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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 03 Nov 2012 09:28:58 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/03/t1351950136kxor1dqh9nxo5md.htm/, Retrieved Tue, 09 Aug 2022 19:32:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185721, Retrieved Tue, 09 Aug 2022 19:32:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-         [Multiple Regression] [] [2012-11-03 13:28:58] [54fc54845b7758f1604e617c5518fca2] [Current]
-   P       [Multiple Regression] [Paper_4.3_Model4] [2012-12-21 19:22:31] [be6dd99035eed41c2358246baf91f928]
-   P       [Multiple Regression] [Paper_4.3_Model4] [2012-12-21 19:22:31] [be6dd99035eed41c2358246baf91f928]
-   P       [Multiple Regression] [Paper_4.3_Model42] [2012-12-21 19:22:31] [be6dd99035eed41c2358246baf91f928]
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Dataseries X:
41	38	13	12	14	12	53
39	32	16	11	18	11	83
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	9	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
32	33	16	11	18	9.5	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	12	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	31	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	10	7	16	8	71
29	31	10	9	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	15	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	35	14	6	15	14.5	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	80
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	12.5	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	85
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	67
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
36	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	85
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	11	9	13	12	69
32	34	16	8	15	10	54
30	37	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
38	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	38	12	8	12	18	84
34	36	13	8	13	16	69
27	26	13	10	10	24	66
31	26	12	8	14	14	81
38	33	17	14	16	20	82
34	39	15	10	10	18	72
24	30	10	8	11	23	54
30	33	14	11	14	12	78
26	25	11	12	12	14	74
34	38	13	12	9	16	82
27	37	16	12	9	18	73
37	31	12	5	11	20	55
36	37	16	12	16	12	72
41	35	12	10	9	12	78
29	25	9	7	13	17	59
36	28	12	12	16	13	72
32	35	15	11	13	9	78
37	33	12	8	9	16	68
30	30	12	9	12	18	69
31	31	14	10	16	10	67
38	37	12	9	11	14	74
36	36	16	12	14	11	54
35	30	11	6	13	9	67
31	36	19	15	15	11	70
38	32	15	12	14	10	80
22	28	8	12	16	11	89
32	36	16	12	13	19	76
36	34	17	11	14	14	74
39	31	12	7	15	12	87
28	28	11	7	13	14	54
32	36	11	5	11	21	61
32	36	14	12	11	13	38
38	40	16	12	14	10	75
32	33	12	3	15	15	69
35	37	16	11	11	16	62
32	32	13	10	15	14	72
37	38	15	12	12	12	70
34	31	16	9	14	19	79
33	37	16	12	14	15	87
33	33	14	9	8	19	62
26	32	16	12	13	13	77
30	30	16	12	9	17	69
24	30	14	10	15	12	69
34	31	11	9	17	11	75
34	32	12	12	13	14	54
33	34	15	8	15	11	72
34	36	15	11	15	13	74
35	37	16	11	14	12	85
35	36	16	12	16	15	52
36	33	11	10	13	14	70
34	33	15	10	16	12	84
34	33	12	12	9	17	64
41	44	12	12	16	11	84
32	39	15	11	11	18	87
30	32	15	8	10	13	79
35	35	16	12	11	17	67
28	25	14	10	15	13	65
33	35	17	11	17	11	85
39	34	14	10	14	12	83
36	35	13	8	8	22	61
36	39	15	12	15	14	82
35	33	13	12	11	12	76
38	36	14	10	16	12	58
33	32	15	12	10	17	72
31	32	12	9	15	9	72
34	36	13	9	9	21	38
32	36	8	6	16	10	78
31	32	14	10	19	11	54
33	34	14	9	12	12	63
34	33	11	9	8	23	66
34	35	12	9	11	13	70
34	30	13	6	14	12	71
33	38	10	10	9	16	67
32	34	16	6	15	9	58
41	33	18	14	13	17	72
34	32	13	10	16	9	72
36	31	11	10	11	14	70
37	30	4	6	12	17	76
36	27	13	12	13	13	50
29	31	16	12	10	11	72
37	30	10	7	11	12	72
27	32	12	8	12	10	88
35	35	12	11	8	19	53
28	28	10	3	12	16	58
35	33	13	6	12	16	66
37	31	15	10	15	14	82
29	35	12	8	11	20	69
32	35	14	9	13	15	68
36	32	10	9	14	23	44
19	21	12	8	10	20	56
21	20	12	9	12	16	53
31	34	11	7	15	14	70
33	32	10	7	13	17	78
36	34	12	6	13	11	71
33	32	16	9	13	13	72
37	33	12	10	12	17	68
34	33	14	11	12	15	67
35	37	16	12	9	21	75
31	32	14	8	9	18	62
37	34	13	11	15	15	67
35	30	4	3	10	8	83
27	30	15	11	14	12	64
34	38	11	12	15	12	68
40	36	11	7	7	22	62
29	32	14	9	14	12	72




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 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 & 12 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185721&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]12 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=185721&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185721&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.45153304933178 + 0.0321668940555693Connected[t] + 0.0427653888947305Separate[t] + 0.558489671132888Software[t] + 0.0702334998707421Happiness[t] -0.031126424436301Depression[t] + 0.00747850801958789Belonging[t] -0.00491058171792578t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.45153304933178 +  0.0321668940555693Connected[t] +  0.0427653888947305Separate[t] +  0.558489671132888Software[t] +  0.0702334998707421Happiness[t] -0.031126424436301Depression[t] +  0.00747850801958789Belonging[t] -0.00491058171792578t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185721&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.45153304933178 +  0.0321668940555693Connected[t] +  0.0427653888947305Separate[t] +  0.558489671132888Software[t] +  0.0702334998707421Happiness[t] -0.031126424436301Depression[t] +  0.00747850801958789Belonging[t] -0.00491058171792578t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185721&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185721&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.45153304933178 + 0.0321668940555693Connected[t] + 0.0427653888947305Separate[t] + 0.558489671132888Software[t] + 0.0702334998707421Happiness[t] -0.031126424436301Depression[t] + 0.00747850801958789Belonging[t] -0.00491058171792578t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.451533049331781.9427942.8060.0054010.0027
Connected0.03216689405556930.0344240.93440.3509670.175483
Separate0.04276538889473050.0350761.21920.2238860.111943
Software0.5584896711328880.05372610.395200
Happiness0.07023349987074210.0578091.21490.2255180.112759
Depression-0.0311264244363010.041759-0.74540.4567250.228362
Belonging0.007478508019587890.0118690.63010.5291910.264595
t-0.004910581717925780.00168-2.92270.003780.00189

\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) & 5.45153304933178 & 1.942794 & 2.806 & 0.005401 & 0.0027 \tabularnewline
Connected & 0.0321668940555693 & 0.034424 & 0.9344 & 0.350967 & 0.175483 \tabularnewline
Separate & 0.0427653888947305 & 0.035076 & 1.2192 & 0.223886 & 0.111943 \tabularnewline
Software & 0.558489671132888 & 0.053726 & 10.3952 & 0 & 0 \tabularnewline
Happiness & 0.0702334998707421 & 0.057809 & 1.2149 & 0.225518 & 0.112759 \tabularnewline
Depression & -0.031126424436301 & 0.041759 & -0.7454 & 0.456725 & 0.228362 \tabularnewline
Belonging & 0.00747850801958789 & 0.011869 & 0.6301 & 0.529191 & 0.264595 \tabularnewline
t & -0.00491058171792578 & 0.00168 & -2.9227 & 0.00378 & 0.00189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185721&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]5.45153304933178[/C][C]1.942794[/C][C]2.806[/C][C]0.005401[/C][C]0.0027[/C][/ROW]
[ROW][C]Connected[/C][C]0.0321668940555693[/C][C]0.034424[/C][C]0.9344[/C][C]0.350967[/C][C]0.175483[/C][/ROW]
[ROW][C]Separate[/C][C]0.0427653888947305[/C][C]0.035076[/C][C]1.2192[/C][C]0.223886[/C][C]0.111943[/C][/ROW]
[ROW][C]Software[/C][C]0.558489671132888[/C][C]0.053726[/C][C]10.3952[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0702334998707421[/C][C]0.057809[/C][C]1.2149[/C][C]0.225518[/C][C]0.112759[/C][/ROW]
[ROW][C]Depression[/C][C]-0.031126424436301[/C][C]0.041759[/C][C]-0.7454[/C][C]0.456725[/C][C]0.228362[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00747850801958789[/C][C]0.011869[/C][C]0.6301[/C][C]0.529191[/C][C]0.264595[/C][/ROW]
[ROW][C]t[/C][C]-0.00491058171792578[/C][C]0.00168[/C][C]-2.9227[/C][C]0.00378[/C][C]0.00189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185721&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185721&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.451533049331781.9427942.8060.0054010.0027
Connected0.03216689405556930.0344240.93440.3509670.175483
Separate0.04276538889473050.0350761.21920.2238860.111943
Software0.5584896711328880.05372610.395200
Happiness0.07023349987074210.0578091.21490.2255180.112759
Depression-0.0311264244363010.041759-0.74540.4567250.228362
Belonging0.007478508019587890.0118690.63010.5291910.264595
t-0.004910581717925780.00168-2.92270.003780.00189







Multiple Linear Regression - Regression Statistics
Multiple R0.667212929789437
R-squared0.445173093678205
Adjusted R-squared0.430002045458468
F-TEST (value)29.3435949336093
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85421524491435
Sum Squared Residuals880.15722866503

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.667212929789437 \tabularnewline
R-squared & 0.445173093678205 \tabularnewline
Adjusted R-squared & 0.430002045458468 \tabularnewline
F-TEST (value) & 29.3435949336093 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85421524491435 \tabularnewline
Sum Squared Residuals & 880.15722866503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185721&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.667212929789437[/C][/ROW]
[ROW][C]R-squared[/C][C]0.445173093678205[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.430002045458468[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.3435949336093[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]256[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85421524491435[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]880.15722866503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185721&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185721&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.667212929789437
R-squared0.445173093678205
Adjusted R-squared0.430002045458468
F-TEST (value)29.3435949336093
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85421524491435
Sum Squared Residuals880.15722866503







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0985387854795-3.09853878547954
21615.75062807565610.249371924343879
31917.10632188991671.89367811008327
41512.16160524103182.83839475896815
51416.4017873467223-2.40178734672232
61314.8470133702108-1.84701337021082
71915.39411954043473.60588045956526
81517.1034911605066-2.10349116050658
91416.0596585119288-2.0596585119288
101514.46131972028220.53868027971776
111615.00398977859880.996010221401191
121616.1957780658441-0.195778065844102
131615.54520079549590.454799204504098
141615.4975704227910.50242957720904
151718.0026152345095-1.00261523450953
161515.4966907599622-0.496690759962226
171514.58783765963910.412162340360936
182016.42213291884223.57786708115779
191815.53184129240092.46815870759907
201615.5983979603540.40160203964601
211615.39447915384270.605520846157265
221615.21361417839330.786385821606707
231916.49214037872652.50785962127348
241615.16091922295150.839080777048499
251716.14837639146470.851623608535261
261716.221803070780.778196929220023
271614.9083527902141.09164720978603
281516.8091087350266-1.80910873502655
291615.68482375194730.31517624805274
301414.263029558757-0.263029558756992
311515.7659606607022-0.765960660702232
321212.8492889523859-0.849288952385888
331414.8039620225922-0.803962022592169
341616.0017496144186-0.00174961441855281
351415.4902098814983-1.49020988149829
361013.0304769205532-3.0304769205532
371013.0355345646574-3.03553456465737
381415.7470324913881-1.74703249138812
391614.5545559199381.445444080062
401614.49489392472291.50510607527713
411614.72053770649711.27946229350293
421415.7009510594818-1.70095105948177
432017.48712155669652.51287844330348
441414.1533777639022-0.153377763902224
451414.4623553291315-0.462355329131477
461115.4909495615117-4.49094956151171
471416.6307612737306-2.63076127373058
481515.1467421663187-0.146742166318652
491615.4178766163230.582123383676952
501415.6302473667626-1.63024736676259
511617.0252984963352-1.02529849633517
521414.1419408813432-0.14194088134322
531215.0171855791395-3.01718557913948
541616.0361860907356-0.0361860907355932
55911.3432897998035-2.34328979980353
561412.38497814452031.61502185547966
571615.85454010930220.145459890697824
581615.52988322876590.470116771234096
591515.1123930302181-0.112393030218105
601614.22106056522641.77893943477358
611211.55955340846610.440446591533867
621615.64068175553980.359318244460184
631616.5488168530774-0.548816853077399
641414.7109860019853-0.710986001985265
651615.30632115833420.693678841665768
661716.05418603320130.945813966798677
671816.33317707986111.6668229201389
681814.39588818744263.60411181255739
691215.8997083739692-3.89970837396924
701615.65157867302610.348421326973927
711013.412380608575-3.41238060857495
721414.9242951062704-0.924295106270409
731816.92805867454331.07194132545668
741817.1409836239110.85901637608902
751615.23229487529480.767705124705194
761713.49188698970083.50811301029925
771616.419099992442-0.419099992441992
781614.53119884767541.46880115232456
791315.2165773344004-2.21657733440038
801615.14214025715680.857859742843149
811615.65862745062780.341372549372198
821615.76324083063160.236759169368356
831515.6434137240469-0.64341372404695
841514.88423238192270.115767618077311
851614.16073547223411.83926452776585
861414.1555692846506-0.155569284650592
871615.36748817802790.632511821972136
881614.87258884882471.12741115117526
891514.51948139025870.480518609741335
901213.907972516408-1.90797251640802
911716.75849437545860.241505624541391
921615.80556191940360.194438080596368
931515.0531067912421-0.0531067912420967
941315.0298518503278-2.02985185032776
951614.74361333653481.25638666346519
961615.78824288132970.21175711867026
971613.67738396548952.32261603451052
981615.77370837563650.22629162436346
991414.4297816437613-0.429781643761329
1001616.9922981106499-0.992298110649857
1011614.67662466255271.32337533744731
1022017.45421844256342.54578155743657
1031514.23966037274950.760339627250478
1041614.95985844448531.04014155551469
1051314.8422042092773-1.84220420927732
1061715.70172996487481.29827003512525
1071615.70112345875230.298876541247731
1081614.35865146879431.64134853120572
1091212.3043422991135-0.30434229911349
1101615.28568089566040.71431910433959
1111615.99282714923440.00717285076563753
1121715.05640936909211.9435906309079
1131314.2811780773625-1.28117807736248
1141214.5798742612205-2.57987426122045
1151816.16147284443871.83852715556127
1161415.8657328435429-1.86573284354287
1171413.2288878289070.771112171092987
1181314.7929151358429-1.79291513584291
1191615.5203842062240.479615793776011
1201314.4292215835434-1.42922158354341
1211615.38542326764150.614576732358463
1221315.8212093173508-2.82120931735076
1231616.8545044640026-0.854504464002574
1241515.8437647974654-0.84376479746541
1251616.7849339139009-0.784933913900918
1261514.65872160107570.341278398924287
1271715.49058390584561.50941609415437
1281514.04825211200880.951747887991231
1291214.6858650734517-2.68586507345171
1301613.94027147954572.05972852045429
1311013.7239532443361-3.72395324433605
1321613.45833215416292.54166784583713
1331214.1659890224415-2.16598902244154
1341415.5440460090552-1.54404600905523
1351515.0836566962205-0.0836566962204929
1361312.11211273473090.887887265269134
1371514.53127780695890.468722193041143
1381113.4989500572246-2.49895005722465
1391213.0064804164844-1.00648041648441
1401113.339956434492-2.33995643449198
1411612.85649991512233.14350008487773
1421513.64412442582351.35587557417654
1431716.84136395828370.158636041716298
1441614.12440384704751.87559615295252
1451013.266751430131-3.26675143013095
1461815.4975454673632.50245453263695
1471314.9457512386837-1.94575123868367
1481614.8430101053071.15698989469303
1491312.827227670110.172772329889988
1501012.9004577169405-2.90045771694049
1511515.8784182905414-0.878418290541449
1521613.82486309168812.17513690831192
1531611.85258101474954.1474189852505
1541412.39630251826531.60369748173469
1551012.4816282956809-2.48162829568091
1561716.43930656379340.560693436206567
1571311.70668585473171.29331414526825
1581513.9009346604711.099065339529
