832321786796385 \tabularnewline
81 & 0.159224772589031 & 0.318449545178061 & 0.840775227410969 \tabularnewline
82 & 0.138930711950385 & 0.27786142390077 & 0.861069288049615 \tabularnewline
83 & 0.132601974875089 & 0.265203949750178 & 0.867398025124911 \tabularnewline
84 & 0.126369466762447 & 0.252738933524895 & 0.873630533237553 \tabularnewline
85 & 0.110373634080142 & 0.220747268160284 & 0.889626365919858 \tabularnewline
86 & 0.0981849076540841 & 0.196369815308168 & 0.901815092345916 \tabularnewline
87 & 0.0871239837783234 & 0.174247967556647 & 0.912876016221677 \tabularnewline
88 & 0.103948800856204 & 0.207897601712408 & 0.896051199143796 \tabularnewline
89 & 0.0873575629894415 & 0.174715125978883 & 0.912642437010558 \tabularnewline
90 & 0.0929360288814446 & 0.185872057762889 & 0.907063971118555 \tabularnewline
91 & 0.0861081960396277 & 0.172216392079255 & 0.913891803960372 \tabularnewline
92 & 0.0772866719609364 & 0.154573343921873 & 0.922713328039064 \tabularnewline
93 & 0.065533107576394 & 0.131066215152788 & 0.934466892423606 \tabularnewline
94 & 0.0672047201660242 & 0.134409440332048 & 0.932795279833976 \tabularnewline
95 & 0.066854899895737 & 0.133709799791474 & 0.933145100104263 \tabularnewline
96 & 0.0614958691122286 & 0.122991738224457 & 0.938504130887771 \tabularnewline
97 & 0.0529889001946291 & 0.105977800389258 & 0.947011099805371 \tabularnewline
98 & 0.0473720863567187 & 0.0947441727134374 & 0.952627913643281 \tabularnewline
99 & 0.0473909685423723 & 0.0947819370847447 & 0.952609031457628 \tabularnewline
100 & 0.0438723379724941 & 0.0877446759449882 & 0.956127662027506 \tabularnewline
101 & 0.0392693238370743 & 0.0785386476741485 & 0.960730676162926 \tabularnewline
102 & 0.0327151686657823 & 0.0654303373315647 & 0.967284831334218 \tabularnewline
103 & 0.0269796591432791 & 0.0539593182865581 & 0.973020340856721 \tabularnewline
104 & 0.0271204534274806 & 0.0542409068549612 & 0.972879546572519 \tabularnewline
105 & 0.062948532422639 & 0.125897064845278 & 0.937051467577361 \tabularnewline
106 & 0.0588639906180108 & 0.117727981236022 & 0.941136009381989 \tabularnewline
107 & 0.0718431107845513 & 0.143686221569103 & 0.928156889215449 \tabularnewline
108 & 0.0622188957257121 & 0.124437791451424 & 0.937781104274288 \tabularnewline
109 & 0.0994282740990329 & 0.198856548198066 & 0.900571725900967 \tabularnewline
110 & 0.116171649187503 & 0.232343298375007 & 0.883828350812497 \tabularnewline
111 & 0.11168475685591 & 0.22336951371182 & 0.88831524314409 \tabularnewline
112 & 0.147536116305575 & 0.29507223261115 & 0.852463883694425 \tabularnewline
113 & 0.137880685580301 & 0.275761371160602 & 0.862119314419699 \tabularnewline
114 & 0.1329363472322 & 0.2658726944644 & 0.8670636527678 \tabularnewline
115 & 0.126647028919236 & 0.253294057838472 & 0.873352971080764 \tabularnewline
116 & 0.124410464645976 & 0.248820929291952 & 0.875589535354024 \tabularnewline
117 & 0.121688250735778 & 0.243376501471555 & 0.878311749264223 \tabularnewline
118 & 0.10721859310293 & 0.214437186205859 & 0.89278140689707 \tabularnewline
119 & 0.101433508772511 & 0.202867017545023 & 0.898566491227489 \tabularnewline
120 & 0.0909101251928961 & 0.181820250385792 & 0.909089874807104 \tabularnewline
121 & 0.0801100373793495 & 0.160220074758699 & 0.91988996262065 \tabularnewline
122 & 0.0729469806447183 & 0.145893961289437 & 0.927053019355282 \tabularnewline
123 & 0.0621840421959034 & 0.124368084391807 & 0.937815957804097 \tabularnewline
124 & 0.059408759243906 & 0.118817518487812 & 0.940591240756094 \tabularnewline
125 & 0.0561309354646045 & 0.112261870929209 & 0.943869064535395 \tabularnewline
126 & 0.0693255666280723 & 0.138651133256145 & 0.930674433371928 \tabularnewline
127 & 0.12525904928085 & 0.250518098561699 & 0.87474095071915 \tabularnewline
128 & 0.123099564412786 & 0.246199128825573 & 0.876900435587214 \tabularnewline
129 & 0.109525389514828 & 0.219050779029657 & 0.890474610485172 \tabularnewline
130 & 0.101358481454208 & 0.202716962908416 & 0.898641518545792 \tabularnewline
131 & 0.109626870961314 & 0.219253741922629 & 0.890373129038686 \tabularnewline
132 & 0.117712144147309 & 0.235424288294618 & 0.882287855852691 \tabularnewline
133 & 0.141904122335345 & 0.28380824467069 & 0.858095877664655 \tabularnewline
134 & 0.125281897245489 & 0.250563794490978 & 0.874718102754511 \tabularnewline
