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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 13:14:12 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322158491hbavgzbrga4a1jd.htm/, Retrieved Fri, 29 Mar 2024 09:40:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147127, Retrieved Fri, 29 Mar 2024 09:40:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 18:14:12] [46e17293cd0520480fa187e99449b207] [Current]
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Dataseries X:
14	13	41	12	53
18	16	39	11	86
11	19	30	14	66
12	15	31	12	67
16	14	34	21	76
18	13	35	12	78
14	19	39	22	53
14	15	34	11	80
15	14	36	10	74
15	15	37	13	76
17	16	38	10	79
19	16	36	8	54
10	16	38	15	67
16	16	39	14	54
18	17	33	10	87
14	15	32	14	58
14	15	36	14	75
17	20	38	11	88
14	18	39	10	64
16	16	32	13	57
18	16	32	7	66
11	16	31	14	68
14	19	39	12	54
12	16	37	14	56
17	17	39	11	86
9	17	41	9	80
16	16	36	11	76
14	15	33	15	69
15	16	33	14	78
11	14	34	13	67
16	15	31	9	80
13	12	27	15	54
17	14	37	10	71
15	16	34	11	84
14	14	34	13	74
16	7	32	8	71
9	10	29	20	63
15	14	36	12	71
17	16	29	10	76
13	16	35	10	69
15	16	37	9	74
16	14	34	14	75
16	20	38	8	54
12	14	35	14	52
12	14	38	11	69
11	11	37	13	68
15	14	38	9	65
15	15	33	11	75
17	16	36	15	74
13	14	38	11	75
16	16	32	10	72
14	14	32	14	67
11	12	32	18	63
12	16	34	14	62
12	9	32	11	63
15	14	37	12	76
16	16	39	13	74
15	16	29	9	67
12	15	37	10	73
12	16	35	15	70
8	12	30	20	53
13	16	38	12	77
11	16	34	12	77
14	14	31	14	52
15	16	34	13	54
10	17	35	11	80
11	18	36	17	66
12	18	30	12	73
15	12	39	13	63
15	16	35	14	69
14	10	38	13	67
16	14	31	15	54
15	18	34	13	81
15	18	38	10	69
13	16	34	11	84
12	17	39	19	80
17	16	37	13	70
13	16	34	17	69
15	13	28	13	77
13	16	37	9	54
15	16	33	11	79
16	20	37	10	30
15	16	35	9	71
16	15	37	12	73
15	15	32	12	72
14	16	33	13	77
15	14	38	13	75
14	16	33	12	69
13	16	29	15	54
7	15	33	22	70
17	12	31	13	73
13	17	36	15	54
15	16	35	13	77
14	15	32	15	82
13	13	29	10	80
16	16	39	11	80
12	16	37	16	69
14	16	35	11	78
17	16	37	11	81
15	14	32	10	76
17	16	38	10	76
12	16	37	16	73
16	20	36	12	85
11	15	32	11	66
15	16	33	16	79
9	13	40	19	68
16	17	38	11	76
15	16	41	16	71
10	16	36	15	54
10	12	43	24	46
15	16	30	14	82
11	16	31	15	74
13	17	32	11	88
14	13	32	15	38
18	12	37	12	76
16	18	37	10	86
14	14	33	14	54
14	14	34	13	70
14	13	33	9	69
14	16	38	15	90
12	13	33	15	54
14	16	31	14	76
15	13	38	11	89
15	16	37	8	76
15	15	33	11	73
13	16	31	11	79
17	15	39	8	90
17	17	44	10	74
19	15	33	11	81
15	12	35	13	72
13	16	32	11	71
9	10	28	20	66
15	16	40	10	77
15	12	27	15	65
15	14	37	12	74
16	15	32	14	82
11	13	28	23	54
14	15	34	14	63
11	11	30	16	54
15	12	35	11	64
13	8	31	12	69
15	16	32	10	54
16	15	30	14	84
14	17	30	12	86
15	16	31	12	77
16	10	40	11	89
16	18	32	12	76
11	13	36	13	60
12	16	32	11	75
9	13	35	19	73
16	10	38	12	85
13	15	42	17	79
16	16	34	9	71
12	16	35	12	72
9	14	35	19	69
13	10	33	18	78
13	17	36	15	54
14	13	32	14	69
19	15	33	11	81
13	16	34	9	84
12	12	32	18	84
13	13	34	16	69




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=147127&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=147127&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147127&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 14.6633477594121 + 0.062586920850308Learning[t] + 0.0501534777877726Connected[t] -0.34600999963173Depression[t] + 0.024722046127967`Belonging `[t] -1.05075722573654M1[t] -0.724924910489709M2[t] + 0.00389266642390334M3[t] -1.17093606188741M4[t] -0.0626785758889235M5[t] -1.21148726676641M6[t] + 0.0958045316136582M7[t] -0.93618993019189M8[t] + 0.302874051916047M9[t] -0.975158158591014M10[t] -1.12689558410354M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  14.6633477594121 +  0.062586920850308Learning[t] +  0.0501534777877726Connected[t] -0.34600999963173Depression[t] +  0.024722046127967`Belonging
`[t] -1.05075722573654M1[t] -0.724924910489709M2[t] +  0.00389266642390334M3[t] -1.17093606188741M4[t] -0.0626785758889235M5[t] -1.21148726676641M6[t] +  0.0958045316136582M7[t] -0.93618993019189M8[t] +  0.302874051916047M9[t] -0.975158158591014M10[t] -1.12689558410354M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147127&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  14.6633477594121 +  0.062586920850308Learning[t] +  0.0501534777877726Connected[t] -0.34600999963173Depression[t] +  0.024722046127967`Belonging
`[t] -1.05075722573654M1[t] -0.724924910489709M2[t] +  0.00389266642390334M3[t] -1.17093606188741M4[t] -0.0626785758889235M5[t] -1.21148726676641M6[t] +  0.0958045316136582M7[t] -0.93618993019189M8[t] +  0.302874051916047M9[t] -0.975158158591014M10[t] -1.12689558410354M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147127&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147127&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
Happiness[t] = + 14.6633477594121 + 0.062586920850308Learning[t] + 0.0501534777877726Connected[t] -0.34600999963173Depression[t] + 0.024722046127967`Belonging `[t] -1.05075722573654M1[t] -0.724924910489709M2[t] + 0.00389266642390334M3[t] -1.17093606188741M4[t] -0.0626785758889235M5[t] -1.21148726676641M6[t] + 0.0958045316136582M7[t] -0.93618993019189M8[t] + 0.302874051916047M9[t] -0.975158158591014M10[t] -1.12689558410354M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.66334775941212.3803346.160200
Learning0.0625869208503080.0727980.85970.3913440.195672
Connected0.05015347778777260.0482971.03840.3007810.15039
Depression-0.346009999631730.054574-6.340200
`Belonging `0.0247220461279670.0153591.60960.1096370.054818
M1-1.050757225736540.752902-1.39560.1649510.082476
M2-0.7249249104897090.750422-0.9660.335630.167815
M30.003892666423903340.7663420.00510.9959540.497977
M4-1.170936061887410.752593-1.55590.1219050.060952
M5-0.06267857588892350.753656-0.08320.9338330.466917
M6-1.211487266766410.752536-1.60990.1095860.054793
M70.09580453161365820.7612920.12580.9000280.450014
M8-0.936189930191890.768661-1.21790.2252080.112604
M90.3028740519160470.7656960.39560.6930120.346506
M10-0.9751581585910140.764683-1.27520.2042480.102124
M11-1.126895584103540.765763-1.47160.1432810.071641

\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) & 14.6633477594121 & 2.380334 & 6.1602 & 0 & 0 \tabularnewline
Learning & 0.062586920850308 & 0.072798 & 0.8597 & 0.391344 & 0.195672 \tabularnewline
Connected & 0.0501534777877726 & 0.048297 & 1.0384 & 0.300781 & 0.15039 \tabularnewline
Depression & -0.34600999963173 & 0.054574 & -6.3402 & 0 & 0 \tabularnewline
`Belonging
` & 0.024722046127967 & 0.015359 & 1.6096 & 0.109637 & 0.054818 \tabularnewline
M1 & -1.05075722573654 & 0.752902 & -1.3956 & 0.164951 & 0.082476 \tabularnewline
M2 & -0.724924910489709 & 0.750422 & -0.966 & 0.33563 & 0.167815 \tabularnewline
M3 & 0.00389266642390334 & 0.766342 & 0.0051 & 0.995954 & 0.497977 \tabularnewline
M4 & -1.17093606188741 & 0.752593 & -1.5559 & 0.121905 & 0.060952 \tabularnewline
M5 & -0.0626785758889235 & 0.753656 & -0.0832 & 0.933833 & 0.466917 \tabularnewline
M6 & -1.21148726676641 & 0.752536 & -1.6099 & 0.109586 & 0.054793 \tabularnewline
M7 & 0.0958045316136582 & 0.761292 & 0.1258 & 0.900028 & 0.450014 \tabularnewline
M8 & -0.93618993019189 & 0.768661 & -1.2179 & 0.225208 & 0.112604 \tabularnewline
M9 & 0.302874051916047 & 0.765696 & 0.3956 & 0.693012 & 0.346506 \tabularnewline
M10 & -0.975158158591014 & 0.764683 & -1.2752 & 0.204248 & 0.102124 \tabularnewline
M11 & -1.12689558410354 & 0.765763 & -1.4716 & 0.143281 & 0.071641 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147127&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]14.6633477594121[/C][C]2.380334[/C][C]6.1602[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Learning[/C][C]0.062586920850308[/C][C]0.072798[/C][C]0.8597[/C][C]0.391344[/C][C]0.195672[/C][/ROW]
[ROW][C]Connected[/C][C]0.0501534777877726[/C][C]0.048297[/C][C]1.0384[/C][C]0.300781[/C][C]0.15039[/C][/ROW]
[ROW][C]Depression[/C][C]-0.34600999963173[/C][C]0.054574[/C][C]-6.3402[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Belonging
`[/C][C]0.024722046127967[/C][C]0.015359[/C][C]1.6096[/C][C]0.109637[/C][C]0.054818[/C][/ROW]
[ROW][C]M1[/C][C]-1.05075722573654[/C][C]0.752902[/C][C]-1.3956[/C][C]0.164951[/C][C]0.082476[/C][/ROW]
[ROW][C]M2[/C][C]-0.724924910489709[/C][C]0.750422[/C][C]-0.966[/C][C]0.33563[/C][C]0.167815[/C][/ROW]
[ROW][C]M3[/C][C]0.00389266642390334[/C][C]0.766342[/C][C]0.0051[/C][C]0.995954[/C][C]0.497977[/C][/ROW]
[ROW][C]M4[/C][C]-1.17093606188741[/C][C]0.752593[/C][C]-1.5559[/C][C]0.121905[/C][C]0.060952[/C][/ROW]
[ROW][C]M5[/C][C]-0.0626785758889235[/C][C]0.753656[/C][C]-0.0832[/C][C]0.933833[/C][C]0.466917[/C][/ROW]
[ROW][C]M6[/C][C]-1.21148726676641[/C][C]0.752536[/C][C]-1.6099[/C][C]0.109586[/C][C]0.054793[/C][/ROW]
[ROW][C]M7[/C][C]0.0958045316136582[/C][C]0.761292[/C][C]0.1258[/C][C]0.900028[/C][C]0.450014[/C][/ROW]
[ROW][C]M8[/C][C]-0.93618993019189[/C][C]0.768661[/C][C]-1.2179[/C][C]0.225208[/C][C]0.112604[/C][/ROW]
[ROW][C]M9[/C][C]0.302874051916047[/C][C]0.765696[/C][C]0.3956[/C][C]0.693012[/C][C]0.346506[/C][/ROW]
[ROW][C]M10[/C][C]-0.975158158591014[/C][C]0.764683[/C][C]-1.2752[/C][C]0.204248[/C][C]0.102124[/C][/ROW]
[ROW][C]M11[/C][C]-1.12689558410354[/C][C]0.765763[/C][C]-1.4716[/C][C]0.143281[/C][C]0.071641[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147127&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147127&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)14.66334775941212.3803346.160200
Learning0.0625869208503080.0727980.85970.3913440.195672