1591614.53739613320241.46260386679759
1601212.6989037635456-0.698903763545609
1611312.69310492059890.306895079401113
1621312.6702041146490.329795885350951
1631212.3813576310274-0.381357631027421
1641716.21309801790220.78690198209781
1651513.66822027825111.33177972174891
1661011.6197611469958-1.61976114699577
1671414.3441924705758-0.344192470575766
1681114.1943469919182-3.19434699191817
1691314.7895963339482-1.78959633394817
1701614.38719268389761.61280731610237
1711210.48153201795311.5184679820469
1721615.33778810465530.66221189534468
1731213.8444384221823-1.84443842218234
174911.3336124343809-2.3336124343809
1751214.9070414350127-2.90704143501267
1761514.47300757445320.526992425546806
1771212.294327621092-0.294327621092034
1781212.650368444193-0.650368444193032
1791413.79386819549250.206131804507504
1801213.2889048934373-1.28890489343725
1811615.00687376064150.993126239358508
1821111.4515058779588-0.451505877958786
1831916.70157676851062.29842323148939
1841515.1109818809655-0.110981880965518
185814.596986586261-6.59698658626104
1861614.69893555689941.30106444310063
1871714.38958070849442.61041929150556
1881212.3486229107255-0.348622910725466
1891111.4120697144516-0.4120697144516
1901110.45496806318950.545031936810458
1911414.4364908904417-0.436490890441723
1921615.3764277982820.623572201717984
193129.722481419343272.27751858065673
1941614.08864046437771.9113595356223
1951313.632884513438-0.632884513437976
1961514.99897541085330.00102458914674308
1971612.91262601217053.08737398782949
1981614.9919436450661.00805635493403
1991412.40763309691111.59236690308889
2001614.46036154757351.53963845242654
2011614.03332000290351.96667999709652
2021413.29546183599230.704538164007681
2031113.3129603848872-2.31296038488724
2041214.4949222642595-2.49492226425949
2051512.67787629914692.32212370085313
2061514.41883656983920.581163430160781
2071614.53201478385261.46798521614738
2081614.8431254461591.15687455384097
2091113.5801453187234-2.58014531872336
2101513.88855340965341.11144659034664
2111214.2037853885328-2.20378538853275
2121215.7224255491506-3.7224255491506
2131514.1090793589770.89092064102304
2141512.09057881164022.90942118835982
2151614.46474325730631.53525674269367
2161413.08051386717530.919486132824671
2171714.57487132482132.42512867517871
2181413.90492310732150.0950768926785438
2191311.83210547004741.16789452995262
2201515.1299096914369-0.129909691436878
2211314.5726876835671-1.57268768356711
2221413.89214896343540.107851036564568
2231514.19998768899480.800012311005213
2241213.0554532006112-1.05545320061118
2251312.26891749151280.731082508487173
226811.6573695969631-3.65736959696312
2271413.68327913284810.316720867151928
2281412.81428909454271.18571090545735
2291112.197890873762-1.19789087376205
2301212.8303898458872-0.830389845887172
2311311.1854887383651.81451126163496
2321013.2189058291037-3.21890582910368
2331611.3487875113224.65121248867802
2341815.77375167331492.22624832668511
2351313.7266606548843-0.726660654884331
2361113.2215618348084-2.22156183480842
237410.9938193483991-6.99381934839915
2381314.1796817218455-1.17968172184545
2391614.13674396300881.86325603699123
2401011.5930618646107-1.59306186461067
2411212.1626452683162-0.162645268316151
2421213.3960154190304-1.39601541903038
243108.810367300487061.18963269951294
2441310.97974899918712.02025100081286
2451513.58020958912071.41979041087932
2461211.80713291791590.192867082084071
2471412.7458333034011.254166696599
2481012.3830320431314-2.38303204313139
2491210.70456268355481.29543731644515
2501211.52224734561410.477752654385857
2511111.7208297915302-0.720829791530183
2521011.5207040112403-1.52070401124025
2531211.27374420882630.726255791173704
2541612.70749683969793.29250316030215
2551213.2078556645355-1.20785566453552
2561413.71970841263680.280291587363206
2571614.13888496961291.86111503038708
2581411.55367985172182.44632014827823
2591314.0549432381567-1.05494323815668
26049.33309354369939-5.33309354369939
2611513.55310182796561.44689817203439
2621114.7741198188765-3.7741198188765
2631111.1662281765916-0.16622817659161
2641412.63108337060341.36891662939664

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.0985387854795 & -3.09853878547954 \tabularnewline
2 & 16 & 15.7506280756561 & 0.249371924343879 \tabularnewline
3 & 19 & 17.1063218899167 & 1.89367811008327 \tabularnewline
4 & 15 & 12.1616052410318 & 2.83839475896815 \tabularnewline
5 & 14 & 16.4017873467223 & -2.40178734672232 \tabularnewline
6 & 13 & 14.8470133702108 & -1.84701337021082 \tabularnewline
7 & 19 & 15.3941195404347 & 3.60588045956526 \tabularnewline
8 & 15 & 17.1034911605066 & -2.10349116050658 \tabularnewline
9 & 14 & 16.0596585119288 & -2.0596585119288 \tabularnewline
10 & 15 & 14.4613197202822 & 0.53868027971776 \tabularnewline
11 & 16 & 15.0039897785988 & 0.996010221401191 \tabularnewline
12 & 16 & 16.1957780658441 & -0.195778065844102 \tabularnewline
13 & 16 & 15.5452007954959 & 0.454799204504098 \tabularnewline
14 & 16 & 15.497570422791 & 0.50242957720904 \tabularnewline
15 & 17 & 18.0026152345095 & -1.00261523450953 \tabularnewline
16 & 15 & 15.4966907599622 & -0.496690759962226 \tabularnewline
17 & 15 & 14.5878376596391 & 0.412162340360936 \tabularnewline
18 & 20 & 16.4221329188422 & 3.57786708115779 \tabularnewline
19 & 18 & 15.5318412924009 & 2.46815870759907 \tabularnewline
20 & 16 & 15.598397960354 & 0.40160203964601 \tabularnewline
21 & 16 & 15.3944791538427 & 0.605520846157265 \tabularnewline
22 & 16 & 15.2136141783933 & 0.786385821606707 \tabularnewline
23 & 19 & 16.4921403787265 & 2.50785962127348 \tabularnewline
24 & 16 & 15.1609192229515 & 0.839080777048499 \tabularnewline
25 & 17 & 16.1483763914647 & 0.851623608535261 \tabularnewline
26 & 17 & 16.22180307078 & 0.778196929220023 \tabularnewline
27 & 16 & 14.908352790214 & 1.09164720978603 \tabularnewline
28 & 15 & 16.8091087350266 & -1.80910873502655 \tabularnewline
29 & 16 & 15.6848237519473 & 0.31517624805274 \tabularnewline
30 & 14 & 14.263029558757 & -0.263029558756992 \tabularnewline
31 & 15 & 15.7659606607022 & -0.765960660702232 \tabularnewline
32 & 12 & 12.8492889523859 & -0.849288952385888 \tabularnewline
33 & 14 & 14.8039620225922 & -0.803962022592169 \tabularnewline
34 & 16 & 16.0017496144186 & -0.00174961441855281 \tabularnewline
35 & 14 & 15.4902098814983 & -1.49020988149829 \tabularnewline
36 & 10 & 13.0304769205532 & -3.0304769205532 \tabularnewline
37 & 10 & 13.0355345646574 & -3.03553456465737 \tabularnewline
38 & 14 & 15.7470324913881 & -1.74703249138812 \tabularnewline
39 & 16 & 14.554555919938 & 1.445444080062 \tabularnewline
40 & 16 & 14.4948939247229 & 1.50510607527713 \tabularnewline
41 & 16 & 14.7205377064971 & 1.27946229350293 \tabularnewline
42 & 14 & 15.7009510594818 & -1.70095105948177 \tabularnewline
43 & 20 & 17.4871215566965 & 2.51287844330348 \tabularnewline
44 & 14 & 14.1533777639022 & -0.153377763902224 \tabularnewline
45 & 14 & 14.4623553291315 & -0.462355329131477 \tabularnewline
46 & 11 & 15.4909495615117 & -4.49094956151171 \tabularnewline
47 & 14 & 16.6307612737306 & -2.63076127373058 \tabularnewline
48 & 15 & 15.1467421663187 & -0.146742166318652 \tabularnewline
49 & 16 & 15.417876616323 & 0.582123383676952 \tabularnewline
50 & 14 & 15.6302473667626 & -1.63024736676259 \tabularnewline
51 & 16 & 17.0252984963352 & -1.02529849633517 \tabularnewline
52 & 14 & 14.1419408813432 & -0.14194088134322 \tabularnewline
53 & 12 & 15.0171855791395 & -3.01718557913948 \tabularnewline
54 & 16 & 16.0361860907356 & -0.0361860907355932 \tabularnewline
55 & 9 & 11.3432897998035 & -2.34328979980353 \tabularnewline
56 & 14 & 12.3849781445203 & 1.61502185547966 \tabularnewline
57 & 16 & 15.8545401093022 & 0.145459890697824 \tabularnewline
58 & 16 & 15.5298832287659 & 0.470116771234096 \tabularnewline
59 & 15 & 15.1123930302181 & -0.112393030218105 \tabularnewline
60 & 16 & 14.2210605652264 & 1.77893943477358 \tabularnewline
61 & 12 & 11.5595534084661 & 0.440446591533867 \tabularnewline
62 & 16 & 15.6406817555398 & 0.359318244460184 \tabularnewline
63 & 16 & 16.5488168530774 & -0.548816853077399 \tabularnewline
64 & 14 & 14.7109860019853 & -0.710986001985265 \tabularnewline
65 & 16 & 15.3063211583342 & 0.693678841665768 \tabularnewline
66 & 17 & 16.0541860332013 & 0.945813966798677 \tabularnewline
67 & 18 & 16.3331770798611 & 1.6668229201389 \tabularnewline
68 & 18 & 14.3958881874426 & 3.60411181255739 \tabularnewline
69 & 12 & 15.8997083739692 & -3.89970837396924 \tabularnewline
70 & 16 & 15.6515786730261 & 0.348421326973927 \tabularnewline
71 & 10 & 13.412380608575 & -3.41238060857495 \tabularnewline
72 & 14 & 14.9242951062704 & -0.924295106270409 \tabularnewline
73 & 18 & 16.9280586745433 & 1.07194132545668 \tabularnewline
74 & 18 & 17.140983623911 & 0.85901637608902 \tabularnewline
75 & 16 & 15.2322948752948 & 0.767705124705194 \tabularnewline
76 & 17 & 13.4918869897008 & 3.50811301029925 \tabularnewline
77 & 16 & 16.419099992442 & -0.419099992441992 \tabularnewline
78 & 16 & 14.5311988476754 & 1.46880115232456 \tabularnewline
79 & 13 & 15.2165773344004 & -2.21657733440038 \tabularnewline
80 & 16 & 15.1421402571568 & 0.857859742843149 \tabularnewline
81 & 16 & 15.6586274506278 & 0.341372549372198 \tabularnewline
82 & 16 & 15.7632408306316 & 0.236759169368356 \tabularnewline
83 & 15 & 15.6434137240469 & -0.64341372404695 \tabularnewline
84 & 15 & 14.8842323819227 & 0.115767618077311 \tabularnewline
85 & 16 & 14.1607354722341 & 1.83926452776585 \tabularnewline
86 & 14 & 14.1555692846506 & -0.155569284650592 \tabularnewline
87 & 16 & 15.3674881780279 & 0.632511821972136 \tabularnewline
88 & 16 & 14.8725888488247 & 1.12741115117526 \tabularnewline
89 & 15 & 14.5194813902587 & 0.480518609741335 \tabularnewline
90 & 12 & 13.907972516408 & -1.90797251640802 \tabularnewline
91 & 17 & 16.7584943754586 & 0.241505624541391 \tabularnewline
92 & 16 & 15.8055619194036 & 0.194438080596368 \tabularnewline
93 & 15 & 15.0531067912421 & -0.0531067912420967 \tabularnewline
94 & 13 & 15.0298518503278 & -2.02985185032776 \tabularnewline
95 & 16 & 14.7436133365348 & 1.25638666346519 \tabularnewline
96 & 16 & 15.7882428813297 & 0.21175711867026 \tabularnewline
97 & 16 & 13.6773839654895 & 2.32261603451052 \tabularnewline
98 & 16 & 15.7737083756365 & 0.22629162436346 \tabularnewline
99 & 14 & 14.4297816437613 & -0.429781643761329 \tabularnewline
100 & 16 & 16.9922981106499 & -0.992298110649857 \tabularnewline
101 & 16 & 14.6766246625527 & 1.32337533744731 \tabularnewline
102 & 20 & 17.4542184425634 & 2.54578155743657 \tabularnewline
103 & 15 & 14.2396603727495 & 0.760339627250478 \tabularnewline
104 & 16 & 14.9598584444853 & 1.04014155551469 \tabularnewline
105 & 13 & 14.8422042092773 & -1.84220420927732 \tabularnewline
106 & 17 & 15.7017299648748 & 1.29827003512525 \tabularnewline
107 & 16 & 15.7011234587523 & 0.298876541247731 \tabularnewline
108 & 16 & 14.3586514687943 & 1.64134853120572 \tabularnewline
109 & 12 & 12.3043422991135 & -0.30434229911349 \tabularnewline
110 & 16 & 15.2856808956604 & 0.71431910433959 \tabularnewline
111 & 16 & 15.9928271492344 & 0.00717285076563753 \tabularnewline
112 & 17 & 15.0564093690921 & 1.9435906309079 \tabularnewline
113 & 13 & 14.2811780773625 & -1.28117807736248 \tabularnewline
114 & 12 & 14.5798742612205 & -2.57987426122045 \tabularnewline
115 & 18 & 16.1614728444387 & 1.83852715556127 \tabularnewline
116 & 14 & 15.8657328435429 & -1.86573284354287 \tabularnewline
117 & 14 & 13.228887828907 & 0.771112171092987 \tabularnewline
118 & 13 & 14.7929151358429 & -1.79291513584291 \tabularnewline
119 & 16 & 15.520384206224 & 0.479615793776011 \tabularnewline
120 & 13 & 14.4292215835434 & -1.42922158354341 \tabularnewline
121 & 16 & 15.3854232676415 & 0.614576732358463 \tabularnewline
122 & 13 & 15.8212093173508 & -2.82120931735076 \tabularnewline
123 & 16 & 16.8545044640026 & -0.854504464002574 \tabularnewline
124 & 15 & 15.8437647974654 & -0.84376479746541 \tabularnewline
125 & 16 & 16.7849339139009 & -0.784933913900918 \tabularnewline
126 & 15 & 14.6587216010757 & 0.341278398924287 \tabularnewline
127 & 17 & 15.4905839058456 & 1.50941609415437 \tabularnewline
128 & 15 & 14.0482521120088 & 0.951747887991231 \tabularnewline
129 & 12 & 14.6858650734517 & -2.68586507345171 \tabularnewline
130 & 16 & 13.9402714795457 & 2.05972852045429 \tabularnewline
131 & 10 & 13.7239532443361 & -3.72395324433605 \tabularnewline
132 & 16 & 13.4583321541629 & 2.54166784583713 \tabularnewline
133 & 12 & 14.1659890224415 & -2.16598902244154 \tabularnewline
134 & 14 & 15.5440460090552 & -1.54404600905523 \tabularnewline
135 & 15 & 15.0836566962205 & -0.0836566962204929 \tabularnewline
136 & 13 & 12.1121127347309 & 0.887887265269134 \tabularnewline
137 & 15 & 14.5312778069589 & 0.468722193041143 \tabularnewline
138 & 11 & 13.4989500572246 & -2.49895005722465 \tabularnewline
139 & 12 & 13.0064804164844 & -1.00648041648441 \tabularnewline
140 & 11 & 13.339956434492 & -2.33995643449198 \tabularnewline
141 & 16 & 12.8564999151223 & 3.14350008487773 \tabularnewline
142 & 15 & 13.6441244258235 & 1.35587557417654 \tabularnewline
143 & 17 & 16.8413639582837 & 0.158636041716298 \tabularnewline
144 & 16 & 14.1244038470475 & 1.87559615295252 \tabularnewline
145 & 10 & 13.266751430131 & -3.26675143013095 \tabularnewline
146 & 18 & 15.497545467363 & 2.50245453263695 \tabularnewline
147 & 13 & 14.9457512386837 & -1.94575123868367 \tabularnewline
148 & 16 & 14.843010105307 & 1.15698989469303 \tabularnewline
149 & 13 & 12.82722767011 & 0.172772329889988 \tabularnewline
150 & 10 & 12.9004577169405 & -2.90045771694049 \tabularnewline
151 & 15 & 15.8784182905414 & -0.878418290541449 \tabularnewline
152 & 16 & 13.8248630916881 & 2.17513690831192 \tabularnewline
153 & 16 & 11.8525810147495 & 4.1474189852505 \tabularnewline
154 & 14 & 12.3963025182653 & 1.60369748173469 \tabularnewline
155 & 10 & 12.4816282956809 & -2.48162829568091 \tabularnewline
156 & 17 & 16.4393065637934 & 0.560693436206567 \tabularnewline
157 & 13 & 11.7066858547317 & 1.29331414526825 \tabularnewline
158 & 15 & 13.900934660471 & 1.099065339529 \tabularnewline
159 & 16 & 14.5373961332024 & 1.46260386679759 \tabularnewline
160 & 12 & 12.6989037635456 & -0.698903763545609 \tabularnewline
161 & 13 & 12.6931049205989 & 0.306895079401113 \tabularnewline
162 & 13 & 12.670204114649 & 0.329795885350951 \tabularnewline
163 & 12 & 12.3813576310274 & -0.381357631027421 \tabularnewline
164 & 17 & 16.2130980179022 & 0.78690198209781 \tabularnewline
165 & 15 & 13.6682202782511 & 1.33177972174891 \tabularnewline
166 & 10 & 11.6197611469958 & -1.61976114699577 \tabularnewline
167 & 14 & 14.3441924705758 & -0.344192470575766 \tabularnewline
168 & 11 & 14.1943469919182 & -3.19434699191817 \tabularnewline
169 & 13 & 14.7895963339482 & -1.78959633394817 \tabularnewline
170 & 16 & 14.3871926838976 & 1.61280731610237 \tabularnewline
171 & 12 & 10.4815320179531 & 1.5184679820469 \tabularnewline
172 & 16 & 15.3377881046553 & 0.66221189534468 \tabularnewline
173 & 12 & 13.8444384221823 & -1.84443842218234 \tabularnewline
174 & 9 & 11.3336124343809 & -2.3336124343809 \tabularnewline
175 & 12 & 14.9070414350127 & -2.90704143501267 \tabularnewline
176 & 15 & 14.4730075744532 & 0.526992425546806 \tabularnewline
177 & 12 & 12.294327621092 & -0.294327621092034 \tabularnewline
178 & 12 & 12.650368444193 & -0.650368444193032 \tabularnewline
179 & 14 & 13.7938681954925 & 0.206131804507504 \tabularnewline
180 & 12 & 13.2889048934373 & -1.28890489343725 \tabularnewline
181 & 16 & 15.0068737606415 & 0.993126239358508 \tabularnewline
182 & 11 & 11.4515058779588 & -0.451505877958786 \tabularnewline
183 & 19 & 16.7015767685106 & 2.29842323148939 \tabularnewline
184 & 15 & 15.1109818809655 & -0.110981880965518 \tabularnewline
185 & 8 & 14.596986586261 & -6.59698658626104 \tabularnewline
186 & 16 & 14.6989355568994 & 1.30106444310063 \tabularnewline
187 & 17 & 14.3895807084944 & 2.61041929150556 \tabularnewline
188 & 12 & 12.3486229107255 & -0.348622910725466 \tabularnewline
189 & 11 & 11.4120697144516 & -0.4120697144516 \tabularnewline