135 & 0.108616925858996 & 0.217233851717992 & 0.891383074141004 \tabularnewline
136 & 0.0952013384513035 & 0.190402676902607 & 0.904798661548696 \tabularnewline
137 & 0.0818384723651544 & 0.163676944730309 & 0.918161527634846 \tabularnewline
138 & 0.0889245443080696 & 0.177849088616139 & 0.91107545569193 \tabularnewline
139 & 0.0760204206054234 & 0.152040841210847 & 0.923979579394577 \tabularnewline
140 & 0.0742700559967712 & 0.148540111993542 & 0.925729944003229 \tabularnewline
141 & 0.0724015380704459 & 0.144803076140892 & 0.927598461929554 \tabularnewline
142 & 0.100541831496459 & 0.201083662992917 & 0.899458168503541 \tabularnewline
143 & 0.119877970978207 & 0.239755941956413 & 0.880122029021793 \tabularnewline
144 & 0.113709953678407 & 0.227419907356814 & 0.886290046321593 \tabularnewline
145 & 0.196021917288855 & 0.392043834577711 & 0.803978082711145 \tabularnewline
146 & 0.180650627395794 & 0.361301254791588 & 0.819349372604206 \tabularnewline
147 & 0.161956449192188 & 0.323912898384377 & 0.838043550807812 \tabularnewline
148 & 0.143654192055569 & 0.287308384111137 & 0.856345807944431 \tabularnewline
149 & 0.131607939361356 & 0.263215878722712 & 0.868392060638644 \tabularnewline
150 & 0.117069124540104 & 0.234138249080208 & 0.882930875459896 \tabularnewline
151 & 0.18738240500228 & 0.374764810004561 & 0.81261759499772 \tabularnewline
152 & 0.17528304485399 & 0.35056608970798 & 0.82471695514601 \tabularnewline
153 & 0.160722043678269 & 0.321444087356538 & 0.839277956321731 \tabularnewline
154 & 0.171808781009072 & 0.343617562018143 & 0.828191218990928 \tabularnewline
155 & 0.149724235739774 & 0.299448471479549 & 0.850275764260226 \tabularnewline
156 & 0.146420314704907 & 0.292840629409814 & 0.853579685295093 \tabularnewline
157 & 0.152715747948916 & 0.305431495897832 & 0.847284252051084 \tabularnewline
158 & 0.148689172367317 & 0.297378344734633 & 0.851310827632683 \tabularnewline
159 & 0.131593871129187 & 0.263187742258374 & 0.868406128870813 \tabularnewline
160 & 0.134020216043572 & 0.268040432087145 & 0.865979783956428 \tabularnewline
161 & 0.120572018867799 & 0.241144037735597 & 0.879427981132201 \tabularnewline
162 & 0.115822662895266 & 0.231645325790531 & 0.884177337104734 \tabularnewline
163 & 0.106756323385561 & 0.213512646771122 & 0.893243676614439 \tabularnewline
164 & 0.13564579542441 & 0.271291590848821 & 0.86435420457559 \tabularnewline
165 & 0.123780473837684 & 0.247560947675367 & 0.876219526162316 \tabularnewline
166 & 0.211170926613592 & 0.422341853227184 & 0.788829073386408 \tabularnewline
167 & 0.213308356666753 & 0.426616713333506 & 0.786691643333247 \tabularnewline
168 & 0.203832527820957 & 0.407665055641915 & 0.796167472179043 \tabularnewline
169 & 0.18485696116651 & 0.36971392233302 & 0.81514303883349 \tabularnewline
170 & 0.302001039816469 & 0.604002079632939 & 0.697998960183531 \tabularnewline
171 & 0.334744749848837 & 0.669489499697675 & 0.665255250151163 \tabularnewline
172 & 0.301538468347653 & 0.603076936695306 & 0.698461531652347 \tabularnewline
173 & 0.40510001187514 & 0.810200023750279 & 0.59489998812486 \tabularnewline
174 & 0.369416843639227 & 0.738833687278454 & 0.630583156360773 \tabularnewline
175 & 0.435071048624201 & 0.870142097248403 & 0.564928951375799 \tabularnewline
176 & 0.411636404274516 & 0.823272808549033 & 0.588363595725484 \tabularnewline
177 & 0.412111410731905 & 0.824222821463809 & 0.587888589268095 \tabularnewline
178 & 0.380539625235789 & 0.761079250471578 & 0.619460374764211 \tabularnewline
179 & 0.353385997662469 & 0.706771995324938 & 0.646614002337531 \tabularnewline
180 & 0.34804410677499 & 0.69608821354998 & 0.65195589322501 \tabularnewline
181 & 0.314260988647906 & 0.628521977295811 & 0.685739011352094 \tabularnewline
182 & 0.307508038315001 & 0.615016076630003 & 0.692491961684999 \tabularnewline
183 & 0.331417872689253 & 0.662835745378506 & 0.668582127310747 \tabularnewline
184 & 0.398707327428147 & 0.797414654856295 & 0.601292672571853 \tabularnewline
185 & 0.642885522247814 & 0.714228955504373 & 0.357114477752186 \tabularnewline
186 & 0.626491170724222 & 0.747017658551556 & 0.373508829275778 \tabularnewline
187 & 0.62178065273116 & 0.75643869453768 & 0.37821934726884 \tabularnewline
188 & 0.760620951341184 & 0.478758097317631 & 0.239379048658816 \tabularnewline
189 & 0.741642608346822 & 0.516714783306355 & 0.258357391653178 \tabularnewline