Connected0.05015347778777260.0482971.03840.3007810.15039
Depression-0.346009999631730.054574-6.340200
`Belonging `0.0247220461279670.0153591.60960.1096370.054818
M1-1.050757225736540.752902-1.39560.1649510.082476
M2-0.7249249104897090.750422-0.9660.335630.167815
M30.003892666423903340.7663420.00510.9959540.497977
M4-1.170936061887410.752593-1.55590.1219050.060952
M5-0.06267857588892350.753656-0.08320.9338330.466917
M6-1.211487266766410.752536-1.60990.1095860.054793
M70.09580453161365820.7612920.12580.9000280.450014
M8-0.936189930191890.768661-1.21790.2252080.112604
M90.3028740519160470.7656960.39560.6930120.346506
M10-0.9751581585910140.764683-1.27520.2042480.102124
M11-1.126895584103540.765763-1.47160.1432810.071641







Multiple Linear Regression - Regression Statistics
Multiple R0.615372287306375
R-squared0.37868305198468
Adjusted R-squared0.314849118969408
F-TEST (value)5.93231584044929
F-TEST (DF numerator)15
F-TEST (DF denominator)146
p-value1.64585656214911e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93493627345523
Sum Squared Residuals546.620843820589

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.615372287306375 \tabularnewline
R-squared & 0.37868305198468 \tabularnewline
Adjusted R-squared & 0.314849118969408 \tabularnewline
F-TEST (value) & 5.93231584044929 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 1.64585656214911e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.93493627345523 \tabularnewline
Sum Squared Residuals & 546.620843820589 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147127&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.615372287306375[/C][/ROW]
[ROW][C]R-squared[/C][C]0.37868305198468[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.314849118969408[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.93231584044929[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/C][/ROW]
[ROW][C]p-value[/C][C]1.64585656214911e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.93493627345523[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]546.620843820589[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147127&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147127&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 R0.615372287306375
R-squared0.37868305198468
Adjusted R-squared0.314849118969408
F-TEST (value)5.93231584044929
F-TEST (DF numerator)15
F-TEST (DF denominator)146
p-value1.64585656214911e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93493627345523
Sum Squared Residuals546.620843820589







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.64066154322970.35933845677026
21815.21578518730662.78421481269345
31114.1485113052266-3.1485113052266
41213.4902304166933-1.49023041669326
51611.79476983367094.20523016632911
61813.79706178867244.20293821132763
71411.60233787378892.3976621262111
81414.5428335810474-0.542833581047397
91516.0172953207445-1.0172953207445
101513.86341760223631.13658239776374
111714.93661671264092.0633832873591
121916.03717418723322.96282581276681
131012.9860405193137-2.98604051931366
141613.38664971231642.61335028768358
151816.07700086410351.92299913589647
161412.62586548006591.37413451993413
171414.3550116613909-0.355011661390885
181714.97886112889922.02113887110075
191415.963813455927-1.96381345592699
201613.24448648611552.75551351388454
211816.78210888116551.21789111883452
221113.0812972877045-2.08129728770447
231413.8644598005170.135540199483034
241214.0607117594865-2.06071175948652
251714.952539792912.04746020708997
26915.9223667862281-6.92236678622806
271615.54692186957720.453078130422824
281412.60195146562951.39804853437045
291514.34130428726180.658695712738224
301113.1915427246955-2.19154272469554
311616.1163876087531-0.116387608753087
321311.9771852761281.022814723872
331715.99328266014841.00671733985163
341514.66534045801040.33465954198955
351413.44918873025420.550811269745827
361615.69355277260480.306447227395235
37910.3301995114513-1.33019951145134
381514.22331022069140.776689779308616
391715.54185752469451.4581424753055
401314.4948953402141-1.49489534021405
411516.1730800120596-1.17308001205965
421613.04330909408752.95669090591245
431616.4836333578236-0.483633357823623
441212.8001528475066-0.800152847506597
451215.6479820460485-3.64798204604848
461113.4152935498113-2.4152935498113
471514.81134422478050.18865577521952
481515.3052598028117-0.30525980281168
491713.05878788663393.94121211336612
501314.7685153604105-1.76851536041053
511615.59342977354590.406570226454052
521412.78577697436731.21422302563274
531112.2859324356263-1.28593243562635
541212.8470963361246-0.847096336124594
551214.6787247780001-2.67872477800011
561514.18580890941680.81419109058319
571615.25489959691320.745100403086756
581514.68631828415960.313681715840392
591214.675544037235-2.67554403723502
601213.9605034500708-1.96050345007078
61810.2583063696601-2.25830636966006
621314.5971232947353-1.59712329473535
631115.1253269604979-4.12532696049787
641412.364792804661.63520719534001
651514.14413865761010.855861342389929
661014.4428635639613-4.44286356396127
671113.4407271173975-2.4407271173975
681214.0109161099197-2.01091610991973
691514.73260940610440.267390593895626
701513.30663324498351.69336675501647
711413.22640063510830.773599364891738
721613.23916295917162.7608370408284
731513.94872909491821.05127090508182
741515.2165407666757-0.216540766675686
751315.6443912830254-2.64439128302537
761211.91594868293750.0840513170624827
771714.69015182902092.30984817097914
781311.98212066012521.01787933987483
791514.38254719677830.617452803221667
801314.8051277351973-1.80512773519734
811515.7696089600899-0.769608960089903
821614.07716808349651.92283191650349
831514.93438990988560.0656100901144201
841615.11041962207510.889580377924894
851513.78417296127171.21582703872826
861414.0003459061648-0.000345906164753977
871514.80531293806070.194687061939322
881413.7025683853750.297431614624954
891313.2013512694077-0.201351269407748
90710.1640523094564-3.16405230945641
911714.37153252477952.62846747522052
921312.74150118046950.258498819530502
931515.1284518241461-0.128451824146054
941413.06896249080170.931037509198256
951314.322196696128-1.32219669612799
961615.79237782102850.207622178971542
971212.6393211341501-0.639321134150086
981414.8173949071317-0.817394907131726
991715.72068557800481.27931442199522
1001514.39231538804590.607684611954114
1011715.92666758247161.07333241752838
1021212.5774792776321-0.577479277632088
1031615.76566983368810.234330166311864
1041114.0964179796803-3.09641797968031
1051514.03955896193130.960441038068746
106911.6148678270849-2.61486782708485
1071614.57902749547561.42097250452442
1081513.94013636329371.05986363670635
1091012.5643469640745-2.56434696407454
110109.679039574725240.320960425274757
1111514.35630328072320.643696719276847
1121112.6878416615441-1.68784166154415
1131315.6389881904992-2.63898819049917
1141411.61968951129522.38031048870481
1151815.09262952952172.90737047047826
1161615.37539705336120.624602946638829
1171413.98835396629490.0116460337050773
1181413.50203797125480.497962028745164
1191414.5968780995032-0.596878099503177
1201414.6054048059934-0.605404805993436
1211212.2261257681603-0.226125768160297
1221413.52930690482950.470693095170488
1231515.7808546622654-0.780854662265368
1241515.4602766179488-0.460276617948825
1251515.1931371346668-0.193137134666822
1261314.1549406858319-1.1549406858319
1271717.1108458919667-0.110845891966667
1281715.36721992348971.63278007651033
1291915.75646613149553.24353386850447
1301513.47646169959791.52353830040207
1311314.0919094772588-1.0919094772588
132911.404969397784-2.40496939778401
1331515.0636179343275-0.0636179343275189
1341512.46039280323782.53960719676218
1351515.0764474137767-0.0764474137766686
1361613.21919458713712.78080541286292
1371110.19535703201520.804642967984787
1381412.80923146140231.19076853859775
1391112.7510432508148-1.75104325081483
1401514.00967355823680.990326441763225
1411314.5753761768005-1.57537617680049
1421514.16938211822760.830617881772375
1431613.21237220160132.78762779839867
1441415.2059057189249-1.20590571892489
1451513.92021663497411.07978336502589
1461614.96458327837641.03541672162362
1471615.1254718004950.874528199505028
1481113.096759641404-2.09675964140401
1491215.2550146699853-3.25501466998529
150911.2513815606104-2.25138156061043
1511615.24010758076060.7598924192394
1521312.84327935943120.156720640568767
1531616.3140060681174-0.314006068117399
1541214.0728193826309-2.07281938263089
155911.2996719796117-2.29967197961173
1561312.64442133952190.355578660478068
1571312.62693388492480.373066115075153
1581413.21864529717060.781354702829409
1591915.45748474600343.54251525399661
1601315.1615825539775-2.16158255397751
1611212.8050954043137-0.805095404313654
1621312.1403698972060.859630102794021

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.6406615432297 & 0.35933845677026 \tabularnewline
2 & 18 & 15.2157851873066 & 2.78421481269345 \tabularnewline
3 & 11 & 14.1485113052266 & -3.1485113052266 \tabularnewline
4 & 12 & 13.4902304166933 & -1.49023041669326 \tabularnewline
5 & 16 & 11.7947698336709 & 4.20523016632911 \tabularnewline
6 & 18 & 13.7970617886724 & 4.20293821132763 \tabularnewline
7 & 14 & 11.6023378737889 & 2.3976621262111 \tabularnewline
8 & 14 & 14.5428335810474 & -0.542833581047397 \tabularnewline
9 & 15 & 16.0172953207445 & -1.0172953207445 \tabularnewline
10 & 15 & 13.8634176022363 & 1.13658239776374 \tabularnewline