190 & 11 & 10.4549680631895 & 0.545031936810458 \tabularnewline
191 & 14 & 14.4364908904417 & -0.436490890441723 \tabularnewline
192 & 16 & 15.376427798282 & 0.623572201717984 \tabularnewline
193 & 12 & 9.72248141934327 & 2.27751858065673 \tabularnewline
194 & 16 & 14.0886404643777 & 1.9113595356223 \tabularnewline
195 & 13 & 13.632884513438 & -0.632884513437976 \tabularnewline
196 & 15 & 14.9989754108533 & 0.00102458914674308 \tabularnewline
197 & 16 & 12.9126260121705 & 3.08737398782949 \tabularnewline
198 & 16 & 14.991943645066 & 1.00805635493403 \tabularnewline
199 & 14 & 12.4076330969111 & 1.59236690308889 \tabularnewline
200 & 16 & 14.4603615475735 & 1.53963845242654 \tabularnewline
201 & 16 & 14.0333200029035 & 1.96667999709652 \tabularnewline
202 & 14 & 13.2954618359923 & 0.704538164007681 \tabularnewline
203 & 11 & 13.3129603848872 & -2.31296038488724 \tabularnewline
204 & 12 & 14.4949222642595 & -2.49492226425949 \tabularnewline
205 & 15 & 12.6778762991469 & 2.32212370085313 \tabularnewline
206 & 15 & 14.4188365698392 & 0.581163430160781 \tabularnewline
207 & 16 & 14.5320147838526 & 1.46798521614738 \tabularnewline
208 & 16 & 14.843125446159 & 1.15687455384097 \tabularnewline
209 & 11 & 13.5801453187234 & -2.58014531872336 \tabularnewline
210 & 15 & 13.8885534096534 & 1.11144659034664 \tabularnewline
211 & 12 & 14.2037853885328 & -2.20378538853275 \tabularnewline
212 & 12 & 15.7224255491506 & -3.7224255491506 \tabularnewline
213 & 15 & 14.109079358977 & 0.89092064102304 \tabularnewline
214 & 15 & 12.0905788116402 & 2.90942118835982 \tabularnewline
215 & 16 & 14.4647432573063 & 1.53525674269367 \tabularnewline
216 & 14 & 13.0805138671753 & 0.919486132824671 \tabularnewline
217 & 17 & 14.5748713248213 & 2.42512867517871 \tabularnewline
218 & 14 & 13.9049231073215 & 0.0950768926785438 \tabularnewline
219 & 13 & 11.8321054700474 & 1.16789452995262 \tabularnewline
220 & 15 & 15.1299096914369 & -0.129909691436878 \tabularnewline
221 & 13 & 14.5726876835671 & -1.57268768356711 \tabularnewline
222 & 14 & 13.8921489634354 & 0.107851036564568 \tabularnewline
223 & 15 & 14.1999876889948 & 0.800012311005213 \tabularnewline
224 & 12 & 13.0554532006112 & -1.05545320061118 \tabularnewline
225 & 13 & 12.2689174915128 & 0.731082508487173 \tabularnewline
226 & 8 & 11.6573695969631 & -3.65736959696312 \tabularnewline
227 & 14 & 13.6832791328481 & 0.316720867151928 \tabularnewline
228 & 14 & 12.8142890945427 & 1.18571090545735 \tabularnewline
229 & 11 & 12.197890873762 & -1.19789087376205 \tabularnewline
230 & 12 & 12.8303898458872 & -0.830389845887172 \tabularnewline
231 & 13 & 11.185488738365 & 1.81451126163496 \tabularnewline
232 & 10 & 13.2189058291037 & -3.21890582910368 \tabularnewline
233 & 16 & 11.348787511322 & 4.65121248867802 \tabularnewline
234 & 18 & 15.7737516733149 & 2.22624832668511 \tabularnewline
235 & 13 & 13.7266606548843 & -0.726660654884331 \tabularnewline
236 & 11 & 13.2215618348084 & -2.22156183480842 \tabularnewline
237 & 4 & 10.9938193483991 & -6.99381934839915 \tabularnewline
238 & 13 & 14.1796817218455 & -1.17968172184545 \tabularnewline
239 & 16 & 14.1367439630088 & 1.86325603699123 \tabularnewline
240 & 10 & 11.5930618646107 & -1.59306186461067 \tabularnewline
241 & 12 & 12.1626452683162 & -0.162645268316151 \tabularnewline
242 & 12 & 13.3960154190304 & -1.39601541903038 \tabularnewline
243 & 10 & 8.81036730048706 & 1.18963269951294 \tabularnewline
244 & 13 & 10.9797489991871 & 2.02025100081286 \tabularnewline
245 & 15 & 13.5802095891207 & 1.41979041087932 \tabularnewline
246 & 12 & 11.8071329179159 & 0.192867082084071 \tabularnewline
247 & 14 & 12.745833303401 & 1.254166696599 \tabularnewline
248 & 10 & 12.3830320431314 & -2.38303204313139 \tabularnewline
249 & 12 & 10.7045626835548 & 1.29543731644515 \tabularnewline
250 & 12 & 11.5222473456141 & 0.477752654385857 \tabularnewline
251 & 11 & 11.7208297915302 & -0.720829791530183 \tabularnewline
252 & 10 & 11.5207040112403 & -1.52070401124025 \tabularnewline
253 & 12 & 11.2737442088263 & 0.726255791173704 \tabularnewline
254 & 16 & 12.7074968396979 & 3.29250316030215 \tabularnewline
255 & 12 & 13.2078556645355 & -1.20785566453552 \tabularnewline
256 & 14 & 13.7197084126368 & 0.280291587363206 \tabularnewline
257 & 16 & 14.1388849696129 & 1.86111503038708 \tabularnewline
258 & 14 & 11.5536798517218 & 2.44632014827823 \tabularnewline
259 & 13 & 14.0549432381567 & -1.05494323815668 \tabularnewline
260 & 4 & 9.33309354369939 & -5.33309354369939 \tabularnewline
261 & 15 & 13.5531018279656 & 1.44689817203439 \tabularnewline
262 & 11 & 14.7741198188765 & -3.7741198188765 \tabularnewline
263 & 11 & 11.1662281765916 & -0.16622817659161 \tabularnewline
264 & 14 & 12.6310833706034 & 1.36891662939664 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185721&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.0985387854795[/C][C]-3.09853878547954[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.7506280756561[/C][C]0.249371924343879[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]17.1063218899167[/C][C]1.89367811008327[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1616052410318[/C][C]2.83839475896815[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.4017873467223[/C][C]-2.40178734672232[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8470133702108[/C][C]-1.84701337021082[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3941195404347[/C][C]3.60588045956526[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.1034911605066[/C][C]-2.10349116050658[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.0596585119288[/C][C]-2.0596585119288[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4613197202822[/C][C]0.53868027971776[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.0039897785988[/C][C]0.996010221401191[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1957780658441[/C][C]-0.195778065844102[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.5452007954959[/C][C]0.454799204504098[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.497570422791[/C][C]0.50242957720904[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]18.0026152345095[/C][C]-1.00261523450953[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.4966907599622[/C][C]-0.496690759962226[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5878376596391[/C][C]0.412162340360936[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4221329188422[/C][C]3.57786708115779[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5318412924009[/C][C]2.46815870759907[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.598397960354[/C][C]0.40160203964601[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3944791538427[/C][C]0.605520846157265[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.2136141783933[/C][C]0.786385821606707[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4921403787265[/C][C]2.50785962127348[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.1609192229515[/C][C]0.839080777048499[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.1483763914647[/C][C]0.851623608535261[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.22180307078[/C][C]0.778196929220023[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.908352790214[/C][C]1.09164720978603[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.8091087350266[/C][C]-1.80910873502655[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6848237519473[/C][C]0.31517624805274[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.263029558757[/C][C]-0.263029558756992[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7659606607022[/C][C]-0.765960660702232[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.8492889523859[/C][C]-0.849288952385888[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.8039620225922[/C][C]-0.803962022592169[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]16.0017496144186[/C][C]-0.00174961441855281[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4902098814983[/C][C]-1.49020988149829[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.0304769205532[/C][C]-3.0304769205532[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.0355345646574[/C][C]-3.03553456465737[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7470324913881[/C][C]-1.74703249138812[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.554555919938[/C][C]1.445444080062[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.4948939247229[/C][C]1.50510607527713[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.7205377064971[/C][C]1.27946229350293[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.7009510594818[/C][C]-1.70095105948177[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.4871215566965[/C][C]2.51287844330348[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1533777639022[/C][C]-0.153377763902224[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.4623553291315[/C][C]-0.462355329131477[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4909495615117[/C][C]-4.49094956151171[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.6307612737306[/C][C]-2.63076127373058[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1467421663187[/C][C]-0.146742166318652[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.417876616323[/C][C]0.582123383676952[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.6302473667626[/C][C]-1.63024736676259[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]17.0252984963352[/C][C]-1.02529849633517[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1419408813432[/C][C]-0.14194088134322[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]15.0171855791395[/C][C]-3.01718557913948[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]16.0361860907356[/C][C]-0.0361860907355932[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.3432897998035[/C][C]-2.34328979980353[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.3849781445203[/C][C]1.61502185547966[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.8545401093022[/C][C]0.145459890697824[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.5298832287659[/C][C]0.470116771234096[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.1123930302181[/C][C]-0.112393030218105[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2210605652264[/C][C]1.77893943477358[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5595534084661[/C][C]0.440446591533867[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.6406817555398[/C][C]0.359318244460184[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5488168530774[/C][C]-0.548816853077399[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.7109860019853[/C][C]-0.710986001985265[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.3063211583342[/C][C]0.693678841665768[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.0541860332013[/C][C]0.945813966798677[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3331770798611[/C][C]1.6668229201389[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.3958881874426[/C][C]3.60411181255739[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8997083739692[/C][C]-3.89970837396924[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.6515786730261[/C][C]0.348421326973927[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.412380608575[/C][C]-3.41238060857495[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.9242951062704[/C][C]-0.924295106270409[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.9280586745433[/C][C]1.07194132545668[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.140983623911[/C][C]0.85901637608902[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.2322948752948[/C][C]0.767705124705194[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.4918869897008[/C][C]3.50811301029925[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.419099992442[/C][C]-0.419099992441992[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5311988476754[/C][C]1.46880115232456[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.2165773344004[/C][C]-2.21657733440038[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.1421402571568[/C][C]0.857859742843149[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.6586274506278[/C][C]0.341372549372198[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.7632408306316[/C][C]0.236759169368356[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6434137240469[/C][C]-0.64341372404695[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.8842323819227[/C][C]0.115767618077311[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.1607354722341[/C][C]1.83926452776585[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.1555692846506[/C][C]-0.155569284650592[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.3674881780279[/C][C]0.632511821972136[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.8725888488247[/C][C]1.12741115117526[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.5194813902587[/C][C]0.480518609741335[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.907972516408[/C][C]-1.90797251640802[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.7584943754586[/C][C]0.241505624541391[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.8055619194036[/C][C]0.194438080596368[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.0531067912421[/C][C]-0.0531067912420967[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0298518503278[/C][C]-2.02985185032776[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.7436133365348[/C][C]1.25638666346519[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7882428813297[/C][C]0.21175711867026[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.6773839654895[/C][C]2.32261603451052[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.7737083756365[/C][C]0.22629162436346[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.4297816437613[/C][C]-0.429781643761329[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]16.9922981106499[/C][C]-0.992298110649857[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.6766246625527[/C][C]1.32337533744731[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.4542184425634[/C][C]2.54578155743657[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.2396603727495[/C][C]0.760339627250478[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.9598584444853[/C][C]1.04014155551469[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.8422042092773[/C][C]-1.84220420927732[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.7017299648748[/C][C]1.29827003512525[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.7011234587523[/C][C]0.298876541247731[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.3586514687943[/C][C]1.64134853120572[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.3043422991135[/C][C]-0.30434229911349[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.2856808956604[/C][C]0.71431910433959[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.9928271492344[/C][C]0.00717285076563753[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]15.0564093690921[/C][C]1.9435906309079[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.2811780773625[/C][C]-1.28117807736248[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.5798742612205[/C][C]-2.57987426122045[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.1614728444387[/C][C]1.83852715556127[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.8657328435429[/C][C]-1.86573284354287[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.228887828907[/C][C]0.771112171092987[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.7929151358429[/C][C]-1.79291513584291[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.520384206224[/C][C]0.479615793776011[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.4292215835434[/C][C]-1.42922158354341[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.3854232676415[/C][C]0.614576732358463[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.8212093173508[/C][C]-2.82120931735076[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.8545044640026[/C][C]-0.854504464002574[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