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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 17 Nov 2013 06:00:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/17/t1384686088d8gs40t052lzjen.htm/, Retrieved Sun, 28 Apr 2024 21:06:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=225701, Retrieved Sun, 28 Apr 2024 21:06:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R P     [Multiple Regression] [] [2013-11-17 11:00:50] [eed7bd73d5c44ddc39c9fbee8149d07c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time14 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225701&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225701&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225701&T=0

As an alternative you can also use a QR Code:  
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'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 29.9485992087432 -0.0403029501747092Connected[t] + 0.0060167528984596Separate[t] -0.0846506179942974Learning[t] -0.0234886684060424Software[t] -0.706983475750363Happiness[t] -0.146225555870815Belonging[t] + 0.142997159492399Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  29.9485992087432 -0.0403029501747092Connected[t] +  0.0060167528984596Separate[t] -0.0846506179942974Learning[t] -0.0234886684060424Software[t] -0.706983475750363Happiness[t] -0.146225555870815Belonging[t] +  0.142997159492399Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225701&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  29.9485992087432 -0.0403029501747092Connected[t] +  0.0060167528984596Separate[t] -0.0846506179942974Learning[t] -0.0234886684060424Software[t] -0.706983475750363Happiness[t] -0.146225555870815Belonging[t] +  0.142997159492399Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225701&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225701&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 29.9485992087432 -0.0403029501747092Connected[t] + 0.0060167528984596Separate[t] -0.0846506179942974Learning[t] -0.0234886684060424Software[t] -0.706983475750363Happiness[t] -0.146225555870815Belonging[t] + 0.142997159492399Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)29.94859920874322.12635814.084500
Connected-0.04030295017470920.051123-0.78840.4312180.215609
Separate0.00601675289845960.0524070.11480.9086870.454343
Learning-0.08465061799429740.091482-0.92530.355670.177835
Software-0.02348866840604240.094201-0.24930.8032930.401647
Happiness-0.7069834757503630.073153-9.664500
Belonging-0.1462255558708150.054745-2.6710.0080470.004023
Belonging_Final0.1429971594923990.0822911.73770.0834680.041734

\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) & 29.9485992087432 & 2.126358 & 14.0845 & 0 & 0 \tabularnewline
Connected & -0.0403029501747092 & 0.051123 & -0.7884 & 0.431218 & 0.215609 \tabularnewline
Separate & 0.0060167528984596 & 0.052407 & 0.1148 & 0.908687 & 0.454343 \tabularnewline
Learning & -0.0846506179942974 & 0.091482 & -0.9253 & 0.35567 & 0.177835 \tabularnewline
Software & -0.0234886684060424 & 0.094201 & -0.2493 & 0.803293 & 0.401647 \tabularnewline
Happiness & -0.706983475750363 & 0.073153 & -9.6645 & 0 & 0 \tabularnewline
Belonging & -0.146225555870815 & 0.054745 & -2.671 & 0.008047 & 0.004023 \tabularnewline
Belonging_Final & 0.142997159492399 & 0.082291 & 1.7377 & 0.083468 & 0.041734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225701&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]29.9485992087432[/C][C]2.126358[/C][C]14.0845[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0403029501747092[/C][C]0.051123[/C][C]-0.7884[/C][C]0.431218[/C][C]0.215609[/C][/ROW]
[ROW][C]Separate[/C][C]0.0060167528984596[/C][C]0.052407[/C][C]0.1148[/C][C]0.908687[/C][C]0.454343[/C][/ROW]
[ROW][C]Learning[/C][C]-0.0846506179942974[/C][C]0.091482[/C][C]-0.9253[/C][C]0.35567[/C][C]0.177835[/C][/ROW]
[ROW][C]Software[/C][C]-0.0234886684060424[/C][C]0.094201[/C][C]-0.2493[/C][C]0.803293[/C][C]0.401647[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.706983475750363[/C][C]0.073153[/C][C]-9.6645[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.146225555870815[/C][C]0.054745[/C][C]-2.671[/C][C]0.008047[/C][C]0.004023[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.142997159492399[/C][C]0.082291[/C][C]1.7377[/C][C]0.083468[/C][C]0.041734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225701&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225701&T=2

As an alternative you can also use a QR Code:  
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)29.94859920874322.12635814.084500
Connected-0.04030295017470920.051123-0.78840.4312180.215609
Separate0.00601675289845960.0524070.11480.9086870.454343
Learning-0.08465061799429740.091482-0.92530.355670.177835
Software-0.02348866840604240.094201-0.24930.8032930.401647
Happiness-0.7069834757503630.073153-9.664500
Belonging-0.1462255558708150.054745-2.6710.0080470.004023
Belonging_Final0.1429971594923990.0822911.73770.0834680.041734






Multiple Linear Regression - Regression Statistics
Multiple R0.618652608318161
R-squared0.382731049778864
Adjusted R-squared0.365852601921255
F-TEST (value)22.6757254581511
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.76292760702527
Sum Squared Residuals1954.24485418557

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.618652608318161 \tabularnewline
R-squared & 0.382731049778864 \tabularnewline
Adjusted R-squared & 0.365852601921255 \tabularnewline
F-TEST (value) & 22.6757254581511 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.76292760702527 \tabularnewline
Sum Squared Residuals & 1954.24485418557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225701&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.618652608318161[/C][/ROW]
[ROW][C]R-squared[/C][C]0.382731049778864[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.365852601921255[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.6757254581511[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]256[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.76292760702527[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1954.24485418557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225701&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225701&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.618652608318161
R-squared0.382731049778864
Adjusted R-squared0.365852601921255
F-TEST (value)22.6757254581511
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.76292760702527
Sum Squared Residuals1954.24485418557






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.0706787890217-2.07067878902172
2119.386966437633221.61303356236677
31415.5675810649187-1.56758106491873
41215.0690389054651-3.0690389054651
52111.463448897319.53655110268998
6129.96670764341012.0332923565899
72214.31624950857827.68375049142184
81112.6073490645059-1.60734906450588
91012.2507355875693-2.25073558756932
101312.2078414905570.792158509443017
111010.4506326835643-0.450632683564343
12810.4560288991883-2.45602889918833
131516.1303332426016-1.13033324260163
141412.50362615591521.49637384408483
15109.181354707056850.818645292943152
161414.0748811987288-0.0748811987287531
171412.75576996757131.24423003242873
18119.506125939688941.49387406031106
191013.4300135886583-3.43001358865834
201312.71647243220610.283527567793914
219.510.7309666966345-1.23096669663452
221415.8211721695944-1.82117216959437
231213.6166639166209-1.61666391662092
241415.6977202383925-1.69772023839247
25119.69013111886321.3098688811368
26915.4042718938735-6.40427189387354
271110.8429827819160.157017218083986
281513.5471640846961.45283591530398
291411.60540747282472.39459252717533
301316.0748009535387-3.07480095353866
31911.3311845358491-2.33118453584907
321515.4811799450673-0.481179945067328
331010.9720583597398-0.972058359739775
341111.9987926344505-0.998792634450497
351312.57924972069960.420750279300398
36812.4446557288483-4.44465572884832
372017.62426239753572.37573760246429
381212.3913844304-0.391384430400045
391010.823044424272-0.823044424271974
401013.8727843851482-3.8727843851482
41912.0700584795227-3.07005847952272
421411.32310179100822.67689820899176
43812.0752714430978-4.07527144309776
441415.6619347922746-1.66193479227463
451112.4771260546196-1.47712605461964
461315.4546859321418-2.45468593214185
47913.2007437142602-4.20074371426023
481112.3012373392283-1.30123733922831
491510.28001484870424.71998515129578
501113.4198208241544-2.41982082415443
511011.6561895506617-1.65618955066172
521413.4444175299720.555582470027986
531815.86165286010112.13834713989894
541416.1752560994127-2.17525609941275
551115.424028004785-4.42402800478502
5614.512.3449078550742.15509214492597
571311.24752527240781.75247472759223
58912.9521256625237-3.95212566252374
591014.1364691048419-4.13646910484192
601514.58255621741860.417443782581392
612018.89783852648561.10216147351436
621212.9701019822211-0.970101982221123
631214.4946349613718-2.49463496137181
641414.3977244788636-0.397724478863617
651313.3465184438438-0.346518443843819
661115.2551842026485-4.25518420264853
671715.43390265027081.56609734972918
681213.8244928070111-1.82449280701106
691312.93671273565830.0632872643417436
701412.43590710842921.56409289157077
711314.1962687930734-1.19626879307339
721512.99535099330142.00464900669856
731311.37524127810481.62475872189521
741011.8367923777135-1.83679237771346
751112.780603418997-1.78060341899703
761913.73184285378395.26815714621608
771310.91461719179462.08538280820537
781714.16355948010472.83644051989525
791312.48106044673980.518939553260199
80914.6155095332266-5.61550953322658
811111.6202293351416-0.620229335141631
82912.5724474751648-3.5724474751648
831211.2669962747390.733003725260956
841212.4942226380307-0.49422263803067
851312.77955465444580.22044534555419
861312.1184371481610.881562851838952
871213.2054462258336-1.20544622583363
881514.96801689911660.0319831008834498
892218.22973602480363.77026397519641
901311.57329009641051.42670990358947
911514.53077962468120.469220375318838
921311.86215831102611.13784168897386
931512.25135000724572.74864999275432
9412.513.4100310275549-0.910031027554912
951110.71517709877670.284822901223319
961614.58372662062031.41627337937966
971112.4332147071392-1.43321470713922
981110.02656028438830.973439715611683
991012.3154875257295-2.31548752572947
1001010.2534498040753-0.253449804075322
1011614.3317960529341.66820394706604
1021210.36991490629981.63008509370021
1031115.8774150398815-4.87741503988146
1041611.80476542173544.19523457826455
1051916.70338969792692.29661030207313
1061111.2027375647298-0.20273756472981
1071612.05666896092873.94333103907134
1081516.8483856022461-1.8483856022461
1092417.7817523354036.21824766459696
1101411.70973430159832.29026569840166
1111514.90887500335430.091124996645684
1121113.0143654843154-2.01436548431536
1131513.61725323322971.38274676677028
1141210.29328723728891.70671276271109
1151010.512578368055-0.512578368055042
1161414.2937682134376-0.29376821343758
1171314.0429451333889-1.04294513338887
118913.488903501121-4.48890350112103
1191511.80419138087113.19580861912893
1201515.8213128610493-0.821312861049309
1211412.76885593436171.23114406563832
1221111.3724384141774-0.372438414177395
123812.0057475305325-4.00574753053247
1241112.264754845446-1.26475484544595
1251113.1692830825707-2.16928308257067
12689.75049894665782-1.75049894665782
1271010.1649517011955-0.164951701195507
128119.655941060057771.34405893994223
1291312.6212488885910.378751111409025
1301113.7012421239307-2.7012421239307
1312017.61391957466152.38608042533845
1321011.7305312221829-1.73053122218293
1331513.37721248441941.62278751558062
1341212.3714800555824-0.371480055582385
1351411.1197089384932.88029106150704
1362316.68653713100846.31346286899159
1371414.1566526501826-0.156652650182614
1381617.1159559049063-1.11595590490632
1391113.2955474557045-2.29554745570449
1401214.3497742905291-2.34977429052909
1411013.5391292765128-3.53912927651279
1421411.17914610413422.8208538958658
1431212.2643895106563-0.264389510656324
1441211.93855722795870.0614427720412523
1451111.0276187019894-0.0276187019893981
1461211.288281956190.711718043809951
1471316.2155145272081-3.21551452720815
1481114.3363011715584-3.33630117155839
1491916.95922847212652.04077152787354
1501211.4787551984310.521244801569002
1511712.88164559704914.11835440295091
152911.4102610697361-2.41026106973613
1531214.5571407032858-2.55714070328584
1541916.94510192281712.05489807718294
1551814.46718225707283.53281774292718
1561514.53077962468120.469220375318838
1571413.80168045861530.198319541384735
158119.655941060057771.34405893994223
159912.9771730113878-3.97717301138777
1601814.06693422258123.93306577741877
1611614.80405542278291.19594457721714
1622417.10966682459556.89033317540452
1631412.63075437560891.36924562439106
1642010.69536486537319.30463513462689
1651815.8591093898392.14089061016099
1662317.17332347728395.82667652271614
1671213.055081181971-1.05508118197099
1681414.8255026820734-0.825502682073394
1691616.2211245696061-0.221124569606062
1701816.27233218135341.72766781864658
1712016.55333825076223.44666174923779
1721211.53593833387650.464061666123491
1731216.2084923595002-4.20849235950016
1741715.19076383644831.80923616355165
1751311.82039002976761.17960997023244
176913.036853007205-4.03685300720496
1771617.2949149119527-1.29491491195272
1781814.98232633396763.01767366603244
1791012.2197674410369-2.21976744103686
1801414.8208728587478-0.820872858747793
1811114.0029797837371-3.00297978373706
182914.5213958422977-5.52139584229771
1831112.4064530601426-1.40645306014257
1841012.3260504295329-2.32605042953289
1851111.8103681088032-0.81036810880321
1861913.22718158544475.77281841455529
1871412.43524480585961.56475519414043
1881211.49255219359630.50744780640365
1891415.0959577912219-1.09595779122192
1902116.70721984635954.29278015364054
1911315.6481146327758-2.64811463277585
1921012.4486729761107-2.44867297611075
1931513.08274943499111.91725056500891
1941616.16791141144-0.167911411440032
1951412.67497903662841.32502096337161
1961214.7066877693376-2.70668776933764
1971912.61328642042796.38671357957209
1981512.16440523327072.83559476672928
1991917.98969066227781.01030933772222
2001313.2987067190283-0.298706719028315
2011717.1231997625005-0.123199762500533
2021212.7674065438777-0.767406543877694
2031110.64250834967720.357491650322823
2041415.391098939205-1.39109893920499
2051112.3813885335817-1.38138853358169
2061312.27619629122890.723803708771061
2071212.6857334320538-0.685733432053776
2081512.63577257516792.36422742483212
2091414.395503288059-0.395503288058962
2101210.82738146394311.17261853605692
2111717.4777798338587-0.477779833858746
2121111.2474110902512-0.247411090251178
2131814.58882856238623.41117143761382
2141315.8595853229194-2.8595853229194
2151715.25826429839061.74173570160941
2161313.3040103616622-0.30401036166222
2171110.11971330288370.880286697116307
2181212.5627209103186-0.562720910318632
2192217.99218300032934.00781699967067
2201412.02132765064911.97867234935087
2211215.0421356006936-3.04213560069362
2221211.85378896221950.146211037780463
2231715.95331489240481.04668510759519
224912.8234212732034-3.82342127320344
2252117.13659230713963.86340769286042
2261011.9179113198274-1.91791131982742
2271110.86178871706560.138211282934366
2281214.5925564778268-2.59255647782678
2292318.33342314006474.6665768599353
2301315.6979505366254-2.69795053662537