11 & 17 & 14.9366167126409 & 2.0633832873591 \tabularnewline
12 & 19 & 16.0371741872332 & 2.96282581276681 \tabularnewline
13 & 10 & 12.9860405193137 & -2.98604051931366 \tabularnewline
14 & 16 & 13.3866497123164 & 2.61335028768358 \tabularnewline
15 & 18 & 16.0770008641035 & 1.92299913589647 \tabularnewline
16 & 14 & 12.6258654800659 & 1.37413451993413 \tabularnewline
17 & 14 & 14.3550116613909 & -0.355011661390885 \tabularnewline
18 & 17 & 14.9788611288992 & 2.02113887110075 \tabularnewline
19 & 14 & 15.963813455927 & -1.96381345592699 \tabularnewline
20 & 16 & 13.2444864861155 & 2.75551351388454 \tabularnewline
21 & 18 & 16.7821088811655 & 1.21789111883452 \tabularnewline
22 & 11 & 13.0812972877045 & -2.08129728770447 \tabularnewline
23 & 14 & 13.864459800517 & 0.135540199483034 \tabularnewline
24 & 12 & 14.0607117594865 & -2.06071175948652 \tabularnewline
25 & 17 & 14.95253979291 & 2.04746020708997 \tabularnewline
26 & 9 & 15.9223667862281 & -6.92236678622806 \tabularnewline
27 & 16 & 15.5469218695772 & 0.453078130422824 \tabularnewline
28 & 14 & 12.6019514656295 & 1.39804853437045 \tabularnewline
29 & 15 & 14.3413042872618 & 0.658695712738224 \tabularnewline
30 & 11 & 13.1915427246955 & -2.19154272469554 \tabularnewline
31 & 16 & 16.1163876087531 & -0.116387608753087 \tabularnewline
32 & 13 & 11.977185276128 & 1.022814723872 \tabularnewline
33 & 17 & 15.9932826601484 & 1.00671733985163 \tabularnewline
34 & 15 & 14.6653404580104 & 0.33465954198955 \tabularnewline
35 & 14 & 13.4491887302542 & 0.550811269745827 \tabularnewline
36 & 16 & 15.6935527726048 & 0.306447227395235 \tabularnewline
37 & 9 & 10.3301995114513 & -1.33019951145134 \tabularnewline
38 & 15 & 14.2233102206914 & 0.776689779308616 \tabularnewline
39 & 17 & 15.5418575246945 & 1.4581424753055 \tabularnewline
40 & 13 & 14.4948953402141 & -1.49489534021405 \tabularnewline
41 & 15 & 16.1730800120596 & -1.17308001205965 \tabularnewline
42 & 16 & 13.0433090940875 & 2.95669090591245 \tabularnewline
43 & 16 & 16.4836333578236 & -0.483633357823623 \tabularnewline
44 & 12 & 12.8001528475066 & -0.800152847506597 \tabularnewline
45 & 12 & 15.6479820460485 & -3.64798204604848 \tabularnewline
46 & 11 & 13.4152935498113 & -2.4152935498113 \tabularnewline
47 & 15 & 14.8113442247805 & 0.18865577521952 \tabularnewline
48 & 15 & 15.3052598028117 & -0.30525980281168 \tabularnewline
49 & 17 & 13.0587878866339 & 3.94121211336612 \tabularnewline
50 & 13 & 14.7685153604105 & -1.76851536041053 \tabularnewline
51 & 16 & 15.5934297735459 & 0.406570226454052 \tabularnewline
52 & 14 & 12.7857769743673 & 1.21422302563274 \tabularnewline
53 & 11 & 12.2859324356263 & -1.28593243562635 \tabularnewline
54 & 12 & 12.8470963361246 & -0.847096336124594 \tabularnewline
55 & 12 & 14.6787247780001 & -2.67872477800011 \tabularnewline
56 & 15 & 14.1858089094168 & 0.81419109058319 \tabularnewline
57 & 16 & 15.2548995969132 & 0.745100403086756 \tabularnewline
58 & 15 & 14.6863182841596 & 0.313681715840392 \tabularnewline
59 & 12 & 14.675544037235 & -2.67554403723502 \tabularnewline
60 & 12 & 13.9605034500708 & -1.96050345007078 \tabularnewline
61 & 8 & 10.2583063696601 & -2.25830636966006 \tabularnewline
62 & 13 & 14.5971232947353 & -1.59712329473535 \tabularnewline
63 & 11 & 15.1253269604979 & -4.12532696049787 \tabularnewline
64 & 14 & 12.36479280466 & 1.63520719534001 \tabularnewline
65 & 15 & 14.1441386576101 & 0.855861342389929 \tabularnewline
66 & 10 & 14.4428635639613 & -4.44286356396127 \tabularnewline
67 & 11 & 13.4407271173975 & -2.4407271173975 \tabularnewline
68 & 12 & 14.0109161099197 & -2.01091610991973 \tabularnewline
69 & 15 & 14.7326094061044 & 0.267390593895626 \tabularnewline
70 & 15 & 13.3066332449835 & 1.69336675501647 \tabularnewline
71 & 14 & 13.2264006351083 & 0.773599364891738 \tabularnewline
72 & 16 & 13.2391629591716 & 2.7608370408284 \tabularnewline
73 & 15 & 13.9487290949182 & 1.05127090508182 \tabularnewline
74 & 15 & 15.2165407666757 & -0.216540766675686 \tabularnewline
75 & 13 & 15.6443912830254 & -2.64439128302537 \tabularnewline
76 & 12 & 11.9159486829375 & 0.0840513170624827 \tabularnewline
77 & 17 & 14.6901518290209 & 2.30984817097914 \tabularnewline
78 & 13 & 11.9821206601252 & 1.01787933987483 \tabularnewline
79 & 15 & 14.3825471967783 & 0.617452803221667 \tabularnewline
80 & 13 & 14.8051277351973 & -1.80512773519734 \tabularnewline
81 & 15 & 15.7696089600899 & -0.769608960089903 \tabularnewline
82 & 16 & 14.0771680834965 & 1.92283191650349 \tabularnewline
83 & 15 & 14.9343899098856 & 0.0656100901144201 \tabularnewline
84 & 16 & 15.1104196220751 & 0.889580377924894 \tabularnewline
85 & 15 & 13.7841729612717 & 1.21582703872826 \tabularnewline
86 & 14 & 14.0003459061648 & -0.000345906164753977 \tabularnewline
87 & 15 & 14.8053129380607 & 0.194687061939322 \tabularnewline
88 & 14 & 13.702568385375 & 0.297431614624954 \tabularnewline
89 & 13 & 13.2013512694077 & -0.201351269407748 \tabularnewline
90 & 7 & 10.1640523094564 & -3.16405230945641 \tabularnewline
91 & 17 & 14.3715325247795 & 2.62846747522052 \tabularnewline
92 & 13 & 12.7415011804695 & 0.258498819530502 \tabularnewline
93 & 15 & 15.1284518241461 & -0.128451824146054 \tabularnewline
94 & 14 & 13.0689624908017 & 0.931037509198256 \tabularnewline
95 & 13 & 14.322196696128 & -1.32219669612799 \tabularnewline
96 & 16 & 15.7923778210285 & 0.207622178971542 \tabularnewline
97 & 12 & 12.6393211341501 & -0.639321134150086 \tabularnewline
98 & 14 & 14.8173949071317 & -0.817394907131726 \tabularnewline
99 & 17 & 15.7206855780048 & 1.27931442199522 \tabularnewline
100 & 15 & 14.3923153880459 & 0.607684611954114 \tabularnewline
101 & 17 & 15.9266675824716 & 1.07333241752838 \tabularnewline
102 & 12 & 12.5774792776321 & -0.577479277632088 \tabularnewline
103 & 16 & 15.7656698336881 & 0.234330166311864 \tabularnewline
104 & 11 & 14.0964179796803 & -3.09641797968031 \tabularnewline
105 & 15 & 14.0395589619313 & 0.960441038068746 \tabularnewline
106 & 9 & 11.6148678270849 & -2.61486782708485 \tabularnewline
107 & 16 & 14.5790274954756 & 1.42097250452442 \tabularnewline
108 & 15 & 13.9401363632937 & 1.05986363670635 \tabularnewline
109 & 10 & 12.5643469640745 & -2.56434696407454 \tabularnewline
110 & 10 & 9.67903957472524 & 0.320960425274757 \tabularnewline
111 & 15 & 14.3563032807232 & 0.643696719276847 \tabularnewline
112 & 11 & 12.6878416615441 & -1.68784166154415 \tabularnewline
113 & 13 & 15.6389881904992 & -2.63898819049917 \tabularnewline
114 & 14 & 11.6196895112952 & 2.38031048870481 \tabularnewline
115 & 18 & 15.0926295295217 & 2.90737047047826 \tabularnewline
116 & 16 & 15.3753970533612 & 0.624602946638829 \tabularnewline
117 & 14 & 13.9883539662949 & 0.0116460337050773 \tabularnewline
118 & 14 & 13.5020379712548 & 0.497962028745164 \tabularnewline
119 & 14 & 14.5968780995032 & -0.596878099503177 \tabularnewline
120 & 14 & 14.6054048059934 & -0.605404805993436 \tabularnewline
121 & 12 & 12.2261257681603 & -0.226125768160297 \tabularnewline
122 & 14 & 13.5293069048295 & 0.470693095170488 \tabularnewline
123 & 15 & 15.7808546622654 & -0.780854662265368 \tabularnewline
124 & 15 & 15.4602766179488 & -0.460276617948825 \tabularnewline
125 & 15 & 15.1931371346668 & -0.193137134666822 \tabularnewline
126 & 13 & 14.1549406858319 & -1.1549406858319 \tabularnewline
127 & 17 & 17.1108458919667 & -0.110845891966667 \tabularnewline
128 & 17 & 15.3672199234897 & 1.63278007651033 \tabularnewline
129 & 19 & 15.7564661314955 & 3.24353386850447 \tabularnewline
130 & 15 & 13.4764616995979 & 1.52353830040207 \tabularnewline
131 & 13 & 14.0919094772588 & -1.0919094772588 \tabularnewline
132 & 9 & 11.404969397784 & -2.40496939778401 \tabularnewline
133 & 15 & 15.0636179343275 & -0.0636179343275189 \tabularnewline
134 & 15 & 12.4603928032378 & 2.53960719676218 \tabularnewline
135 & 15 & 15.0764474137767 & -0.0764474137766686 \tabularnewline
136 & 16 & 13.2191945871371 & 2.78080541286292 \tabularnewline
137 & 11 & 10.1953570320152 & 0.804642967984787 \tabularnewline
138 & 14 & 12.8092314614023 & 1.19076853859775 \tabularnewline
139 & 11 & 12.7510432508148 & -1.75104325081483 \tabularnewline
140 & 15 & 14.0096735582368 & 0.990326441763225 \tabularnewline
141 & 13 & 14.5753761768005 & -1.57537617680049 \tabularnewline
142 & 15 & 14.1693821182276 & 0.830617881772375 \tabularnewline
143 & 16 & 13.2123722016013 & 2.78762779839867 \tabularnewline
144 & 14 & 15.2059057189249 & -1.20590571892489 \tabularnewline
145 & 15 & 13.9202166349741 & 1.07978336502589 \tabularnewline
146 & 16 & 14.9645832783764 & 1.03541672162362 \tabularnewline
147 & 16 & 15.125471800495 & 0.874528199505028 \tabularnewline
148 & 11 & 13.096759641404 & -2.09675964140401 \tabularnewline
149 & 12 & 15.2550146699853 & -3.25501466998529 \tabularnewline
150 & 9 & 11.2513815606104 & -2.25138156061043 \tabularnewline
151 & 16 & 15.2401075807606 & 0.7598924192394 \tabularnewline
152 & 13 & 12.8432793594312 & 0.156720640568767 \tabularnewline
153 & 16 & 16.3140060681174 & -0.314006068117399 \tabularnewline
154 & 12 & 14.0728193826309 & -2.07281938263089 \tabularnewline
155 & 9 & 11.2996719796117 & -2.29967197961173 \tabularnewline
156 & 13 & 12.6444213395219 & 0.355578660478068 \tabularnewline
157 & 13 & 12.6269338849248 & 0.373066115075153 \tabularnewline
158 & 14 & 13.2186452971706 & 0.781354702829409 \tabularnewline
159 & 19 & 15.4574847460034 & 3.54251525399661 \tabularnewline