.8437647974654[/C][C]-0.84376479746541[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.7849339139009[/C][C]-0.784933913900918[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.6587216010757[/C][C]0.341278398924287[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.4905839058456[/C][C]1.50941609415437[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]14.0482521120088[/C][C]0.951747887991231[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.6858650734517[/C][C]-2.68586507345171[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.9402714795457[/C][C]2.05972852045429[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.7239532443361[/C][C]-3.72395324433605[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.4583321541629[/C][C]2.54166784583713[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.1659890224415[/C][C]-2.16598902244154[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5440460090552[/C][C]-1.54404600905523[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.0836566962205[/C][C]-0.0836566962204929[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.1121127347309[/C][C]0.887887265269134[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.5312778069589[/C][C]0.468722193041143[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.4989500572246[/C][C]-2.49895005722465[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.0064804164844[/C][C]-1.00648041648441[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.339956434492[/C][C]-2.33995643449198[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8564999151223[/C][C]3.14350008487773[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.6441244258235[/C][C]1.35587557417654[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.8413639582837[/C][C]0.158636041716298[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.1244038470475[/C][C]1.87559615295252[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.266751430131[/C][C]-3.26675143013095[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.497545467363[/C][C]2.50245453263695[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.9457512386837[/C][C]-1.94575123868367[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.843010105307[/C][C]1.15698989469303[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.82722767011[/C][C]0.172772329889988[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.9004577169405[/C][C]-2.90045771694049[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]15.8784182905414[/C][C]-0.878418290541449[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.8248630916881[/C][C]2.17513690831192[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.8525810147495[/C][C]4.1474189852505[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.3963025182653[/C][C]1.60369748173469[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.4816282956809[/C][C]-2.48162829568091[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.4393065637934[/C][C]0.560693436206567[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.7066858547317[/C][C]1.29331414526825[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.900934660471[/C][C]1.099065339529[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.5373961332024[/C][C]1.46260386679759[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.6989037635456[/C][C]-0.698903763545609[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6931049205989[/C][C]0.306895079401113[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.670204114649[/C][C]0.329795885350951[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.3813576310274[/C][C]-0.381357631027421[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.2130980179022[/C][C]0.78690198209781[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.6682202782511[/C][C]1.33177972174891[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.6197611469958[/C][C]-1.61976114699577[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.3441924705758[/C][C]-0.344192470575766[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.1943469919182[/C][C]-3.19434699191817[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.7895963339482[/C][C]-1.78959633394817[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.3871926838976[/C][C]1.61280731610237[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.4815320179531[/C][C]1.5184679820469[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.3377881046553[/C][C]0.66221189534468[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.8444384221823[/C][C]-1.84443842218234[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.3336124343809[/C][C]-2.3336124343809[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.9070414350127[/C][C]-2.90704143501267[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.4730075744532[/C][C]0.526992425546806[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.294327621092[/C][C]-0.294327621092034[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.650368444193[/C][C]-0.650368444193032[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.7938681954925[/C][C]0.206131804507504[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.2889048934373[/C][C]-1.28890489343725[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.0068737606415[/C][C]0.993126239358508[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.4515058779588[/C][C]-0.451505877958786[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]16.7015767685106[/C][C]2.29842323148939[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.1109818809655[/C][C]-0.110981880965518[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.596986586261[/C][C]-6.59698658626104[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.6989355568994[/C][C]1.30106444310063[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.3895807084944[/C][C]2.61041929150556[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.3486229107255[/C][C]-0.348622910725466[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.4120697144516[/C][C]-0.4120697144516[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.4549680631895[/C][C]0.545031936810458[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]14.4364908904417[/C][C]-0.436490890441723[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.376427798282[/C][C]0.623572201717984[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.72248141934327[/C][C]2.27751858065673[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.0886404643777[/C][C]1.9113595356223[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.632884513438[/C][C]-0.632884513437976[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]14.9989754108533[/C][C]0.00102458914674308[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]12.9126260121705[/C][C]3.08737398782949[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]14.991943645066[/C][C]1.00805635493403[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4076330969111[/C][C]1.59236690308889[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.4603615475735[/C][C]1.53963845242654[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]14.0333200029035[/C][C]1.96667999709652[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.2954618359923[/C][C]0.704538164007681[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.3129603848872[/C][C]-2.31296038488724[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.4949222642595[/C][C]-2.49492226425949[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.6778762991469[/C][C]2.32212370085313[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.4188365698392[/C][C]0.581163430160781[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.5320147838526[/C][C]1.46798521614738[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]14.843125446159[/C][C]1.15687455384097[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.5801453187234[/C][C]-2.58014531872336[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.8885534096534[/C][C]1.11144659034664[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.2037853885328[/C][C]-2.20378538853275[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]15.7224255491506[/C][C]-3.7224255491506[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.109079358977[/C][C]0.89092064102304[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]12.0905788116402[/C][C]2.90942118835982[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.4647432573063[/C][C]1.53525674269367[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.0805138671753[/C][C]0.919486132824671[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.5748713248213[/C][C]2.42512867517871[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.9049231073215[/C][C]0.0950768926785438[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.8321054700474[/C][C]1.16789452995262[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.1299096914369[/C][C]-0.129909691436878[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.5726876835671[/C][C]-1.57268768356711[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]13.8921489634354[/C][C]0.107851036564568[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.1999876889948[/C][C]0.800012311005213[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]13.0554532006112[/C][C]-1.05545320061118[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.2689174915128[/C][C]0.731082508487173[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.6573695969631[/C][C]-3.65736959696312[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]13.6832791328481[/C][C]0.316720867151928[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]12.8142890945427[/C][C]1.18571090545735[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.197890873762[/C][C]-1.19789087376205[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.8303898458872[/C][C]-0.830389845887172[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.185488738365[/C][C]1.81451126163496[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.2189058291037[/C][C]-3.21890582910368[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.348787511322[/C][C]4.65121248867802[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]15.7737516733149[/C][C]2.22624832668511[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.7266606548843[/C][C]-0.726660654884331[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.2215618348084[/C][C]-2.22156183480842[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]10.9938193483991[/C][C]-6.99381934839915[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.1796817218455[/C][C]-1.17968172184545[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.1367439630088[/C][C]1.86325603699123[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.5930618646107[/C][C]-1.59306186461067[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.1626452683162[/C][C]-0.162645268316151[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.3960154190304[/C][C]-1.39601541903038[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.81036730048706[/C][C]1.18963269951294[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]10.9797489991871[/C][C]2.02025100081286[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]13.5802095891207[/C][C]1.41979041087932[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]11.8071329179159[/C][C]0.192867082084071[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]12.745833303401[/C][C]1.254166696599[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.3830320431314[/C][C]-2.38303204313139[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.7045626835548[/C][C]1.29543731644515[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.5222473456141[/C][C]0.477752654385857[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]11.7208297915302[/C][C]-0.720829791530183[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.5207040112403[/C][C]-1.52070401124025[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.2737442088263[/C][C]0.726255791173704[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.7074968396979[/C][C]3.29250316030215[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.2078556645355[/C][C]-1.20785566453552[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.7197084126368[/C][C]0.280291587363206[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.1388849696129[/C][C]1.86111503038708[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.5536798517218[/C][C]2.44632014827823[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.0549432381567[/C][C]-1.05494323815668[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.33309354369939[/C][C]-5.33309354369939[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.5531018279656[/C][C]1.44689817203439[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]14.7741198188765[/C][C]-3.7741198188765[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.1662281765916[/C][C]-0.16622817659161[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.6310833706034[/C][C]1.36891662939664[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185721&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185721&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0985387854795-3.09853878547954
21615.75062807565610.249371924343879
31917.10632188991671.89367811008327
41512.16160524103182.83839475896815
51416.4017873467223-2.40178734672232
61314.8470133702108-1.84701337021082
71915.39411954043473.60588045956526
81517.1034911605066-2.10349116050658
91416.0596585119288-2.0596585119288
101514.46131972028220.53868027971776
111615.00398977859880.996010221401191
121616.1957780658441-0.195778065844102
131615.54520079549590.454799204504098
141615.4975704227910.50242957720904
151718.0026152345095-1.00261523450953
161515.4966907599622-0.496690759962226
171514.58783765963910.412162340360936
182016.42213291884223.57786708115779
191815.53184129240092.46815870759907
201615.5983979603540.40160203964601
211615.39447915384270.605520846157265
221615.21361417839330.786385821606707
231916.49214037872652.50785962127348
241615.16091922295150.839080777048499
251716.14837639146470.851623608535261
261716.221803070780.778196929220023
271614.9083527902141.09164720978603
281516.8091087350266-1.80910873502655
291615.68482375194730.31517624805274
301414.263029558757-0.263029558756992
311515.7659606607022-0.765960660702232
321212.8492889523859-0.849288952385888
331414.8039620225922-0.803962022592169
341616.0017496144186-0.00174961441855281
351415.4902098814983-1.49020988149829
361013.0304769205532-3.0304769205532
371013.0355345646574-3.03553456465737
381415.7470324913881-1.74703249138812
391614.5545559199381.445444080062
401614.49489392472291.50510607527713
411614.72053770649711.27946229350293
421415.7009510594818-1.70095105948177
432017.48712155669652.51287844330348
441414.1533777639022-0.153377763902224
451414.4623553291315-0.462355329131477
461115.4909495615117-4.49094956151171
471416.6307612737306-2.63076127373058
481515.1467421663187-0.146742166318652
491615.4178766163230.582123383676952
501415.6302473667626-1.63024736676259
511617.0252984963352-1.02529849633517
521414.1419408813432-0.14194088134322
531215.0171855791395-3.01718557913948
541616.0361860907356-0.0361860907355932
55911.3432897998035-2.34328979980353
561412.38497814452031.61502185547966
571615.85454010930220.145459890697824
581615.52988322876590.470116771234096
591515.1123930302181-0.112393030218105
601614.22106056522641.77893943477358
611211.55955340846610.440446591533867
621615.64068175553980.359318244460184
631616.5488168530774-0.548816853077399
641414.7109860019853-0.710986001985265
651615.30632115833420.693678841665768
661716.05418603320130.945813966798677
671816.33317707986111.6668229201389
681814.39588818744263.60411181255739
691215.8997083739692-3.89970837396924
701615.65157867302610.348421326973927
711013.412380608575-3.41238060857495
721414.9242951062704-0.924295106270409
731816.92805867454331.07194132545668
741817.1409836239110.85901637608902
751615.23229487529480.767705124705194
761713.49188698970083.50811301029925
771616.419099992442-0.419099992441992
781614.53119884767541.46880115232456
791315.2165773344004-2.21657733440038