2311213.386506176235-1.386506176235
2321617.8977570916836-1.89775709168358
233913.4301958683188-4.43019586831881
2341713.50102274484433.49897725515567
235911.8873896605286-2.88738966052861
2361415.6544395742704-1.65443957427037
2371715.7112721762531.28872782374701
2381315.4946665608674-2.49466656086744
2391115.8808621627185-4.88086216271849
2401215.6137825421604-3.61378254216039
2411014.076437711058-4.07643771105795
2421918.93048069126190.0695193087380646
2431616.1116301325695-0.111630132569486
2441615.08035673713330.919643262866727
2451412.12286517359121.87713482640877
2462015.92618235228344.07381764771662
2471514.48773936122710.5122606387729
2482315.44754940606577.55245059393431
2492017.99491005856862.0050899414314
2501616.051525496072-0.0515254960720058
2511412.97353952656361.02646047343643
2521713.92469904040773.07530095959227
2531114.2645985407165-3.26459854071648
2541313.96117701187-0.961177011869954
2551715.26998430738191.73001569261805
2561515.2013316498899-0.201331649889855
2572115.94345178719895.05654821280109
2581817.80977648086160.19022351913838
2591512.76414542402262.2358545759774
260816.5387264612104-8.53872646121035
2611213.6904753430729-1.69047534307287
2621212.9087082978523-0.908708297852302
2632219.01952725528032.98047274471967
2641213.2987122538222-1.29871225382222

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 14.0706787890217 & -2.07067878902172 \tabularnewline
2 & 11 & 9.38696643763322 & 1.61303356236677 \tabularnewline
3 & 14 & 15.5675810649187 & -1.56758106491873 \tabularnewline
4 & 12 & 15.0690389054651 & -3.0690389054651 \tabularnewline
5 & 21 & 11.46344889731 & 9.53655110268998 \tabularnewline
6 & 12 & 9.9667076434101 & 2.0332923565899 \tabularnewline
7 & 22 & 14.3162495085782 & 7.68375049142184 \tabularnewline
8 & 11 & 12.6073490645059 & -1.60734906450588 \tabularnewline
9 & 10 & 12.2507355875693 & -2.25073558756932 \tabularnewline
10 & 13 & 12.207841490557 & 0.792158509443017 \tabularnewline
11 & 10 & 10.4506326835643 & -0.450632683564343 \tabularnewline
12 & 8 & 10.4560288991883 & -2.45602889918833 \tabularnewline
13 & 15 & 16.1303332426016 & -1.13033324260163 \tabularnewline
14 & 14 & 12.5036261559152 & 1.49637384408483 \tabularnewline
15 & 10 & 9.18135470705685 & 0.818645292943152 \tabularnewline
16 & 14 & 14.0748811987288 & -0.0748811987287531 \tabularnewline
17 & 14 & 12.7557699675713 & 1.24423003242873 \tabularnewline
18 & 11 & 9.50612593968894 & 1.49387406031106 \tabularnewline
19 & 10 & 13.4300135886583 & -3.43001358865834 \tabularnewline
20 & 13 & 12.7164724322061 & 0.283527567793914 \tabularnewline
21 & 9.5 & 10.7309666966345 & -1.23096669663452 \tabularnewline
22 & 14 & 15.8211721695944 & -1.82117216959437 \tabularnewline
23 & 12 & 13.6166639166209 & -1.61666391662092 \tabularnewline
24 & 14 & 15.6977202383925 & -1.69772023839247 \tabularnewline
25 & 11 & 9.6901311188632 & 1.3098688811368 \tabularnewline
26 & 9 & 15.4042718938735 & -6.40427189387354 \tabularnewline
27 & 11 & 10.842982781916 & 0.157017218083986 \tabularnewline
28 & 15 & 13.547164084696 & 1.45283591530398 \tabularnewline
29 & 14 & 11.6054074728247 & 2.39459252717533 \tabularnewline
30 & 13 & 16.0748009535387 & -3.07480095353866 \tabularnewline
31 & 9 & 11.3311845358491 & -2.33118453584907 \tabularnewline
32 & 15 & 15.4811799450673 & -0.481179945067328 \tabularnewline
33 & 10 & 10.9720583597398 & -0.972058359739775 \tabularnewline
34 & 11 & 11.9987926344505 & -0.998792634450497 \tabularnewline
35 & 13 & 12.5792497206996 & 0.420750279300398 \tabularnewline
36 & 8 & 12.4446557288483 & -4.44465572884832 \tabularnewline
37 & 20 & 17.6242623975357 & 2.37573760246429 \tabularnewline
38 & 12 & 12.3913844304 & -0.391384430400045 \tabularnewline
39 & 10 & 10.823044424272 & -0.823044424271974 \tabularnewline
40 & 10 & 13.8727843851482 & -3.8727843851482 \tabularnewline
41 & 9 & 12.0700584795227 & -3.07005847952272 \tabularnewline
42 & 14 & 11.3231017910082 & 2.67689820899176 \tabularnewline
43 & 8 & 12.0752714430978 & -4.07527144309776 \tabularnewline
44 & 14 & 15.6619347922746 & -1.66193479227463 \tabularnewline
45 & 11 & 12.4771260546196 & -1.47712605461964 \tabularnewline
46 & 13 & 15.4546859321418 & -2.45468593214185 \tabularnewline
47 & 9 & 13.2007437142602 & -4.20074371426023 \tabularnewline
48 & 11 & 12.3012373392283 & -1.30123733922831 \tabularnewline
49 & 15 & 10.2800148487042 & 4.71998515129578 \tabularnewline
50 & 11 & 13.4198208241544 & -2.41982082415443 \tabularnewline
51 & 10 & 11.6561895506617 & -1.65618955066172 \tabularnewline
52 & 14 & 13.444417529972 & 0.555582470027986 \tabularnewline
53 & 18 & 15.8616528601011 & 2.13834713989894 \tabularnewline
54 & 14 & 16.1752560994127 & -2.17525609941275 \tabularnewline
55 & 11 & 15.424028004785 & -4.42402800478502 \tabularnewline
56 & 14.5 & 12.344907855074 & 2.15509214492597 \tabularnewline
57 & 13 & 11.2475252724078 & 1.75247472759223 \tabularnewline
58 & 9 & 12.9521256625237 & -3.95212566252374 \tabularnewline
59 & 10 & 14.1364691048419 & -4.13646910484192 \tabularnewline
60 & 15 & 14.5825562174186 & 0.417443782581392 \tabularnewline
61 & 20 & 18.8978385264856 & 1.10216147351436 \tabularnewline
62 & 12 & 12.9701019822211 & -0.970101982221123 \tabularnewline
63 & 12 & 14.4946349613718 & -2.49463496137181 \tabularnewline
64 & 14 & 14.3977244788636 & -0.397724478863617 \tabularnewline
65 & 13 & 13.3465184438438 & -0.346518443843819 \tabularnewline
66 & 11 & 15.2551842026485 & -4.25518420264853 \tabularnewline
67 & 17 & 15.4339026502708 & 1.56609734972918 \tabularnewline
68 & 12 & 13.8244928070111 & -1.82449280701106 \tabularnewline
69 & 13 & 12.9367127356583 & 0.0632872643417436 \tabularnewline
70 & 14 & 12.4359071084292 & 1.56409289157077 \tabularnewline
71 & 13 & 14.1962687930734 & -1.19626879307339 \tabularnewline
72 & 15 & 12.9953509933014 & 2.00464900669856 \tabularnewline
73 & 13 & 11.3752412781048 & 1.62475872189521 \tabularnewline
74 & 10 & 11.8367923777135 & -1.83679237771346 \tabularnewline
75 & 11 & 12.780603418997 & -1.78060341899703 \tabularnewline
76 & 19 & 13.7318428537839 & 5.26815714621608 \tabularnewline
77 & 13 & 10.9146171917946 & 2.08538280820537 \tabularnewline
78 & 17 & 14.1635594801047 & 2.83644051989525 \tabularnewline
79 & 13 & 12.4810604467398 & 0.518939553260199 \tabularnewline
80 & 9 & 14.6155095332266 & -5.61550953322658 \tabularnewline
81 & 11 & 11.6202293351416 & -0.620229335141631 \tabularnewline
82 & 9 & 12.5724474751648 & -3.5724474751648 \tabularnewline
83 & 12 & 11.266996274739 & 0.733003725260956 \tabularnewline
84 & 12 & 12.4942226380307 & -0.49422263803067 \tabularnewline
85 & 13 & 12.7795546544458 & 0.22044534555419 \tabularnewline
86 & 13 & 12.118437148161 & 0.881562851838952 \tabularnewline
87 & 12 & 13.2054462258336 & -1.20544622583363 \tabularnewline
88 & 15 & 14.9680168991166 & 0.0319831008834498 \tabularnewline
89 & 22 & 18.2297360248036 & 3.77026397519641 \tabularnewline
90 & 13 & 11.5732900964105 & 1.42670990358947 \tabularnewline
91 & 15 & 14.5307796246812 & 0.469220375318838 \tabularnewline
92 & 13 & 11.8621583110261 & 1.13784168897386 \tabularnewline
93 & 15 & 12.2513500072457 & 2.74864999275432 \tabularnewline
94 & 12.5 & 13.4100310275549 & -0.910031027554912 \tabularnewline
95 & 11 & 10.7151770987767 & 0.284822901223319 \tabularnewline
96 & 16 & 14.5837266206203 & 1.41627337937966 \tabularnewline
97 & 11 & 12.4332147071392 & -1.43321470713922 \tabularnewline
98 & 11 & 10.0265602843883 & 0.973439715611683 \tabularnewline
99 & 10 & 12.3154875257295 & -2.31548752572947 \tabularnewline
100 & 10 & 10.2534498040753 & -0.253449804075322 \tabularnewline
101 & 16 & 14.331796052934 & 1.66820394706604 \tabularnewline
102 & 12 & 10.3699149062998 & 1.63008509370021 \tabularnewline
103 & 11 & 15.8774150398815 & -4.87741503988146 \tabularnewline
104 & 16 & 11.8047654217354 & 4.19523457826455 \tabularnewline
105 & 19 & 16.7033896979269 & 2.29661030207313 \tabularnewline
106 & 11 & 11.2027375647298 & -0.20273756472981 \tabularnewline
107 & 16 & 12.0566689609287 & 3.94333103907134 \tabularnewline
108 & 15 & 16.8483856022461 & -1.8483856022461 \tabularnewline
109 & 24 & 17.781752335403 & 6.21824766459696 \tabularnewline
110 & 14 & 11.7097343015983 & 2.29026569840166 \tabularnewline
111 & 15 & 14.9088750033543 & 0.091124996645684 \tabularnewline
112 & 11 & 13.0143654843154 & -2.01436548431536 \tabularnewline
113 & 15 & 13.6172532332297 & 1.38274676677028 \tabularnewline
114 & 12 & 10.2932872372889 & 1.70671276271109 \tabularnewline
115 & 10 & 10.512578368055 & -0.512578368055042 \tabularnewline
116 & 14 & 14.2937682134376 & -0.29376821343758 \tabularnewline
117 & 13 & 14.0429451333889 & -1.04294513338887 \tabularnewline
118 & 9 & 13.488903501121 & -4.48890350112103 \tabularnewline
119 & 15 & 11.8041913808711 & 3.19580861912893 \tabularnewline
120 & 15 & 15.8213128610493 & -0.821312861049309 \tabularnewline
121 & 14 & 12.7688559343617 & 1.23114406563832 \tabularnewline
122 & 11 & 11.3724384141774 & -0.372438414177395 \tabularnewline
123 & 8 & 12.0057475305325 & -4.00574753053247 \tabularnewline
124 & 11 & 12.264754845446 & -1.26475484544595 \tabularnewline
125 & 11 & 13.1692830825707 & -2.16928308257067 \tabularnewline
126 & 8 & 9.75049894665782 & -1.75049894665782 \tabularnewline
127 & 10 & 10.1649517011955 & -0.164951701195507 \tabularnewline
128 & 11 & 9.65594106005777 & 1.34405893994223 \tabularnewline
129 & 13 & 12.621248888591 & 0.378751111409025 \tabularnewline
130 & 11 & 13.7012421239307 & -2.7012421239307 \tabularnewline
131 & 20 & 17.6139195746615 & 2.38608042533845 \tabularnewline
132 & 10 & 11.7305312221829 & -1.73053122218293 \tabularnewline
133 & 15 & 13.3772124844194 & 1.62278751558062 \tabularnewline
134 & 12 & 12.3714800555824 & -0.371480055582385 \tabularnewline
135 & 14 & 11.119708938493 & 2.88029106150704 \tabularnewline
136 & 23 & 16.6865371310084 & 6.31346286899159 \tabularnewline
137 & 14 & 14.1566526501826 & -0.156652650182614 \tabularnewline
138 & 16 & 17.1159559049063 & -1.11595590490632 \tabularnewline
139 & 11 & 13.2955474557045 & -2.29554745570449 \tabularnewline
140 & 12 & 14.3497742905291 & -2.34977429052909 \tabularnewline
141 & 10 & 13.5391292765128 & -3.53912927651279 \tabularnewline
142 & 14 & 11.1791461041342 & 2.8208538958658 \tabularnewline
143 & 12 & 12.2643895106563 & -0.264389510656324 \tabularnewline
144 & 12 & 11.9385572279587 & 0.0614427720412523 \tabularnewline
145 & 11 & 11.0276187019894 & -0.0276187019893981 \tabularnewline
146 & 12 & 11.28828195619 & 0.711718043809951 \tabularnewline
147 & 13 & 16.2155145272081 & -3.21551452720815 \tabularnewline
148 & 11 & 14.3363011715584 & -3.33630117155839 \tabularnewline
149 & 19 & 16.9592284721265 & 2.04077152787354 \tabularnewline
150 & 12 & 11.478755198431 & 0.521244801569002 \tabularnewline
151 & 17 & 12.8816455970491 & 4.11835440295091 \tabularnewline
152 & 9 & 11.4102610697361 & -2.41026106973613 \tabularnewline
153 & 12 & 14.5571407032858 & -2.55714070328584 \tabularnewline
154 & 19 & 16.9451019228171 & 2.05489807718294 \tabularnewline
155 & 18 & 14.4671822570728 & 3.53281774292718 \tabularnewline
156 & 15 & 14.5307796246812 & 0.469220375318838 \tabularnewline
157 & 14 & 13.8016804586153 & 0.198319541384735 \tabularnewline
158 & 11 & 9.65594106005777 & 1.34405893994223 \tabularnewline
159 & 9 & 12.9771730113878 & -3.97717301138777 \tabularnewline
160 & 18 & 14.0669342225812 & 3.93306577741877 \tabularnewline
161 & 16 & 14.8040554227829 & 1.19594457721714 \tabularnewline
162 & 24 & 17.1096668245955 & 6.89033317540452 \tabularnewline
163 & 14 & 12.6307543756089 & 1.36924562439106 \tabularnewline
164 & 20 & 10.6953648653731 & 9.30463513462689 \tabularnewline
165 & 18 & 15.859109389839 & 2.14089061016099 \tabularnewline
166 & 23 & 17.1733234772839 & 5.82667652271614 \tabularnewline
167 & 12 & 13.055081181971 & -1.05508118197099 \tabularnewline
168 & 14 & 14.8255026820734 & -0.825502682073394 \tabularnewline
169 & 16 & 16.2211245696061 & -0.221124569606062 \tabularnewline
170 & 18 & 16.2723321813534 & 1.72766781864658 \tabularnewline
171 & 20 & 16.5533382507622 & 3.44666174923779 \tabularnewline
172 & 12 & 11.5359383338765 & 0.464061666123491 \tabularnewline
173 & 12 & 16.2084923595002 & -4.20849235950016 \tabularnewline
174 & 17 & 15.1907638364483 & 1.80923616355165 \tabularnewline
175 & 13 & 11.8203900297676 & 1.17960997023244 \tabularnewline
176 & 9 & 13.036853007205 & -4.03685300720496 \tabularnewline
177 & 16 & 17.2949149119527 & -1.29491491195272 \tabularnewline
178 & 18 & 14.9823263339676 & 3.01767366603244 \tabularnewline
179 & 10 & 12.2197674410369 & -2.21976744103686 \tabularnewline
180 & 14 & 14.8208728587478 & -0.820872858747793 \tabularnewline
181 & 11 & 14.0029797837371 & -3.00297978373706 \tabularnewline
182 & 9 & 14.5213958422977 & -5.52139584229771 \tabularnewline
183 & 11 & 12.4064530601426 & -1.40645306014257 \tabularnewline
184 & 10 & 12.3260504295329 & -2.32605042953289 \tabularnewline
185 & 11 & 11.8103681088032 & -0.81036810880321 \tabularnewline
186 & 19 & 13.2271815854447 & 5.77281841455529 \tabularnewline
187 & 14 & 12.4352448058596 & 1.56475519414043 \tabularnewline
188 & 12 & 11.4925521935963 & 0.50744780640365 \tabularnewline
189 & 14 & 15.0959577912219 & -1.09595779122192 \tabularnewline
190 & 21 & 16.7072198463595 & 4.29278015364054 \tabularnewline
191 & 13 & 15.6481146327758 & -2.64811463277585 \tabularnewline
192 & 10 & 12.4486729761107 & -2.44867297611075 \tabularnewline
193 & 15 & 13.0827494349911 & 1.91725056500891 \tabularnewline
194 & 16 & 16.16791141144 & -0.167911411440032 \tabularnewline
195 & 14 & 12.6749790366284 & 1.32502096337161 \tabularnewline
196 & 12 & 14.7066877693376 & -2.70668776933764 \tabularnewline
197 & 19 & 12.6132864204279 & 6.38671357957209 \tabularnewline
198 & 15 & 12.1644052332707 & 2.83559476672928 \tabularnewline
199 & 19 & 17.9896906622778 & 1.01030933772222 \tabularnewline
200 & 13 & 13.2987067190283 & -0.298706719028315 \tabularnewline
201 & 17 & 17.1231997625005 & -0.123199762500533 \tabularnewline
202 & 12 & 12.7674065438777 & -0.767406543877694 \tabularnewline
203 & 11 & 10.6425083496772 & 0.357491650322823 \tabularnewline
204 & 14 & 15.391098939205 & -1.39109893920499 \tabularnewline
205 & 11 & 12.3813885335817 & -1.38138853358169 \tabularnewline
206 & 13 & 12.2761962912289 & 0.723803708771061 \tabularnewline
207 & 12 & 12.6857334320538 & -0.685733432053776 \tabularnewline
208 & 15 & 12.6357725751679 & 2.36422742483212 \tabularnewline
209 & 14 & 14.395503288059 & -0.395503288058962 \tabularnewline
210 & 12 & 10.8273814639431 & 1.17261853605692 \tabularnewline
211 & 17 & 17.4777798338587 & -0.477779833858746 \tabularnewline
212 & 11 & 11.2474110902512 & -0.247411090251178 \tabularnewline
213 & 18 & 14.5888285623862 & 3.41117143761382 \tabularnewline
214 & 13 & 15.8595853229194 & -2.8595853229194 \tabularnewline
215 & 17 & 15.2582642983906 & 1.74173570160941 \tabularnewline
216 & 13 & 13.3040103616622 & -0.30401036166222 \tabularnewline
217 & 11 & 10.1197133028837 & 0.880286697116307 \tabularnewline
218 & 12 & 12.5627209103186 & -0.562720910318632 \tabularnewline
219 & 22 & 17.9921830003293 & 4.00781699967067 \tabularnewline
220 & 14 & 12.0213276506491 & 1.97867234935087 \tabularnewline
221 & 12 & 15.0421356006936 & -3.04213560069362 \tabularnewline
222 & 12 & 11.8537889622195 & 0.146211037780463 \tabularnewline