160 & 13 & 15.1615825539775 & -2.16158255397751 \tabularnewline
161 & 12 & 12.8050954043137 & -0.805095404313654 \tabularnewline
162 & 13 & 12.140369897206 & 0.859630102794021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147127&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]14[/C][C]13.6406615432297[/C][C]0.35933845677026[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.2157851873066[/C][C]2.78421481269345[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.1485113052266[/C][C]-3.1485113052266[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.4902304166933[/C][C]-1.49023041669326[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.7947698336709[/C][C]4.20523016632911[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]13.7970617886724[/C][C]4.20293821132763[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]11.6023378737889[/C][C]2.3976621262111[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.5428335810474[/C][C]-0.542833581047397[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]16.0172953207445[/C][C]-1.0172953207445[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]13.8634176022363[/C][C]1.13658239776374[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]14.9366167126409[/C][C]2.0633832873591[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]16.0371741872332[/C][C]2.96282581276681[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]12.9860405193137[/C][C]-2.98604051931366[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.3866497123164[/C][C]2.61335028768358[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]16.0770008641035[/C][C]1.92299913589647[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.6258654800659[/C][C]1.37413451993413[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.3550116613909[/C][C]-0.355011661390885[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]14.9788611288992[/C][C]2.02113887110075[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.963813455927[/C][C]-1.96381345592699[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.2444864861155[/C][C]2.75551351388454[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]16.7821088811655[/C][C]1.21789111883452[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.0812972877045[/C][C]-2.08129728770447[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.864459800517[/C][C]0.135540199483034[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.0607117594865[/C][C]-2.06071175948652[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.95253979291[/C][C]2.04746020708997[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.9223667862281[/C][C]-6.92236678622806[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.5469218695772[/C][C]0.453078130422824[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]12.6019514656295[/C][C]1.39804853437045[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.3413042872618[/C][C]0.658695712738224[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.1915427246955[/C][C]-2.19154272469554[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]16.1163876087531[/C][C]-0.116387608753087[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]11.977185276128[/C][C]1.022814723872[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.9932826601484[/C][C]1.00671733985163[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.6653404580104[/C][C]0.33465954198955[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.4491887302542[/C][C]0.550811269745827[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.6935527726048[/C][C]0.306447227395235[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.3301995114513[/C][C]-1.33019951145134[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.2233102206914[/C][C]0.776689779308616[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.5418575246945[/C][C]1.4581424753055[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.4948953402141[/C][C]-1.49489534021405[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]16.1730800120596[/C][C]-1.17308001205965[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.0433090940875[/C][C]2.95669090591245[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]16.4836333578236[/C][C]-0.483633357823623[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.8001528475066[/C][C]-0.800152847506597[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]15.6479820460485[/C][C]-3.64798204604848[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.4152935498113[/C][C]-2.4152935498113[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.8113442247805[/C][C]0.18865577521952[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.3052598028117[/C][C]-0.30525980281168[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.0587878866339[/C][C]3.94121211336612[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.7685153604105[/C][C]-1.76851536041053[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.5934297735459[/C][C]0.406570226454052[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.7857769743673[/C][C]1.21422302563274[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]12.2859324356263[/C][C]-1.28593243562635[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]12.8470963361246[/C][C]-0.847096336124594[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.6787247780001[/C][C]-2.67872477800011[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.1858089094168[/C][C]0.81419109058319[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.2548995969132[/C][C]0.745100403086756[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.6863182841596[/C][C]0.313681715840392[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.675544037235[/C][C]-2.67554403723502[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.9605034500708[/C][C]-1.96050345007078[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.2583063696601[/C][C]-2.25830636966006[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.5971232947353[/C][C]-1.59712329473535[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]15.1253269604979[/C][C]-4.12532696049787[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.36479280466[/C][C]1.63520719534001[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.1441386576101[/C][C]0.855861342389929[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.4428635639613[/C][C]-4.44286356396127[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.4407271173975[/C][C]-2.4407271173975[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.0109161099197[/C][C]-2.01091610991973[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.7326094061044[/C][C]0.267390593895626[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.3066332449835[/C][C]1.69336675501647[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.2264006351083[/C][C]0.773599364891738[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.2391629591716[/C][C]2.7608370408284[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]13.9487290949182[/C][C]1.05127090508182[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.2165407666757[/C][C]-0.216540766675686[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.6443912830254[/C][C]-2.64439128302537[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.9159486829375[/C][C]0.0840513170624827[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.6901518290209[/C][C]2.30984817097914[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.9821206601252[/C][C]1.01787933987483[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.3825471967783[/C][C]0.617452803221667[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.8051277351973[/C][C]-1.80512773519734[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.7696089600899[/C][C]-0.769608960089903[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.0771680834965[/C][C]1.92283191650349[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.9343899098856[/C][C]0.0656100901144201[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]15.1104196220751[/C][C]0.889580377924894[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]13.7841729612717[/C][C]1.21582703872826[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0003459061648[/C][C]-0.000345906164753977[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.8053129380607[/C][C]0.194687061939322[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]13.702568385375[/C][C]0.297431614624954[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.2013512694077[/C][C]-0.201351269407748[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.1640523094564[/C][C]-3.16405230945641[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.3715325247795[/C][C]2.62846747522052[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.7415011804695[/C][C]0.258498819530502[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.1284518241461[/C][C]-0.128451824146054[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.0689624908017[/C][C]0.931037509198256[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.322196696128[/C][C]-1.32219669612799[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7923778210285[/C][C]0.207622178971542[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.6393211341501[/C][C]-0.639321134150086[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.8173949071317[/C][C]-0.817394907131726[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]15.7206855780048[/C][C]1.27931442199