801615.14214025715680.857859742843149
811615.65862745062780.341372549372198
821615.76324083063160.236759169368356
831515.6434137240469-0.64341372404695
841514.88423238192270.115767618077311
851614.16073547223411.83926452776585
861414.1555692846506-0.155569284650592
871615.36748817802790.632511821972136
881614.87258884882471.12741115117526
891514.51948139025870.480518609741335
901213.907972516408-1.90797251640802
911716.75849437545860.241505624541391
921615.80556191940360.194438080596368
931515.0531067912421-0.0531067912420967
941315.0298518503278-2.02985185032776
951614.74361333653481.25638666346519
961615.78824288132970.21175711867026
971613.67738396548952.32261603451052
981615.77370837563650.22629162436346
991414.4297816437613-0.429781643761329
1001616.9922981106499-0.992298110649857
1011614.67662466255271.32337533744731
1022017.45421844256342.54578155743657
1031514.23966037274950.760339627250478
1041614.95985844448531.04014155551469
1051314.8422042092773-1.84220420927732
1061715.70172996487481.29827003512525
1071615.70112345875230.298876541247731
1081614.35865146879431.64134853120572
1091212.3043422991135-0.30434229911349
1101615.28568089566040.71431910433959
1111615.99282714923440.00717285076563753
1121715.05640936909211.9435906309079
1131314.2811780773625-1.28117807736248
1141214.5798742612205-2.57987426122045
1151816.16147284443871.83852715556127
1161415.8657328435429-1.86573284354287
1171413.2288878289070.771112171092987
1181314.7929151358429-1.79291513584291
1191615.5203842062240.479615793776011
1201314.4292215835434-1.42922158354341
1211615.38542326764150.614576732358463
1221315.8212093173508-2.82120931735076
1231616.8545044640026-0.854504464002574
1241515.8437647974654-0.84376479746541
1251616.7849339139009-0.784933913900918
1261514.65872160107570.341278398924287
1271715.49058390584561.50941609415437
1281514.04825211200880.951747887991231
1291214.6858650734517-2.68586507345171
1301613.94027147954572.05972852045429
1311013.7239532443361-3.72395324433605
1321613.45833215416292.54166784583713
1331214.1659890224415-2.16598902244154
1341415.5440460090552-1.54404600905523
1351515.0836566962205-0.0836566962204929
1361312.11211273473090.887887265269134
1371514.53127780695890.468722193041143
1381113.4989500572246-2.49895005722465
1391213.0064804164844-1.00648041648441
1401113.339956434492-2.33995643449198
1411612.85649991512233.14350008487773
1421513.64412442582351.35587557417654
1431716.84136395828370.158636041716298
1441614.12440384704751.87559615295252
1451013.266751430131-3.26675143013095
1461815.4975454673632.50245453263695
1471314.9457512386837-1.94575123868367
1481614.8430101053071.15698989469303
1491312.827227670110.172772329889988
1501012.9004577169405-2.90045771694049
1511515.8784182905414-0.878418290541449
1521613.82486309168812.17513690831192
1531611.85258101474954.1474189852505
1541412.39630251826531.60369748173469
1551012.4816282956809-2.48162829568091
1561716.43930656379340.560693436206567
1571311.70668585473171.29331414526825
1581513.9009346604711.099065339529
1591614.53739613320241.46260386679759
1601212.6989037635456-0.698903763545609
1611312.69310492059890.306895079401113
1621312.6702041146490.329795885350951
1631212.3813576310274-0.381357631027421
1641716.21309801790220.78690198209781
1651513.66822027825111.33177972174891
1661011.6197611469958-1.61976114699577
1671414.3441924705758-0.344192470575766
1681114.1943469919182-3.19434699191817
1691314.7895963339482-1.78959633394817
1701614.38719268389761.61280731610237
1711210.48153201795311.5184679820469
1721615.33778810465530.66221189534468
1731213.8444384221823-1.84443842218234
174911.3336124343809-2.3336124343809
1751214.9070414350127-2.90704143501267
1761514.47300757445320.526992425546806
1771212.294327621092-0.294327621092034
1781212.650368444193-0.650368444193032
1791413.79386819549250.206131804507504
1801213.2889048934373-1.28890489343725
1811615.00687376064150.993126239358508
1821111.4515058779588-0.451505877958786
1831916.70157676851062.29842323148939
1841515.1109818809655-0.110981880965518
185814.596986586261-6.59698658626104
1861614.69893555689941.30106444310063
1871714.38958070849442.61041929150556
1881212.3486229107255-0.348622910725466
1891111.4120697144516-0.4120697144516
1901110.45496806318950.545031936810458
1911414.4364908904417-0.436490890441723
1921615.3764277982820.623572201717984
193129.722481419343272.27751858065673
1941614.08864046437771.9113595356223
1951313.632884513438-0.632884513437976
1961514.99897541085330.00102458914674308
1971612.91262601217053.08737398782949
1981614.9919436450661.00805635493403
1991412.40763309691111.59236690308889
2001614.46036154757351.53963845242654
2011614.03332000290351.96667999709652
2021413.29546183599230.704538164007681
2031113.3129603848872-2.31296038488724
2041214.4949222642595-2.49492226425949
2051512.67787629914692.32212370085313
2061514.41883656983920.581163430160781
2071614.53201478385261.46798521614738
2081614.8431254461591.15687455384097
2091113.5801453187234-2.58014531872336
2101513.88855340965341.11144659034664
2111214.2037853885328-2.20378538853275
2121215.7224255491506-3.7224255491506
2131514.1090793589770.89092064102304
2141512.09057881164022.90942118835982
2151614.46474325730631.53525674269367
2161413.08051386717530.919486132824671
2171714.57487132482132.42512867517871
2181413.90492310732150.0950768926785438
2191311.83210547004741.16789452995262
2201515.1299096914369-0.129909691436878
2211314.5726876835671-1.57268768356711
2221413.89214896343540.107851036564568
2231514.19998768899480.800012311005213
2241213.0554532006112-1.05545320061118
2251312.26891749151280.731082508487173
226811.6573695969631-3.65736959696312
2271413.68327913284810.316720867151928
2281412.81428909454271.18571090545735
2291112.197890873762-1.19789087376205
2301212.8303898458872-0.830389845887172
2311311.1854887383651.81451126163496
2321013.2189058291037-3.21890582910368
2331611.3487875113224.65121248867802
2341815.77375167331492.22624832668511
2351313.7266606548843-0.726660654884331
2361113.2215618348084-2.22156183480842
237410.9938193483991-6.99381934839915
2381314.1796817218455-1.17968172184545
2391614.13674396300881.86325603699123
2401011.5930618646107-1.59306186461067
2411212.1626452683162-0.162645268316151
2421213.3960154190304-1.39601541903038
243108.810367300487061.18963269951294
2441310.97974899918712.02025100081286
2451513.58020958912071.41979041087932
2461211.80713291791590.192867082084071
2471412.7458333034011.254166696599
2481012.3830320431314-2.38303204313139
2491210.70456268355481.29543731644515
2501211.52224734561410.477752654385857
2511111.7208297915302-0.720829791530183
2521011.5207040112403-1.52070401124025
2531211.27374420882630.726255791173704
2541612.70749683969793.29250316030215
2551213.2078556645355-1.20785566453552
2561413.71970841263680.280291587363206
2571614.13888496961291.86111503038708
2581411.55367985172182.44632014827823
2591314.0549432381567-1.05494323815668
26049.33309354369939-5.33309354369939
2611513.55310182796561.44689817203439
2621114.7741198188765-3.7741198188765
2631111.1662281765916-0.16622817659161
2641412.63108337060341.36891662939664







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4024752294716530.8049504589433070.597524770528347
120.7998130195522850.400373960895430.200186980447715
130.7608266649872480.4783466700255040.239173335012752
140.6735526211782490.6528947576435010.326447378821751
150.6181146999759330.7637706000481350.381885300024067
160.6808332009748050.6383335980503890.319166799025195
170.6081426280273630.7837147439452740.391857371972637
180.8576943804218130.2846112391563750.142305619578187
190.8309802892137430.3380394215725140.169019710786257
200.7804673886153940.4390652227692110.219532611384606
210.7164350906766020.5671298186467950.283564909323398
220.6779248597001320.6441502805997360.322075140299868
230.6400806239680920.7198387520638160.359919376031908
240.6080028194115910.7839943611768180.391997180588409
250.5446025202534540.9107949594930920.455397479746546
260.4951188739165580.9902377478331160.504881126083442
270.4356950665268060.8713901330536110.564304933473194
280.4894287276521350.978857455304270.510571272347865
290.4295311932516830.8590623865033670.570468806748317
300.4391106913414210.8782213826828430.560889308658579
310.3907776313005610.7815552626011230.609222368699439
320.3865362509458760.7730725018917520.613463749054124
330.3675737435383950.7351474870767890.632426256461605
340.3119461115168850.6238922230337690.688053888483115
350.3007900756320090.6015801512640180.699209924367991
360.3994469562214780.7988939124429550.600553043778522
370.4802336405472560.9604672810945130.519766359452744
380.4564144938699520.9128289877399050.543585506130048
390.5040367816736210.9919264366527580.495963218326379
400.484662564347350.9693251286946990.51533743565265
410.4489815388141230.8979630776282450.551018461185877
420.4193309291449650.838661858289930.580669070855035
430.4354887871740710.8709775743481420.564511212825929
440.3883570187665220.7767140375330440.611642981233478
450.3497844417106930.6995688834213850.650215558289307
460.5744902589496390.8510194821007220.425509741050361
470.592513782373670.814972435252660.40748621762633
480.5540651435835930.8918697128328150.445934856416407
490.5413440298923260.9173119402153490.458655970107675
500.5141860993611120.9716278012777760.485813900638888
510.4699707815049490.9399415630098990.530029218495051
520.428427599419140.856855198838280.57157240058086
530.4356612904445350.871322580889070.564338709555465
540.4060088780298160.8120177560596330.593991121970184
550.4042776889552210.8085553779104430.595722311044779
560.4207346972468480.8414693944936960.579265302753152
570.3801415205875410.7602830411750820.619858479412459
580.3656289476085520.7312578952171040.634371052391448
590.3265080055678580.6530160111357170.673491994432141
600.3533720372327280.7067440744654550.646627962767272
610.3272204306739440.6544408613478880.672779569326056
620.2931319780199710.5862639560399410.706868021980029
630.2591175374011110.5182350748022220.740882462598889
640.2265144769622940.4530289539245870.773485523037706
650.2029094885878310.4058189771756630.797090511412169
660.1931317947470220.3862635894940440.806868205252978
670.1958312451995280.3916624903990550.804168754800472
680.3093029909179420.6186059818358850.690697009082058
690.4222184651265260.8444369302530520.577781534873474
700.3901976574227390.7803953148454780.609802342577261
710.4626709970512260.9253419941024520.537329002948774
720.4277994404901990.8555988809803990.572200559509801
730.4210243957462350.842048791492470.578975604253765
740.3996317935083080.7992635870166160.600368206491692
750.3635917953613020.7271835907226050.636408204638698
760.4415616385406130.8831232770812270.558438361459387
770.4034167204255080.8068334408510160.596583279574492
780.3826230797418060.7652461594836120.617376920258194
790.3920519671908410.7841039343816820.607948032809159
800.3577742307192110.7155484614384230.642225769280789
810.3260492214260720.6520984428521440.673950778573928
820.2940411602945140.5880823205890280.705958839705486
830.2670596465294420.5341192930588840.732940353470558
840.2370940357481770.4741880714963530.762905964251823
850.2342196076945590.4684392153891170.765780392305441
860.2054573319940010.4109146639880020.794542668005999
870.1823600852479830.3647201704959660.817639914752017
880.168667246980520.337334493961040.83133275301948
890.1460354047632350.2920708095264710.853964595236764
900.1414013986751060.2828027973502130.858598601324894
910.1214534031524870.2429068063049750.878546596847512
920.1055383353625640.2110766707251280.894461664637436
930.09047315031226920.1809463006245380.909526849687731
940.09445170453054310.1889034090610860.905548295469457
950.08514528825709260.1702905765141850.914854711742907
960.071343759710940.142687519421880.92865624028906
970.07653904197525380.1530780839505080.923460958024746
980.06391729571907330.1278345914381470.936082704280927
990.05325605400948650.1065121080189730.946743945990513
1000.04719646434776450.0943929286955290.952803535652236
1010.04182377086681880.08364754173363760.958176229133181
1020.04956466407379190.09912932814758390.950435335926208
1030.04181593253059760.08363186506119530.958184067469402
1040.03715462474442260.07430924948884520.962845375255577
1050.04437996451127950.08875992902255910.95562003548872
1060.03924672407584380.07849344815168760.960753275924156
1070.0321015407475390.06420308149507810.967898459252461
1080.03063896195038420.06127792390076850.969361038049616
1090.02497665092764810.04995330185529620.975023349072352
1100.02068028807217360.04136057614434720.979319711927826
1110.01653670186114490.03307340372228980.983463298138855
1120.01746109681401090.03492219362802170.982538903185989
1130.01518680487295420.03037360974590830.984813195127046
1140.02034278401881580.04068556803763150.979657215981184
1150.01955457891592430.03910915783184870.980445421084076
1160.0191565828176050.038313165635210.980843417182395
1170.01618954154275630.03237908308551270.983810458457244
1180.01614287982130670.03228575964261350.983857120178693
1190.0131115966255750.02622319325115010.986888403374425
1200.01185128625019640.02370257250039280.988148713749804
1210.009611389131385440.01922277826277090.990388610868615
1220.01429761320826160.02859522641652320.985702386791738
1230.01191824859867930.02383649719735870.988081751401321
1240.01004172312432350.0200834462486470.989958276875677
1250.008203863469081220.01640772693816240.991796136530919
1260.006524021410002810.01304804282000560.993475978589997
1270.005924994717088630.01184998943417730.994075005282911
1280.004989580314376040.009979160628752070.995010419685624
1290.006796767787733840.01359353557546770.993203232212266
1300.007378964708857440.01475792941771490.992621035291143
1310.01503800357014760.03007600714029510.984961996429852
1320.01790200110102970.03580400220205950.98209799889897
1330.01900142777862720.03800285555725430.980998572221373
1340.01793995121491990.03587990242983990.98206004878508
1350.01432808310788750.0286561662157750.985671916892113
1360.01223094262633360.02446188525266710.987769057373666
1370.009906487418236290.01981297483647260.990093512581764
1380.01253842108576520.02507684217153030.987461578914235
1390.01076182162125860.02152364324251710.989238178378741
1400.012905295518090.02581059103617990.98709470448191
1410.02015601666196180.04031203332392360.979843983338038
1420.01852428145511670.03704856291023350.981475718544883
1430.01502053120934960.03004106241869930.98497946879065
1440.01572360125878750.0314472025175750.984276398741213
1450.02669745511910140.05339491023820280.973302544880899
1460.0328992839312780.0657985678625560.967100716068722
1470.03471066253461560.06942132506923130.965289337465384
1480.03082681579115920.06165363158231840.969173184208841
1490.02516944911618040.05033889823236080.97483055088382
1500.03474316691801150.06948633383602310.965256833081989
1510.02956496453309840.05912992906619680.970435035466902
1520.03121189996585850.06242379993171690.968788100034142
1530.06149216472039650.1229843294407930.938507835279603
1540.05926934843115770.1185386968623150.940730651568842
1550.06685146729498350.1337029345899670.933148532705017
1560.05688283720363720.1137656744072740.943117162796363
1570.05072635077972830.1014527015594570.949273649220272
1580.0443069457737580.0886138915475160.955693054226242
1590.0428963705597270.08579274111945390.957103629440273
1600.03682892885311180.07365785770622360.963171071146888
1610.03014940476039780.06029880952079570.969850595239602
1620.02468825111722130.04937650223444270.975311748882779
1630.02045569225456290.04091138450912580.979544307745437
1640.01746633422788520.03493266845577030.982533665772115
1650.01528997122958660.03057994245917320.984710028770413
1660.01621585711291260.03243171422582530.983784142887087
1670.01295191907958690.02590383815917390.987048080920413
1680.01900979434554740.03801958869109470.980990205654453
1690.01931450605355480.03862901210710960.980685493946445
1700.01780193125318540.03560386250637080.982198068746815
1710.0163272135588430.0326544271176860.983672786441157
1720.01317765009859480.02635530019718960.986822349901405