223 & 17 & 15.9533148924048 & 1.04668510759519 \tabularnewline
224 & 9 & 12.8234212732034 & -3.82342127320344 \tabularnewline
225 & 21 & 17.1365923071396 & 3.86340769286042 \tabularnewline
226 & 10 & 11.9179113198274 & -1.91791131982742 \tabularnewline
227 & 11 & 10.8617887170656 & 0.138211282934366 \tabularnewline
228 & 12 & 14.5925564778268 & -2.59255647782678 \tabularnewline
229 & 23 & 18.3334231400647 & 4.6665768599353 \tabularnewline
230 & 13 & 15.6979505366254 & -2.69795053662537 \tabularnewline
231 & 12 & 13.386506176235 & -1.386506176235 \tabularnewline
232 & 16 & 17.8977570916836 & -1.89775709168358 \tabularnewline
233 & 9 & 13.4301958683188 & -4.43019586831881 \tabularnewline
234 & 17 & 13.5010227448443 & 3.49897725515567 \tabularnewline
235 & 9 & 11.8873896605286 & -2.88738966052861 \tabularnewline
236 & 14 & 15.6544395742704 & -1.65443957427037 \tabularnewline
237 & 17 & 15.711272176253 & 1.28872782374701 \tabularnewline
238 & 13 & 15.4946665608674 & -2.49466656086744 \tabularnewline
239 & 11 & 15.8808621627185 & -4.88086216271849 \tabularnewline
240 & 12 & 15.6137825421604 & -3.61378254216039 \tabularnewline
241 & 10 & 14.076437711058 & -4.07643771105795 \tabularnewline
242 & 19 & 18.9304806912619 & 0.0695193087380646 \tabularnewline
243 & 16 & 16.1116301325695 & -0.111630132569486 \tabularnewline
244 & 16 & 15.0803567371333 & 0.919643262866727 \tabularnewline
245 & 14 & 12.1228651735912 & 1.87713482640877 \tabularnewline
246 & 20 & 15.9261823522834 & 4.07381764771662 \tabularnewline
247 & 15 & 14.4877393612271 & 0.5122606387729 \tabularnewline
248 & 23 & 15.4475494060657 & 7.55245059393431 \tabularnewline
249 & 20 & 17.9949100585686 & 2.0050899414314 \tabularnewline
250 & 16 & 16.051525496072 & -0.0515254960720058 \tabularnewline
251 & 14 & 12.9735395265636 & 1.02646047343643 \tabularnewline
252 & 17 & 13.9246990404077 & 3.07530095959227 \tabularnewline
253 & 11 & 14.2645985407165 & -3.26459854071648 \tabularnewline
254 & 13 & 13.96117701187 & -0.961177011869954 \tabularnewline
255 & 17 & 15.2699843073819 & 1.73001569261805 \tabularnewline
256 & 15 & 15.2013316498899 & -0.201331649889855 \tabularnewline
257 & 21 & 15.9434517871989 & 5.05654821280109 \tabularnewline
258 & 18 & 17.8097764808616 & 0.19022351913838 \tabularnewline
259 & 15 & 12.7641454240226 & 2.2358545759774 \tabularnewline
260 & 8 & 16.5387264612104 & -8.53872646121035 \tabularnewline
261 & 12 & 13.6904753430729 & -1.69047534307287 \tabularnewline
262 & 12 & 12.9087082978523 & -0.908708297852302 \tabularnewline
263 & 22 & 19.0195272552803 & 2.98047274471967 \tabularnewline
264 & 12 & 13.2987122538222 & -1.29871225382222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225701&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]12[/C][C]14.0706787890217[/C][C]-2.07067878902172[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.38696643763322[/C][C]1.61303356236677[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]15.5675810649187[/C][C]-1.56758106491873[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]15.0690389054651[/C][C]-3.0690389054651[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.46344889731[/C][C]9.53655110268998[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.9667076434101[/C][C]2.0332923565899[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]14.3162495085782[/C][C]7.68375049142184[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.6073490645059[/C][C]-1.60734906450588[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.2507355875693[/C][C]-2.25073558756932[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.207841490557[/C][C]0.792158509443017[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.4506326835643[/C][C]-0.450632683564343[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]10.4560288991883[/C][C]-2.45602889918833[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]16.1303332426016[/C][C]-1.13033324260163[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.5036261559152[/C][C]1.49637384408483[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.18135470705685[/C][C]0.818645292943152[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.0748811987288[/C][C]-0.0748811987287531[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.7557699675713[/C][C]1.24423003242873[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.50612593968894[/C][C]1.49387406031106[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]13.4300135886583[/C][C]-3.43001358865834[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.7164724322061[/C][C]0.283527567793914[/C][/ROW]
[ROW][C]21[/C][C]9.5[/C][C]10.7309666966345[/C][C]-1.23096669663452[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.8211721695944[/C][C]-1.82117216959437[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]13.6166639166209[/C][C]-1.61666391662092[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]15.6977202383925[/C][C]-1.69772023839247[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]9.6901311188632[/C][C]1.3098688811368[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.4042718938735[/C][C]-6.40427189387354[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.842982781916[/C][C]0.157017218083986[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.547164084696[/C][C]1.45283591530398[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]11.6054074728247[/C][C]2.39459252717533[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]16.0748009535387[/C][C]-3.07480095353866[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.3311845358491[/C][C]-2.33118453584907[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]15.4811799450673[/C][C]-0.481179945067328[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.9720583597398[/C][C]-0.972058359739775[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.9987926344505[/C][C]-0.998792634450497[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.5792497206996[/C][C]0.420750279300398[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.4446557288483[/C][C]-4.44465572884832[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.6242623975357[/C][C]2.37573760246429[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.3913844304[/C][C]-0.391384430400045[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]10.823044424272[/C][C]-0.823044424271974[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.8727843851482[/C][C]-3.8727843851482[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.0700584795227[/C][C]-3.07005847952272[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.3231017910082[/C][C]2.67689820899176[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.0752714430978[/C][C]-4.07527144309776[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]15.6619347922746[/C][C]-1.66193479227463[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.4771260546196[/C][C]-1.47712605461964[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.4546859321418[/C][C]-2.45468593214185[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]13.2007437142602[/C][C]-4.20074371426023[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.3012373392283[/C][C]-1.30123733922831[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.2800148487042[/C][C]4.71998515129578[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.4198208241544[/C][C]-2.41982082415443[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.6561895506617[/C][C]-1.65618955066172[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.444417529972[/C][C]0.555582470027986[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.8616528601011[/C][C]2.13834713989894[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]16.1752560994127[/C][C]-2.17525609941275[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.424028004785[/C][C]-4.42402800478502[/C][/ROW]
[ROW][C]56[/C][C]14.5[/C][C]12.344907855074[/C][C]2.15509214492597[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.2475252724078[/C][C]1.75247472759223[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.9521256625237[/C][C]-3.95212566252374[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]14.1364691048419[/C][C]-4.13646910484192[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.5825562174186[/C][C]0.417443782581392[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]18.8978385264856[/C][C]1.10216147351436[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.9701019822211[/C][C]-0.970101982221123[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.4946349613718[/C][C]-2.49463496137181[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3977244788636[/C][C]-0.397724478863617[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.3465184438438[/C][C]-0.346518443843819[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]15.2551842026485[/C][C]-4.25518420264853[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]15.4339026502708[/C][C]1.56609734972918[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.8244928070111[/C][C]-1.82449280701106[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.9367127356583[/C][C]0.0632872643417436[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.4359071084292[/C][C]1.56409289157077[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]14.1962687930734[/C][C]-1.19626879307339[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]12.9953509933014[/C][C]2.00464900669856[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.3752412781048[/C][C]1.62475872189521[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.8367923777135[/C][C]-1.83679237771346[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.780603418997[/C][C]-1.78060341899703[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]13.7318428537839[/C][C]5.26815714621608[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.9146171917946[/C][C]2.08538280820537[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]14.1635594801047[/C][C]2.83644051989525[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.4810604467398[/C][C]0.518939553260199[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]14.6155095332266[/C][C]-5.61550953322658[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.6202293351416[/C][C]-0.620229335141631[/C][/ROW]
[ROW][C]82[/C][C]9[/C][C]12.5724474751648[/C][C]-3.5724474751648[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.266996274739[/C][C]0.733003725260956[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.4942226380307[/C][C]-0.49422263803067[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]12.7795546544458[/C][C]0.22044534555419[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.118437148161[/C][C]0.881562851838952[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]13.2054462258336[/C][C]-1.20544622583363[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]14.9680168991166[/C][C]0.0319831008834498[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]18.2297360248036[/C][C]3.77026397519641[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]11.5732900964105[/C][C]1.42670990358947[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]14.5307796246812[/C][C]0.469220375318838[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]11.8621583110261[/C][C]1.13784168897386[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]12.2513500072457[/C][C]2.74864999275432[/C][/ROW]
[ROW][C]94[/C][C]12.5[/C][C]13.4100310275549[/C][C]-0.910031027554912[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]10.7151770987767[/C][C]0.284822901223319[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.5837266206203[/C][C]1.41627337937966[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]12.4332147071392[/C][C]-1.43321470713922[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]10.0265602843883[/C][C]0.973439715611683[/C][/ROW]
[ROW][C]99[/C][C]10[/C][C]12.3154875257295[/C][C]-2.31548752572947[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.2534498040753[/C][C]-0.253449804075322[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.331796052934[/C][C]1.66820394706604[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]10.3699149062998[/C][C]1.63008509370021[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]15.8774150398815[/C][C]-4.87741503988146[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]11.8047654217354[/C][C]4.19523457826455[/C][/ROW]
[ROW][C]105[/C][C]19[/C][C]16.7033896979269[/C][C]2.29661030207313[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]11.2027375647298[/C][C]-0.20273756472981[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]12.0566689609287[/C][C]3.94333103907134[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]16.8483856022461[/C][C]-1.8483856022461[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]17.781752335403[/C][C]6.21824766459696[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]11.7097343015983[/C][C]2.29026569840166[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.9088750033543[/C][C]0.091124996645684[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.0143654843154[/C][C]-2.01436548431536[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]13.6172532332297[/C][C]1.38274676677028[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]10.2932872372889[/C][C]1.70671276271109[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]10.512578368055[/C][C]-0.512578368055042[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.2937682134376[/C][C]-0.29376821343758[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]14.0429451333889[/C][C]-1.04294513338887[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]13.488903501121[/C][C]-4.48890350112103[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]11.8041913808711[/C][C]3.19580861912893[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.8213128610493[/C][C]-0.821312861049309[/C][/ROW]
[ROW][C]121[/C][C]14[/C][C]12.7688559343617[/C][C]1.23114406563832[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]11.3724384141774[/C][C]-0.372438414177395[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]12.0057475305325[/C][C]-4.00574753053247[/C][/ROW]
[ROW][C]124[/C][C]11[/C][C]12.264754845446[/C][C]-1.26475484544595[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]13.1692830825707[/C][C]-2.16928308257067[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]9.75049894665782[/C][C]-1.75049894665782[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.1649517011955[/C][C]-0.164951701195507[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]9.65594106005777[/C][C]1.34405893994223[/C][/ROW]
[ROW][C]129[/C][C]13[/C][C]12.621248888591[/C][C]0.378751111409025[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]13.7012421239307[/C][C]-2.7012421239307[/C][/ROW]
[ROW][C]131[/C][C]20[/C][C]17.6139195746615[/C][C]2.38608042533845[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]11.7305312221829[/C][C]-1.73053122218293[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]13.3772124844194[/C][C]1.62278751558062[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]12.3714800555824[/C][C]-0.371480055582385[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]11.119708938493[/C][C]2.88029106150704[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]16.6865371310084[/C][C]6.31346286899159[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]14.1566526501826[/C][C]-0.156652650182614[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]17.1159559049063[/C][C]-1.11595590490632[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2955474557045[/C][C]-2.29554745570449[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]14.3497742905291[/C][C]-2.34977429052909[/C][/ROW]