190 & 0.747442723919818 & 0.505114552160365 & 0.252557276080182 \tabularnewline
191 & 0.715423553303686 & 0.569152893392629 & 0.284576446696314 \tabularnewline
192 & 0.686301604325982 & 0.627396791348035 & 0.313698395674018 \tabularnewline
193 & 0.655524010654788 & 0.688951978690424 & 0.344475989345212 \tabularnewline
194 & 0.62308690403169 & 0.753826191936621 & 0.37691309596831 \tabularnewline
195 & 0.585439828860762 & 0.829120342278477 & 0.414560171139238 \tabularnewline
196 & 0.545788614584992 & 0.908422770830016 & 0.454211385415008 \tabularnewline
197 & 0.538471918281299 & 0.923056163437402 & 0.461528081718701 \tabularnewline
198 & 0.512333233930684 & 0.975333532138631 & 0.487666766069316 \tabularnewline
199 & 0.470692677667062 & 0.941385355334125 & 0.529307322332938 \tabularnewline
200 & 0.55045366906526 & 0.899092661869479 & 0.44954633093474 \tabularnewline
201 & 0.519117163585802 & 0.961765672828397 & 0.480882836414198 \tabularnewline
202 & 0.674552256206 & 0.650895487588 & 0.325447743794 \tabularnewline
203 & 0.65398436079324 & 0.69203127841352 & 0.34601563920676 \tabularnewline
204 & 0.616137578247862 & 0.767724843504277 & 0.383862421752138 \tabularnewline
205 & 0.576698944365753 & 0.846602111268495 & 0.423301055634247 \tabularnewline
206 & 0.53674888757661 & 0.926502224846779 & 0.46325111242339 \tabularnewline
207 & 0.497560031833015 & 0.99512006366603 & 0.502439968166985 \tabularnewline
208 & 0.453413579779906 & 0.906827159559812 & 0.546586420220094 \tabularnewline
209 & 0.423405248606779 & 0.846810497213558 & 0.576594751393221 \tabularnewline
210 & 0.389724149176161 & 0.779448298352323 & 0.610275850823839 \tabularnewline
211 & 0.34933278399142 & 0.69866556798284 & 0.65066721600858 \tabularnewline
212 & 0.318267027941331 & 0.636534055882662 & 0.681732972058669 \tabularnewline
213 & 0.372310907463718 & 0.744621814927436 & 0.627689092536282 \tabularnewline
214 & 0.351717880716683 & 0.703435761433366 & 0.648282119283317 \tabularnewline
215 & 0.309204786534971 & 0.618409573069942 & 0.690795213465029 \tabularnewline
216 & 0.273770253887784 & 0.547540507775568 & 0.726229746112216 \tabularnewline
217 & 0.244010160563435 & 0.48802032112687 & 0.755989839436565 \tabularnewline
218 & 0.274845752119696 & 0.549691504239393 & 0.725154247880303 \tabularnewline
219 & 0.247112472452333 & 0.494224944904666 & 0.752887527547667 \tabularnewline
220 & 0.224929514026426 & 0.449859028052852 & 0.775070485973574 \tabularnewline
221 & 0.194353913796659 & 0.388707827593317 & 0.805646086203341 \tabularnewline
222 & 0.207407493905554 & 0.414814987811108 & 0.792592506094446 \tabularnewline
223 & 0.172499065994756 & 0.344998131989512 & 0.827500934005244 \tabularnewline
224 & 0.14881072526358 & 0.29762145052716 & 0.85118927473642 \tabularnewline
225 & 0.216193441227129 & 0.432386882454258 & 0.783806558772871 \tabularnewline
226 & 0.199039381340509 & 0.398078762681018 & 0.800960618659491 \tabularnewline
227 & 0.176725203675869 & 0.353450407351738 & 0.823274796324131 \tabularnewline
228 & 0.186570742838805 & 0.373141485677609 & 0.813429257161195 \tabularnewline
229 & 0.153001656529007 & 0.306003313058015 & 0.846998343470992 \tabularnewline
230 & 0.122270903338829 & 0.244541806677659 & 0.877729096661171 \tabularnewline
231 & 0.121777718944427 & 0.243555437888854 & 0.878222281055573 \tabularnewline
232 & 0.268101474148319 & 0.536202948296638 & 0.731898525851681 \tabularnewline
233 & 0.226922280292321 & 0.453844560584642 & 0.773077719707679 \tabularnewline
234 & 0.34749543513397 & 0.694990870267941 & 0.652504564866029 \tabularnewline
235 & 0.29761665527398 & 0.595233310547959 & 0.70238334472602 \tabularnewline
236 & 0.302253120698433 & 0.604506241396865 & 0.697746879301567 \tabularnewline
237 & 0.26390522836446 & 0.527810456728921 & 0.73609477163554 \tabularnewline
238 & 0.354505161279184 & 0.709010322558367 & 0.645494838720816 \tabularnewline
239 & 0.300448349048348 & 0.600896698096695 & 0.699551650951652 \tabularnewline
240 & 0.474333086682347 & 0.948666173364694 & 0.525666913317653 \tabularnewline
241 & 0.548150294096355 & 0.903699411807291 & 0.451849705903645 \tabularnewline
242 & 0.470593216969826 & 0.941186433939652 & 0.529406783030174 \tabularnewline
243 & 0.395726470679002 & 0.791452941358003 & 0.604273529320998 \tabularnewline
244 & 0.384088153782452 & 0.768176307564904 & 0.615911846217548 \tabularnewline