1730.01281280658023820.02562561316047640.987187193419762
1740.0146859512169980.02937190243399610.985314048783002
1750.01889299089026780.03778598178053560.981107009109732
1760.01514540637781190.03029081275562370.984854593622188
1770.01198087198819830.02396174397639660.988019128011802
1780.01007815072384070.02015630144768150.989921849276159
1790.007837987409293120.01567597481858620.992162012590707
1800.006892283844467760.01378456768893550.993107716155532
1810.005579292334205570.01115858466841110.994420707665794
1820.004301702273412540.008603404546825080.995698297726587
1830.004542724867746630.009085449735493250.995457275132253
1840.003463407804699890.006926815609399780.9965365921953
1850.09454855289349380.1890971057869880.905451447106506
1860.08376732863415990.167534657268320.91623267136584
1870.09373198735774520.187463974715490.906268012642255
1880.07914224297580170.1582844859516030.920857757024198
1890.07054186547488870.1410837309497770.929458134525111
1900.05967056132143360.1193411226428670.940329438678566
1910.05168880038133290.1033776007626660.948311199618667
1920.04301097668946360.08602195337892710.956989023310536
1930.04331207625446860.08662415250893730.956687923745531
1940.04106966838062690.08213933676125390.958930331619373
1950.03570277389819840.07140554779639680.964297226101802
1960.02845501402156620.05691002804313250.971544985978434
1970.03732224023959860.07464448047919710.962677759760401
1980.03051949953524940.06103899907049870.969480500464751
1990.02740578193646910.05481156387293820.972594218063531
2000.02327653896244020.04655307792488050.97672346103756
2010.0213113555866380.0426227111732760.978688644413362
2020.01781655444151850.0356331088830370.982183445558481
2030.0198469109119110.03969382182382210.980153089088089
2040.02636750807308050.0527350161461610.973632491926919
2050.02782370670694510.05564741341389020.972176293293055
2060.02175032821015880.04350065642031750.978249671789841
2070.0193869270812980.03877385416259590.980613072918702
2080.01549037224012230.03098074448024460.984509627759878
2090.01807789947052060.03615579894104120.981922100529479
2100.01496369412228890.02992738824457780.985036305877711
2110.01839583923516740.03679167847033490.981604160764833
2120.03247176111908930.06494352223817850.967528238880911
2130.02537109455514540.05074218911029070.974628905444855
2140.03147911690339730.06295823380679470.968520883096603
2150.02676125047967240.05352250095934480.973238749520328
2160.0207293671329080.0414587342658160.979270632867092
2170.02313706630027140.04627413260054270.976862933699729
2180.01960675534811750.0392135106962350.980393244651882
2190.01829439039192630.03658878078385260.981705609608074
2200.01374190380798160.02748380761596320.986258096192018
2210.01140251372411710.02280502744823410.988597486275883
2220.008311548018902670.01662309603780530.991688451981097
2230.006416313818357130.01283262763671430.993583686181643
2240.004916911599013890.009833823198027770.995083088400986
2250.003439601113773190.006879202227546380.996560398886227
2260.007101991232983660.01420398246596730.992898008767016
2270.005651567358694880.01130313471738980.994348432641305
2280.004122617759569460.008245235519138910.995877382240431
2290.002931619834977250.00586323966995450.997068380165023
2300.002075990459030020.004151980918060040.99792400954097
2310.002268509892050130.004537019784100260.99773149010795
2320.007798519167070090.01559703833414020.99220148083293
2330.02936984049576280.05873968099152560.970630159504237
2340.0449507472439720.0899014944879440.955049252756028
2350.03309141058089440.06618282116178880.966908589419106
2360.02710115142265260.05420230284530510.972898848577347
2370.2389590159114660.4779180318229320.761040984088534
2380.1929492720192480.3858985440384960.807050727980752
2390.1632899251623070.3265798503246140.836710074837693
2400.130447620662190.260895241324380.86955237933781
2410.1168174708181940.2336349416363870.883182529181806
2420.1779079840648210.3558159681296430.822092015935179
2430.1504539502042450.3009079004084890.849546049795755
2440.1514528225349930.3029056450699870.848547177465007
2450.1328522494438550.265704498887710.867147750556145
2460.1164758208434460.2329516416868930.883524179156554
2470.0792562973088710.1585125946177420.920743702691129
2480.07042908849462310.1408581769892460.929570911505377
2490.05349779011521510.106995580230430.946502209884785
2500.1307798132060180.2615596264120370.869220186793981
2510.1074728615960920.2149457231921850.892527138403908
2520.5439783895035810.9120432209928390.456021610496419
2530.3822887274810980.7645774549621970.617711272518902

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.402475229471653 & 0.804950458943307 & 0.597524770528347 \tabularnewline
12 & 0.799813019552285 & 0.40037396089543 & 0.200186980447715 \tabularnewline
13 & 0.760826664987248 & 0.478346670025504 & 0.239173335012752 \tabularnewline
14 & 0.673552621178249 & 0.652894757643501 & 0.326447378821751 \tabularnewline
15 & 0.618114699975933 & 0.763770600048135 & 0.381885300024067 \tabularnewline
16 & 0.680833200974805 & 0.638333598050389 & 0.319166799025195 \tabularnewline
17 & 0.608142628027363 & 0.783714743945274 & 0.391857371972637 \tabularnewline
18 & 0.857694380421813 & 0.284611239156375 & 0.142305619578187 \tabularnewline
19 & 0.830980289213743 & 0.338039421572514 & 0.169019710786257 \tabularnewline
20 & 0.780467388615394 & 0.439065222769211 & 0.219532611384606 \tabularnewline
21 & 0.716435090676602 & 0.567129818646795 & 0.283564909323398 \tabularnewline
22 & 0.677924859700132 & 0.644150280599736 & 0.322075140299868 \tabularnewline
23 & 0.640080623968092 & 0.719838752063816 & 0.359919376031908 \tabularnewline
24 & 0.608002819411591 & 0.783994361176818 & 0.391997180588409 \tabularnewline
25 & 0.544602520253454 & 0.910794959493092 & 0.455397479746546 \tabularnewline
26 & 0.495118873916558 & 0.990237747833116 & 0.504881126083442 \tabularnewline
27 & 0.435695066526806 & 0.871390133053611 & 0.564304933473194 \tabularnewline
28 & 0.489428727652135 & 0.97885745530427 & 0.510571272347865 \tabularnewline
29 & 0.429531193251683 & 0.859062386503367 & 0.570468806748317 \tabularnewline
30 & 0.439110691341421 & 0.878221382682843 & 0.560889308658579 \tabularnewline
31 & 0.390777631300561 & 0.781555262601123 & 0.609222368699439 \tabularnewline
32 & 0.386536250945876 & 0.773072501891752 & 0.613463749054124 \tabularnewline
33 & 0.367573743538395 & 0.735147487076789 & 0.632426256461605 \tabularnewline
34 & 0.311946111516885 & 0.623892223033769 & 0.688053888483115 \tabularnewline
35 & 0.300790075632009 & 0.601580151264018 & 0.699209924367991 \tabularnewline
36 & 0.399446956221478 & 0.798893912442955 & 0.600553043778522 \tabularnewline
37 & 0.480233640547256 & 0.960467281094513 & 0.519766359452744 \tabularnewline
38 & 0.456414493869952 & 0.912828987739905 & 0.543585506130048 \tabularnewline
39 & 0.504036781673621 & 0.991926436652758 & 0.495963218326379 \tabularnewline
40 & 0.48466256434735 & 0.969325128694699 & 0.51533743565265 \tabularnewline
41 & 0.448981538814123 & 0.897963077628245 & 0.551018461185877 \tabularnewline
42 & 0.419330929144965 & 0.83866185828993 & 0.580669070855035 \tabularnewline
43 & 0.435488787174071 & 0.870977574348142 & 0.564511212825929 \tabularnewline
44 & 0.388357018766522 & 0.776714037533044 & 0.611642981233478 \tabularnewline
45 & 0.349784441710693 & 0.699568883421385 & 0.650215558289307 \tabularnewline
46 & 0.574490258949639 & 0.851019482100722 & 0.425509741050361 \tabularnewline
47 & 0.59251378237367 & 0.81497243525266 & 0.40748621762633 \tabularnewline
48 & 0.554065143583593 & 0.891869712832815 & 0.445934856416407 \tabularnewline
49 & 0.541344029892326 & 0.917311940215349 & 0.458655970107675 \tabularnewline
50 & 0.514186099361112 & 0.971627801277776 & 0.485813900638888 \tabularnewline
51 & 0.469970781504949 & 0.939941563009899 & 0.530029218495051 \tabularnewline
52 & 0.42842759941914 & 0.85685519883828 & 0.57157240058086 \tabularnewline
53 & 0.435661290444535 & 0.87132258088907 & 0.564338709555465 \tabularnewline
54 & 0.406008878029816 & 0.812017756059633 & 0.593991121970184 \tabularnewline
55 & 0.404277688955221 & 0.808555377910443 & 0.595722311044779 \tabularnewline
56 & 0.420734697246848 & 0.841469394493696 & 0.579265302753152 \tabularnewline
57 & 0.380141520587541 & 0.760283041175082 & 0.619858479412459 \tabularnewline
58 & 0.365628947608552 & 0.731257895217104 & 0.634371052391448 \tabularnewline
59 & 0.326508005567858 & 0.653016011135717 & 0.673491994432141 \tabularnewline
60 & 0.353372037232728 & 0.706744074465455 & 0.646627962767272 \tabularnewline
61 & 0.327220430673944 & 0.654440861347888 & 0.672779569326056 \tabularnewline
62 & 0.293131978019971 & 0.586263956039941 & 0.706868021980029 \tabularnewline
63 & 0.259117537401111 & 0.518235074802222 & 0.740882462598889 \tabularnewline
64 & 0.226514476962294 & 0.453028953924587 & 0.773485523037706 \tabularnewline
65 & 0.202909488587831 & 0.405818977175663 & 0.797090511412169 \tabularnewline
66 & 0.193131794747022 & 0.386263589494044 & 0.806868205252978 \tabularnewline
67 & 0.195831245199528 & 0.391662490399055 & 0.804168754800472 \tabularnewline
68 & 0.309302990917942 & 0.618605981835885 & 0.690697009082058 \tabularnewline
69 & 0.422218465126526 & 0.844436930253052 & 0.577781534873474 \tabularnewline
70 & 0.390197657422739 & 0.780395314845478 & 0.609802342577261 \tabularnewline
71 & 0.462670997051226 & 0.925341994102452 & 0.537329002948774 \tabularnewline
72 & 0.427799440490199 & 0.855598880980399 & 0.572200559509801 \tabularnewline
73 & 0.421024395746235 & 0.84204879149247 & 0.578975604253765 \tabularnewline
74 & 0.399631793508308 & 0.799263587016616 & 0.600368206491692 \tabularnewline
75 & 0.363591795361302 & 0.727183590722605 & 0.636408204638698 \tabularnewline
76 & 0.441561638540613 & 0.883123277081227 & 0.558438361459387 \tabularnewline
77 & 0.403416720425508 & 0.806833440851016 & 0.596583279574492 \tabularnewline
78 & 0.382623079741806 & 0.765246159483612 & 0.617376920258194 \tabularnewline
79 & 0.392051967190841 & 0.784103934381682 & 0.607948032809159 \tabularnewline
80 & 0.357774230719211 & 0.715548461438423 & 0.642225769280789 \tabularnewline
81 & 0.326049221426072 & 0.652098442852144 & 0.673950778573928 \tabularnewline
82 & 0.294041160294514 & 0.588082320589028 & 0.705958839705486 \tabularnewline
83 & 0.267059646529442 & 0.534119293058884 & 0.732940353470558 \tabularnewline
84 & 0.237094035748177 & 0.474188071496353 & 0.762905964251823 \tabularnewline
85 & 0.234219607694559 & 0.468439215389117 & 0.765780392305441 \tabularnewline
86 & 0.205457331994001 & 0.410914663988002 & 0.794542668005999 \tabularnewline
87 & 0.182360085247983 & 0.364720170495966 & 0.817639914752017 \tabularnewline
88 & 0.16866724698052 & 0.33733449396104 & 0.83133275301948 \tabularnewline
89 & 0.146035404763235 & 0.292070809526471 & 0.853964595236764 \tabularnewline
90 & 0.141401398675106 & 0.282802797350213 & 0.858598601324894 \tabularnewline
91 & 0.121453403152487 & 0.242906806304975 & 0.878546596847512 \tabularnewline
92 & 0.105538335362564 & 0.211076670725128 & 0.894461664637436 \tabularnewline
93 & 0.0904731503122692 & 0.180946300624538 & 0.909526849687731 \tabularnewline
94 & 0.0944517045305431 & 0.188903409061086 & 0.905548295469457 \tabularnewline
95 & 0.0851452882570926 & 0.170290576514185 & 0.914854711742907 \tabularnewline
96 & 0.07134375971094 & 0.14268751942188 & 0.92865624028906 \tabularnewline
97 & 0.0765390419752538 & 0.153078083950508 & 0.923460958024746 \tabularnewline
98 & 0.0639172957190733 & 0.127834591438147 & 0.936082704280927 \tabularnewline
99 & 0.0532560540094865 & 0.106512108018973 & 0.946743945990513 \tabularnewline
100 & 0.0471964643477645 & 0.094392928695529 & 0.952803535652236 \tabularnewline
101 & 0.0418237708668188 & 0.0836475417336376 & 0.958176229133181 \tabularnewline
102 & 0.0495646640737919 & 0.0991293281475839 & 0.950435335926208 \tabularnewline
103 & 0.0418159325305976 & 0.0836318650611953 & 0.958184067469402 \tabularnewline
104 & 0.0371546247444226 & 0.0743092494888452 & 0.962845375255577 \tabularnewline
105 & 0.0443799645112795 & 0.0887599290225591 & 0.95562003548872 \tabularnewline
106 & 0.0392467240758438 & 0.0784934481516876 & 0.960753275924156 \tabularnewline
107 & 0.032101540747539 & 0.0642030814950781 & 0.967898459252461 \tabularnewline
108 & 0.0306389619503842 & 0.0612779239007685 & 0.969361038049616 \tabularnewline
109 & 0.0249766509276481 & 0.0499533018552962 & 0.975023349072352 \tabularnewline
110 & 0.0206802880721736 & 0.0413605761443472 & 0.979319711927826 \tabularnewline
111 & 0.0165367018611449 & 0.0330734037222898 & 0.983463298138855 \tabularnewline
112 & 0.0174610968140109 & 0.0349221936280217 & 0.982538903185989 \tabularnewline
113 & 0.0151868048729542 & 0.0303736097459083 & 0.984813195127046 \tabularnewline
114 & 0.0203427840188158 & 0.0406855680376315 & 0.979657215981184 \tabularnewline
115 & 0.0195545789159243 & 0.0391091578318487 & 0.980445421084076 \tabularnewline
116 & 0.019156582817605 & 0.03831316563521 & 0.980843417182395 \tabularnewline
117 & 0.0161895415427563 & 0.0323790830855127 & 0.983810458457244 \tabularnewline
118 & 0.0161428798213067 & 0.0322857596426135 & 0.983857120178693 \tabularnewline
119 & 0.013111596625575 & 0.0262231932511501 & 0.986888403374425 \tabularnewline
120 & 0.0118512862501964 & 0.0237025725003928 & 0.988148713749804 \tabularnewline
121 & 0.00961138913138544 & 0.0192227782627709 & 0.990388610868615 \tabularnewline
122 & 0.0142976132082616 & 0.0285952264165232 & 0.985702386791738 \tabularnewline
123 & 0.0119182485986793 & 0.0238364971973587 & 0.988081751401321 \tabularnewline
124 & 0.0100417231243235 & 0.020083446248647 & 0.989958276875677 \tabularnewline
125 & 0.00820386346908122 & 0.0164077269381624 & 0.991796136530919 \tabularnewline
126 & 0.00652402141000281 & 0.0130480428200056 & 0.993475978589997 \tabularnewline
127 & 0.00592499471708863 & 0.0118499894341773 & 0.994075005282911 \tabularnewline
128 & 0.00498958031437604 & 0.00997916062875207 & 0.995010419685624 \tabularnewline
129 & 0.00679676778773384 & 0.0135935355754677 & 0.993203232212266 \tabularnewline
130 & 0.00737896470885744 & 0.0147579294177149 & 0.992621035291143 \tabularnewline
131 & 0.0150380035701476 & 0.0300760071402951 & 0.984961996429852 \tabularnewline
132 & 0.0179020011010297 & 0.0358040022020595 & 0.98209799889897 \tabularnewline
133 & 0.0190014277786272 & 0.0380028555572543 & 0.980998572221373 \tabularnewline
134 & 0.0179399512149199 & 0.0358799024298399 & 0.98206004878508 \tabularnewline
135 & 0.0143280831078875 & 0.028656166215775 & 0.985671916892113 \tabularnewline
136 & 0.0122309426263336 & 0.0244618852526671 & 0.987769057373666 \tabularnewline
137 & 0.00990648741823629 & 0.0198129748364726 & 0.990093512581764 \tabularnewline
138 & 0.0125384210857652 & 0.0250768421715303 & 0.987461578914235 \tabularnewline
139 & 0.0107618216212586 & 0.0215236432425171 & 0.989238178378741 \tabularnewline
140 & 0.01290529551809 & 0.0258105910361799 & 0.98709470448191 \tabularnewline
141 & 0.0201560166619618 & 0.0403120333239236 & 0.979843983338038 \tabularnewline
142 & 0.0185242814551167 & 0.0370485629102335 & 0.981475718544883 \tabularnewline
143 & 0.0150205312093496 & 0.0300410624186993 & 0.98497946879065 \tabularnewline
144 & 0.0157236012587875 & 0.031447202517575 & 0.984276398741213 \tabularnewline
145 & 0.0266974551191014 & 0.0533949102382028 & 0.973302544880899 \tabularnewline
146 & 0.032899283931278 & 0.065798567862556 & 0.967100716068722 \tabularnewline
147 & 0.0347106625346156 & 0.0694213250692313 & 0.965289337465384 \tabularnewline
148 & 0.0308268157911592 & 0.0616536315823184 & 0.969173184208841 \tabularnewline
149 & 0.0251694491161804 & 0.0503388982323608 & 0.97483055088382 \tabularnewline
150 & 0.0347431669180115 & 0.0694863338360231 & 0.965256833081989 \tabularnewline
151 & 0.0295649645330984 & 0.0591299290661968 & 0.970435035466902 \tabularnewline
152 & 0.0312118999658585 & 0.0624237999317169 & 0.968788100034142 \tabularnewline
153 & 0.0614921647203965 & 0.122984329440793 & 0.938507835279603 \tabularnewline