[ROW][C]141[/C][C]10[/C][C]13.5391292765128[/C][C]-3.53912927651279[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]11.1791461041342[/C][C]2.8208538958658[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]12.2643895106563[/C][C]-0.264389510656324[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.9385572279587[/C][C]0.0614427720412523[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.0276187019894[/C][C]-0.0276187019893981[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]11.28828195619[/C][C]0.711718043809951[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]16.2155145272081[/C][C]-3.21551452720815[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]14.3363011715584[/C][C]-3.33630117155839[/C][/ROW]
[ROW][C]149[/C][C]19[/C][C]16.9592284721265[/C][C]2.04077152787354[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]11.478755198431[/C][C]0.521244801569002[/C][/ROW]
[ROW][C]151[/C][C]17[/C][C]12.8816455970491[/C][C]4.11835440295091[/C][/ROW]
[ROW][C]152[/C][C]9[/C][C]11.4102610697361[/C][C]-2.41026106973613[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]14.5571407032858[/C][C]-2.55714070328584[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]16.9451019228171[/C][C]2.05489807718294[/C][/ROW]
[ROW][C]155[/C][C]18[/C][C]14.4671822570728[/C][C]3.53281774292718[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.5307796246812[/C][C]0.469220375318838[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]13.8016804586153[/C][C]0.198319541384735[/C][/ROW]
[ROW][C]158[/C][C]11[/C][C]9.65594106005777[/C][C]1.34405893994223[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]12.9771730113878[/C][C]-3.97717301138777[/C][/ROW]
[ROW][C]160[/C][C]18[/C][C]14.0669342225812[/C][C]3.93306577741877[/C][/ROW]
[ROW][C]161[/C][C]16[/C][C]14.8040554227829[/C][C]1.19594457721714[/C][/ROW]
[ROW][C]162[/C][C]24[/C][C]17.1096668245955[/C][C]6.89033317540452[/C][/ROW]
[ROW][C]163[/C][C]14[/C][C]12.6307543756089[/C][C]1.36924562439106[/C][/ROW]
[ROW][C]164[/C][C]20[/C][C]10.6953648653731[/C][C]9.30463513462689[/C][/ROW]
[ROW][C]165[/C][C]18[/C][C]15.859109389839[/C][C]2.14089061016099[/C][/ROW]
[ROW][C]166[/C][C]23[/C][C]17.1733234772839[/C][C]5.82667652271614[/C][/ROW]
[ROW][C]167[/C][C]12[/C][C]13.055081181971[/C][C]-1.05508118197099[/C][/ROW]
[ROW][C]168[/C][C]14[/C][C]14.8255026820734[/C][C]-0.825502682073394[/C][/ROW]
[ROW][C]169[/C][C]16[/C][C]16.2211245696061[/C][C]-0.221124569606062[/C][/ROW]
[ROW][C]170[/C][C]18[/C][C]16.2723321813534[/C][C]1.72766781864658[/C][/ROW]
[ROW][C]171[/C][C]20[/C][C]16.5533382507622[/C][C]3.44666174923779[/C][/ROW]
[ROW][C]172[/C][C]12[/C][C]11.5359383338765[/C][C]0.464061666123491[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]16.2084923595002[/C][C]-4.20849235950016[/C][/ROW]
[ROW][C]174[/C][C]17[/C][C]15.1907638364483[/C][C]1.80923616355165[/C][/ROW]
[ROW][C]175[/C][C]13[/C][C]11.8203900297676[/C][C]1.17960997023244[/C][/ROW]
[ROW][C]176[/C][C]9[/C][C]13.036853007205[/C][C]-4.03685300720496[/C][/ROW]
[ROW][C]177[/C][C]16[/C][C]17.2949149119527[/C][C]-1.29491491195272[/C][/ROW]
[ROW][C]178[/C][C]18[/C][C]14.9823263339676[/C][C]3.01767366603244[/C][/ROW]
[ROW][C]179[/C][C]10[/C][C]12.2197674410369[/C][C]-2.21976744103686[/C][/ROW]
[ROW][C]180[/C][C]14[/C][C]14.8208728587478[/C][C]-0.820872858747793[/C][/ROW]
[ROW][C]181[/C][C]11[/C][C]14.0029797837371[/C][C]-3.00297978373706[/C][/ROW]
[ROW][C]182[/C][C]9[/C][C]14.5213958422977[/C][C]-5.52139584229771[/C][/ROW]
[ROW][C]183[/C][C]11[/C][C]12.4064530601426[/C][C]-1.40645306014257[/C][/ROW]
[ROW][C]184[/C][C]10[/C][C]12.3260504295329[/C][C]-2.32605042953289[/C][/ROW]
[ROW][C]185[/C][C]11[/C][C]11.8103681088032[/C][C]-0.81036810880321[/C][/ROW]
[ROW][C]186[/C][C]19[/C][C]13.2271815854447[/C][C]5.77281841455529[/C][/ROW]
[ROW][C]187[/C][C]14[/C][C]12.4352448058596[/C][C]1.56475519414043[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]11.4925521935963[/C][C]0.50744780640365[/C][/ROW]
[ROW][C]189[/C][C]14[/C][C]15.0959577912219[/C][C]-1.09595779122192[/C][/ROW]
[ROW][C]190[/C][C]21[/C][C]16.7072198463595[/C][C]4.29278015364054[/C][/ROW]
[ROW][C]191[/C][C]13[/C][C]15.6481146327758[/C][C]-2.64811463277585[/C][/ROW]
[ROW][C]192[/C][C]10[/C][C]12.4486729761107[/C][C]-2.44867297611075[/C][/ROW]
[ROW][C]193[/C][C]15[/C][C]13.0827494349911[/C][C]1.91725056500891[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]16.16791141144[/C][C]-0.167911411440032[/C][/ROW]
[ROW][C]195[/C][C]14[/C][C]12.6749790366284[/C][C]1.32502096337161[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]14.7066877693376[/C][C]-2.70668776933764[/C][/ROW]
[ROW][C]197[/C][C]19[/C][C]12.6132864204279[/C][C]6.38671357957209[/C][/ROW]
[ROW][C]198[/C][C]15[/C][C]12.1644052332707[/C][C]2.83559476672928[/C][/ROW]
[ROW][C]199[/C][C]19[/C][C]17.9896906622778[/C][C]1.01030933772222[/C][/ROW]
[ROW][C]200[/C][C]13[/C][C]13.2987067190283[/C][C]-0.298706719028315[/C][/ROW]
[ROW][C]201[/C][C]17[/C][C]17.1231997625005[/C][C]-0.123199762500533[/C][/ROW]
[ROW][C]202[/C][C]12[/C][C]12.7674065438777[/C][C]-0.767406543877694[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]10.6425083496772[/C][C]0.357491650322823[/C][/ROW]
[ROW][C]204[/C][C]14[/C][C]15.391098939205[/C][C]-1.39109893920499[/C][/ROW]
[ROW][C]205[/C][C]11[/C][C]12.3813885335817[/C][C]-1.38138853358169[/C][/ROW]
[ROW][C]206[/C][C]13[/C][C]12.2761962912289[/C][C]0.723803708771061[/C][/ROW]
[ROW][C]207[/C][C]12[/C][C]12.6857334320538[/C][C]-0.685733432053776[/C][/ROW]
[ROW][C]208[/C][C]15[/C][C]12.6357725751679[/C][C]2.36422742483212[/C][/ROW]
[ROW][C]209[/C][C]14[/C][C]14.395503288059[/C][C]-0.395503288058962[/C][/ROW]
[ROW][C]210[/C][C]12[/C][C]10.8273814639431[/C][C]1.17261853605692[/C][/ROW]
[ROW][C]211[/C][C]17[/C][C]17.4777798338587[/C][C]-0.477779833858746[/C][/ROW]
[ROW][C]212[/C][C]11[/C][C]11.2474110902512[/C][C]-0.247411090251178[/C][/ROW]
[ROW][C]213[/C][C]18[/C][C]14.5888285623862[/C][C]3.41117143761382[/C][/ROW]
[ROW][C]214[/C][C]13[/C][C]15.8595853229194[/C][C]-2.8595853229194[/C][/ROW]
[ROW][C]215[/C][C]17[/C][C]15.2582642983906[/C][C]1.74173570160941[/C][/ROW]
[ROW][C]216[/C][C]13[/C][C]13.3040103616622[/C][C]-0.30401036166222[/C][/ROW]
[ROW][C]217[/C][C]11[/C][C]10.1197133028837[/C][C]0.880286697116307[/C][/ROW]
[ROW][C]218[/C][C]12[/C][C]12.5627209103186[/C][C]-0.562720910318632[/C][/ROW]
[ROW][C]219[/C][C]22[/C][C]17.9921830003293[/C][C]4.00781699967067[/C][/ROW]
[ROW][C]220[/C][C]14[/C][C]12.0213276506491[/C][C]1.97867234935087[/C][/ROW]
[ROW][C]221[/C][C]12[/C][C]15.0421356006936[/C][C]-3.04213560069362[/C][/ROW]
[ROW][C]222[/C][C]12[/C][C]11.8537889622195[/C][C]0.146211037780463[/C][/ROW]
[ROW][C]223[/C][C]17[/C][C]15.9533148924048[/C][C]1.04668510759519[/C][/ROW]
[ROW][C]224[/C][C]9[/C][C]12.8234212732034[/C][C]-3.82342127320344[/C][/ROW]
[ROW][C]225[/C][C]21[/C][C]17.1365923071396[/C][C]3.86340769286042[/C][/ROW]
[ROW][C]226[/C][C]10[/C][C]11.9179113198274[/C][C]-1.91791131982742[/C][/ROW]
[ROW][C]227[/C][C]11[/C][C]10.8617887170656[/C][C]0.138211282934366[/C][/ROW]
[ROW][C]228[/C][C]12[/C][C]14.5925564778268[/C][C]-2.59255647782678[/C][/ROW]
[ROW][C]229[/C][C]23[/C][C]18.3334231400647[/C][C]4.6665768599353[/C][/ROW]
[ROW][C]230[/C][C]13[/C][C]15.6979505366254[/C][C]-2.69795053662537[/C][/ROW]
[ROW][C]231[/C][C]12[/C][C]13.386506176235[/C][C]-1.386506176235[/C][/ROW]
[ROW][C]232[/C][C]16[/C][C]17.8977570916836[/C][C]-1.89775709168358[/C][/ROW]
[ROW][C]233[/C][C]9[/C][C]13.4301958683188[/C][C]-4.43019586831881[/C][/ROW]
[ROW][C]234[/C][C]17[/C][C]13.5010227448443[/C][C]3.49897725515567[/C][/ROW]
[ROW][C]235[/C][C]9[/C][C]11.8873896605286[/C][C]-2.88738966052861[/C][/ROW]
[ROW][C]236[/C][C]14[/C][C]15.6544395742704[/C][C]-1.65443957427037[/C][/ROW]
[ROW][C]237[/C][C]17[/C][C]15.711272176253[/C][C]1.28872782374701[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]15.4946665608674[/C][C]-2.49466656086744[/C][/ROW]
[ROW][C]239[/C][C]11[/C][C]15.8808621627185[/C][C]-4.88086216271849[/C][/ROW]
[ROW][C]240[/C][C]12[/C][C]15.6137825421604[/C][C]-3.61378254216039[/C][/ROW]
[ROW][C]241[/C][C]10[/C][C]14.076437711058[/C][C]-4.07643771105795[/C][/ROW]
[ROW][C]242[/C][C]19[/C][C]18.9304806912619[/C][C]0.0695193087380646[/C][/ROW]
[ROW][C]243[/C][C]16[/C][C]16.1116301325695[/C][C]-0.111630132569486[/C][/ROW]
[ROW][C]244[/C][C]16[/C][C]15.0803567371333[/C][C]0.919643262866727[/C][/ROW]
[ROW][C]245[/C][C]14[/C][C]12.1228651735912[/C][C]1.87713482640877[/C][/ROW]
[ROW][C]246[/C][C]20[/C][C]15.9261823522834[/C][C]4.07381764771662[/C][/ROW]
[ROW][C]247[/C][C]15[/C][C]14.4877393612271[/C][C]0.5122606387729[/C][/ROW]
[ROW][C]248[/C][C]23[/C][C]15.4475494060657[/C][C]7.55245059393431[/C][/ROW]
[ROW][C]249[/C][C]20[/C][C]17.9949100585686[/C][C]2.0050899414314[/C][/ROW]
[ROW][C]250[/C][C]16[/C][C]16.051525496072[/C][C]-0.0515254960720058[/C][/ROW]
[ROW][C]251[/C][C]14[/C][C]12.9735395265636[/C][C]1.02646047343643[/C][/ROW]
[ROW][C]252[/C][C]17[/C][C]13.9246990404077[/C][C]3.07530095959227[/C][/ROW]
[ROW][C]253[/C][C]11[/C][C]14.2645985407165[/C][C]-3.26459854071648[/C][/ROW]
[ROW][C]254[/C][C]13[/C][C]13.96117701187[/C][C]-0.961177011869954[/C][/ROW]
[ROW][C]255[/C][C]17[/C][C]15.2699843073819[/C][C]1.73001569261805[/C][/ROW]
[ROW][C]256[/C][C]15[/C][C]15.2013316498899[/C][C]-0.201331649889855[/C][/ROW]
[ROW][C]257[/C][C]21[/C][C]15.9434517871989[/C][C]5.05654821280109[/C][/ROW]
[ROW][C]258[/C][C]18[/C][C]17.8097764808616[/C][C]0.19022351913838[/C][/ROW]
[ROW][C]259[/C][C]15[/C][C]12.7641454240226[/C][C]2.2358545759774[/C][/ROW]
[ROW][C]260[/C][C]8[/C][C]16.5387264612104[/C][C]-8.53872646121035[/C][/ROW]
[ROW][C]261[/C][C]12[/C][C]13.6904753430729[/C][C]-1.69047534307287[/C][/ROW]
[ROW][C]262[/C][C]12[/C][C]12.9087082978523[/C][C]-0.908708297852302[/C][/ROW]
[ROW][C]263[/C][C]22[/C][C]19.0195272552803[/C][C]2.98047274471967[/C][/ROW]
[ROW][C]264[/C][C]12[/C][C]13.2987122538222[/C][C]-1.29871225382222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225701&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225701&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.0706787890217-2.07067878902172
2119.386966437633221.61303356236677
31415.5675810649187-1.56758106491873
41215.0690389054651-3.0690389054651
52111.463448897319.53655110268998
6129.96670764341012.0332923565899
72214.31624950857827.68375049142184
81112.6073490645059-1.60734906450588
91012.2507355875693-2.25073558756932
101312.2078414905570.792158509443017
111010.4506326835643-0.450632683564343
12810.4560288991883-2.45602889918833
131516.1303332426016-1.13033324260163
141412.50362615591521.49637384408483
15109.181354707056850.818645292943152
161414.0748811987288-0.0748811987287531
171412.75576996757131.24423003242873
18119.506125939688941.49387406031106
191013.4300135886583-3.43001358865834
201312.71647243220610.283527567793914
219.510.7309666966345-1.23096669663452
221415.8211721695944-1.82117216959437
231213.6166639166209-1.61666391662092
241415.6977202383925-1.69772023839247
25119.69013111886321.3098688811368
26915.4042718938735-6.40427189387354
271110.8429827819160.157017218083986
281513.5471640846961.45283591530398
291411.60540747282472.39459252717533
301316.0748009535387-3.07480095353866
31911.3311845358491-2.33118453584907
321515.4811799450673-0.481179945067328
331010.9720583597398-0.972058359739775
341111.9987926344505-0.998792634450497
351312.57924972069960.420750279300398
36812.4446557288483-4.44465572884832
372017.62426239753572.37573760246429
381212.3913844304-0.391384430400045
391010.823044424272-0.823044424271974
401013.8727843851482-3.8727843851482
41912.0700584795227-3.07005847952272
421411.32310179100822.67689820899176
43812.0752714430978-4.07527144309776
441415.6619347922746-1.66193479227463
451112.4771260546196-1.47712605461964
461315.4546859321418-2.45468593214185
47913.2007437142602-4.20074371426023
481112.3012373392283-1.30123733922831
491510.28001484870424.71998515129578
501113.4198208241544-2.41982082415443
511011.6561895506617-1.65618955066172
521413.4444175299720.555582470027986
531815.86165286010112.13834713989894
541416.1752560994127-2.17525609941275
551115.424028004785-4.42402800478502
5614.512.3449078550742.15509214492597
571311.24752527240781.75247472759223
58912.9521256625237-3.95212566252374
591014.1364691048419-4.13646910484192
601514.58255621741860.417443782581392
612018.89783852648561.10216147351436
621212.9701019822211-0.970101982221123
631214.4946349613718-2.49463496137181
641414.3977244788636-0.397724478863617
651313.3465184438438-0.346518443843819
661115.2551842026485-4.25518420264853
671715.43390265027081.56609734972918
681213.8244928070111-1.82449280701106
691312.93671273565830.0632872643417436
701412.43590710842921.56409289157077
711314.1962687930734-1.19626879307339
721512.99535099330142.00464900669856
731311.37524127810481.62475872189521
741011.8367923777135-1.83679237771346
751112.780603418997-1.78060341899703
761913.73184285378395.26815714621608
771310.91461719179462.08538280820537
781714.16355948010472.83644051989525
791312.48106044673980.518939553260199
80914.6155095332266-5.61550953322658
811111.6202293351416-0.620229335141631
82912.5724474751648-3.5724474751648
831211.2669962747390.733003725260956
841212.4942226380307-0.49422263803067
851312.77955465444580.22044534555419
861312.1184371481610.881562851838952
871213.2054462258336-1.20544622583363
881514.96801689911660.0319831008834498
892218.22973602480363.77026397519641
901311.57329009641051.42670990358947
911514.53077962468120.469220375318838
921311.86215831102611.13784168897386
931512.25135000724572.74864999275432
9412.513.4100310275549-0.910031027554912
951110.71517709877670.284822901223319
961614.58372662062031.41627337937966
971112.4332147071392-1.43321470713922
981110.02656028438830.973439715611683
991012.3154875257295-2.31548752572947
1001010.2534498040753-0.253449804075322
1011614.3317960529341.66820394706604
1021210.36991490629981.63008509370021
1031115.8774150398815-4.87741503988146
1041611.80476542173544.19523457826455
1051916.70338969792692.29661030207313
1061111.2027375647298-0.20273756472981
1071612.05666896092873.94333103907134
1081516.8483856022461-1.8483856022461
1092417.7817523354036.21824766459696
1101411.70973430159832.29026569840166
1111514.90887500335430.091124996645684
1121113.0143654843154-2.01436548431536
1131513.61725323322971.38274676677028
1141210.29328723728891.70671276271109
1151010.512578368055-0.512578368055042
1161414.2937682134376-0.29376821343758
1171314.0429451333889-1.04294513338887
118913.488903501121-4.48890350112103
1191511.80419138087113.19580861912893
1201515.8213128610493-0.821312861049309
1211412.76885593436171.23114406563832
1221111.3724384141774-0.372438414177395
123812.0057475305325-4.00574753053247
1241112.264754845446-1.26475484544595
1251113.1692830825707-2.16928308257067
12689.75049894665782-1.75049894665782
1271010.1649517011955-0.164951701195507
128119.655941060057771.34405893994223
1291312.6212488885910.378751111409025
1301113.7012421239307-2.7012421239307
1312017.61391957466152.38608042533845
1321011.7305312221829-1.73053122218293