522[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.3923153880459[/C][C]0.607684611954114[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.9266675824716[/C][C]1.07333241752838[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.5774792776321[/C][C]-0.577479277632088[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.7656698336881[/C][C]0.234330166311864[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.0964179796803[/C][C]-3.09641797968031[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]14.0395589619313[/C][C]0.960441038068746[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.6148678270849[/C][C]-2.61486782708485[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.5790274954756[/C][C]1.42097250452442[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.9401363632937[/C][C]1.05986363670635[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.5643469640745[/C][C]-2.56434696407454[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.67903957472524[/C][C]0.320960425274757[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.3563032807232[/C][C]0.643696719276847[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]12.6878416615441[/C][C]-1.68784166154415[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.6389881904992[/C][C]-2.63898819049917[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.6196895112952[/C][C]2.38031048870481[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]15.0926295295217[/C][C]2.90737047047826[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.3753970533612[/C][C]0.624602946638829[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.9883539662949[/C][C]0.0116460337050773[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.5020379712548[/C][C]0.497962028745164[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.5968780995032[/C][C]-0.596878099503177[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.6054048059934[/C][C]-0.605404805993436[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.2261257681603[/C][C]-0.226125768160297[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.5293069048295[/C][C]0.470693095170488[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.7808546622654[/C][C]-0.780854662265368[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.4602766179488[/C][C]-0.460276617948825[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.1931371346668[/C][C]-0.193137134666822[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.1549406858319[/C][C]-1.1549406858319[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]17.1108458919667[/C][C]-0.110845891966667[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.3672199234897[/C][C]1.63278007651033[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.7564661314955[/C][C]3.24353386850447[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.4764616995979[/C][C]1.52353830040207[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.0919094772588[/C][C]-1.0919094772588[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]11.404969397784[/C][C]-2.40496939778401[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.0636179343275[/C][C]-0.0636179343275189[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.4603928032378[/C][C]2.53960719676218[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.0764474137767[/C][C]-0.0764474137766686[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.2191945871371[/C][C]2.78080541286292[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]10.1953570320152[/C][C]0.804642967984787[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.8092314614023[/C][C]1.19076853859775[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.7510432508148[/C][C]-1.75104325081483[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.0096735582368[/C][C]0.990326441763225[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]14.5753761768005[/C][C]-1.57537617680049[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.1693821182276[/C][C]0.830617881772375[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.2123722016013[/C][C]2.78762779839867[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]15.2059057189249[/C][C]-1.20590571892489[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.9202166349741[/C][C]1.07978336502589[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.9645832783764[/C][C]1.03541672162362[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]15.125471800495[/C][C]0.874528199505028[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.096759641404[/C][C]-2.09675964140401[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]15.2550146699853[/C][C]-3.25501466998529[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.2513815606104[/C][C]-2.25138156061043[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.2401075807606[/C][C]0.7598924192394[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.8432793594312[/C][C]0.156720640568767[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]16.3140060681174[/C][C]-0.314006068117399[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.0728193826309[/C][C]-2.07281938263089[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.2996719796117[/C][C]-2.29967197961173[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.6444213395219[/C][C]0.355578660478068[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]12.6269338849248[/C][C]0.373066115075153[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.2186452971706[/C][C]0.781354702829409[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.4574847460034[/C][C]3.54251525399661[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.1615825539775[/C][C]-2.16158255397751[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.8050954043137[/C][C]-0.805095404313654[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.140369897206[/C][C]0.859630102794021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147127&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147127&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
11413.64066154322970.35933845677026
21815.21578518730662.78421481269345
31114.1485113052266-3.1485113052266
41213.4902304166933-1.49023041669326
51611.79476983367094.20523016632911
61813.79706178867244.20293821132763
71411.60233787378892.3976621262111
81414.5428335810474-0.542833581047397
91516.0172953207445-1.0172953207445
101513.86341760223631.13658239776374
111714.93661671264092.0633832873591
121916.03717418723322.96282581276681
131012.9860405193137-2.98604051931366
141613.38664971231642.61335028768358
151816.07700086410351.92299913589647
161412.62586548006591.37413451993413
171414.3550116613909-0.355011661390885
181714.97886112889922.02113887110075
191415.963813455927-1.96381345592699
201613.24448648611552.75551351388454
211816.78210888116551.21789111883452
221113.0812972877045-2.08129728770447
231413.8644598005170.135540199483034
241214.0607117594865-2.06071175948652
251714.952539792912.04746020708997
26915.9223667862281-6.92236678622806
271615.54692186957720.453078130422824
281412.60195146562951.39804853437045
291514.34130428726180.658695712738224
301113.1915427246955-2.19154272469554
311616.1163876087531-0.116387608753087
321311.9771852761281.022814723872
331715.99328266014841.00671733985163
341514.66534045801040.33465954198955
351413.44918873025420.550811269745827
361615.69355277260480.306447227395235
37910.3301995114513-1.33019951145134
381514.22331022069140.776689779308616
391715.54185752469451.4581424753055
401314.4948953402141-1.49489534021405
411516.1730800120596-1.17308001205965
421613.04330909408752.95669090591245
431616.4836333578236-0.483633357823623
441212.8001528475066-0.800152847506597
451215.6479820460485-3.64798204604848
461113.4152935498113-2.4152935498113
471514.81134422478050.18865577521952
481515.3052598028117-0.30525980281168
491713.05878788663393.94121211336612
501314.7685153604105-1.76851536041053
511615.59342977354590.406570226454052
521412.78577697436731.21422302563274
531112.2859324356263-1.28593243562635
541212.8470963361246-0.847096336124594
551214.6787247780001-2.67872477800011
561514.18580890941680.81419109058319
571615.25489959691320.745100403086756
581514.68631828415960.313681715840392
591214.675544037235-2.67554403723502
601213.9605034500708-1.96050345007078
61810.2583063696601-2.25830636966006
621314.5971232947353-1.59712329473535
631115.1253269604979-4.12532696049787
641412.364792804661.63520719534001
651514.14413865761010.855861342389929
661014.4428635639613-4.44286356396127
671113.4407271173975-2.4407271173975
681214.0109161099197-2.01091610991973
691514.73260940610440.267390593895626
701513.30663324498351.69336675501647
711413.22640063510830.773599364891738
721613.23916295917162.7608370408284
731513.94872909491821.05127090508182
741515.2165407666757-0.216540766675686
751315.6443912830254-2.64439128302537
761211.91594868293750.0840513170624827
771714.69015182902092.30984817097914
781311.98212066012521.01787933987483
791514.38254719677830.617452803221667
801314.8051277351973-1.80512773519734
811515.7696089600899-0.769608960089903
821614.07716808349651.92283191650349
831514.93438990988560.0656100901144201
841615.11041962207510.889580377924894
851513.78417296127171.21582703872826
861414.0003459061648-0.000345906164753977
871514.80531293806070.194687061939322
881413.7025683853750.297431614624954
891313.2013512694077-0.201351269407748
90710.1640523094564-3.16405230945641
911714.37153252477952.62846747522052