245 & 0.41076722842203 & 0.821534456844061 & 0.589232771577969 \tabularnewline
246 & 0.633615828089549 & 0.732768343820903 & 0.366384171910451 \tabularnewline
247 & 0.63249794510883 & 0.735004109782339 & 0.36750205489117 \tabularnewline
248 & 0.570389303610123 & 0.859221392779755 & 0.429610696389877 \tabularnewline
249 & 0.779238858634696 & 0.441522282730608 & 0.220761141365304 \tabularnewline
250 & 0.728063536469991 & 0.543872927060018 & 0.271936463530009 \tabularnewline
251 & 0.707269309395553 & 0.585461381208893 & 0.292730690604447 \tabularnewline
252 & 0.814608097647106 & 0.370783804705789 & 0.185391902352894 \tabularnewline
253 & 0.723380668763824 & 0.553238662472353 & 0.276619331236176 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185708&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]0.0462821839600423[/C][C]0.0925643679200845[/C][C]0.953717816039958[/C][/ROW]
[ROW][C]12[/C][C]0.388048359907123[/C][C]0.776096719814247[/C][C]0.611951640092877[/C][/ROW]
[ROW][C]13[/C][C]0.561637648490375[/C][C]0.876724703019249[/C][C]0.438362351509625[/C][/ROW]
[ROW][C]14[/C][C]0.558195287917515[/C][C]0.88360942416497[/C][C]0.441804712082485[/C][/ROW]
[ROW][C]15[/C][C]0.458185636321224[/C][C]0.916371272642448[/C][C]0.541814363678776[/C][/ROW]
[ROW][C]16[/C][C]0.493551916084238[/C][C]0.987103832168476[/C][C]0.506448083915762[/C][/ROW]
[ROW][C]17[/C][C]0.561892733674785[/C][C]0.876214532650429[/C][C]0.438107266325215[/C][/ROW]
[ROW][C]18[/C][C]0.562987527338652[/C][C]0.874024945322696[/C][C]0.437012472661348[/C][/ROW]
[ROW][C]19[/C][C]0.474933306446246[/C][C]0.949866612892492[/C][C]0.525066693553754[/C][/ROW]
[ROW][C]20[/C][C]0.506449829858887[/C][C]0.987100340282225[/C][C]0.493550170141113[/C][/ROW]
[ROW][C]21[/C][C]0.569061348144402[/C][C]0.861877303711195[/C][C]0.430938651855598[/C][/ROW]
[ROW][C]22[/C][C]0.538460564878025[/C][C]0.923078870243951[/C][C]0.461539435121975[/C][/ROW]
[ROW][C]23[/C][C]0.526756224193485[/C][C]0.94648755161303[/C][C]0.473243775806515[/C][/ROW]
[ROW][C]24[/C][C]0.460330855329919[/C][C]0.920661710659838[/C][C]0.539669144670081[/C][/ROW]
[ROW][C]25[/C][C]0.514532711994642[/C][C]0.970934576010716[/C][C]0.485467288005358[/C][/ROW]
[ROW][C]26[/C][C]0.689236791452495[/C][C]0.62152641709501[/C][C]0.310763208547505[/C][/ROW]
[ROW][C]27[/C][C]0.661675332409354[/C][C]0.676649335181292[/C][C]0.338324667590646[/C][/ROW]
[ROW][C]28[/C][C]0.615495285874825[/C][C]0.76900942825035[/C][C]0.384504714125175[/C][/ROW]
[ROW][C]29[/C][C]0.606014607015785[/C][C]0.787970785968429[/C][C]0.393985392984215[/C][/ROW]
[ROW][C]30[/C][C]0.543324136903499[/C][C]0.913351726193002[/C][C]0.456675863096501[/C][/ROW]
[ROW][C]31[/C][C]0.533898147677729[/C][C]0.932203704644542[/C][C]0.466101852322271[/C][/ROW]
[ROW][C]32[/C][C]0.679867003737882[/C][C]0.640265992524235[/C][C]0.320132996262118[/C][/ROW]
[ROW][C]33[/C][C]0.66915085455028[/C][C]0.66169829089944[/C][C]0.33084914544972[/C][/ROW]
[ROW][C]34[/C][C]0.628011742894091[/C][C]0.743976514211819[/C][C]0.371988257105909[/C][/ROW]
[ROW][C]35[/C][C]0.574183169238479[/C][C]0.851633661523042[/C][C]0.425816830761521[/C][/ROW]
[ROW][C]36[/C][C]0.519578042779462[/C][C]0.960843914441076[/C][C]0.480421957220538[/C][/ROW]
[ROW][C]37[/C][C]0.476208805035735[/C][C]0.95241761007147[/C][C]0.523791194964265[/C][/ROW]
[ROW][C]38[/C][C]0.441940996504053[/C][C]0.883881993008106[/C][C]0.558059003495947[/C][/ROW]
[ROW][C]39[/C][C]0.557868093167478[/C][C]0.884263813665044[/C][C]0.442131906832522[/C][/ROW]
[ROW][C]40[/C][C]0.504698760892395[/C][C]0.990602478215209[/C][C]0.495301239107605[/C][/ROW]
[ROW][C]41[/C][C]0.471353908538391[/C][C]0.942707817076781[/C][C]0.528646091461609[/C][/ROW]
[ROW][C]42[/C][C]0.418974509341366[/C][C]0.837949018682733[/C][C]0.581025490658634[/C][/ROW]
[ROW][C]43[/C][C]0.380667791403479[/C][C]0.761335582806957[/C][C]0.619332208596521[/C][/ROW]
[ROW][C]44[/C][C]0.336534983355132[/C][C]0.673069966710263[/C][C]0.663465016644868[/C][/ROW]
[ROW][C]45[/C][C]0.408474872646309[/C][C]0.816949745292618[/C][C]0.591525127353691[/C][/ROW]
[ROW][C]46[/C][C]0.418231301825954[/C][C]0.836462603651909[/C][C]0.581768698174046[/C][/ROW]
[ROW][C]47[/C][C]0.389508109158566[/C][C]0.779016218317131[/C][C]0.610491890841434[/C][/ROW]
[ROW][C]48[/C][C]0.359667529805819[/C][C]0.719335059611639[/C][C]0.64033247019418[/C][/ROW]