1331513.37721248441941.62278751558062
1341212.3714800555824-0.371480055582385
1351411.1197089384932.88029106150704
1362316.68653713100846.31346286899159
1371414.1566526501826-0.156652650182614
1381617.1159559049063-1.11595590490632
1391113.2955474557045-2.29554745570449
1401214.3497742905291-2.34977429052909
1411013.5391292765128-3.53912927651279
1421411.17914610413422.8208538958658
1431212.2643895106563-0.264389510656324
1441211.93855722795870.0614427720412523
1451111.0276187019894-0.0276187019893981
1461211.288281956190.711718043809951
1471316.2155145272081-3.21551452720815
1481114.3363011715584-3.33630117155839
1491916.95922847212652.04077152787354
1501211.4787551984310.521244801569002
1511712.88164559704914.11835440295091
152911.4102610697361-2.41026106973613
1531214.5571407032858-2.55714070328584
1541916.94510192281712.05489807718294
1551814.46718225707283.53281774292718
1561514.53077962468120.469220375318838
1571413.80168045861530.198319541384735
158119.655941060057771.34405893994223
159912.9771730113878-3.97717301138777
1601814.06693422258123.93306577741877
1611614.80405542278291.19594457721714
1622417.10966682459556.89033317540452
1631412.63075437560891.36924562439106
1642010.69536486537319.30463513462689
1651815.8591093898392.14089061016099
1662317.17332347728395.82667652271614
1671213.055081181971-1.05508118197099
1681414.8255026820734-0.825502682073394
1691616.2211245696061-0.221124569606062
1701816.27233218135341.72766781864658
1712016.55333825076223.44666174923779
1721211.53593833387650.464061666123491
1731216.2084923595002-4.20849235950016
1741715.19076383644831.80923616355165
1751311.82039002976761.17960997023244
176913.036853007205-4.03685300720496
1771617.2949149119527-1.29491491195272
1781814.98232633396763.01767366603244
1791012.2197674410369-2.21976744103686
1801414.8208728587478-0.820872858747793
1811114.0029797837371-3.00297978373706
182914.5213958422977-5.52139584229771
1831112.4064530601426-1.40645306014257
1841012.3260504295329-2.32605042953289
1851111.8103681088032-0.81036810880321
1861913.22718158544475.77281841455529
1871412.43524480585961.56475519414043
1881211.49255219359630.50744780640365
1891415.0959577912219-1.09595779122192
1902116.70721984635954.29278015364054
1911315.6481146327758-2.64811463277585
1921012.4486729761107-2.44867297611075
1931513.08274943499111.91725056500891
1941616.16791141144-0.167911411440032
1951412.67497903662841.32502096337161
1961214.7066877693376-2.70668776933764
1971912.61328642042796.38671357957209
1981512.16440523327072.83559476672928
1991917.98969066227781.01030933772222
2001313.2987067190283-0.298706719028315
2011717.1231997625005-0.123199762500533
2021212.7674065438777-0.767406543877694
2031110.64250834967720.357491650322823
2041415.391098939205-1.39109893920499
2051112.3813885335817-1.38138853358169
2061312.27619629122890.723803708771061
2071212.6857334320538-0.685733432053776
2081512.63577257516792.36422742483212
2091414.395503288059-0.395503288058962
2101210.82738146394311.17261853605692
2111717.4777798338587-0.477779833858746
2121111.2474110902512-0.247411090251178
2131814.58882856238623.41117143761382
2141315.8595853229194-2.8595853229194
2151715.25826429839061.74173570160941
2161313.3040103616622-0.30401036166222
2171110.11971330288370.880286697116307
2181212.5627209103186-0.562720910318632
2192217.99218300032934.00781699967067
2201412.02132765064911.97867234935087
2211215.0421356006936-3.04213560069362
2221211.85378896221950.146211037780463
2231715.95331489240481.04668510759519
224912.8234212732034-3.82342127320344
2252117.13659230713963.86340769286042
2261011.9179113198274-1.91791131982742
2271110.86178871706560.138211282934366
2281214.5925564778268-2.59255647782678
2292318.33342314006474.6665768599353
2301315.6979505366254-2.69795053662537
2311213.386506176235-1.386506176235
2321617.8977570916836-1.89775709168358
233913.4301958683188-4.43019586831881
2341713.50102274484433.49897725515567
235911.8873896605286-2.88738966052861
2361415.6544395742704-1.65443957427037
2371715.7112721762531.28872782374701
2381315.4946665608674-2.49466656086744
2391115.8808621627185-4.88086216271849
2401215.6137825421604-3.61378254216039
2411014.076437711058-4.07643771105795
2421918.93048069126190.0695193087380646
2431616.1116301325695-0.111630132569486
2441615.08035673713330.919643262866727
2451412.12286517359121.87713482640877
2462015.92618235228344.07381764771662
2471514.48773936122710.5122606387729
2482315.44754940606577.55245059393431
2492017.99491005856862.0050899414314
2501616.051525496072-0.0515254960720058
2511412.97353952656361.02646047343643
2521713.92469904040773.07530095959227
2531114.2645985407165-3.26459854071648
2541313.96117701187-0.961177011869954
2551715.26998430738191.73001569261805
2561515.2013316498899-0.201331649889855
2572115.94345178719895.05654821280109
2581817.80977648086160.19022351913838
2591512.76414542402262.2358545759774
260816.5387264612104-8.53872646121035
2611213.6904753430729-1.69047534307287
2621212.9087082978523-0.908708297852302
2632219.01952725528032.98047274471967
2641213.2987122538222-1.29871225382222






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4463279045029760.8926558090059520.553672095497024
120.9522335594823190.09553288103536250.0477664405176813
130.9458207250446130.1083585499107750.0541792749553874
140.9597723579432340.08045528411353240.0402276420567662
150.9360392148426660.1279215703146680.0639607851573339
160.9012585924158040.1974828151683920.0987414075841959
170.9501853656235760.09962926875284790.049814634376424
180.932218847449530.1355623051009410.0677811525504703
190.957108219318430.08578356136313990.0428917806815699
200.9376045387289390.1247909225421220.0623954612710609
210.9265416443843070.1469167112313850.0734583556156926
220.9218152935290540.1563694129418910.0781847064709457
230.8944365219774740.2111269560450520.105563478022526
240.8750700872314670.2498598255370670.124929912768533
250.8366461930843140.3267076138313720.163353806915686
260.8377232857289070.3245534285421850.162276714271093
270.8488010577639040.3023978844721910.151198942236096
280.8089633526593130.3820732946813730.191036647340687
290.8402573784053480.3194852431893030.159742621594652
300.8307678352445920.3384643295108160.169232164755408
310.8365683433842030.3268633132315940.163431656615797
320.8052945886912250.389410822617550.194705411308775
330.7742716786984410.4514566426031180.225728321301559
340.7497873161015220.5004253677969560.250212683898478
350.7163104782806460.5673790434387070.283689521719354
360.7506892793099310.4986214413801380.249310720690069
370.8321416639029020.3357166721941970.167858336097098
380.7970177607635650.4059644784728690.202982239236435
390.7632041505531370.4735916988937270.236795849446863
400.7628261784492090.4743476431015820.237173821550791
410.7510926770852210.4978146458295590.248907322914779
420.737533734437480.524932531125040.26246626556252
430.7902789260450660.4194421479098690.209721073954934
440.7550514410991220.4898971178017550.244948558900878
450.7187057693861050.5625884612277890.281294230613895
460.6928635648430740.6142728703138520.307136435156926
470.7487027706028620.5025944587942770.251297229397138
480.7148595691689840.5702808616620310.285140430831016
490.7683180043557950.463363991288410.231681995644205
500.7426765207757820.5146469584484360.257323479224218
510.7306888053118480.5386223893763030.269311194688152
520.6963511996007530.6072976007984940.303648800399247
530.6985556677843220.6028886644313560.301444332215678
540.6661088505996950.6677822988006090.333891149400304
550.6781576288469320.6436847423061350.321842371153068
560.6939825138123940.6120349723752130.306017486187606
570.6716192299567740.6567615400864510.328380770043226
580.708330875637240.5833382487255190.29166912436276
590.7233295430206740.5533409139586520.276670456979326
600.7001469331438070.5997061337123870.299853066856193
610.7122493527147740.5755012945704510.287750647285226
620.6757692867682580.6484614264634840.324230713231742
630.6520898764601430.6958202470797140.347910123539857
640.6120835992326910.7758328015346180.387916400767309
650.5711704408992690.8576591182014630.428829559100731
660.5813684543883590.8372630912232810.418631545611641
670.5841118642724090.8317762714551810.415888135727591
680.5542636364549280.8914727270901450.445736363545072
690.5135644480605660.9728711038788680.486435551939434
700.4895370834587790.9790741669175590.510462916541221
710.4507582157643490.9015164315286980.549241784235651
720.4269952860384330.8539905720768650.573004713961567
730.4002773084587880.8005546169175770.599722691541212
740.3793882995555760.7587765991111510.620611700444424
750.3495254783122040.6990509566244070.650474521687796
760.5113807088215060.9772385823569870.488619291178494
770.4901661626436210.9803323252872410.509833837356379
780.5064315491175940.9871369017648130.493568450882406
790.4681799037321280.9363598074642570.531820096267872
800.5661954074955740.8676091850088530.433804592504427
810.529343775063860.941312449872280.47065622493614
820.5534282633448950.893143473310210.446571736655105
830.5170414609891820.9659170780216360.482958539010818
840.479057917505190.9581158350103810.52094208249481
850.4411858671171250.882371734234250.558814132882875
860.4076287078188250.815257415637650.592371292181175
870.3757107706130030.7514215412260060.624289229386997
880.3426718404698680.6853436809397370.657328159530132
890.4330312208022080.8660624416044170.566968779197792
900.4035111459534770.8070222919069530.596488854046523
910.3749560808269720.7499121616539430.625043919173028
920.3446894256757470.6893788513514950.655310574324253
930.3444014501020.6888029002040.655598549898
940.3131897596974140.6263795193948280.686810240302586
950.2804810772852730.5609621545705450.719518922714727
960.2645877308376670.5291754616753340.735412269162333
970.2419151873073970.4838303746147950.758084812692603
980.2154561424731430.4309122849462850.784543857526857
990.2074787967542140.4149575935084270.792521203245786
1000.1823278790564770.3646557581129540.817672120943523
1010.1718499077582390.3436998155164790.828150092241761
1020.1537133562165160.3074267124330320.846286643783484
1030.1940613467901810.3881226935803630.805938653209819
1040.2249135884204770.4498271768409540.775086411579523
1050.2337294192911490.4674588385822990.766270580708851
1060.2067241767188530.4134483534377060.793275823281147
1070.2318868631907570.4637737263815150.768113136809243
1080.2154442016107830.4308884032215650.784555798389217
1090.3440330705239440.6880661410478870.655966929476056
1100.3353864076125240.6707728152250480.664613592387476
1110.3046271747159040.6092543494318080.695372825284096
1120.2935575170452570.5871150340905130.706442482954743
1130.2693810554387170.5387621108774350.730618944561283
1140.2505102698888750.501020539777750.749489730111125
1150.2235721227080250.4471442454160490.776427877291975
1160.1975902596189830.3951805192379660.802409740381017
1170.1767884547438590.3535769094877180.823211545256141
1180.2154226031821260.4308452063642520.784577396817874
1190.2213570659079660.4427141318159330.778642934092034
1200.197430252913640.394860505827280.80256974708636
1210.1823231958038390.3646463916076780.817676804196161
1220.1614518606780190.3229037213560390.838548139321981
1230.190643306125460.3812866122509210.80935669387454
1240.171640477511590.3432809550231790.82835952248841
1250.1619494256583670.3238988513167340.838050574341633
1260.1507609962400760.3015219924801510.849239003759924
1270.1325745175190820.2651490350381640.867425482480918
1280.1182261000678330.2364522001356660.881773899932167
1290.1018591543258390.2037183086516790.898140845674161
1300.1008898027982140.2017796055964290.899110197201786
1310.1025617069879710.2051234139759420.897438293012029
1320.09454795021399910.1890959004279980.905452049786001
1330.08634471828627720.1726894365725540.913655281713723
1340.07337994000482680.1467598800096540.926620059995173
1350.07485014738018850.1497002947603770.925149852619812
1360.1492236745265760.2984473490531520.850776325473424
1370.1300289751445160.2600579502890320.869971024855484
1380.1152080566027990.2304161132055980.884791943397201
1390.1103981390584440.2207962781168890.889601860941556
1400.1051487826693080.2102975653386150.894851217330693
1410.117775411949440.2355508238988810.88222458805056
1420.1185327024955450.237065404991090.881467297504455
1430.1019432809901290.2038865619802580.898056719009871
1440.08679032564508770.1735806512901750.913209674354912
1450.07390058715235920.1478011743047180.926099412847641
1460.0626117513178320.1252235026356640.937388248682168
1470.06863007274242270.1372601454848450.931369927257577
1480.0747856749949540.1495713499899080.925214325005046
1490.07027419971161090.1405483994232220.929725800288389
1500.0597081758730350.119416351746070.940291824126965
1510.07280106342161190.1456021268432240.927198936578388
1520.06989169674600760.1397833934920150.930108303253992
1530.0685425223792520.1370850447585040.931457477620748
1540.06373037303029090.1274607460605820.936269626969709
1550.07091898407611320.1418379681522260.929081015923887
1560.06046151681820130.1209230336364030.939538483181799
1570.05050120659676390.1010024131935280.949498793403236
1580.04306938929076890.08613877858153770.956930610709231
1590.0523639136862030.1047278273724060.947636086313797
1600.06526640303742370.1305328060748470.934733596962576
1610.05582610266015020.11165220532030.94417389733985
1620.1230237884097330.2460475768194660.876976211590267
1630.1124770216894380.2249540433788760.887522978310562
1640.3959741345179050.791948269035810.604025865482095
1650.3788859547381580.7577719094763160.621114045261842
1660.4870899917554890.9741799835109780.512910008244511
1670.4550028797796770.9100057595593530.544997120220324
1680.4232520557689990.8465041115379990.576747944231001
1690.3870156930575490.7740313861150980.612984306942451
1700.3651125562784440.7302251125568870.634887443721556
1710.3749106550169230.7498213100338460.625089344983077
1720.3401638197535030.6803276395070050.659836180246497
1730.3835019050238350.7670038100476710.616498094976165
1740.3707500871152640.7415001742305280.629249912884736
1750.3485076478747470.6970152957494950.651492352125253
1760.3850932120296310.7701864240592610.614906787970369
1770.3591536525917180.7183073051834360.640846347408282
1780.3716937705375350.743387541075070.628306229462465
1790.3568329159571070.7136658319142140.643167084042893
1800.3245210691516390.6490421383032770.675478930848361
1810.3519327704173870.7038655408347750.648067229582613
1820.4570749952548260.9141499905096510.542925004745174
1830.4421090672556130.8842181345112260.557890932744387
1840.4306230020464740.8612460040929480.569376997953526
1850.4140617383990550.8281234767981090.585938261600945
1860.5412294913835780.9175410172328450.458770508616422
1870.5092431138806530.9815137722386950.490756886119347
1880.4765167141802440.9530334283604880.523483285819756
1890.4402804590057520.8805609180115050.559719540994248
1900.479035042445310.958070084890620.52096495755469
1910.5084526219468440.9830947561063130.491547378053156
1920.5254128699941480.9491742600117040.474587130005852
1930.5161614425373150.967677114925370.483838557462685
1940.4853519660992740.9707039321985480.514648033900726
1950.4575474386124280.9150948772248560.542452561387572
1960.4928153287641080.9856306575282170.507184671235892
1970.6973941421641390.6052117156717210.302605857835861