921312.74150118046950.258498819530502
931515.1284518241461-0.128451824146054
941413.06896249080170.931037509198256
951314.322196696128-1.32219669612799
961615.79237782102850.207622178971542
971212.6393211341501-0.639321134150086
981414.8173949071317-0.817394907131726
991715.72068557800481.27931442199522
1001514.39231538804590.607684611954114
1011715.92666758247161.07333241752838
1021212.5774792776321-0.577479277632088
1031615.76566983368810.234330166311864
1041114.0964179796803-3.09641797968031
1051514.03955896193130.960441038068746
106911.6148678270849-2.61486782708485
1071614.57902749547561.42097250452442
1081513.94013636329371.05986363670635
1091012.5643469640745-2.56434696407454
110109.679039574725240.320960425274757
1111514.35630328072320.643696719276847
1121112.6878416615441-1.68784166154415
1131315.6389881904992-2.63898819049917
1141411.61968951129522.38031048870481
1151815.09262952952172.90737047047826
1161615.37539705336120.624602946638829
1171413.98835396629490.0116460337050773
1181413.50203797125480.497962028745164
1191414.5968780995032-0.596878099503177
1201414.6054048059934-0.605404805993436
1211212.2261257681603-0.226125768160297
1221413.52930690482950.470693095170488
1231515.7808546622654-0.780854662265368
1241515.4602766179488-0.460276617948825
1251515.1931371346668-0.193137134666822
1261314.1549406858319-1.1549406858319
1271717.1108458919667-0.110845891966667
1281715.36721992348971.63278007651033
1291915.75646613149553.24353386850447
1301513.47646169959791.52353830040207
1311314.0919094772588-1.0919094772588
132911.404969397784-2.40496939778401
1331515.0636179343275-0.0636179343275189
1341512.46039280323782.53960719676218
1351515.0764474137767-0.0764474137766686
1361613.21919458713712.78080541286292
1371110.19535703201520.804642967984787
1381412.80923146140231.19076853859775
1391112.7510432508148-1.75104325081483
1401514.00967355823680.990326441763225
1411314.5753761768005-1.57537617680049
1421514.16938211822760.830617881772375
1431613.21237220160132.78762779839867
1441415.2059057189249-1.20590571892489
1451513.92021663497411.07978336502589
1461614.96458327837641.03541672162362
1471615.1254718004950.874528199505028
1481113.096759641404-2.09675964140401
1491215.2550146699853-3.25501466998529
150911.2513815606104-2.25138156061043
1511615.24010758076060.7598924192394
1521312.84327935943120.156720640568767
1531616.3140060681174-0.314006068117399
1541214.0728193826309-2.07281938263089
155911.2996719796117-2.29967197961173
1561312.64442133952190.355578660478068
1571312.62693388492480.373066115075153
1581413.21864529717060.781354702829409
1591915.45748474600343.54251525399661
1601315.1615825539775-2.16158255397751
1611212.8050954043137-0.805095404313654
1621312.1403698972060.859630102794021







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.1801474991544890.3602949983089780.819852500845511
200.802797217292110.394405565415780.19720278270789
210.9466693158501190.1066613682997610.0533306841498807
220.9259315443483680.1481369113032640.074068455651632
230.8917598425208610.2164803149582780.108240157479139
240.9715649598887190.05687008022256240.0284350401112812
250.9866795147280410.02664097054391790.013320485271959
260.999939240780780.0001215184384401366.0759219220068e-05
270.9998772324390870.0002455351218269620.000122767560913481
280.9997822807210370.0004354385579249720.000217719278962486
290.9995872246021460.0008255507957070980.000412775397853549
300.9999227498645820.0001545002708361347.72501354180669e-05
310.9998479637523290.0003040724953425160.000152036247671258
320.9997334930731830.0005330138536341850.000266506926817092
330.9995286856927420.0009426286145153140.000471314307257657
340.9992062685200250.00158746295995080.000793731479975401
350.9988245892229180.00235082155416420.0011754107770821
360.9981424392598990.003715121480202020.00185756074010101
370.997445576133180.005108847733640970.00255442386682048
380.9963896757643820.007220648471235380.00361032423561769
390.9958474863248990.008305027350201210.0041525136751006
400.9947465931271490.01050681374570140.00525340687285069
410.9929270424628660.01414591507426810.00707295753713405
420.9930704458594080.01385910828118460.0069295541405923
430.9902809657187280.01943806856254330.00971903428127163
440.988121644705430.02375671058914050.0118783552945703
450.9951540816616260.009691836676748950.00484591833837448
460.9950957223541660.009808555291667060.00490427764583353
470.9927250988220430.01454980235591410.00727490117795706
480.9902773863725780.0194452272548430.0097226136274215
490.9960105316575470.007978936684905850.00398946834245293
500.9954442201089670.009111559782066690.00455577989103335
510.9934453937597670.01310921248046540.00655460624023268
520.9914493114027720.01710137719445620.0085506885972281
530.9899700126192960.02005997476140750.0100299873807038
540.988247069502450.02350586099510060.0117529304975503
550.989957619403650.02008476119269940.0100423805963497
560.9865328617616770.02693427647664560.0134671382383228
570.9816842548738760.03663149025224750.0183157451261238
580.9769897694589480.04602046108210470.0230102305410523
590.9833634584074610.03327308318507820.0166365415925391
600.9864336499424320.02713270011513630.0135663500575681
610.9873787945366460.02524241092670750.0126212054633537
620.9858975628262580.02820487434748320.0141024371737416
630.9953881621461550.009223675707689360.00461183785384468
640.9948127839627120.01037443207457580.00518721603728791
650.9930524500241650.01389509995166910.00694754997583457
660.9988344724339060.002331055132187070.00116552756609353
670.9991373753067970.001725249386405490.000862624693202747
680.9991997679836650.001600464032670270.000800232016335137
690.9987967966682480.002406406663504510.00120320333175226
700.998662160566580.002675678866839790.00133783943341989
710.9981208780453750.003758243909250540.00187912195462527
720.998625994763750.00274801047250080.0013740052362504
730.9981742978891770.003651404221645770.00182570211082288
740.9975483585745750.004903282850850190.0024516414254251
750.9984435198498410.003112960300317690.00155648015015884
760.9978308892924150.004338221415169360.00216911070758468
770.9982737195230620.003452560953876620.00172628047693831
780.9977606228734560.004478754253087680.00223937712654384
790.9969028871826880.006194225634623160.00309711281731158
800.9969594165419480.006081166916103230.00304058345805161
810.9960171493212970.007965701357406030.00398285067870302
820.9954725556756690.009054888648662760.00452744432433138
830.9935039643698670.01299207126026620.00649603563013309
840.9914421407204520.01711571855909540.00855785927954771
850.989663692259350.02067261548129950.0103363077406498
860.9861474856218990.02770502875620250.0138525143781013
870.982113281892930.03577343621413930.0178867181070696
880.9764162273462910.04716754530741770.0235837726537089
890.9697969252659650.06040614946807090.0302030747340354
900.9806548646570820.03869027068583610.019345135342918
910.9844104455966240.03117910880675280.0155895544033764
920.9787573289050760.04248534218984720.0212426710949236
930.9718248039706920.0563503920586150.0281751960293075
940.9657455727193820.06850885456123570.0342544272806178
950.9602510743045550.07949785139089090.0397489256954454
960.9482955188611770.1034089622776460.0517044811388228
970.9341010812345710.1317978375308570.0658989187654285
980.9291730851565270.1416538296869450.0708269148434726
990.9155078844904170.1689842310191650.0844921155095827
1000.8996304002772370.2007391994455260.100369599722763
1010.8932177522349760.2135644955300480.106782247765024
1020.8702599602174890.2594800795650210.129740039782511
1030.8415718007223150.316856398555370.158428199277685
1040.905371142382270.189257715235460.0946288576177299
1050.8841098523358290.2317802953283420.115890147664171
1060.8951600284637670.2096799430724650.104839971536233
1070.8862391457721330.2275217084557340.113760854227867
1080.8906085387829650.218782922434070.109391461217035
1090.9075311693687060.1849376612625880.0924688306312939
1100.8836524641931990.2326950716136020.116347535806801
1110.8570046823377970.2859906353244060.142995317662203
1120.8470083508165190.3059832983669630.152991649183481
1130.8533326328719190.2933347342561630.146667367128081
1140.8993614322480310.2012771355039380.100638567751969
1150.9355601766553840.1288796466892330.0644398233446164
1160.922844036570760.1543119268584790.0771559634292396
1170.8982467418918230.2035065162163540.101753258108177
1180.8680572386575620.2638855226848750.131942761342438
1190.8333570813288170.3332858373423670.166642918671183
1200.79758531460280.40482937079440.2024146853972
1210.749936109159390.5001277816812210.25006389084061
1220.7252965732344150.549406853531170.274703426765585
1230.7094317905892720.5811364188214560.290568209410728
1240.6549742386342740.6900515227314520.345025761365726
1250.6237030599766750.7525938800466490.376296940023325
1260.6049514070306370.7900971859387260.395048592969363
1270.5356467864451170.9287064271097660.464353213554883
1280.5321636709827960.9356726580344080.467836329017204
1290.6286194487032670.7427611025934670.371380551296733
1300.5909485492950250.818102901409950.409051450704975
1310.524380761654640.951238476690720.47561923834536
1320.5295220556221930.9409558887556150.470477944377807
1330.4491199128726870.8982398257453730.550880087127313
1340.3837642362928790.7675284725857590.616235763707121
1350.3186386763265130.6372773526530270.681361323673487