[ROW][C]49[/C][C]0.315857245121566[/C][C]0.631714490243132[/C][C]0.684142754878434[/C][/ROW]
[ROW][C]50[/C][C]0.328231096357611[/C][C]0.656462192715222[/C][C]0.671768903642389[/C][/ROW]
[ROW][C]51[/C][C]0.372437210057635[/C][C]0.74487442011527[/C][C]0.627562789942365[/C][/ROW]
[ROW][C]52[/C][C]0.337506529338198[/C][C]0.675013058676396[/C][C]0.662493470661802[/C][/ROW]
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[ROW][C]188[/C][C]0.760620951341184[/C][C]0.478758097317631[/C][C]0.239379048658816[/C][/ROW]
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[ROW][C]191[/C][C]0.715423553303686[/C][C]0.569152893392629[/C][C]0.284576446696314[/C][/ROW]
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[ROW][C]197[/C][C]0.538471918281299[/C][C]0.923056163437402[/C][C]0.461528081718701[/C][/ROW]
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[ROW][C]199[/C][C]0.470692677667062[/C][C]0.941385355334125[/C][C]0.529307322332938[/C][/ROW]
[ROW][C]200[/C][C]0.55045366906526[/C][C]0.899092661869479[/C][C]0.44954633093474[/C][/ROW]
[ROW][C]201[/C][C]0.519117163585802[/C][C]0.961765672828397[/C][C]0.480882836414198[/C][/ROW]
[ROW][C]202[/C][C]0.674552256206[/C][C]0.650895487588[/C][C]0.325447743794[/C][/ROW]
[ROW][C]203[/C][C]0.65398436079324[/C][C]0.69203127841352[/C][C]0.34601563920676[/C][/ROW]
[ROW][C]204[/C][C]0.616137578247862[/C][C]0.767724843504277[/C][C]0.383862421752138[/C][/ROW]
[ROW][C]205[/C][C]0.576698944365753[/C][C]0.846602111268495[/C][C]0.423301055634247[/C][/ROW]
[ROW][C]206[/C][C]0.53674888757661[/C][C]0.926502224846779[/C][C]0.46325111242339[/C][/ROW]
[ROW][C]207[/C][C]0.497560031833015[/C][C]0.99512006366603[/C][C]0.502439968166985[/C][/ROW]
[ROW][C]208[/C][C]0.453413579779906[/C][C]0.906827159559812[/C][C]0.546586420220094[/C][/ROW]
[ROW][C]209[/C][C]0.423405248606779[/C][C]0.846810497213558[/C][C]0.576594751393221[/C][/ROW]
[ROW][C]210[/C][C]0.389724149176161[/C][C]0.779448298352323[/C][C]0.610275850823839[/C][/ROW]
[ROW][C]211[/C][C]0.34933278399142[/C][C]0.69866556798284[/C][C]0.65066721600858[/C][/ROW]
[ROW][C]212[/C][C]0.318267027941331[/C][C]0.636534055882662[/C][C]0.681732972058669[/C][/ROW]
[ROW][C]213[/C][C]0.372310907463718[/C][C]0.744621814927436[/C][C]0.627689092536282[/C][/ROW]
[ROW][C]214[/C][C]0.351717880716683[/C][C]0.703435761433366[/C][C]0.648282119283317[/C][/ROW]
[ROW][C]215[/C][C]0.309204786534971[/C][C]0.618409573069942[/C][C]0.690795213465029[/C][/ROW]
[ROW][C]216[/C][C]0.273770253887784[/C][C]0.547540507775568[/C][C]0.726229746112216[/C][/ROW]
[ROW][C]217[/C][C]0.244010160563435[/C][C]0.48802032112687[/C][C]0.755989839436565[/C][/ROW]
[ROW][C]218[/C][C]0.274845752119696[/C][C]0.549691504239393[/C][C]0.725154247880303[/C][/ROW]
[ROW][C]219[/C][C]0.247112472452333[/C][C]0.494224944904666[/C][C]0.752887527547667[/C][/ROW]
[ROW][C]220[/C][C]0.224929514026426[/C][C]0.449859028052852[/C][C]0.775070485973574[/C][/ROW]
[ROW][C]221[/C][C]0.194353913796659[/C][C]0.388707827593317[/C][C]0.805646086203341[/C][/ROW]
[ROW][C]222[/C][C]0.207407493905554[/C][C]0.414814987811108[/C][C]0.792592506094446[/C][/ROW]
[ROW][C]223[/C][C]0.172499065994756[/C][C]0.344998131989512[/C][C]0.827500934005244[/C][/ROW]
[ROW][C]224[/C][C]0.14881072526358[/C][C]0.29762145052716[/C][C]0.85118927473642[/C][/ROW]
[ROW][C]225[/C][C]0.216193441227129[/C][C]0.432386882454258[/C][C]0.783806558772871[/C][/ROW]
[ROW][C]226[/C][C]0.199039381340509[/C][C]0.398078762681018[/C][C]0.800960618659491[/C][/ROW]
[ROW][C]227[/C][C]0.176725203675869[/C][C]0.353450407351738[/C][C]0.823274796324131[/C][/ROW]
[ROW][C]228[/C][C]0.186570742838805[/C][C]0.373141485677609[/C][C]0.813429257161195[/C][/ROW]
[ROW][C]229[/C][C]0.153001656529007[/C][C]0.306003313058015[/C][C]0.846998343470992[/C][/ROW]
[ROW][C]230[/C][C]0.122270903338829[/C][C]0.244541806677659[/C][C]0.877729096661171[/C][/ROW]
[ROW][C]231[/C][C]0.121777718944427[/C][C]0.243555437888854[/C][C]0.878222281055573[/C][/ROW]
[ROW][C]232[/C][C]0.268101474148319[/C][C]0.536202948296638[/C][C]0.731898525851681[/C][/ROW]
[ROW][C]233[/C][C]0.226922280292321[/C][C]0.453844560584642[/C][C]0.773077719707679[/C][/ROW]
[ROW][C]234[/C][C]0.34749543513397[/C][C]0.694990870267941[/C][C]0.652504564866029[/C][/ROW]
[ROW][C]235[/C][C]0.29761665527398[/C][C]0.595233310547959[/C][C]0.70238334472602[/C][/ROW]