1980.7055090881210030.5889818237579940.294490911878997
1990.6688720435673940.6622559128652130.331127956432606
2000.6293520206935750.741295958612850.370647979306425
2010.5890264798364220.8219470403271560.410973520163578
2020.5482287393486310.9035425213027380.451771260651369
2030.5235464485417310.9529071029165380.476453551458269
2040.5193202831772510.9613594336454980.480679716822749
2050.4820681032720730.9641362065441460.517931896727927
2060.4394459041264320.8788918082528640.560554095873568
2070.3982527243632660.7965054487265330.601747275636734
2080.3648431767218830.7296863534437670.635156823278117
2090.3237661163656560.6475322327313120.676233883634344
2100.3164326090042410.6328652180084820.683567390995759
2110.2909111370468070.5818222740936140.709088862953193
2120.2565793185206930.5131586370413860.743420681479307
2130.2837074456867590.5674148913735190.716292554313241
2140.2659421842460070.5318843684920140.734057815753993
2150.2329721559085530.4659443118171060.767027844091447
2160.2002631541081940.4005263082163890.799736845891806
2170.1901394597651880.3802789195303760.809860540234812
2180.1604920094659510.3209840189319020.839507990534049
2190.1634965271407640.3269930542815270.836503472859236
2200.155155275284890.3103105505697790.84484472471511
2210.1541199822585190.3082399645170370.845880017741481
2220.1275378421660030.2550756843320060.872462157833997
2230.1049216758889310.2098433517778620.895078324111069
2240.1058394081198390.2116788162396770.894160591880161
2250.09576817764201280.1915363552840260.904231822357987
2260.07798028019421730.1559605603884350.922019719805783
2270.0607195064556360.1214390129112720.939280493544364
2280.05700005452223940.1140001090444790.942999945477761
2290.0771587296965820.1543174593931640.922841270303418
2300.07343508624837280.1468701724967460.926564913751627
2310.05652088001458270.1130417600291650.943479119985417
2320.05802216100957010.116044322019140.94197783899043
2330.1207749026978660.2415498053957320.879225097302134
2340.1285174940888360.2570349881776720.871482505911164
2350.1221870354616680.2443740709233360.877812964538332
2360.09518633202041160.1903726640408230.904813667979588
2370.1238311602012730.2476623204025450.876168839798727
2380.1684373205137990.3368746410275980.831562679486201
2390.2707810599278020.5415621198556040.729218940072198
2400.2691496220083360.5382992440166720.730850377991664
2410.2231843482776760.4463686965553520.776815651722324
2420.271165268434970.5423305368699390.72883473156503
2430.2114672018764460.4229344037528930.788532798123553
2440.1585675716058880.3171351432117760.841432428394112
2450.235039722950060.470079445900120.76496027704994
2460.2565838079439840.5131676158879680.743416192056016
2470.1896872213523810.3793744427047630.810312778647619
2480.2345499276889950.4690998553779910.765450072311005
2490.3441553042563320.6883106085126630.655844695743668
2500.24757472177560.4951494435511990.7524252782244
2510.2786775396300770.5573550792601550.721322460369923
2520.9092773389400060.1814453221199880.0907226610599941
2530.8430764456808850.3138471086382290.156923554319115

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.446327904502976 & 0.892655809005952 & 0.553672095497024 \tabularnewline
12 & 0.952233559482319 & 0.0955328810353625 & 0.0477664405176813 \tabularnewline
13 & 0.945820725044613 & 0.108358549910775 & 0.0541792749553874 \tabularnewline
14 & 0.959772357943234 & 0.0804552841135324 & 0.0402276420567662 \tabularnewline
15 & 0.936039214842666 & 0.127921570314668 & 0.0639607851573339 \tabularnewline
16 & 0.901258592415804 & 0.197482815168392 & 0.0987414075841959 \tabularnewline
17 & 0.950185365623576 & 0.0996292687528479 & 0.049814634376424 \tabularnewline
18 & 0.93221884744953 & 0.135562305100941 & 0.0677811525504703 \tabularnewline
19 & 0.95710821931843 & 0.0857835613631399 & 0.0428917806815699 \tabularnewline
20 & 0.937604538728939 & 0.124790922542122 & 0.0623954612710609 \tabularnewline
21 & 0.926541644384307 & 0.146916711231385 & 0.0734583556156926 \tabularnewline
22 & 0.921815293529054 & 0.156369412941891 & 0.0781847064709457 \tabularnewline
23 & 0.894436521977474 & 0.211126956045052 & 0.105563478022526 \tabularnewline
24 & 0.875070087231467 & 0.249859825537067 & 0.124929912768533 \tabularnewline
25 & 0.836646193084314 & 0.326707613831372 & 0.163353806915686 \tabularnewline
26 & 0.837723285728907 & 0.324553428542185 & 0.162276714271093 \tabularnewline
27 & 0.848801057763904 & 0.302397884472191 & 0.151198942236096 \tabularnewline
28 & 0.808963352659313 & 0.382073294681373 & 0.191036647340687 \tabularnewline
29 & 0.840257378405348 & 0.319485243189303 & 0.159742621594652 \tabularnewline
30 & 0.830767835244592 & 0.338464329510816 & 0.169232164755408 \tabularnewline
31 & 0.836568343384203 & 0.326863313231594 & 0.163431656615797 \tabularnewline
32 & 0.805294588691225 & 0.38941082261755 & 0.194705411308775 \tabularnewline
33 & 0.774271678698441 & 0.451456642603118 & 0.225728321301559 \tabularnewline
34 & 0.749787316101522 & 0.500425367796956 & 0.250212683898478 \tabularnewline
35 & 0.716310478280646 & 0.567379043438707 & 0.283689521719354 \tabularnewline
36 & 0.750689279309931 & 0.498621441380138 & 0.249310720690069 \tabularnewline
37 & 0.832141663902902 & 0.335716672194197 & 0.167858336097098 \tabularnewline
38 & 0.797017760763565 & 0.405964478472869 & 0.202982239236435 \tabularnewline
39 & 0.763204150553137 & 0.473591698893727 & 0.236795849446863 \tabularnewline
40 & 0.762826178449209 & 0.474347643101582 & 0.237173821550791 \tabularnewline
41 & 0.751092677085221 & 0.497814645829559 & 0.248907322914779 \tabularnewline
42 & 0.73753373443748 & 0.52493253112504 & 0.26246626556252 \tabularnewline
43 & 0.790278926045066 & 0.419442147909869 & 0.209721073954934 \tabularnewline
44 & 0.755051441099122 & 0.489897117801755 & 0.244948558900878 \tabularnewline
45 & 0.718705769386105 & 0.562588461227789 & 0.281294230613895 \tabularnewline
46 & 0.692863564843074 & 0.614272870313852 & 0.307136435156926 \tabularnewline
47 & 0.748702770602862 & 0.502594458794277 & 0.251297229397138 \tabularnewline
48 & 0.714859569168984 & 0.570280861662031 & 0.285140430831016 \tabularnewline
49 & 0.768318004355795 & 0.46336399128841 & 0.231681995644205 \tabularnewline
50 & 0.742676520775782 & 0.514646958448436 & 0.257323479224218 \tabularnewline
51 & 0.730688805311848 & 0.538622389376303 & 0.269311194688152 \tabularnewline
52 & 0.696351199600753 & 0.607297600798494 & 0.303648800399247 \tabularnewline
53 & 0.698555667784322 & 0.602888664431356 & 0.301444332215678 \tabularnewline
54 & 0.666108850599695 & 0.667782298800609 & 0.333891149400304 \tabularnewline
55 & 0.678157628846932 & 0.643684742306135 & 0.321842371153068 \tabularnewline
56 & 0.693982513812394 & 0.612034972375213 & 0.306017486187606 \tabularnewline
57 & 0.671619229956774 & 0.656761540086451 & 0.328380770043226 \tabularnewline
58 & 0.70833087563724 & 0.583338248725519 & 0.29166912436276 \tabularnewline
59 & 0.723329543020674 & 0.553340913958652 & 0.276670456979326 \tabularnewline
60 & 0.700146933143807 & 0.599706133712387 & 0.299853066856193 \tabularnewline
61 & 0.712249352714774 & 0.575501294570451 & 0.287750647285226 \tabularnewline
62 & 0.675769286768258 & 0.648461426463484 & 0.324230713231742 \tabularnewline
63 & 0.652089876460143 & 0.695820247079714 & 0.347910123539857 \tabularnewline
64 & 0.612083599232691 & 0.775832801534618 & 0.387916400767309 \tabularnewline
65 & 0.571170440899269 & 0.857659118201463 & 0.428829559100731 \tabularnewline
66 & 0.581368454388359 & 0.837263091223281 & 0.418631545611641 \tabularnewline
67 & 0.584111864272409 & 0.831776271455181 & 0.415888135727591 \tabularnewline
68 & 0.554263636454928 & 0.891472727090145 & 0.445736363545072 \tabularnewline
69 & 0.513564448060566 & 0.972871103878868 & 0.486435551939434 \tabularnewline
70 & 0.489537083458779 & 0.979074166917559 & 0.510462916541221 \tabularnewline
71 & 0.450758215764349 & 0.901516431528698 & 0.549241784235651 \tabularnewline
72 & 0.426995286038433 & 0.853990572076865 & 0.573004713961567 \tabularnewline
73 & 0.400277308458788 & 0.800554616917577 & 0.599722691541212 \tabularnewline
74 & 0.379388299555576 & 0.758776599111151 & 0.620611700444424 \tabularnewline
75 & 0.349525478312204 & 0.699050956624407 & 0.650474521687796 \tabularnewline
76 & 0.511380708821506 & 0.977238582356987 & 0.488619291178494 \tabularnewline
77 & 0.490166162643621 & 0.980332325287241 & 0.509833837356379 \tabularnewline
78 & 0.506431549117594 & 0.987136901764813 & 0.493568450882406 \tabularnewline
79 & 0.468179903732128 & 0.936359807464257 & 0.531820096267872 \tabularnewline
80 & 0.566195407495574 & 0.867609185008853 & 0.433804592504427 \tabularnewline
81 & 0.52934377506386 & 0.94131244987228 & 0.47065622493614 \tabularnewline
82 & 0.553428263344895 & 0.89314347331021 & 0.446571736655105 \tabularnewline
83 & 0.517041460989182 & 0.965917078021636 & 0.482958539010818 \tabularnewline
84 & 0.47905791750519 & 0.958115835010381 & 0.52094208249481 \tabularnewline
85 & 0.441185867117125 & 0.88237173423425 & 0.558814132882875 \tabularnewline
86 & 0.407628707818825 & 0.81525741563765 & 0.592371292181175 \tabularnewline
87 & 0.375710770613003 & 0.751421541226006 & 0.624289229386997 \tabularnewline
88 & 0.342671840469868 & 0.685343680939737 & 0.657328159530132 \tabularnewline
89 & 0.433031220802208 & 0.866062441604417 & 0.566968779197792 \tabularnewline
90 & 0.403511145953477 & 0.807022291906953 & 0.596488854046523 \tabularnewline
91 & 0.374956080826972 & 0.749912161653943 & 0.625043919173028 \tabularnewline
92 & 0.344689425675747 & 0.689378851351495 & 0.655310574324253 \tabularnewline
93 & 0.344401450102 & 0.688802900204 & 0.655598549898 \tabularnewline
94 & 0.313189759697414 & 0.626379519394828 & 0.686810240302586 \tabularnewline
95 & 0.280481077285273 & 0.560962154570545 & 0.719518922714727 \tabularnewline
96 & 0.264587730837667 & 0.529175461675334 & 0.735412269162333 \tabularnewline
97 & 0.241915187307397 & 0.483830374614795 & 0.758084812692603 \tabularnewline
98 & 0.215456142473143 & 0.430912284946285 & 0.784543857526857 \tabularnewline
99 & 0.207478796754214 & 0.414957593508427 & 0.792521203245786 \tabularnewline
100 & 0.182327879056477 & 0.364655758112954 & 0.817672120943523 \tabularnewline
101 & 0.171849907758239 & 0.343699815516479 & 0.828150092241761 \tabularnewline
102 & 0.153713356216516 & 0.307426712433032 & 0.846286643783484 \tabularnewline
103 & 0.194061346790181 & 0.388122693580363 & 0.805938653209819 \tabularnewline
104 & 0.224913588420477 & 0.449827176840954 & 0.775086411579523 \tabularnewline
105 & 0.233729419291149 & 0.467458838582299 & 0.766270580708851 \tabularnewline
106 & 0.206724176718853 & 0.413448353437706 & 0.793275823281147 \tabularnewline
107 & 0.231886863190757 & 0.463773726381515 & 0.768113136809243 \tabularnewline
108 & 0.215444201610783 & 0.430888403221565 & 0.784555798389217 \tabularnewline
109 & 0.344033070523944 & 0.688066141047887 & 0.655966929476056 \tabularnewline
110 & 0.335386407612524 & 0.670772815225048 & 0.664613592387476 \tabularnewline
111 & 0.304627174715904 & 0.609254349431808 & 0.695372825284096 \tabularnewline
112 & 0.293557517045257 & 0.587115034090513 & 0.706442482954743 \tabularnewline
113 & 0.269381055438717 & 0.538762110877435 & 0.730618944561283 \tabularnewline
114 & 0.250510269888875 & 0.50102053977775 & 0.749489730111125 \tabularnewline
115 & 0.223572122708025 & 0.447144245416049 & 0.776427877291975 \tabularnewline
116 & 0.197590259618983 & 0.395180519237966 & 0.802409740381017 \tabularnewline
117 & 0.176788454743859 & 0.353576909487718 & 0.823211545256141 \tabularnewline
118 & 0.215422603182126 & 0.430845206364252 & 0.784577396817874 \tabularnewline
119 & 0.221357065907966 & 0.442714131815933 & 0.778642934092034 \tabularnewline
120 & 0.19743025291364 & 0.39486050582728 & 0.80256974708636 \tabularnewline
121 & 0.182323195803839 & 0.364646391607678 & 0.817676804196161 \tabularnewline
122 & 0.161451860678019 & 0.322903721356039 & 0.838548139321981 \tabularnewline
123 & 0.19064330612546 & 0.381286612250921 & 0.80935669387454 \tabularnewline
124 & 0.17164047751159 & 0.343280955023179 & 0.82835952248841 \tabularnewline
125 & 0.161949425658367 & 0.323898851316734 & 0.838050574341633 \tabularnewline
126 & 0.150760996240076 & 0.301521992480151 & 0.849239003759924 \tabularnewline
127 & 0.132574517519082 & 0.265149035038164 & 0.867425482480918 \tabularnewline
128 & 0.118226100067833 & 0.236452200135666 & 0.881773899932167 \tabularnewline
129 & 0.101859154325839 & 0.203718308651679 & 0.898140845674161 \tabularnewline
130 & 0.100889802798214 & 0.201779605596429 & 0.899110197201786 \tabularnewline
131 & 0.102561706987971 & 0.205123413975942 & 0.897438293012029 \tabularnewline
132 & 0.0945479502139991 & 0.189095900427998 & 0.905452049786001 \tabularnewline
133 & 0.0863447182862772 & 0.172689436572554 & 0.913655281713723 \tabularnewline
134 & 0.0733799400048268 & 0.146759880009654 & 0.926620059995173 \tabularnewline
135 & 0.0748501473801885 & 0.149700294760377 & 0.925149852619812 \tabularnewline
136 & 0.149223674526576 & 0.298447349053152 & 0.850776325473424 \tabularnewline
137 & 0.130028975144516 & 0.260057950289032 & 0.869971024855484 \tabularnewline
138 & 0.115208056602799 & 0.230416113205598 & 0.884791943397201 \tabularnewline
139 & 0.110398139058444 & 0.220796278116889 & 0.889601860941556 \tabularnewline
140 & 0.105148782669308 & 0.210297565338615 & 0.894851217330693 \tabularnewline
141 & 0.11777541194944 & 0.235550823898881 & 0.88222458805056 \tabularnewline
142 & 0.118532702495545 & 0.23706540499109 & 0.881467297504455 \tabularnewline
143 & 0.101943280990129 & 0.203886561980258 & 0.898056719009871 \tabularnewline
144 & 0.0867903256450877 & 0.173580651290175 & 0.913209674354912 \tabularnewline
145 & 0.0739005871523592 & 0.147801174304718 & 0.926099412847641 \tabularnewline
146 & 0.062611751317832 & 0.125223502635664 & 0.937388248682168 \tabularnewline
147 & 0.0686300727424227 & 0.137260145484845 & 0.931369927257577 \tabularnewline
148 & 0.074785674994954 & 0.149571349989908 & 0.925214325005046 \tabularnewline
149 & 0.0702741997116109 & 0.140548399423222 & 0.929725800288389 \tabularnewline
150 & 0.059708175873035 & 0.11941635174607 & 0.940291824126965 \tabularnewline
151 & 0.0728010634216119 & 0.145602126843224 & 0.927198936578388 \tabularnewline
152 & 0.0698916967460076 & 0.139783393492015 & 0.930108303253992 \tabularnewline
153 & 0.068542522379252 & 0.137085044758504 & 0.931457477620748 \tabularnewline
154 & 0.0637303730302909 & 0.127460746060582 & 0.936269626969709 \tabularnewline
155 & 0.0709189840761132 & 0.141837968152226 & 0.929081015923887 \tabularnewline
156 & 0.0604615168182013 & 0.120923033636403 & 0.939538483181799 \tabularnewline
157 & 0.0505012065967639 & 0.101002413193528 & 0.949498793403236 \tabularnewline
158 & 0.0430693892907689 & 0.0861387785815377 & 0.956930610709231 \tabularnewline
159 & 0.052363913686203 & 0.104727827372406 & 0.947636086313797 \tabularnewline
160 & 0.0652664030374237 & 0.130532806074847 & 0.934733596962576 \tabularnewline
161 & 0.0558261026601502 & 0.1116522053203 & 0.94417389733985 \tabularnewline
162 & 0.123023788409733 & 0.246047576819466 & 0.876976211590267 \tabularnewline
163 & 0.112477021689438 & 0.224954043378876 & 0.887522978310562 \tabularnewline
164 & 0.395974134517905 & 0.79194826903581 & 0.604025865482095 \tabularnewline