154 & 0.0592693484311577 & 0.118538696862315 & 0.940730651568842 \tabularnewline
155 & 0.0668514672949835 & 0.133702934589967 & 0.933148532705017 \tabularnewline
156 & 0.0568828372036372 & 0.113765674407274 & 0.943117162796363 \tabularnewline
157 & 0.0507263507797283 & 0.101452701559457 & 0.949273649220272 \tabularnewline
158 & 0.044306945773758 & 0.088613891547516 & 0.955693054226242 \tabularnewline
159 & 0.042896370559727 & 0.0857927411194539 & 0.957103629440273 \tabularnewline
160 & 0.0368289288531118 & 0.0736578577062236 & 0.963171071146888 \tabularnewline
161 & 0.0301494047603978 & 0.0602988095207957 & 0.969850595239602 \tabularnewline
162 & 0.0246882511172213 & 0.0493765022344427 & 0.975311748882779 \tabularnewline
163 & 0.0204556922545629 & 0.0409113845091258 & 0.979544307745437 \tabularnewline
164 & 0.0174663342278852 & 0.0349326684557703 & 0.982533665772115 \tabularnewline
165 & 0.0152899712295866 & 0.0305799424591732 & 0.984710028770413 \tabularnewline
166 & 0.0162158571129126 & 0.0324317142258253 & 0.983784142887087 \tabularnewline
167 & 0.0129519190795869 & 0.0259038381591739 & 0.987048080920413 \tabularnewline
168 & 0.0190097943455474 & 0.0380195886910947 & 0.980990205654453 \tabularnewline
169 & 0.0193145060535548 & 0.0386290121071096 & 0.980685493946445 \tabularnewline
170 & 0.0178019312531854 & 0.0356038625063708 & 0.982198068746815 \tabularnewline
171 & 0.016327213558843 & 0.032654427117686 & 0.983672786441157 \tabularnewline
172 & 0.0131776500985948 & 0.0263553001971896 & 0.986822349901405 \tabularnewline
173 & 0.0128128065802382 & 0.0256256131604764 & 0.987187193419762 \tabularnewline
174 & 0.014685951216998 & 0.0293719024339961 & 0.985314048783002 \tabularnewline
175 & 0.0188929908902678 & 0.0377859817805356 & 0.981107009109732 \tabularnewline
176 & 0.0151454063778119 & 0.0302908127556237 & 0.984854593622188 \tabularnewline
177 & 0.0119808719881983 & 0.0239617439763966 & 0.988019128011802 \tabularnewline
178 & 0.0100781507238407 & 0.0201563014476815 & 0.989921849276159 \tabularnewline
179 & 0.00783798740929312 & 0.0156759748185862 & 0.992162012590707 \tabularnewline
180 & 0.00689228384446776 & 0.0137845676889355 & 0.993107716155532 \tabularnewline
181 & 0.00557929233420557 & 0.0111585846684111 & 0.994420707665794 \tabularnewline
182 & 0.00430170227341254 & 0.00860340454682508 & 0.995698297726587 \tabularnewline
183 & 0.00454272486774663 & 0.00908544973549325 & 0.995457275132253 \tabularnewline
184 & 0.00346340780469989 & 0.00692681560939978 & 0.9965365921953 \tabularnewline
185 & 0.0945485528934938 & 0.189097105786988 & 0.905451447106506 \tabularnewline
186 & 0.0837673286341599 & 0.16753465726832 & 0.91623267136584 \tabularnewline
187 & 0.0937319873577452 & 0.18746397471549 & 0.906268012642255 \tabularnewline
188 & 0.0791422429758017 & 0.158284485951603 & 0.920857757024198 \tabularnewline
189 & 0.0705418654748887 & 0.141083730949777 & 0.929458134525111 \tabularnewline
190 & 0.0596705613214336 & 0.119341122642867 & 0.940329438678566 \tabularnewline
191 & 0.0516888003813329 & 0.103377600762666 & 0.948311199618667 \tabularnewline
192 & 0.0430109766894636 & 0.0860219533789271 & 0.956989023310536 \tabularnewline
193 & 0.0433120762544686 & 0.0866241525089373 & 0.956687923745531 \tabularnewline
194 & 0.0410696683806269 & 0.0821393367612539 & 0.958930331619373 \tabularnewline
195 & 0.0357027738981984 & 0.0714055477963968 & 0.964297226101802 \tabularnewline
196 & 0.0284550140215662 & 0.0569100280431325 & 0.971544985978434 \tabularnewline
197 & 0.0373222402395986 & 0.0746444804791971 & 0.962677759760401 \tabularnewline
198 & 0.0305194995352494 & 0.0610389990704987 & 0.969480500464751 \tabularnewline
199 & 0.0274057819364691 & 0.0548115638729382 & 0.972594218063531 \tabularnewline
200 & 0.0232765389624402 & 0.0465530779248805 & 0.97672346103756 \tabularnewline
201 & 0.021311355586638 & 0.042622711173276 & 0.978688644413362 \tabularnewline
202 & 0.0178165544415185 & 0.035633108883037 & 0.982183445558481 \tabularnewline
203 & 0.019846910911911 & 0.0396938218238221 & 0.980153089088089 \tabularnewline
204 & 0.0263675080730805 & 0.052735016146161 & 0.973632491926919 \tabularnewline
205 & 0.0278237067069451 & 0.0556474134138902 & 0.972176293293055 \tabularnewline
206 & 0.0217503282101588 & 0.0435006564203175 & 0.978249671789841 \tabularnewline
207 & 0.019386927081298 & 0.0387738541625959 & 0.980613072918702 \tabularnewline
208 & 0.0154903722401223 & 0.0309807444802446 & 0.984509627759878 \tabularnewline
209 & 0.0180778994705206 & 0.0361557989410412 & 0.981922100529479 \tabularnewline
210 & 0.0149636941222889 & 0.0299273882445778 & 0.985036305877711 \tabularnewline
211 & 0.0183958392351674 & 0.0367916784703349 & 0.981604160764833 \tabularnewline
212 & 0.0324717611190893 & 0.0649435222381785 & 0.967528238880911 \tabularnewline
213 & 0.0253710945551454 & 0.0507421891102907 & 0.974628905444855 \tabularnewline
214 & 0.0314791169033973 & 0.0629582338067947 & 0.968520883096603 \tabularnewline
215 & 0.0267612504796724 & 0.0535225009593448 & 0.973238749520328 \tabularnewline
216 & 0.020729367132908 & 0.041458734265816 & 0.979270632867092 \tabularnewline
217 & 0.0231370663002714 & 0.0462741326005427 & 0.976862933699729 \tabularnewline
218 & 0.0196067553481175 & 0.039213510696235 & 0.980393244651882 \tabularnewline
219 & 0.0182943903919263 & 0.0365887807838526 & 0.981705609608074 \tabularnewline
220 & 0.0137419038079816 & 0.0274838076159632 & 0.986258096192018 \tabularnewline
221 & 0.0114025137241171 & 0.0228050274482341 & 0.988597486275883 \tabularnewline
222 & 0.00831154801890267 & 0.0166230960378053 & 0.991688451981097 \tabularnewline
223 & 0.00641631381835713 & 0.0128326276367143 & 0.993583686181643 \tabularnewline
224 & 0.00491691159901389 & 0.00983382319802777 & 0.995083088400986 \tabularnewline
225 & 0.00343960111377319 & 0.00687920222754638 & 0.996560398886227 \tabularnewline
226 & 0.00710199123298366 & 0.0142039824659673 & 0.992898008767016 \tabularnewline
227 & 0.00565156735869488 & 0.0113031347173898 & 0.994348432641305 \tabularnewline
228 & 0.00412261775956946 & 0.00824523551913891 & 0.995877382240431 \tabularnewline
229 & 0.00293161983497725 & 0.0058632396699545 & 0.997068380165023 \tabularnewline
230 & 0.00207599045903002 & 0.00415198091806004 & 0.99792400954097 \tabularnewline
231 & 0.00226850989205013 & 0.00453701978410026 & 0.99773149010795 \tabularnewline
232 & 0.00779851916707009 & 0.0155970383341402 & 0.99220148083293 \tabularnewline
233 & 0.0293698404957628 & 0.0587396809915256 & 0.970630159504237 \tabularnewline
234 & 0.044950747243972 & 0.089901494487944 & 0.955049252756028 \tabularnewline
235 & 0.0330914105808944 & 0.0661828211617888 & 0.966908589419106 \tabularnewline
236 & 0.0271011514226526 & 0.0542023028453051 & 0.972898848577347 \tabularnewline
237 & 0.238959015911466 & 0.477918031822932 & 0.761040984088534 \tabularnewline
238 & 0.192949272019248 & 0.385898544038496 & 0.807050727980752 \tabularnewline
239 & 0.163289925162307 & 0.326579850324614 & 0.836710074837693 \tabularnewline
240 & 0.13044762066219 & 0.26089524132438 & 0.86955237933781 \tabularnewline
241 & 0.116817470818194 & 0.233634941636387 & 0.883182529181806 \tabularnewline
242 & 0.177907984064821 & 0.355815968129643 & 0.822092015935179 \tabularnewline
243 & 0.150453950204245 & 0.300907900408489 & 0.849546049795755 \tabularnewline
244 & 0.151452822534993 & 0.302905645069987 & 0.848547177465007 \tabularnewline
245 & 0.132852249443855 & 0.26570449888771 & 0.867147750556145 \tabularnewline
246 & 0.116475820843446 & 0.232951641686893 & 0.883524179156554 \tabularnewline
247 & 0.079256297308871 & 0.158512594617742 & 0.920743702691129 \tabularnewline
248 & 0.0704290884946231 & 0.140858176989246 & 0.929570911505377 \tabularnewline
249 & 0.0534977901152151 & 0.10699558023043 & 0.946502209884785 \tabularnewline
250 & 0.130779813206018 & 0.261559626412037 & 0.869220186793981 \tabularnewline
251 & 0.107472861596092 & 0.214945723192185 & 0.892527138403908 \tabularnewline
252 & 0.543978389503581 & 0.912043220992839 & 0.456021610496419 \tabularnewline
253 & 0.382288727481098 & 0.764577454962197 & 0.617711272518902 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185721&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]0.402475229471653[/C][C]0.804950458943307[/C][C]0.597524770528347[/C][/ROW]
[ROW][C]12[/C][C]0.799813019552285[/C][C]0.40037396089543[/C][C]0.200186980447715[/C][/ROW]
[ROW][C]13[/C][C]0.760826664987248[/C][C]0.478346670025504[/C][C]0.239173335012752[/C][/ROW]
[ROW][C]14[/C][C]0.673552621178249[/C][C]0.652894757643501[/C][C]0.326447378821751[/C][/ROW]
[ROW][C]15[/C][C]0.618114699975933[/C][C]0.763770600048135[/C][C]0.381885300024067[/C][/ROW]
[ROW][C]16[/C][C]0.680833200974805[/C][C]0.638333598050389[/C][C]0.319166799025195[/C][/ROW]
[ROW][C]17[/C][C]0.608142628027363[/C][C]0.783714743945274[/C][C]0.391857371972637[/C][/ROW]
[ROW][C]18[/C][C]0.857694380421813[/C][C]0.284611239156375[/C][C]0.142305619578187[/C][/ROW]
[ROW][C]19[/C][C]0.830980289213743[/C][C]0.338039421572514[/C][C]0.169019710786257[/C][/ROW]
[ROW][C]20[/C][C]0.780467388615394[/C][C]0.439065222769211[/C][C]0.219532611384606[/C][/ROW]
[ROW][C]21[/C][C]0.716435090676602[/C][C]0.567129818646795[/C][C]0.283564909323398[/C][/ROW]
[ROW][C]22[/C][C]0.677924859700132[/C][C]0.644150280599736[/C][C]0.322075140299868[/C][/ROW]
[ROW][C]23[/C][C]0.640080623968092[/C][C]0.719838752063816[/C][C]0.359919376031908[/C][/ROW]
[ROW][C]24[/C][C]0.608002819411591[/C][C]0.783994361176818[/C][C]0.391997180588409[/C][/ROW]
[ROW][C]25[/C][C]0.544602520253454[/C][C]0.910794959493092[/C][C]0.455397479746546[/C][/ROW]
[ROW][C]26[/C][C]0.495118873916558[/C][C]0.990237747833116[/C][C]0.504881126083442[/C][/ROW]
[ROW][C]27[/C][C]0.435695066526806[/C][C]0.871390133053611[/C][C]0.564304933473194[/C][/ROW]
[ROW][C]28[/C][C]0.489428727652135[/C][C]0.97885745530427[/C][C]0.510571272347865[/C][/ROW]
[ROW][C]29[/C][C]0.429531193251683[/C][C]0.859062386503367[/C][C]0.570468806748317[/C][/ROW]
[ROW][C]30[/C][C]0.439110691341421[/C][C]0.878221382682843[/C][C]0.560889308658579[/C][/ROW]
[ROW][C]31[/C][C]0.390777631300561[/C][C]0.781555262601123[/C][C]0.609222368699439[/C][/ROW]
[ROW][C]32[/C][C]0.386536250945876[/C][C]0.773072501891752[/C][C]0.613463749054124[/C][/ROW]
[ROW][C]33[/C][C]0.367573743538395[/C][C]0.735147487076789[/C][C]0.632426256461605[/C][/ROW]
[ROW][C]34[/C][C]0.311946111516885[/C][C]0.623892223033769[/C][C]0.688053888483115[/C][/ROW]
[ROW][C]35[/C][C]0.300790075632009[/C][C]0.601580151264018[/C][C]0.699209924367991[/C][/ROW]
[ROW][C]36[/C][C]0.399446956221478[/C][C]0.798893912442955[/C][C]0.600553043778522[/C][/ROW]
[ROW][C]37[/C][C]0.480233640547256[/C][C]0.960467281094513[/C][C]0.519766359452744[/C][/ROW]
[ROW][C]38[/C][C]0.456414493869952[/C][C]0.912828987739905[/C][C]0.543585506130048[/C][/ROW]
[ROW][C]39[/C][C]0.504036781673621[/C][C]0.991926436652758[/C][C]0.495963218326379[/C][/ROW]
[ROW][C]40[/C][C]0.48466256434735[/C][C]0.969325128694699[/C][C]0.51533743565265[/C][/ROW]
[ROW][C]41[/C][C]0.448981538814123[/C][C]0.897963077628245[/C][C]0.551018461185877[/C][/ROW]
[ROW][C]42[/C][C]0.419330929144965[/C][C]0.83866185828993[/C][C]0.580669070855035[/C][/ROW]
[ROW][C]43[/C][C]0.435488787174071[/C][C]0.870977574348142[/C][C]0.564511212825929[/C][/ROW]
[ROW][C]44[/C][C]0.388357018766522[/C][C]0.776714037533044[/C][C]0.611642981233478[/C][/ROW]
[ROW][C]45[/C][C]0.349784441710693[/C][C]0.699568883421385[/C][C]0.650215558289307[/C][/ROW]
[ROW][C]46[/C][C]0.574490258949639[/C][C]0.851019482100722[/C][C]0.425509741050361[/C][/ROW]
[ROW][C]47[/C][C]0.59251378237367[/C][C]0.81497243525266[/C][C]0.40748621762633[/C][/ROW]
[ROW][C]48[/C][C]0.554065143583593[/C][C]0.891869712832815[/C][C]0.445934856416407[/C][/ROW]
[ROW][C]49[/C][C]0.541344029892326[/C][C]0.917311940215349[/C][C]0.458655970107675[/C][/ROW]
[ROW][C]50[/C][C]0.514186099361112[/C][C]0.971627801277776[/C][C]0.485813900638888[/C][/ROW]
[ROW][C]51[/C][C]0.469970781504949[/C][C]0.939941563009899[/C][C]0.530029218495051[/C][/ROW]
[ROW][C]52[/C][C]0.42842759941914[/C][C]0.85685519883828[/C][C]0.57157240058086[/C][/ROW]
[ROW][C]53[/C][C]0.435661290444535[/C][C]0.87132258088907[/C][C]0.564338709555465[/C][/ROW]
[ROW][C]54[/C][C]0.406008878029816[/C][C]0.812017756059633[/C][C]0.593991121970184[/C][/ROW]
[ROW][C]55[/C][C]0.404277688955221[/C][C]0.808555377910443[/C][C]0.595722311044779[/C][/ROW]
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[ROW][C]201[/C][C]0.021311355586638[/C][C]0.042622711173276[/C][C]0.978688644413362[/C][/ROW]
[ROW][C]202[/C][C]0.0178165544415185[/C][C]0.035633108883037[/C][C]0.982183445558481[/C][/ROW]
[ROW][C]203[/C][C]0.019846910911911[/C][C]0.0396938218238221[/C][C]0.980153089088089[/C][/ROW]
[ROW][C]204[/C][C]0.0263675080730805[/C][C]0.052735016146161[/C][C]0.973632491926919[/C][/ROW]
[ROW][C]205[/C][C]0.0278237067069451[/C][C]0.0556474134138902[/C][C]0.972176293293055[/C][/ROW]
[ROW][C]206[/C][C]0.0217503282101588[/C][C]0.0435006564203175[/C][C]0.978249671789841[/C][/ROW]
[ROW][C]207[/C][C]0.019386927081298[/C][C]0.0387738541625959[/C][C]0.980613072918702[/C][/ROW]
[ROW][C]208[/C][C]0.0154903722401223[/C][C]0.0309807444802446[/C][C]0.984509627759878[/C][/ROW]
[ROW][C]209[/C][C]0.0180778994705206[/C][C]0.0361557989410412[/C][C]0.981922100529479[/C][/ROW]
[ROW][C]210[/C][C]0.0149636941222889[/C][C]0.0299273882445778[/C][C]0.985036305877711[/C][/ROW]
[ROW][C]211[/C][C]0.0183958392351674[/C][C]0.0367916784703349[/C][C]0.981604160764833[/C][/ROW]
[ROW][C]212[/C][C]0.0324717611190893[/C][C]0.0649435222381785[/C][C]0.967528238880911[/C][/ROW]
[ROW][C]213[/C][C]0.0253710945551454[/C][C]0.0507421891102907[/C][C]0.974628905444855[/C][/ROW]
[ROW][C]214[/C][C]0.0314791169033973[/C][C]0.0629582338067947[/C][C]0.968520883096603[/C][/ROW]
[ROW][C]215[/C][C]0.0267612504796724[/C][C]0.0535225009593448[/C][C]0.973238749520328[/C][/ROW]
[ROW][C]216[/C][C]0.020729367132908[/C][C]0.041458734265816[/C][C]0.979270632867092[/C][/ROW]
[ROW][C]217[/C][C]0.0231370663002714[/C][C]0.0462741326005427[/C][C]0.976862933699729[/C][/ROW]
[ROW][C]218[/C][C]0.0196067553481175[/C][C]0.039213510696235[/C][C]0.980393244651882[/C][/ROW]
[ROW][C]219[/C][C]0.0182943903919263[/C][C]0.0365887807838526[/C][C]0.981705609608074[/C][/ROW]
[ROW][C]220[/C][C]0.0137419038079816[/C][C]0.0274838076159632[/C][C]0.986258096192018[/C][/ROW]
[ROW][C]221[/C][C]0.0114025137241171[/C][C]0.0228050274482341[/C][C]0.988597486275883[/C][/ROW]
[ROW][C]222[/C][C]0.00831154801890267[/C][C]0.0166230960378053[/C][C]0.991688451981097[/C][/ROW]
[ROW][C]223[/C][C]0.00641631381835713[/C][C]0.0128326276367143[/C][C]0.993583686181643[/C][/ROW]
[ROW][C]224[/C][C]0.00491691159901389[/C][C]0.00983382319802777[/C][C]0.995083088400986[/C][/ROW]
[ROW][C]225[/C][C]0.00343960111377319[/C][C]0.00687920222754638[/C][C]0.996560398886227[/C][/ROW]
[ROW][C]226[/C][C]0.00710199123298366[/C][C]0.0142039824659673[/C][C]0.992898008767016[/C][/ROW]
[ROW][C]227[/C][C]0.00565156735869488[/C][C]0.0113031347173898[/C][C]0.994348432641305[/C][/ROW]
[ROW][C]228[/C][C]0.00412261775956946[/C][C]0.00824523551913891[/C][C]0.995877382240431[/C][/ROW]
[ROW][C]229[/C][C]0.00293161983497725[/C][C]0.0058632396699545[/C][C]0.997068380165023[/C][/ROW]
[ROW][C]230[/C][C]0.00207599045903002[/C][C]0.00415198091806004[/C][C]0.99792400954097[/C][/ROW]
[ROW][C]231[/C][C]0.00226850989205013[/C][C]0.00453701978410026[/C][C]0.99773149010795[/C][/ROW]
[ROW][C]232[/C][C]0.00779851916707009[/C][C]0.0155970383341402[/C][C]0.99220148083293[/C][/ROW]
[ROW][C]233[/C][C]0.0293698404957628[/C][C]0.0587396809915256[/C][C]0.970630159504237[/C][/ROW]
[ROW][C]234[/C][C]0.044950747243972[/C][C]0.089901494487944[/C][C]0.955049252756028[/C][/ROW]
[ROW][C]235[/C][C]0.0330914105808944[/C][C]0.0661828211617888[/C][C]0.966908589419106[/C][/ROW]
[ROW][C]236[/C][C]0.0271011514226526[/C][C]0.0542023028453051[/C][C]0.972898848577347[/C][/ROW]
[ROW][C]237[/C][C]0.238959015911466[/C][C]0.477918031822932[/C][C]0.761040984088534[/C][/ROW]
[ROW][C]238[/C][C]0.192949272019248[/C][C]0.385898544038496[/C][C]0.807050727980752[/C][/ROW]