[ROW][C]236[/C][C]0.302253120698433[/C][C]0.604506241396865[/C][C]0.697746879301567[/C][/ROW]
[ROW][C]237[/C][C]0.26390522836446[/C][C]0.527810456728921[/C][C]0.73609477163554[/C][/ROW]
[ROW][C]238[/C][C]0.354505161279184[/C][C]0.709010322558367[/C][C]0.645494838720816[/C][/ROW]
[ROW][C]239[/C][C]0.300448349048348[/C][C]0.600896698096695[/C][C]0.699551650951652[/C][/ROW]
[ROW][C]240[/C][C]0.474333086682347[/C][C]0.948666173364694[/C][C]0.525666913317653[/C][/ROW]
[ROW][C]241[/C][C]0.548150294096355[/C][C]0.903699411807291[/C][C]0.451849705903645[/C][/ROW]
[ROW][C]242[/C][C]0.470593216969826[/C][C]0.941186433939652[/C][C]0.529406783030174[/C][/ROW]
[ROW][C]243[/C][C]0.395726470679002[/C][C]0.791452941358003[/C][C]0.604273529320998[/C][/ROW]
[ROW][C]244[/C][C]0.384088153782452[/C][C]0.768176307564904[/C][C]0.615911846217548[/C][/ROW]
[ROW][C]245[/C][C]0.41076722842203[/C][C]0.821534456844061[/C][C]0.589232771577969[/C][/ROW]
[ROW][C]246[/C][C]0.633615828089549[/C][C]0.732768343820903[/C][C]0.366384171910451[/C][/ROW]
[ROW][C]247[/C][C]0.63249794510883[/C][C]0.735004109782339[/C][C]0.36750205489117[/C][/ROW]
[ROW][C]248[/C][C]0.570389303610123[/C][C]0.859221392779755[/C][C]0.429610696389877[/C][/ROW]
[ROW][C]249[/C][C]0.779238858634696[/C][C]0.441522282730608[/C][C]0.220761141365304[/C][/ROW]
[ROW][C]250[/C][C]0.728063536469991[/C][C]0.543872927060018[/C][C]0.271936463530009[/C][/ROW]
[ROW][C]251[/C][C]0.707269309395553[/C][C]0.585461381208893[/C][C]0.292730690604447[/C][/ROW]
[ROW][C]252[/C][C]0.814608097647106[/C][C]0.370783804705789[/C][C]0.185391902352894[/C][/ROW]
[ROW][C]253[/C][C]0.723380668763824[/C][C]0.553238662472353[/C][C]0.276619331236176[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185708&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185708&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
110.04628218396004230.09256436792008450.953717816039958
120.3880483599071230.7760967198142470.611951640092877
130.5616376484903750.8767247030192490.438362351509625
140.5581952879175150.883609424164970.441804712082485
150.4581856363212240.9163712726424480.541814363678776
160.4935519160842380.9871038321684760.506448083915762
170.5618927336747850.8762145326504290.438107266325215
180.5629875273386520.8740249453226960.437012472661348
190.4749333064462460.9498666128924920.525066693553754
200.5064498298588870.9871003402822250.493550170141113
210.5690613481444020.8618773037111950.430938651855598
220.5384605648780250.9230788702439510.461539435121975
230.5267562241934850.946487551613030.473243775806515
240.4603308553299190.9206617106598380.539669144670081
250.5145327119946420.9709345760107160.485467288005358
260.6892367914524950.621526417095010.310763208547505
270.6616753324093540.6766493351812920.338324667590646
280.6154952858748250.769009428250350.384504714125175
290.6060146070157850.7879707859684290.393985392984215
300.5433241369034990.9133517261930020.456675863096501
310.5338981476777290.9322037046445420.466101852322271
320.6798670037378820.6402659925242350.320132996262118
330.669150854550280.661698290899440.33084914544972
340.6280117428940910.7439765142118190.371988257105909
350.5741831692384790.8516336615230420.425816830761521
360.5195780427794620.9608439144410760.480421957220538
370.4762088050357350.952417610071470.523791194964265
380.4419409965040530.8838819930081060.558059003495947
390.5578680931674780.8842638136650440.442131906832522
400.5046987608923950.9906024782152090.495301239107605
410.4713539085383910.9427078170767810.528646091461609
420.4189745093413660.8379490186827330.581025490658634
430.3806677914034790.7613355828069570.619332208596521
440.3365349833551320.6730699667102630.663465016644868
450.4084748726463090.8169497452926180.591525127353691
460.4182313018259540.8364626036519090.581768698174046
470.3895081091585660.7790162183171310.610491890841434
480.3596675298058190.7193350596116390.64033247019418
490.3158572451215660.6317144902431320.684142754878434
500.3282310963576110.6564621927152220.671768903642389
510.3724372100576350.744874420115270.627562789942365
520.3375065293381980.6750130586763960.662493470661802
530.298381164255930.596762328511860.70161883574407
540.2649390958509570.5298781917019150.735060904149043
550.229250937870170.458501875740340.77074906212983
560.2165739497433820.4331478994867650.783426050256617
570.2306672507497330.4613345014994660.769332749250267