165 & 0.378885954738158 & 0.757771909476316 & 0.621114045261842 \tabularnewline
166 & 0.487089991755489 & 0.974179983510978 & 0.512910008244511 \tabularnewline
167 & 0.455002879779677 & 0.910005759559353 & 0.544997120220324 \tabularnewline
168 & 0.423252055768999 & 0.846504111537999 & 0.576747944231001 \tabularnewline
169 & 0.387015693057549 & 0.774031386115098 & 0.612984306942451 \tabularnewline
170 & 0.365112556278444 & 0.730225112556887 & 0.634887443721556 \tabularnewline
171 & 0.374910655016923 & 0.749821310033846 & 0.625089344983077 \tabularnewline
172 & 0.340163819753503 & 0.680327639507005 & 0.659836180246497 \tabularnewline
173 & 0.383501905023835 & 0.767003810047671 & 0.616498094976165 \tabularnewline
174 & 0.370750087115264 & 0.741500174230528 & 0.629249912884736 \tabularnewline
175 & 0.348507647874747 & 0.697015295749495 & 0.651492352125253 \tabularnewline
176 & 0.385093212029631 & 0.770186424059261 & 0.614906787970369 \tabularnewline
177 & 0.359153652591718 & 0.718307305183436 & 0.640846347408282 \tabularnewline
178 & 0.371693770537535 & 0.74338754107507 & 0.628306229462465 \tabularnewline
179 & 0.356832915957107 & 0.713665831914214 & 0.643167084042893 \tabularnewline
180 & 0.324521069151639 & 0.649042138303277 & 0.675478930848361 \tabularnewline
181 & 0.351932770417387 & 0.703865540834775 & 0.648067229582613 \tabularnewline
182 & 0.457074995254826 & 0.914149990509651 & 0.542925004745174 \tabularnewline
183 & 0.442109067255613 & 0.884218134511226 & 0.557890932744387 \tabularnewline
184 & 0.430623002046474 & 0.861246004092948 & 0.569376997953526 \tabularnewline
185 & 0.414061738399055 & 0.828123476798109 & 0.585938261600945 \tabularnewline
186 & 0.541229491383578 & 0.917541017232845 & 0.458770508616422 \tabularnewline
187 & 0.509243113880653 & 0.981513772238695 & 0.490756886119347 \tabularnewline
188 & 0.476516714180244 & 0.953033428360488 & 0.523483285819756 \tabularnewline
189 & 0.440280459005752 & 0.880560918011505 & 0.559719540994248 \tabularnewline
190 & 0.47903504244531 & 0.95807008489062 & 0.52096495755469 \tabularnewline
191 & 0.508452621946844 & 0.983094756106313 & 0.491547378053156 \tabularnewline
192 & 0.525412869994148 & 0.949174260011704 & 0.474587130005852 \tabularnewline
193 & 0.516161442537315 & 0.96767711492537 & 0.483838557462685 \tabularnewline
194 & 0.485351966099274 & 0.970703932198548 & 0.514648033900726 \tabularnewline
195 & 0.457547438612428 & 0.915094877224856 & 0.542452561387572 \tabularnewline
196 & 0.492815328764108 & 0.985630657528217 & 0.507184671235892 \tabularnewline
197 & 0.697394142164139 & 0.605211715671721 & 0.302605857835861 \tabularnewline
198 & 0.705509088121003 & 0.588981823757994 & 0.294490911878997 \tabularnewline
199 & 0.668872043567394 & 0.662255912865213 & 0.331127956432606 \tabularnewline
200 & 0.629352020693575 & 0.74129595861285 & 0.370647979306425 \tabularnewline
201 & 0.589026479836422 & 0.821947040327156 & 0.410973520163578 \tabularnewline
202 & 0.548228739348631 & 0.903542521302738 & 0.451771260651369 \tabularnewline
203 & 0.523546448541731 & 0.952907102916538 & 0.476453551458269 \tabularnewline
204 & 0.519320283177251 & 0.961359433645498 & 0.480679716822749 \tabularnewline
205 & 0.482068103272073 & 0.964136206544146 & 0.517931896727927 \tabularnewline
206 & 0.439445904126432 & 0.878891808252864 & 0.560554095873568 \tabularnewline
207 & 0.398252724363266 & 0.796505448726533 & 0.601747275636734 \tabularnewline
208 & 0.364843176721883 & 0.729686353443767 & 0.635156823278117 \tabularnewline
209 & 0.323766116365656 & 0.647532232731312 & 0.676233883634344 \tabularnewline
210 & 0.316432609004241 & 0.632865218008482 & 0.683567390995759 \tabularnewline
211 & 0.290911137046807 & 0.581822274093614 & 0.709088862953193 \tabularnewline
212 & 0.256579318520693 & 0.513158637041386 & 0.743420681479307 \tabularnewline
213 & 0.283707445686759 & 0.567414891373519 & 0.716292554313241 \tabularnewline
214 & 0.265942184246007 & 0.531884368492014 & 0.734057815753993 \tabularnewline
215 & 0.232972155908553 & 0.465944311817106 & 0.767027844091447 \tabularnewline
216 & 0.200263154108194 & 0.400526308216389 & 0.799736845891806 \tabularnewline
217 & 0.190139459765188 & 0.380278919530376 & 0.809860540234812 \tabularnewline
218 & 0.160492009465951 & 0.320984018931902 & 0.839507990534049 \tabularnewline
219 & 0.163496527140764 & 0.326993054281527 & 0.836503472859236 \tabularnewline
220 & 0.15515527528489 & 0.310310550569779 & 0.84484472471511 \tabularnewline
221 & 0.154119982258519 & 0.308239964517037 & 0.845880017741481 \tabularnewline
222 & 0.127537842166003 & 0.255075684332006 & 0.872462157833997 \tabularnewline
223 & 0.104921675888931 & 0.209843351777862 & 0.895078324111069 \tabularnewline
224 & 0.105839408119839 & 0.211678816239677 & 0.894160591880161 \tabularnewline
225 & 0.0957681776420128 & 0.191536355284026 & 0.904231822357987 \tabularnewline
226 & 0.0779802801942173 & 0.155960560388435 & 0.922019719805783 \tabularnewline
227 & 0.060719506455636 & 0.121439012911272 & 0.939280493544364 \tabularnewline
228 & 0.0570000545222394 & 0.114000109044479 & 0.942999945477761 \tabularnewline
229 & 0.077158729696582 & 0.154317459393164 & 0.922841270303418 \tabularnewline
230 & 0.0734350862483728 & 0.146870172496746 & 0.926564913751627 \tabularnewline
231 & 0.0565208800145827 & 0.113041760029165 & 0.943479119985417 \tabularnewline
232 & 0.0580221610095701 & 0.11604432201914 & 0.94197783899043 \tabularnewline
233 & 0.120774902697866 & 0.241549805395732 & 0.879225097302134 \tabularnewline
234 & 0.128517494088836 & 0.257034988177672 & 0.871482505911164 \tabularnewline
235 & 0.122187035461668 & 0.244374070923336 & 0.877812964538332 \tabularnewline
236 & 0.0951863320204116 & 0.190372664040823 & 0.904813667979588 \tabularnewline
237 & 0.123831160201273 & 0.247662320402545 & 0.876168839798727 \tabularnewline
238 & 0.168437320513799 & 0.336874641027598 & 0.831562679486201 \tabularnewline
239 & 0.270781059927802 & 0.541562119855604 & 0.729218940072198 \tabularnewline
240 & 0.269149622008336 & 0.538299244016672 & 0.730850377991664 \tabularnewline
241 & 0.223184348277676 & 0.446368696555352 & 0.776815651722324 \tabularnewline
242 & 0.27116526843497 & 0.542330536869939 & 0.72883473156503 \tabularnewline
243 & 0.211467201876446 & 0.422934403752893 & 0.788532798123553 \tabularnewline
244 & 0.158567571605888 & 0.317135143211776 & 0.841432428394112 \tabularnewline
245 & 0.23503972295006 & 0.47007944590012 & 0.76496027704994 \tabularnewline
246 & 0.256583807943984 & 0.513167615887968 & 0.743416192056016 \tabularnewline
247 & 0.189687221352381 & 0.379374442704763 & 0.810312778647619 \tabularnewline
248 & 0.234549927688995 & 0.469099855377991 & 0.765450072311005 \tabularnewline
249 & 0.344155304256332 & 0.688310608512663 & 0.655844695743668 \tabularnewline
250 & 0.2475747217756 & 0.495149443551199 & 0.7524252782244 \tabularnewline
251 & 0.278677539630077 & 0.557355079260155 & 0.721322460369923 \tabularnewline
252 & 0.909277338940006 & 0.181445322119988 & 0.0907226610599941 \tabularnewline
253 & 0.843076445680885 & 0.313847108638229 & 0.156923554319115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225701&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]0.446327904502976[/C][C]0.892655809005952[/C][C]0.553672095497024[/C][/ROW]
[ROW][C]12[/C][C]0.952233559482319[/C][C]0.0955328810353625[/C][C]0.0477664405176813[/C][/ROW]
[ROW][C]13[/C][C]0.945820725044613[/C][C]0.108358549910775[/C][C]0.0541792749553874[/C][/ROW]
[ROW][C]14[/C][C]0.959772357943234[/C][C]0.0804552841135324[/C][C]0.0402276420567662[/C][/ROW]
[ROW][C]15[/C][C]0.936039214842666[/C][C]0.127921570314668[/C][C]0.0639607851573339[/C][/ROW]
[ROW][C]16[/C][C]0.901258592415804[/C][C]0.197482815168392[/C][C]0.0987414075841959[/C][/ROW]
[ROW][C]17[/C][C]0.950185365623576[/C][C]0.0996292687528479[/C][C]0.049814634376424[/C][/ROW]
[ROW][C]18[/C][C]0.93221884744953[/C][C]0.135562305100941[/C][C]0.0677811525504703[/C][/ROW]
[ROW][C]19[/C][C]0.95710821931843[/C][C]0.0857835613631399[/C][C]0.0428917806815699[/C][/ROW]
[ROW][C]20[/C][C]0.937604538728939[/C][C]0.124790922542122[/C][C]0.0623954612710609[/C][/ROW]
[ROW][C]21[/C][C]0.926541644384307[/C][C]0.146916711231385[/C][C]0.0734583556156926[/C][/ROW]
[ROW][C]22[/C][C]0.921815293529054[/C][C]0.156369412941891[/C][C]0.0781847064709457[/C][/ROW]
[ROW][C]23[/C][C]0.894436521977474[/C][C]0.211126956045052[/C][C]0.105563478022526[/C][/ROW]
[ROW][C]24[/C][C]0.875070087231467[/C][C]0.249859825537067[/C][C]0.124929912768533[/C][/ROW]
[ROW][C]25[/C][C]0.836646193084314[/C][C]0.326707613831372[/C][C]0.163353806915686[/C][/ROW]
[ROW][C]26[/C][C]0.837723285728907[/C][C]0.324553428542185[/C][C]0.162276714271093[/C][/ROW]
[ROW][C]27[/C][C]0.848801057763904[/C][C]0.302397884472191[/C][C]0.151198942236096[/C][/ROW]
[ROW][C]28[/C][C]0.808963352659313[/C][C]0.382073294681373[/C][C]0.191036647340687[/C][/ROW]
[ROW][C]29[/C][C]0.840257378405348[/C][C]0.319485243189303[/C][C]0.159742621594652[/C][/ROW]
[ROW][C]30[/C][C]0.830767835244592[/C][C]0.338464329510816[/C][C]0.169232164755408[/C][/ROW]
[ROW][C]31[/C][C]0.836568343384203[/C][C]0.326863313231594[/C][C]0.163431656615797[/C][/ROW]
[ROW][C]32[/C][C]0.805294588691225[/C][C]0.38941082261755[/C][C]0.194705411308775[/C][/ROW]
[ROW][C]33[/C][C]0.774271678698441[/C][C]0.451456642603118[/C][C]0.225728321301559[/C][/ROW]
[ROW][C]34[/C][C]0.749787316101522[/C][C]0.500425367796956[/C][C]0.250212683898478[/C][/ROW]
[ROW][C]35[/C][C]0.716310478280646[/C][C]0.567379043438707[/C][C]0.283689521719354[/C][/ROW]
[ROW][C]36[/C][C]0.750689279309931[/C][C]0.498621441380138[/C][C]0.249310720690069[/C][/ROW]
[ROW][C]37[/C][C]0.832141663902902[/C][C]0.335716672194197[/C][C]0.167858336097098[/C][/ROW]
[ROW][C]38[/C][C]0.797017760763565[/C][C]0.405964478472869[/C][C]0.202982239236435[/C][/ROW]
[ROW][C]39[/C][C]0.763204150553137[/C][C]0.473591698893727[/C][C]0.236795849446863[/C][/ROW]
[ROW][C]40[/C][C]0.762826178449209[/C][C]0.474347643101582[/C][C]0.237173821550791[/C][/ROW]
[ROW][C]41[/C][C]0.751092677085221[/C][C]0.497814645829559[/C][C]0.248907322914779[/C][/ROW]
[ROW][C]42[/C][C]0.73753373443748[/C][C]0.52493253112504[/C][C]0.26246626556252[/C][/ROW]
[ROW][C]43[/C][C]0.790278926045066[/C][C]0.419442147909869[/C][C]0.209721073954934[/C][/ROW]
[ROW][C]44[/C][C]0.755051441099122[/C][C]0.489897117801755[/C][C]0.244948558900878[/C][/ROW]
[ROW][C]45[/C][C]0.718705769386105[/C][C]0.562588461227789[/C][C]0.281294230613895[/C][/ROW]
[ROW][C]46[/C][C]0.692863564843074[/C][C]0.614272870313852[/C][C]0.307136435156926[/C][/ROW]
[ROW][C]47[/C][C]0.748702770602862[/C][C]0.502594458794277[/C][C]0.251297229397138[/C][/ROW]
[ROW][C]48[/C][C]0.714859569168984[/C][C]0.570280861662031[/C][C]0.285140430831016[/C][/ROW]
[ROW][C]49[/C][C]0.768318004355795[/C][C]0.46336399128841[/C][C]0.231681995644205[/C][/ROW]
[ROW][C]50[/C][C]0.742676520775782[/C][C]0.514646958448436[/C][C]0.257323479224218[/C][/ROW]
[ROW][C]51[/C][C]0.730688805311848[/C][C]0.538622389376303[/C][C]0.269311194688152[/C][/ROW]
[ROW][C]52[/C][C]0.696351199600753[/C][C]0.607297600798494[/C][C]0.303648800399247[/C][/ROW]
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[ROW][C]187[/C][C]0.509243113880653[/C][C]0.981513772238695[/C][C]0.490756886119347[/C][/ROW]
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[ROW][C]189[/C][C]0.440280459005752[/C][C]0.880560918011505[/C][C]0.559719540994248[/C][/ROW]
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[ROW][C]191[/C][C]0.508452621946844[/C][C]0.983094756106313[/C][C]0.491547378053156[/C][/ROW]
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[ROW][C]197[/C][C]0.697394142164139[/C][C]0.605211715671721[/C][C]0.302605857835861[/C][/ROW]
[ROW][C]198[/C][C]0.705509088121003[/C][C]0.588981823757994[/C][C]0.294490911878997[/C][/ROW]
[ROW][C]199[/C][C]0.668872043567394[/C][C]0.662255912865213[/C][C]0.331127956432606[/C][/ROW]
[ROW][C]200[/C][C]0.629352020693575[/C][C]0.74129595861285[/C][C]0.370647979306425[/C][/ROW]
[ROW][C]201[/C][C]0.589026479836422[/C][C]0.821947040327156[/C][C]0.410973520163578[/C][/ROW]
[ROW][C]202[/C][C]0.548228739348631[/C][C]0.903542521302738[/C][C]0.451771260651369[/C][/ROW]
[ROW][C]203[/C][C]0.523546448541731[/C][C]0.952907102916538[/C][C]0.476453551458269[/C][/ROW]
[ROW][C]204[/C][C]0.519320283177251[/C][C]0.961359433645498[/C][C]0.480679716822749[/C][/ROW]
[ROW][C]205[/C][C]0.482068103272073[/C][C]0.964136206544146[/C][C]0.517931896727927[/C][/ROW]
[ROW][C]206[/C][C]0.439445904126432[/C][C]0.878891808252864[/C][C]0.560554095873568[/C][/ROW]
[ROW][C]207[/C][C]0.398252724363266[/C][C]0.796505448726533[/C][C]0.601747275636734[/C][/ROW]
[ROW][C]208[/C][C]0.364843176721883[/C][C]0.729686353443767[/C][C]0.635156823278117[/C][/ROW]
[ROW][C]209[/C][C]0.323766116365656[/C][C]0.647532232731312[/C][C]0.676233883634344[/C][/ROW]
[ROW][C]210[/C][C]0.316432609004241[/C][C]0.632865218008482[/C][C]0.683567390995759[/C][/ROW]
[ROW][C]211[/C][C]0.290911137046807[/C][C]0.581822274093614[/C][C]0.709088862953193[/C][/ROW]
[ROW][C]212[/C][C]0.256579318520693[/C][C]0.513158637041386[/C][C]0.743420681479307[/C][/ROW]
[ROW][C]213[/C][C]0.283707445686759[/C][C]0.567414891373519[/C][C]0.716292554313241[/C][/ROW]
[ROW][C]214[/C][C]0.265942184246007[/C][C]0.531884368492014[/C][C]0.734057815753993[/C][/ROW]
[ROW][C]215[/C][C]0.232972155908553[/C][C]0.465944311817106[/C][C]0.767027844091447[/C][/ROW]
[ROW][C]216[/C][C]0.200263154108194[/C][C]0.400526308216389[/C][C]0.799736845891806[/C][/ROW]
[ROW][C]217[/C][C]0.190139459765188[/C][C]0.380278919530376[/C][C]0.809860540234812[/C][/ROW]
[ROW][C]218[/C][C]0.160492009465951[/C][C]0.320984018931902[/C][C]0.839507990534049[/C][/ROW]
[ROW][C]219[/C][C]0.163496527140764[/C][C]0.326993054281527[/C][C]0.836503472859236[/C][/ROW]
[ROW][C]220[/C][C]0.15515527528489[/C][C]0.310310550569779[/C][C]0.84484472471511[/C][/ROW]
[ROW][C]221[/C][C]0.154119982258519[/C][C]0.308239964517037[/C][C]0.845880017741481[/C][/ROW]
[ROW][C]222[/C][C]0.127537842166003[/C][C]0.255075684332006[/C][C]0.872462157833997[/C][/ROW]
[ROW][C]223[/C][C]0.104921675888931[/C][C]0.209843351777862[/C][C]0.895078324111069[/C][/ROW]
[ROW][C]224[/C][C]0.105839408119839[/C][C]0.211678816239677[/C][C]0.894160591880161[/C][/ROW]
[ROW][C]225[/C][C]0.0957681776420128[/C][C]0.191536355284026[/C][C]0.904231822357987[/C][/ROW]
[ROW][C]226[/C][C]0.0779802801942173[/C][C]0.155960560388435[/C][C]0.922019719805783[/C][/ROW]
[ROW][C]227[/C][C]0.060719506455636[/C][C]0.121439012911272[/C][C]0.939280493544364[/C][/ROW]
[ROW][C]228[/C][C]0.0570000545222394[/C][C]0.114000109044479[/C][C]0.942999945477761[/C][/ROW]
[ROW][C]229[/C][C]0.077158729696582[/C][C]0.154317459393164[/C][C]0.922841270303418[/C][/ROW]
[ROW][C]230[/C][C]0.0734350862483728[/C][C]0.146870172496746[/C][C]0.926564913751627[/C][/ROW]
[ROW][C]231[/C][C]0.0565208800145827[/C][C]0.113041760029165[/C][C]0.943479119985417[/C][/ROW]
[ROW][C]232[/C][C]0.0580221610095701[/C][C]0.11604432201914[/C][C]0.94197783899043[/C][/ROW]
[ROW][C]233[/C][C]0.120774902697866[/C][C]0.241549805395732[/C][C]0.879225097302134[/C][/ROW]
[ROW][C]234[/C][C]0.128517494088836[/C][C]0.257034988177672[/C][C]0.871482505911164[/C][/ROW]
[ROW][C]235[/C][C]0.122187035461668[/C][C]0.244374070923336[/C][C]0.877812964538332[/C][/ROW]
[ROW][C]236[/C][C]0.0951863320204116[/C][C]0.190372664040823[/C][C]0.904813667979588[/C][/ROW]