1360.4689794571326660.9379589142653320.531020542867334
1370.6128179069671120.7743641860657770.387182093032888
1380.5613381184371310.8773237631257380.438661881562869
1390.4883473939615830.9766947879231650.511652606038417
1400.421710846506820.8434216930136390.57828915349318
1410.7420584879864220.5158830240271550.257941512013578
1420.6134341692674910.7731316614650180.386565830732509
1430.6833926978505920.6332146042988160.316607302149408

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.180147499154489 & 0.360294998308978 & 0.819852500845511 \tabularnewline
20 & 0.80279721729211 & 0.39440556541578 & 0.19720278270789 \tabularnewline
21 & 0.946669315850119 & 0.106661368299761 & 0.0533306841498807 \tabularnewline
22 & 0.925931544348368 & 0.148136911303264 & 0.074068455651632 \tabularnewline
23 & 0.891759842520861 & 0.216480314958278 & 0.108240157479139 \tabularnewline
24 & 0.971564959888719 & 0.0568700802225624 & 0.0284350401112812 \tabularnewline
25 & 0.986679514728041 & 0.0266409705439179 & 0.013320485271959 \tabularnewline
26 & 0.99993924078078 & 0.000121518438440136 & 6.0759219220068e-05 \tabularnewline
27 & 0.999877232439087 & 0.000245535121826962 & 0.000122767560913481 \tabularnewline
28 & 0.999782280721037 & 0.000435438557924972 & 0.000217719278962486 \tabularnewline
29 & 0.999587224602146 & 0.000825550795707098 & 0.000412775397853549 \tabularnewline
30 & 0.999922749864582 & 0.000154500270836134 & 7.72501354180669e-05 \tabularnewline
31 & 0.999847963752329 & 0.000304072495342516 & 0.000152036247671258 \tabularnewline
32 & 0.999733493073183 & 0.000533013853634185 & 0.000266506926817092 \tabularnewline
33 & 0.999528685692742 & 0.000942628614515314 & 0.000471314307257657 \tabularnewline
34 & 0.999206268520025 & 0.0015874629599508 & 0.000793731479975401 \tabularnewline
35 & 0.998824589222918 & 0.0023508215541642 & 0.0011754107770821 \tabularnewline
36 & 0.998142439259899 & 0.00371512148020202 & 0.00185756074010101 \tabularnewline
37 & 0.99744557613318 & 0.00510884773364097 & 0.00255442386682048 \tabularnewline
38 & 0.996389675764382 & 0.00722064847123538 & 0.00361032423561769 \tabularnewline
39 & 0.995847486324899 & 0.00830502735020121 & 0.0041525136751006 \tabularnewline
40 & 0.994746593127149 & 0.0105068137457014 & 0.00525340687285069 \tabularnewline
41 & 0.992927042462866 & 0.0141459150742681 & 0.00707295753713405 \tabularnewline
42 & 0.993070445859408 & 0.0138591082811846 & 0.0069295541405923 \tabularnewline
43 & 0.990280965718728 & 0.0194380685625433 & 0.00971903428127163 \tabularnewline
44 & 0.98812164470543 & 0.0237567105891405 & 0.0118783552945703 \tabularnewline
45 & 0.995154081661626 & 0.00969183667674895 & 0.00484591833837448 \tabularnewline
46 & 0.995095722354166 & 0.00980855529166706 & 0.00490427764583353 \tabularnewline
47 & 0.992725098822043 & 0.0145498023559141 & 0.00727490117795706 \tabularnewline
48 & 0.990277386372578 & 0.019445227254843 & 0.0097226136274215 \tabularnewline
49 & 0.996010531657547 & 0.00797893668490585 & 0.00398946834245293 \tabularnewline
50 & 0.995444220108967 & 0.00911155978206669 & 0.00455577989103335 \tabularnewline
51 & 0.993445393759767 & 0.0131092124804654 & 0.00655460624023268 \tabularnewline
52 & 0.991449311402772 & 0.0171013771944562 & 0.0085506885972281 \tabularnewline
53 & 0.989970012619296 & 0.0200599747614075 & 0.0100299873807038 \tabularnewline
54 & 0.98824706950245 & 0.0235058609951006 & 0.0117529304975503 \tabularnewline
55 & 0.98995761940365 & 0.0200847611926994 & 0.0100423805963497 \tabularnewline
56 & 0.986532861761677 & 0.0269342764766456 & 0.0134671382383228 \tabularnewline
57 & 0.981684254873876 & 0.0366314902522475 & 0.0183157451261238 \tabularnewline
58 & 0.976989769458948 & 0.0460204610821047 & 0.0230102305410523 \tabularnewline
59 & 0.983363458407461 & 0.0332730831850782 & 0.0166365415925391 \tabularnewline
60 & 0.986433649942432 & 0.0271327001151363 & 0.0135663500575681 \tabularnewline
61 & 0.987378794536646 & 0.0252424109267075 & 0.0126212054633537 \tabularnewline
62 & 0.985897562826258 & 0.0282048743474832 & 0.0141024371737416 \tabularnewline
63 & 0.995388162146155 & 0.00922367570768936 & 0.00461183785384468 \tabularnewline
64 & 0.994812783962712 & 0.0103744320745758 & 0.00518721603728791 \tabularnewline
65 & 0.993052450024165 & 0.0138950999516691 & 0.00694754997583457 \tabularnewline
66 & 0.998834472433906 & 0.00233105513218707 & 0.00116552756609353 \tabularnewline
67 & 0.999137375306797 & 0.00172524938640549 & 0.000862624693202747 \tabularnewline
68 & 0.999199767983665 & 0.00160046403267027 & 0.000800232016335137 \tabularnewline
69 & 0.998796796668248 & 0.00240640666350451 & 0.00120320333175226 \tabularnewline
70 & 0.99866216056658 & 0.00267567886683979 & 0.00133783943341989 \tabularnewline
71 & 0.998120878045375 & 0.00375824390925054 & 0.00187912195462527 \tabularnewline
72 & 0.99862599476375 & 0.0027480104725008 & 0.0013740052362504 \tabularnewline
73 & 0.998174297889177 & 0.00365140422164577 & 0.00182570211082288 \tabularnewline
74 & 0.997548358574575 & 0.00490328285085019 & 0.0024516414254251 \tabularnewline
75 & 0.998443519849841 & 0.00311296030031769 & 0.00155648015015884 \tabularnewline
76 & 0.997830889292415 & 0.00433822141516936 & 0.00216911070758468 \tabularnewline
77 & 0.998273719523062 & 0.00345256095387662 & 0.00172628047693831 \tabularnewline
78 & 0.997760622873456 & 0.00447875425308768 & 0.00223937712654384 \tabularnewline
79 & 0.996902887182688 & 0.00619422563462316 & 0.00309711281731158 \tabularnewline
80 & 0.996959416541948 & 0.00608116691610323 & 0.00304058345805161 \tabularnewline
81 & 0.996017149321297 & 0.00796570135740603 & 0.00398285067870302 \tabularnewline
82 & 0.995472555675669 & 0.00905488864866276 & 0.00452744432433138 \tabularnewline
83 & 0.993503964369867 & 0.0129920712602662 & 0.00649603563013309 \tabularnewline
84 & 0.991442140720452 & 0.0171157185590954 & 0.00855785927954771 \tabularnewline
85 & 0.98966369225935 & 0.0206726154812995 & 0.0103363077406498 \tabularnewline
86 & 0.986147485621899 & 0.0277050287562025 & 0.0138525143781013 \tabularnewline
87 & 0.98211328189293 & 0.0357734362141393 & 0.0178867181070696 \tabularnewline
88 & 0.976416227346291 & 0.0471675453074177 & 0.0235837726537089 \tabularnewline
89 & 0.969796925265965 & 0.0604061494680709 & 0.0302030747340354 \tabularnewline
90 & 0.980654864657082 & 0.0386902706858361 & 0.019345135342918 \tabularnewline
91 & 0.984410445596624 & 0.0311791088067528 & 0.0155895544033764 \tabularnewline
92 & 0.978757328905076 & 0.0424853421898472 & 0.0212426710949236 \tabularnewline
93 & 0.971824803970692 & 0.056350392058615 & 0.0281751960293075 \tabularnewline
94 & 0.965745572719382 & 0.0685088545612357 & 0.0342544272806178 \tabularnewline
95 & 0.960251074304555 & 0.0794978513908909 & 0.0397489256954454 \tabularnewline
96 & 0.948295518861177 & 0.103408962277646 & 0.0517044811388228 \tabularnewline
97 & 0.934101081234571 & 0.131797837530857 & 0.0658989187654285 \tabularnewline
98 & 0.929173085156527 & 0.141653829686945 & 0.0708269148434726 \tabularnewline
99 & 0.915507884490417 & 0.168984231019165 & 0.0844921155095827 \tabularnewline
100 & 0.899630400277237 & 0.200739199445526 & 0.100369599722763 \tabularnewline
101 & 0.893217752234976 & 0.213564495530048 & 0.106782247765024 \tabularnewline
102 & 0.870259960217489 & 0.259480079565021 & 0.129740039782511 \tabularnewline
103 & 0.841571800722315 & 0.31685639855537 & 0.158428199277685 \tabularnewline
104 & 0.90537114238227 & 0.18925771523546 & 0.0946288576177299 \tabularnewline
105 & 0.884109852335829 & 0.231780295328342 & 0.115890147664171 \tabularnewline
106 & 0.895160028463767 & 0.209679943072465 & 0.104839971536233 \tabularnewline
107 & 0.886239145772133 & 0.227521708455734 & 0.113760854227867 \tabularnewline
108 & 0.890608538782965 & 0.21878292243407 & 0.109391461217035 \tabularnewline
109 & 0.907531169368706 & 0.184937661262588 & 0.0924688306312939 \tabularnewline
110 & 0.883652464193199 & 0.232695071613602 & 0.116347535806801 \tabularnewline
111 & 0.857004682337797 & 0.285990635324406 & 0.142995317662203 \tabularnewline
112 & 0.847008350816519 & 0.305983298366963 & 0.152991649183481 \tabularnewline
113 & 0.853332632871919 & 0.293334734256163 & 0.146667367128081 \tabularnewline
114 & 0.899361432248031 & 0.201277135503938 & 0.100638567751969 \tabularnewline
115 & 0.935560176655384 & 0.128879646689233 & 0.0644398233446164 \tabularnewline
116 & 0.92284403657076 & 0.154311926858479 & 0.0771559634292396 \tabularnewline
117 & 0.898246741891823 & 0.203506516216354 & 0.101753258108177 \tabularnewline
118 & 0.868057238657562 & 0.263885522684875 & 0.131942761342438 \tabularnewline
119 & 0.833357081328817 & 0.333285837342367 & 0.166642918671183 \tabularnewline
120 & 0.7975853146028 & 0.4048293707944 & 0.2024146853972 \tabularnewline
121 & 0.74993610915939 & 0.500127781681221 & 0.25006389084061 \tabularnewline
122 & 0.725296573234415 & 0.54940685353117 & 0.274703426765585 \tabularnewline
123 & 0.709431790589272 & 0.581136418821456 & 0.290568209410728 \tabularnewline
124 & 0.654974238634274 & 0.690051522731452 & 0.345025761365726 \tabularnewline
125 & 0.623703059976675 & 0.752593880046649 & 0.376296940023325 \tabularnewline
126 & 0.604951407030637 & 0.790097185938726 & 0.395048592969363 \tabularnewline
127 & 0.535646786445117 & 0.928706427109766 & 0.464353213554883 \tabularnewline
128 & 0.532163670982796 & 0.935672658034408 & 0.467836329017204 \tabularnewline