580.3370831283512080.6741662567024150.662916871648792
590.3025067249598810.6050134499197620.697493275040119
600.2650034196990480.5300068393980960.734996580300952
610.2361450362311140.4722900724622290.763854963768886
620.2234708066997850.4469416133995710.776529193300215
630.1985093567458890.3970187134917780.801490643254111
640.2026618535960580.4053237071921170.797338146403942
650.1744547941080330.3489095882160660.825545205891967
660.1527008559699150.3054017119398310.847299144030085
670.1298278072693250.2596556145386510.870172192730675
680.147076434816250.2941528696324990.85292356518375
690.1459428186731370.2918856373462740.854057181326863
700.1249953853011240.2499907706022490.875004614698876
710.1340376469883640.2680752939767270.865962353011636
720.1563968086488580.3127936172977160.843603191351142
730.1524211487690380.3048422975380770.847578851230962
740.1294475742377430.2588951484754850.870552425762257
750.1135670860892870.2271341721785750.886432913910713
760.1524405965359580.3048811930719160.847559403464042
770.1321606100637170.2643212201274330.867839389936284
780.1122119312871920.2244238625743850.887788068712808
790.1435989897000420.2871979794000840.856401010299958
800.1676782132036150.335356426407230.832321786796385
810.1592247725890310.3184495451780610.840775227410969
820.1389307119503850.277861423900770.861069288049615
830.1326019748750890.2652039497501780.867398025124911
840.1263694667624470.2527389335248950.873630533237553
850.1103736340801420.2207472681602840.889626365919858
860.09818490765408410.1963698153081680.901815092345916
870.08712398377832340.1742479675566470.912876016221677
880.1039488008562040.2078976017124080.896051199143796
890.08735756298944150.1747151259788830.912642437010558
900.09293602888144460.1858720577628890.907063971118555
910.08610819603962770.1722163920792550.913891803960372
920.07728667196093640.1545733439218730.922713328039064
930.0655331075763940.1310662151527880.934466892423606
940.06720472016602420.1344094403320480.932795279833976
950.0668548998957370.1337097997914740.933145100104263
960.06149586911222860.1229917382244570.938504130887771
970.05298890019462910.1059778003892580.947011099805371
980.04737208635671870.09474417271343740.952627913643281
990.04739096854237230.09478193708474470.952609031457628
1000.04387233797249410.08774467594498820.956127662027506
1010.03926932383707430.07853864767414850.960730676162926
1020.03271516866578230.06543033733156470.967284831334218
1030.02697965914327910.05395931828655810.973020340856721
1040.02712045342748060.05424090685496120.972879546572519
1050.0629485324226390.1258970648452780.937051467577361
1060.05886399061801080.1177279812360220.941136009381989
1070.07184311078455130.1436862215691030.928156889215449
1080.06221889572571210.1244377914514240.937781104274288
1090.09942827409903290.1988565481980660.900571725900967
1100.1161716491875030.2323432983750070.883828350812497
1110.111684756855910.223369513711820.88831524314409
1120.1475361163055750.295072232611150.852463883694425
1130.1378806855803010.2757613711606020.862119314419699
1140.13293634723220.26587269446440.8670636527678
1150.1266470289192360.2532940578384720.873352971080764
1160.1244104646459760.2488209292919520.875589535354024
1170.1216882507357780.2433765014715550.878311749264223
1180.107218593102930.2144371862058590.89278140689707
1190.1014335087725110.2028670175450230.898566491227489
1200.09091012519289610.1818202503857920.909089874807104
1210.08011003737934950.1602200747586990.91988996262065
1220.07294698064471830.1458939612894370.927053019355282
1230.06218404219590340.1243680843918070.937815957804097
1240.0594087592439060.1188175184878120.940591240756094
1250.05613093546460450.1122618709292090.943869064535395
1260.06932556662807230.1386511332561450.930674433371928
1270.125259049280850.2505180985616990.87474095071915
1280.1230995644127860.2461991288255730.876900435587214
1290.1095253895148280.2190507790296570.890474610485172
1300.1013584814542080.2027169629084160.898641518545792
1310.1096268709613140.2192537419226290.890373129038686
1320.1177121441473090.2354242882946180.882287855852691
1330.1419041223353450.283808244670690.858095877664655
1340.1252818972454890.2505637944909780.874718102754511
1350.1086169258589960.2172338517179920.891383074141004
1360.09520133845130350.1904026769026070.904798661548696
1370.08183847236515440.1636769447303090.918161527634846