[ROW][C]237[/C][C]0.123831160201273[/C][C]0.247662320402545[/C][C]0.876168839798727[/C][/ROW]
[ROW][C]238[/C][C]0.168437320513799[/C][C]0.336874641027598[/C][C]0.831562679486201[/C][/ROW]
[ROW][C]239[/C][C]0.270781059927802[/C][C]0.541562119855604[/C][C]0.729218940072198[/C][/ROW]
[ROW][C]240[/C][C]0.269149622008336[/C][C]0.538299244016672[/C][C]0.730850377991664[/C][/ROW]
[ROW][C]241[/C][C]0.223184348277676[/C][C]0.446368696555352[/C][C]0.776815651722324[/C][/ROW]
[ROW][C]242[/C][C]0.27116526843497[/C][C]0.542330536869939[/C][C]0.72883473156503[/C][/ROW]
[ROW][C]243[/C][C]0.211467201876446[/C][C]0.422934403752893[/C][C]0.788532798123553[/C][/ROW]
[ROW][C]244[/C][C]0.158567571605888[/C][C]0.317135143211776[/C][C]0.841432428394112[/C][/ROW]
[ROW][C]245[/C][C]0.23503972295006[/C][C]0.47007944590012[/C][C]0.76496027704994[/C][/ROW]
[ROW][C]246[/C][C]0.256583807943984[/C][C]0.513167615887968[/C][C]0.743416192056016[/C][/ROW]
[ROW][C]247[/C][C]0.189687221352381[/C][C]0.379374442704763[/C][C]0.810312778647619[/C][/ROW]
[ROW][C]248[/C][C]0.234549927688995[/C][C]0.469099855377991[/C][C]0.765450072311005[/C][/ROW]
[ROW][C]249[/C][C]0.344155304256332[/C][C]0.688310608512663[/C][C]0.655844695743668[/C][/ROW]
[ROW][C]250[/C][C]0.2475747217756[/C][C]0.495149443551199[/C][C]0.7524252782244[/C][/ROW]
[ROW][C]251[/C][C]0.278677539630077[/C][C]0.557355079260155[/C][C]0.721322460369923[/C][/ROW]
[ROW][C]252[/C][C]0.909277338940006[/C][C]0.181445322119988[/C][C]0.0907226610599941[/C][/ROW]
[ROW][C]253[/C][C]0.843076445680885[/C][C]0.313847108638229[/C][C]0.156923554319115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225701&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225701&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4463279045029760.8926558090059520.553672095497024
120.9522335594823190.09553288103536250.0477664405176813
130.9458207250446130.1083585499107750.0541792749553874
140.9597723579432340.08045528411353240.0402276420567662
150.9360392148426660.1279215703146680.0639607851573339
160.9012585924158040.1974828151683920.0987414075841959
170.9501853656235760.09962926875284790.049814634376424
180.932218847449530.1355623051009410.0677811525504703
190.957108219318430.08578356136313990.0428917806815699
200.9376045387289390.1247909225421220.0623954612710609
210.9265416443843070.1469167112313850.0734583556156926
220.9218152935290540.1563694129418910.0781847064709457
230.8944365219774740.2111269560450520.105563478022526
240.8750700872314670.2498598255370670.124929912768533
250.8366461930843140.3267076138313720.163353806915686
260.8377232857289070.3245534285421850.162276714271093
270.8488010577639040.3023978844721910.151198942236096
280.8089633526593130.3820732946813730.191036647340687
290.8402573784053480.3194852431893030.159742621594652
300.8307678352445920.3384643295108160.169232164755408
310.8365683433842030.3268633132315940.163431656615797
320.8052945886912250.389410822617550.194705411308775
330.7742716786984410.4514566426031180.225728321301559
340.7497873161015220.5004253677969560.250212683898478
350.7163104782806460.5673790434387070.283689521719354
360.7506892793099310.4986214413801380.249310720690069
370.8321416639029020.3357166721941970.167858336097098
380.7970177607635650.4059644784728690.202982239236435
390.7632041505531370.4735916988937270.236795849446863
400.7628261784492090.4743476431015820.237173821550791
410.7510926770852210.4978146458295590.248907322914779
420.737533734437480.524932531125040.26246626556252
430.7902789260450660.4194421479098690.209721073954934
440.7550514410991220.4898971178017550.244948558900878
450.7187057693861050.5625884612277890.281294230613895
460.6928635648430740.6142728703138520.307136435156926
470.7487027706028620.5025944587942770.251297229397138
480.7148595691689840.5702808616620310.285140430831016
490.7683180043557950.463363991288410.231681995644205
500.7426765207757820.5146469584484360.257323479224218
510.7306888053118480.5386223893763030.269311194688152
520.6963511996007530.6072976007984940.303648800399247
530.6985556677843220.6028886644313560.301444332215678
540.6661088505996950.6677822988006090.333891149400304
550.6781576288469320.6436847423061350.321842371153068
560.6939825138123940.6120349723752130.306017486187606
570.6716192299567740.6567615400864510.328380770043226
580.708330875637240.5833382487255190.29166912436276
590.7233295430206740.5533409139586520.276670456979326
600.7001469331438070.5997061337123870.299853066856193
610.7122493527147740.5755012945704510.287750647285226
620.6757692867682580.6484614264634840.324230713231742
630.6520898764601430.6958202470797140.347910123539857
640.6120835992326910.7758328015346180.387916400767309
650.5711704408992690.8576591182014630.428829559100731
660.5813684543883590.8372630912232810.418631545611641
670.5841118642724090.8317762714551810.415888135727591
680.5542636364549280.8914727270901450.445736363545072
690.5135644480605660.9728711038788680.486435551939434
700.4895370834587790.9790741669175590.510462916541221
710.4507582157643490.9015164315286980.549241784235651
720.4269952860384330.8539905720768650.573004713961567
730.4002773084587880.8005546169175770.599722691541212
740.3793882995555760.7587765991111510.620611700444424
750.3495254783122040.6990509566244070.650474521687796
760.5113807088215060.9772385823569870.488619291178494
770.4901661626436210.9803323252872410.509833837356379
780.5064315491175940.9871369017648130.493568450882406
790.4681799037321280.9363598074642570.531820096267872
800.5661954074955740.8676091850088530.433804592504427
810.529343775063860.941312449872280.47065622493614
820.5534282633448950.893143473310210.446571736655105
830.5170414609891820.9659170780216360.482958539010818
840.479057917505190.9581158350103810.52094208249481
850.4411858671171250.882371734234250.558814132882875
860.4076287078188250.815257415637650.592371292181175
870.3757107706130030.7514215412260060.624289229386997
880.3426718404698680.6853436809397370.657328159530132
890.4330312208022080.8660624416044170.566968779197792
900.4035111459534770.8070222919069530.596488854046523
910.3749560808269720.7499121616539430.625043919173028
920.3446894256757470.6893788513514950.655310574324253
930.3444014501020.6888029002040.655598549898
940.3131897596974140.6263795193948280.686810240302586
950.2804810772852730.5609621545705450.719518922714727
960.2645877308376670.5291754616753340.735412269162333
970.2419151873073970.4838303746147950.758084812692603
980.2154561424731430.4309122849462850.784543857526857
990.2074787967542140.4149575935084270.792521203245786
1000.1823278790564770.3646557581129540.817672120943523
1010.1718499077582390.3436998155164790.828150092241761
1020.1537133562165160.3074267124330320.846286643783484
1030.1940613467901810.3881226935803630.805938653209819
1040.2249135884204770.4498271768409540.775086411579523
1050.2337294192911490.4674588385822990.766270580708851
1060.2067241767188530.4134483534377060.793275823281147
1070.2318868631907570.4637737263815150.768113136809243
1080.2154442016107830.4308884032215650.784555798389217
1090.3440330705239440.6880661410478870.655966929476056
1100.3353864076125240.6707728152250480.664613592387476
1110.3046271747159040.6092543494318080.695372825284096
1120.2935575170452570.5871150340905130.706442482954743
1130.2693810554387170.5387621108774350.730618944561283
1140.2505102698888750.501020539777750.749489730111125
1150.2235721227080250.4471442454160490.776427877291975
1160.1975902596189830.3951805192379660.802409740381017
1170.1767884547438590.3535769094877180.823211545256141
1180.2154226031821260.4308452063642520.784577396817874
1190.2213570659079660.4427141318159330.778642934092034
1200.197430252913640.394860505827280.80256974708636
1210.1823231958038390.3646463916076780.817676804196161
1220.1614518606780190.3229037213560390.838548139321981
1230.190643306125460.3812866122509210.80935669387454
1240.171640477511590.3432809550231790.82835952248841
1250.1619494256583670.3238988513167340.838050574341633
1260.1507609962400760.3015219924801510.849239003759924
1270.1325745175190820.2651490350381640.867425482480918
1280.1182261000678330.2364522001356660.881773899932167
1290.1018591543258390.2037183086516790.898140845674161
1300.1008898027982140.2017796055964290.899110197201786
1310.1025617069879710.2051234139759420.897438293012029
1320.09454795021399910.1890959004279980.905452049786001
1330.08634471828627720.1726894365725540.913655281713723
1340.07337994000482680.1467598800096540.926620059995173
1350.07485014738018850.1497002947603770.925149852619812
1360.1492236745265760.2984473490531520.850776325473424
1370.1300289751445160.2600579502890320.869971024855484
1380.1152080566027990.2304161132055980.884791943397201
1390.1103981390584440.2207962781168890.889601860941556
1400.1051487826693080.2102975653386150.894851217330693
1410.117775411949440.2355508238988810.88222458805056
1420.1185327024955450.237065404991090.881467297504455
1430.1019432809901290.2038865619802580.898056719009871
1440.08679032564508770.1735806512901750.913209674354912
1450.07390058715235920.1478011743047180.926099412847641
1460.0626117513178320.1252235026356640.937388248682168
1470.06863007274242270.1372601454848450.931369927257577
1480.0747856749949540.1495713499899080.925214325005046
1490.07027419971161090.1405483994232220.929725800288389
1500.0597081758730350.119416351746070.940291824126965
1510.07280106342161190.1456021268432240.927198936578388
1520.06989169674600760.1397833934920150.930108303253992
1530.0685425223792520.1370850447585040.931457477620748
1540.06373037303029090.1274607460605820.936269626969709
1550.07091898407611320.1418379681522260.929081015923887
1560.06046151681820130.1209230336364030.939538483181799
1570.05050120659676390.1010024131935280.949498793403236
1580.04306938929076890.08613877858153770.956930610709231
1590.0523639136862030.1047278273724060.947636086313797
1600.06526640303742370.1305328060748470.934733596962576
1610.05582610266015020.11165220532030.94417389733985
1620.1230237884097330.2460475768194660.876976211590267
1630.1124770216894380.2249540433788760.887522978310562
1640.3959741345179050.791948269035810.604025865482095
1650.3788859547381580.7577719094763160.621114045261842
1660.4870899917554890.9741799835109780.512910008244511
1670.4550028797796770.9100057595593530.544997120220324
1680.4232520557689990.8465041115379990.576747944231001
1690.3870156930575490.7740313861150980.612984306942451
1700.3651125562784440.7302251125568870.634887443721556
1710.3749106550169230.7498213100338460.625089344983077
1720.3401638197535030.6803276395070050.659836180246497
1730.3835019050238350.7670038100476710.616498094976165
1740.3707500871152640.7415001742305280.629249912884736
1750.3485076478747470.6970152957494950.651492352125253
1760.3850932120296310.7701864240592610.614906787970369
1770.3591536525917180.7183073051834360.640846347408282
1780.3716937705375350.743387541075070.628306229462465
1790.3568329159571070.7136658319142140.643167084042893
1800.3245210691516390.6490421383032770.675478930848361
1810.3519327704173870.7038655408347750.648067229582613
1820.4570749952548260.9141499905096510.542925004745174
1830.4421090672556130.8842181345112260.557890932744387
1840.4306230020464740.8612460040929480.569376997953526
1850.4140617383990550.8281234767981090.585938261600945
1860.5412294913835780.9175410172328450.458770508616422
1870.5092431138806530.9815137722386950.490756886119347
1880.4765167141802440.9530334283604880.523483285819756
1890.4402804590057520.8805609180115050.559719540994248
1900.479035042445310.958070084890620.52096495755469
1910.5084526219468440.9830947561063130.491547378053156
1920.5254128699941480.9491742600117040.474587130005852
1930.5161614425373150.967677114925370.483838557462685
1940.4853519660992740.9707039321985480.514648033900726
1950.4575474386124280.9150948772248560.542452561387572
1960.4928153287641080.9856306575282170.507184671235892
1970.6973941421641390.6052117156717210.302605857835861
1980.7055090881210030.5889818237579940.294490911878997
1990.6688720435673940.6622559128652130.331127956432606
2000.6293520206935750.741295958612850.370647979306425
2010.5890264798364220.8219470403271560.410973520163578
2020.5482287393486310.9035425213027380.451771260651369
2030.5235464485417310.9529071029165380.476453551458269
2040.5193202831772510.9613594336454980.480679716822749
2050.4820681032720730.9641362065441460.517931896727927
2060.4394459041264320.8788918082528640.560554095873568
2070.3982527243632660.7965054487265330.601747275636734
2080.3648431767218830.7296863534437670.635156823278117
2090.3237661163656560.6475322327313120.676233883634344
2100.3164326090042410.6328652180084820.683567390995759
2110.2909111370468070.5818222740936140.709088862953193
2120.2565793185206930.5131586370413860.743420681479307
2130.2837074456867590.5674148913735190.716292554313241
2140.2659421842460070.5318843684920140.734057815753993
2150.2329721559085530.4659443118171060.767027844091447
2160.2002631541081940.4005263082163890.799736845891806
2170.1901394597651880.3802789195303760.809860540234812
2180.1604920094659510.3209840189319020.839507990534049
2190.1634965271407640.3269930542815270.836503472859236
2200.155155275284890.3103105505697790.84484472471511
2210.1541199822585190.3082399645170370.845880017741481
2220.1275378421660030.2550756843320060.872462157833997
2230.1049216758889310.2098433517778620.895078324111069
2240.1058394081198390.2116788162396770.894160591880161
2250.09576817764201280.1915363552840260.904231822357987
2260.07798028019421730.1559605603884350.922019719805783
2270.0607195064556360.1214390129112720.939280493544364
2280.05700005452223940.1140001090444790.942999945477761
2290.0771587296965820.1543174593931640.922841270303418
2300.07343508624837280.1468701724967460.926564913751627
2310.05652088001458270.1130417600291650.943479119985417
2320.05802216100957010.116044322019140.94197783899043
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2340.1285174940888360.2570349881776720.871482505911164
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2410.2231843482776760.4463686965553520.776815651722324
2420.271165268434970.5423305368699390.72883473156503
2430.2114672018764460.4229344037528930.788532798123553
2440.1585675716058880.3171351432117760.841432428394112
2450.235039722950060.470079445900120.76496027704994
2460.2565838079439840.5131676158879680.743416192056016
2470.1896872213523810.3793744427047630.810312778647619
2480.2345499276889950.4690998553779910.765450072311005
2490.3441553042563320.6883106085126630.655844695743668
2500.24757472177560.4951494435511990.7524252782244
2510.2786775396300770.5573550792601550.721322460369923
2520.9092773389400060.1814453221199880.0907226610599941
2530.8430764456808850.3138471086382290.156923554319115






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 level50.0205761316872428OK

\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 & 5 & 0.0205761316872428 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225701&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]5[/C][C]0.0205761316872428[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225701&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225701&T=6

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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 level50.0205761316872428OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}