[ROW][C]239[/C][C]0.163289925162307[/C][C]0.326579850324614[/C][C]0.836710074837693[/C][/ROW]
[ROW][C]240[/C][C]0.13044762066219[/C][C]0.26089524132438[/C][C]0.86955237933781[/C][/ROW]
[ROW][C]241[/C][C]0.116817470818194[/C][C]0.233634941636387[/C][C]0.883182529181806[/C][/ROW]
[ROW][C]242[/C][C]0.177907984064821[/C][C]0.355815968129643[/C][C]0.822092015935179[/C][/ROW]
[ROW][C]243[/C][C]0.150453950204245[/C][C]0.300907900408489[/C][C]0.849546049795755[/C][/ROW]
[ROW][C]244[/C][C]0.151452822534993[/C][C]0.302905645069987[/C][C]0.848547177465007[/C][/ROW]
[ROW][C]245[/C][C]0.132852249443855[/C][C]0.26570449888771[/C][C]0.867147750556145[/C][/ROW]
[ROW][C]246[/C][C]0.116475820843446[/C][C]0.232951641686893[/C][C]0.883524179156554[/C][/ROW]
[ROW][C]247[/C][C]0.079256297308871[/C][C]0.158512594617742[/C][C]0.920743702691129[/C][/ROW]
[ROW][C]248[/C][C]0.0704290884946231[/C][C]0.140858176989246[/C][C]0.929570911505377[/C][/ROW]
[ROW][C]249[/C][C]0.0534977901152151[/C][C]0.10699558023043[/C][C]0.946502209884785[/C][/ROW]
[ROW][C]250[/C][C]0.130779813206018[/C][C]0.261559626412037[/C][C]0.869220186793981[/C][/ROW]
[ROW][C]251[/C][C]0.107472861596092[/C][C]0.214945723192185[/C][C]0.892527138403908[/C][/ROW]
[ROW][C]252[/C][C]0.543978389503581[/C][C]0.912043220992839[/C][C]0.456021610496419[/C][/ROW]
[ROW][C]253[/C][C]0.382288727481098[/C][C]0.764577454962197[/C][C]0.617711272518902[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185721&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185721&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4024752294716530.8049504589433070.597524770528347
120.7998130195522850.400373960895430.200186980447715
130.7608266649872480.4783466700255040.239173335012752
140.6735526211782490.6528947576435010.326447378821751
150.6181146999759330.7637706000481350.381885300024067
160.6808332009748050.6383335980503890.319166799025195
170.6081426280273630.7837147439452740.391857371972637
180.8576943804218130.2846112391563750.142305619578187
190.8309802892137430.3380394215725140.169019710786257
200.7804673886153940.4390652227692110.219532611384606
210.7164350906766020.5671298186467950.283564909323398
220.6779248597001320.6441502805997360.322075140299868
230.6400806239680920.7198387520638160.359919376031908
240.6080028194115910.7839943611768180.391997180588409
250.5446025202534540.9107949594930920.455397479746546
260.4951188739165580.9902377478331160.504881126083442
270.4356950665268060.8713901330536110.564304933473194
280.4894287276521350.978857455304270.510571272347865
290.4295311932516830.8590623865033670.570468806748317
300.4391106913414210.8782213826828430.560889308658579
310.3907776313005610.7815552626011230.609222368699439
320.3865362509458760.7730725018917520.613463749054124
330.3675737435383950.7351474870767890.632426256461605
340.3119461115168850.6238922230337690.688053888483115
350.3007900756320090.6015801512640180.699209924367991
360.3994469562214780.7988939124429550.600553043778522
370.4802336405472560.9604672810945130.519766359452744
380.4564144938699520.9128289877399050.543585506130048
390.5040367816736210.9919264366527580.495963218326379
400.484662564347350.9693251286946990.51533743565265
410.4489815388141230.8979630776282450.551018461185877
420.4193309291449650.838661858289930.580669070855035
430.4354887871740710.8709775743481420.564511212825929
440.3883570187665220.7767140375330440.611642981233478
450.3497844417106930.6995688834213850.650215558289307
460.5744902589496390.8510194821007220.425509741050361
470.592513782373670.814972435252660.40748621762633
480.5540651435835930.8918697128328150.445934856416407
490.5413440298923260.9173119402153490.458655970107675
500.5141860993611120.9716278012777760.485813900638888
510.4699707815049490.9399415630098990.530029218495051
520.428427599419140.856855198838280.57157240058086
530.4356612904445350.871322580889070.564338709555465
540.4060088780298160.8120177560596330.593991121970184
550.4042776889552210.8085553779104430.595722311044779
560.4207346972468480.8414693944936960.579265302753152
570.3801415205875410.7602830411750820.619858479412459
580.3656289476085520.7312578952171040.634371052391448
590.3265080055678580.6530160111357170.673491994432141
600.3533720372327280.7067440744654550.646627962767272
610.3272204306739440.6544408613478880.672779569326056
620.2931319780199710.5862639560399410.706868021980029
630.2591175374011110.5182350748022220.740882462598889
640.2265144769622940.4530289539245870.773485523037706
650.2029094885878310.4058189771756630.797090511412169
660.1931317947470220.3862635894940440.806868205252978
670.1958312451995280.3916624903990550.804168754800472
680.3093029909179420.6186059818358850.690697009082058
690.4222184651265260.8444369302530520.577781534873474
700.3901976574227390.7803953148454780.609802342577261
710.4626709970512260.9253419941024520.537329002948774
720.4277994404901990.8555988809803990.572200559509801
730.4210243957462350.842048791492470.578975604253765
740.3996317935083080.7992635870166160.600368206491692
750.3635917953613020.7271835907226050.636408204638698
760.4415616385406130.8831232770812270.558438361459387
770.4034167204255080.8068334408510160.596583279574492
780.3826230797418060.7652461594836120.617376920258194
790.3920519671908410.7841039343816820.607948032809159
800.3577742307192110.7155484614384230.642225769280789
810.3260492214260720.6520984428521440.673950778573928
820.2940411602945140.5880823205890280.705958839705486
830.2670596465294420.5341192930588840.732940353470558
840.2370940357481770.4741880714963530.762905964251823
850.2342196076945590.4684392153891170.765780392305441
860.2054573319940010.4109146639880020.794542668005999
870.1823600852479830.3647201704959660.817639914752017
880.168667246980520.337334493961040.83133275301948
890.1460354047632350.2920708095264710.853964595236764
900.1414013986751060.2828027973502130.858598601324894
910.1214534031524870.2429068063049750.878546596847512
920.1055383353625640.2110766707251280.894461664637436
930.09047315031226920.1809463006245380.909526849687731
940.09445170453054310.1889034090610860.905548295469457
950.08514528825709260.1702905765141850.914854711742907
960.071343759710940.142687519421880.92865624028906
970.07653904197525380.1530780839505080.923460958024746
980.06391729571907330.1278345914381470.936082704280927
990.05325605400948650.1065121080189730.946743945990513
1000.04719646434776450.0943929286955290.952803535652236
1010.04182377086681880.08364754173363760.958176229133181
1020.04956466407379190.09912932814758390.950435335926208
1030.04181593253059760.08363186506119530.958184067469402
1040.03715462474442260.07430924948884520.962845375255577
1050.04437996451127950.08875992902255910.95562003548872
1060.03924672407584380.07849344815168760.960753275924156
1070.0321015407475390.06420308149507810.967898459252461
1080.03063896195038420.06127792390076850.969361038049616
1090.02497665092764810.04995330185529620.975023349072352
1100.02068028807217360.04136057614434720.979319711927826
1110.01653670186114490.03307340372228980.983463298138855
1120.01746109681401090.03492219362802170.982538903185989
1130.01518680487295420.03037360974590830.984813195127046
1140.02034278401881580.04068556803763150.979657215981184
1150.01955457891592430.03910915783184870.980445421084076
1160.0191565828176050.038313165635210.980843417182395
1170.01618954154275630.03237908308551270.983810458457244
1180.01614287982130670.03228575964261350.983857120178693
1190.0131115966255750.02622319325115010.986888403374425
1200.01185128625019640.02370257250039280.988148713749804
1210.009611389131385440.01922277826277090.990388610868615
1220.01429761320826160.02859522641652320.985702386791738
1230.01191824859867930.02383649719735870.988081751401321
1240.01004172312432350.0200834462486470.989958276875677
1250.008203863469081220.01640772693816240.991796136530919
1260.006524021410002810.01304804282000560.993475978589997
1270.005924994717088630.01184998943417730.994075005282911
1280.004989580314376040.009979160628752070.995010419685624
1290.006796767787733840.01359353557546770.993203232212266
1300.007378964708857440.01475792941771490.992621035291143
1310.01503800357014760.03007600714029510.984961996429852
1320.01790200110102970.03580400220205950.98209799889897
1330.01900142777862720.03800285555725430.980998572221373
1340.01793995121491990.03587990242983990.98206004878508
1350.01432808310788750.0286561662157750.985671916892113
1360.01223094262633360.02446188525266710.987769057373666
1370.009906487418236290.01981297483647260.990093512581764
1380.01253842108576520.02507684217153030.987461578914235
1390.01076182162125860.02152364324251710.989238178378741
1400.012905295518090.02581059103617990.98709470448191
1410.02015601666196180.04031203332392360.979843983338038
1420.01852428145511670.03704856291023350.981475718544883
1430.01502053120934960.03004106241869930.98497946879065
1440.01572360125878750.0314472025175750.984276398741213
1450.02669745511910140.05339491023820280.973302544880899
1460.0328992839312780.0657985678625560.967100716068722
1470.03471066253461560.06942132506923130.965289337465384
1480.03082681579115920.06165363158231840.969173184208841
1490.02516944911618040.05033889823236080.97483055088382
1500.03474316691801150.06948633383602310.965256833081989
1510.02956496453309840.05912992906619680.970435035466902
1520.03121189996585850.06242379993171690.968788100034142
1530.06149216472039650.1229843294407930.938507835279603
1540.05926934843115770.1185386968623150.940730651568842
1550.06685146729498350.1337029345899670.933148532705017
1560.05688283720363720.1137656744072740.943117162796363
1570.05072635077972830.1014527015594570.949273649220272
1580.0443069457737580.0886138915475160.955693054226242
1590.0428963705597270.08579274111945390.957103629440273
1600.03682892885311180.07365785770622360.963171071146888
1610.03014940476039780.06029880952079570.969850595239602
1620.02468825111722130.04937650223444270.975311748882779
1630.02045569225456290.04091138450912580.979544307745437
1640.01746633422788520.03493266845577030.982533665772115
1650.01528997122958660.03057994245917320.984710028770413
1660.01621585711291260.03243171422582530.983784142887087
1670.01295191907958690.02590383815917390.987048080920413
1680.01900979434554740.03801958869109470.980990205654453
1690.01931450605355480.03862901210710960.980685493946445
1700.01780193125318540.03560386250637080.982198068746815
1710.0163272135588430.0326544271176860.983672786441157
1720.01317765009859480.02635530019718960.986822349901405
1730.01281280658023820.02562561316047640.987187193419762
1740.0146859512169980.02937190243399610.985314048783002
1750.01889299089026780.03778598178053560.981107009109732
1760.01514540637781190.03029081275562370.984854593622188
1770.01198087198819830.02396174397639660.988019128011802
1780.01007815072384070.02015630144768150.989921849276159
1790.007837987409293120.01567597481858620.992162012590707
1800.006892283844467760.01378456768893550.993107716155532
1810.005579292334205570.01115858466841110.994420707665794
1820.004301702273412540.008603404546825080.995698297726587
1830.004542724867746630.009085449735493250.995457275132253
1840.003463407804699890.006926815609399780.9965365921953
1850.09454855289349380.1890971057869880.905451447106506
1860.08376732863415990.167534657268320.91623267136584
1870.09373198735774520.187463974715490.906268012642255
1880.07914224297580170.1582844859516030.920857757024198
1890.07054186547488870.1410837309497770.929458134525111
1900.05967056132143360.1193411226428670.940329438678566
1910.05168880038133290.1033776007626660.948311199618667
1920.04301097668946360.08602195337892710.956989023310536
1930.04331207625446860.08662415250893730.956687923745531
1940.04106966838062690.08213933676125390.958930331619373
1950.03570277389819840.07140554779639680.964297226101802
1960.02845501402156620.05691002804313250.971544985978434
1970.03732224023959860.07464448047919710.962677759760401
1980.03051949953524940.06103899907049870.969480500464751
1990.02740578193646910.05481156387293820.972594218063531
2000.02327653896244020.04655307792488050.97672346103756
2010.0213113555866380.0426227111732760.978688644413362
2020.01781655444151850.0356331088830370.982183445558481
2030.0198469109119110.03969382182382210.980153089088089
2040.02636750807308050.0527350161461610.973632491926919
2050.02782370670694510.05564741341389020.972176293293055
2060.02175032821015880.04350065642031750.978249671789841
2070.0193869270812980.03877385416259590.980613072918702
2080.01549037224012230.03098074448024460.984509627759878
2090.01807789947052060.03615579894104120.981922100529479
2100.01496369412228890.02992738824457780.985036305877711
2110.01839583923516740.03679167847033490.981604160764833
2120.03247176111908930.06494352223817850.967528238880911
2130.02537109455514540.05074218911029070.974628905444855
2140.03147911690339730.06295823380679470.968520883096603
2150.02676125047967240.05352250095934480.973238749520328
2160.0207293671329080.0414587342658160.979270632867092
2170.02313706630027140.04627413260054270.976862933699729
2180.01960675534811750.0392135106962350.980393244651882
2190.01829439039192630.03658878078385260.981705609608074
2200.01374190380798160.02748380761596320.986258096192018
2210.01140251372411710.02280502744823410.988597486275883
2220.008311548018902670.01662309603780530.991688451981097
2230.006416313818357130.01283262763671430.993583686181643
2240.004916911599013890.009833823198027770.995083088400986
2250.003439601113773190.006879202227546380.996560398886227
2260.007101991232983660.01420398246596730.992898008767016
2270.005651567358694880.01130313471738980.994348432641305
2280.004122617759569460.008245235519138910.995877382240431
2290.002931619834977250.00586323966995450.997068380165023
2300.002075990459030020.004151980918060040.99792400954097
2310.002268509892050130.004537019784100260.99773149010795
2320.007798519167070090.01559703833414020.99220148083293
2330.02936984049576280.05873968099152560.970630159504237
2340.0449507472439720.0899014944879440.955049252756028
2350.03309141058089440.06618282116178880.966908589419106
2360.02710115142265260.05420230284530510.972898848577347
2370.2389590159114660.4779180318229320.761040984088534
2380.1929492720192480.3858985440384960.807050727980752
2390.1632899251623070.3265798503246140.836710074837693
2400.130447620662190.260895241324380.86955237933781
2410.1168174708181940.2336349416363870.883182529181806
2420.1779079840648210.3558159681296430.822092015935179
2430.1504539502042450.3009079004084890.849546049795755
2440.1514528225349930.3029056450699870.848547177465007
2450.1328522494438550.265704498887710.867147750556145
2460.1164758208434460.2329516416868930.883524179156554
2470.0792562973088710.1585125946177420.920743702691129
2480.07042908849462310.1408581769892460.929570911505377
2490.05349779011521510.106995580230430.946502209884785
2500.1307798132060180.2615596264120370.869220186793981
2510.1074728615960920.2149457231921850.892527138403908
2520.5439783895035810.9120432209928390.456021610496419
2530.3822887274810980.7645774549621970.617711272518902







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.0411522633744856NOK
5% type I error level860.353909465020576NOK
10% type I error level1250.51440329218107NOK

\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 & 10 & 0.0411522633744856 & NOK \tabularnewline
5% type I error level & 86 & 0.353909465020576 & NOK \tabularnewline
10% type I error level & 125 & 0.51440329218107 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185721&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]10[/C][C]0.0411522633744856[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]86[/C][C]0.353909465020576[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]125[/C][C]0.51440329218107[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185721&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185721&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.0411522633744856NOK
5% type I error level860.353909465020576NOK
10% type I error level1250.51440329218107NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}