1380.08892454430806960.1778490886161390.91107545569193
1390.07602042060542340.1520408412108470.923979579394577
1400.07427005599677120.1485401119935420.925729944003229
1410.07240153807044590.1448030761408920.927598461929554
1420.1005418314964590.2010836629929170.899458168503541
1430.1198779709782070.2397559419564130.880122029021793
1440.1137099536784070.2274199073568140.886290046321593
1450.1960219172888550.3920438345777110.803978082711145
1460.1806506273957940.3613012547915880.819349372604206
1470.1619564491921880.3239128983843770.838043550807812
1480.1436541920555690.2873083841111370.856345807944431
1490.1316079393613560.2632158787227120.868392060638644
1500.1170691245401040.2341382490802080.882930875459896
1510.187382405002280.3747648100045610.81261759499772
1520.175283044853990.350566089707980.82471695514601
1530.1607220436782690.3214440873565380.839277956321731
1540.1718087810090720.3436175620181430.828191218990928
1550.1497242357397740.2994484714795490.850275764260226
1560.1464203147049070.2928406294098140.853579685295093
1570.1527157479489160.3054314958978320.847284252051084
1580.1486891723673170.2973783447346330.851310827632683
1590.1315938711291870.2631877422583740.868406128870813
1600.1340202160435720.2680404320871450.865979783956428
1610.1205720188677990.2411440377355970.879427981132201
1620.1158226628952660.2316453257905310.884177337104734
1630.1067563233855610.2135126467711220.893243676614439
1640.135645795424410.2712915908488210.86435420457559
1650.1237804738376840.2475609476753670.876219526162316
1660.2111709266135920.4223418532271840.788829073386408
1670.2133083566667530.4266167133335060.786691643333247
1680.2038325278209570.4076650556419150.796167472179043
1690.184856961166510.369713922333020.81514303883349
1700.3020010398164690.6040020796329390.697998960183531
1710.3347447498488370.6694894996976750.665255250151163
1720.3015384683476530.6030769366953060.698461531652347
1730.405100011875140.8102000237502790.59489998812486
1740.3694168436392270.7388336872784540.630583156360773
1750.4350710486242010.8701420972484030.564928951375799
1760.4116364042745160.8232728085490330.588363595725484
1770.4121114107319050.8242228214638090.587888589268095
1780.3805396252357890.7610792504715780.619460374764211
1790.3533859976624690.7067719953249380.646614002337531
1800.348044106774990.696088213549980.65195589322501
1810.3142609886479060.6285219772958110.685739011352094
1820.3075080383150010.6150160766300030.692491961684999
1830.3314178726892530.6628357453785060.668582127310747
1840.3987073274281470.7974146548562950.601292672571853
1850.6428855222478140.7142289555043730.357114477752186
1860.6264911707242220.7470176585515560.373508829275778
1870.621780652731160.756438694537680.37821934726884
1880.7606209513411840.4787580973176310.239379048658816
1890.7416426083468220.5167147833063550.258357391653178
1900.7474427239198180.5051145521603650.252557276080182
1910.7154235533036860.5691528933926290.284576446696314
1920.6863016043259820.6273967913480350.313698395674018
1930.6555240106547880.6889519786904240.344475989345212
1940.623086904031690.7538261919366210.37691309596831
1950.5854398288607620.8291203422784770.414560171139238
1960.5457886145849920.9084227708300160.454211385415008
1970.5384719182812990.9230561634374020.461528081718701
1980.5123332339306840.9753335321386310.487666766069316
1990.4706926776670620.9413853553341250.529307322332938
2000.550453669065260.8990926618694790.44954633093474
2010.5191171635858020.9617656728283970.480882836414198
2020.6745522562060.6508954875880.325447743794
2030.653984360793240.692031278413520.34601563920676
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2520.8146080976471060.3707838047057890.185391902352894
2530.7233806687638240.5532386624723530.276619331236176







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level80.0329218106995885OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 8 & 0.0329218106995885 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185708&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0329218106995885[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185708&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185708&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 level00OK
5% type I error level00OK
10% type I error level80.0329218106995885OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}