129 & 0.628619448703267 & 0.742761102593467 & 0.371380551296733 \tabularnewline
130 & 0.590948549295025 & 0.81810290140995 & 0.409051450704975 \tabularnewline
131 & 0.52438076165464 & 0.95123847669072 & 0.47561923834536 \tabularnewline
132 & 0.529522055622193 & 0.940955888755615 & 0.470477944377807 \tabularnewline
133 & 0.449119912872687 & 0.898239825745373 & 0.550880087127313 \tabularnewline
134 & 0.383764236292879 & 0.767528472585759 & 0.616235763707121 \tabularnewline
135 & 0.318638676326513 & 0.637277352653027 & 0.681361323673487 \tabularnewline
136 & 0.468979457132666 & 0.937958914265332 & 0.531020542867334 \tabularnewline
137 & 0.612817906967112 & 0.774364186065777 & 0.387182093032888 \tabularnewline
138 & 0.561338118437131 & 0.877323763125738 & 0.438661881562869 \tabularnewline
139 & 0.488347393961583 & 0.976694787923165 & 0.511652606038417 \tabularnewline
140 & 0.42171084650682 & 0.843421693013639 & 0.57828915349318 \tabularnewline
141 & 0.742058487986422 & 0.515883024027155 & 0.257941512013578 \tabularnewline
142 & 0.613434169267491 & 0.773131661465018 & 0.386565830732509 \tabularnewline
143 & 0.683392697850592 & 0.633214604298816 & 0.316607302149408 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147127&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]19[/C][C]0.180147499154489[/C][C]0.360294998308978[/C][C]0.819852500845511[/C][/ROW]
[ROW][C]20[/C][C]0.80279721729211[/C][C]0.39440556541578[/C][C]0.19720278270789[/C][/ROW]
[ROW][C]21[/C][C]0.946669315850119[/C][C]0.106661368299761[/C][C]0.0533306841498807[/C][/ROW]
[ROW][C]22[/C][C]0.925931544348368[/C][C]0.148136911303264[/C][C]0.074068455651632[/C][/ROW]
[ROW][C]23[/C][C]0.891759842520861[/C][C]0.216480314958278[/C][C]0.108240157479139[/C][/ROW]
[ROW][C]24[/C][C]0.971564959888719[/C][C]0.0568700802225624[/C][C]0.0284350401112812[/C][/ROW]
[ROW][C]25[/C][C]0.986679514728041[/C][C]0.0266409705439179[/C][C]0.013320485271959[/C][/ROW]
[ROW][C]26[/C][C]0.99993924078078[/C][C]0.000121518438440136[/C][C]6.0759219220068e-05[/C][/ROW]
[ROW][C]27[/C][C]0.999877232439087[/C][C]0.000245535121826962[/C][C]0.000122767560913481[/C][/ROW]
[ROW][C]28[/C][C]0.999782280721037[/C][C]0.000435438557924972[/C][C]0.000217719278962486[/C][/ROW]
[ROW][C]29[/C][C]0.999587224602146[/C][C]0.000825550795707098[/C][C]0.000412775397853549[/C][/ROW]
[ROW][C]30[/C][C]0.999922749864582[/C][C]0.000154500270836134[/C][C]7.72501354180669e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999847963752329[/C][C]0.000304072495342516[/C][C]0.000152036247671258[/C][/ROW]
[ROW][C]32[/C][C]0.999733493073183[/C][C]0.000533013853634185[/C][C]0.000266506926817092[/C][/ROW]
[ROW][C]33[/C][C]0.999528685692742[/C][C]0.000942628614515314[/C][C]0.000471314307257657[/C][/ROW]
[ROW][C]34[/C][C]0.999206268520025[/C][C]0.0015874629599508[/C][C]0.000793731479975401[/C][/ROW]
[ROW][C]35[/C][C]0.998824589222918[/C][C]0.0023508215541642[/C][C]0.0011754107770821[/C][/ROW]
[ROW][C]36[/C][C]0.998142439259899[/C][C]0.00371512148020202[/C][C]0.00185756074010101[/C][/ROW]
[ROW][C]37[/C][C]0.99744557613318[/C][C]0.00510884773364097[/C][C]0.00255442386682048[/C][/ROW]
[ROW][C]38[/C][C]0.996389675764382[/C][C]0.00722064847123538[/C][C]0.00361032423561769[/C][/ROW]
[ROW][C]39[/C][C]0.995847486324899[/C][C]0.00830502735020121[/C][C]0.0041525136751006[/C][/ROW]
[ROW][C]40[/C][C]0.994746593127149[/C][C]0.0105068137457014[/C][C]0.00525340687285069[/C][/ROW]
[ROW][C]41[/C][C]0.992927042462866[/C][C]0.0141459150742681[/C][C]0.00707295753713405[/C][/ROW]
[ROW][C]42[/C][C]0.993070445859408[/C][C]0.0138591082811846[/C][C]0.0069295541405923[/C][/ROW]
[ROW][C]43[/C][C]0.990280965718728[/C][C]0.0194380685625433[/C][C]0.00971903428127163[/C][/ROW]
[ROW][C]44[/C][C]0.98812164470543[/C][C]0.0237567105891405[/C][C]0.0118783552945703[/C][/ROW]
[ROW][C]45[/C][C]0.995154081661626[/C][C]0.00969183667674895[/C][C]0.00484591833837448[/C][/ROW]
[ROW][C]46[/C][C]0.995095722354166[/C][C]0.00980855529166706[/C][C]0.00490427764583353[/C][/ROW]
[ROW][C]47[/C][C]0.992725098822043[/C][C]0.0145498023559141[/C][C]0.00727490117795706[/C][/ROW]
[ROW][C]48[/C][C]0.990277386372578[/C][C]0.019445227254843[/C][C]0.0097226136274215[/C][/ROW]
[ROW][C]49[/C][C]0.996010531657547[/C][C]0.00797893668490585[/C][C]0.00398946834245293[/C][/ROW]
[ROW][C]50[/C][C]0.995444220108967[/C][C]0.00911155978206669[/C][C]0.00455577989103335[/C][/ROW]
[ROW][C]51[/C][C]0.993445393759767[/C][C]0.0131092124804654[/C][C]0.00655460624023268[/C][/ROW]
[ROW][C]52[/C][C]0.991449311402772[/C][C]0.0171013771944562[/C][C]0.0085506885972281[/C][/ROW]
[ROW][C]53[/C][C]0.989970012619296[/C][C]0.0200599747614075[/C][C]0.0100299873807038[/C][/ROW]
[ROW][C]54[/C][C]0.98824706950245[/C][C]0.0235058609951006[/C][C]0.0117529304975503[/C][/ROW]
[ROW][C]55[/C][C]0.98995761940365[/C][C]0.0200847611926994[/C][C]0.0100423805963497[/C][/ROW]
[ROW][C]56[/C][C]0.986532861761677[/C][C]0.0269342764766456[/C][C]0.0134671382383228[/C][/ROW]
[ROW][C]57[/C][C]0.981684254873876[/C][C]0.0366314902522475[/C][C]0.0183157451261238[/C][/ROW]
[ROW][C]58[/C][C]0.976989769458948[/C][C]0.0460204610821047[/C][C]0.0230102305410523[/C][/ROW]
[ROW][C]59[/C][C]0.983363458407461[/C][C]0.0332730831850782[/C][C]0.0166365415925391[/C][/ROW]
[ROW][C]60[/C][C]0.986433649942432[/C][C]0.0271327001151363[/C][C]0.0135663500575681[/C][/ROW]
[ROW][C]61[/C][C]0.987378794536646[/C][C]0.0252424109267075[/C][C]0.0126212054633537[/C][/ROW]
[ROW][C]62[/C][C]0.985897562826258[/C][C]0.0282048743474832[/C][C]0.0141024371737416[/C][/ROW]
[ROW][C]63[/C][C]0.995388162146155[/C][C]0.00922367570768936[/C][C]0.00461183785384468[/C][/ROW]
[ROW][C]64[/C][C]0.994812783962712[/C][C]0.0103744320745758[/C][C]0.00518721603728791[/C][/ROW]
[ROW][C]65[/C][C]0.993052450024165[/C][C]0.0138950999516691[/C][C]0.00694754997583457[/C][/ROW]
[ROW][C]66[/C][C]0.998834472433906[/C][C]0.00233105513218707[/C][C]0.00116552756609353[/C][/ROW]
[ROW][C]67[/C][C]0.999137375306797[/C][C]0.00172524938640549[/C][C]0.000862624693202747[/C][/ROW]
[ROW][C]68[/C][C]0.999199767983665[/C][C]0.00160046403267027[/C][C]0.000800232016335137[/C][/ROW]
[ROW][C]69[/C][C]0.998796796668248[/C][C]0.00240640666350451[/C][C]0.00120320333175226[/C][/ROW]
[ROW][C]70[/C][C]0.99866216056658[/C][C]0.00267567886683979[/C][C]0.00133783943341989[/C][/ROW]
[ROW][C]71[/C][C]0.998120878045375[/C][C]0.00375824390925054[/C][C]0.00187912195462527[/C][/ROW]
[ROW][C]72[/C][C]0.99862599476375[/C][C]0.0027480104725008[/C][C]0.0013740052362504[/C][/ROW]
[ROW][C]73[/C][C]0.998174297889177[/C][C]0.00365140422164577[/C][C]0.00182570211082288[/C][/ROW]
[ROW][C]74[/C][C]0.997548358574575[/C][C]0.00490328285085019[/C][C]0.0024516414254251[/C][/ROW]
[ROW][C]75[/C][C]0.998443519849841[/C][C]0.00311296030031769[/C][C]0.00155648015015884[/C][/ROW]
[ROW][C]76[/C][C]0.997830889292415[/C][C]0.00433822141516936[/C][C]0.00216911070758468[/C][/ROW]
[ROW][C]77[/C][C]0.998273719523062[/C][C]0.00345256095387662[/C][C]0.00172628047693831[/C][/ROW]
[ROW][C]78[/C][C]0.997760622873456[/C][C]0.00447875425308768[/C][C]0.00223937712654384[/C][/ROW]
[ROW][C]79[/C][C]0.996902887182688[/C][C]0.00619422563462316[/C][C]0.00309711281731158[/C][/ROW]
[ROW][C]80[/C][C]0.996959416541948[/C][C]0.00608116691610323[/C][C]0.00304058345805161[/C][/ROW]
[ROW][C]81[/C][C]0.996017149321297[/C][C]0.00796570135740603[/C][C]0.00398285067870302[/C][/ROW]
[ROW][C]82[/C][C]0.995472555675669[/C][C]0.00905488864866276[/C][C]0.00452744432433138[/C][/ROW]
[ROW][C]83[/C][C]0.993503964369867[/C][C]0.0129920712602662[/C][C]0.00649603563013309[/C][/ROW]
[ROW][C]84[/C][C]0.991442140720452[/C][C]0.0171157185590954[/C][C]0.00855785927954771[/C][/ROW]
[ROW][C]85[/C][C]0.98966369225935[/C][C]0.0206726154812995[/C][C]0.0103363077406498[/C][/ROW]
[ROW][C]86[/C][C]0.986147485621899[/C][C]0.0277050287562025[/C][C]0.0138525143781013[/C][/ROW]
[ROW][C]87[/C][C]0.98211328189293[/C][C]0.0357734362141393[/C][C]0.0178867181070696[/C][/ROW]
[ROW][C]88[/C][C]0.976416227346291[/C][C]0.0471675453074177[/C][C]0.0235837726537089[/C][/ROW]
[ROW][C]89[/C][C]0.969796925265965[/C][C]0.0604061494680709[/C][C]0.0302030747340354[/C][/ROW]
[ROW][C]90[/C][C]0.980654864657082[/C][C]0.0386902706858361[/C][C]0.019345135342918[/C][/ROW]
[ROW][C]91[/C][C]0.984410445596624[/C][C]0.0311791088067528[/C][C]0.0155895544033764[/C][/ROW]
[ROW][C]92[/C][C]0.978757328905076[/C][C]0.0424853421898472[/C][C]0.0212426710949236[/C][/ROW]
[ROW][C]93[/C][C]0.971824803970692[/C][C]0.056350392058615[/C][C]0.0281751960293075[/C][/ROW]
[ROW][C]94[/C][C]0.965745572719382[/C][C]0.0685088545612357[/C][C]0.0342544272806178[/C][/ROW]
[ROW][C]95[/C][C]0.960251074304555[/C][C]0.0794978513908909[/C][C]0.0397489256954454[/C][/ROW]
[ROW][C]96[/C][C]0.948295518861177[/C][C]0.103408962277646[/C][C]0.0517044811388228[/C][/ROW]
[ROW][C]97[/C][C]0.934101081234571[/C][C]0.131797837530857[/C][C]0.0658989187654285[/C][/ROW]
[ROW][C]98[/C][C]0.929173085156527[/C][C]0.141653829686945[/C][C]0.0708269148434726[/C][/ROW]
[ROW][C]99[/C][C]0.915507884490417[/C][C]0.168984231019165[/C][C]0.0844921155095827[/C][/ROW]
[ROW][C]100[/C][C]0.899630400277237[/C][C]0.200739199445526[/C][C]0.100369599722763[/C][/ROW]
[ROW][C]101[/C][C]0.893217752234976[/C][C]0.213564495530048[/C][C]0.106782247765024[/C][/ROW]
[ROW][C]102[/C][C]0.870259960217489[/C][C]0.259480079565021[/C][C]0.129740039782511[/C][/ROW]
[ROW][C]103[/C][C]0.841571800722315[/C][C]0.31685639855537[/C][C]0.158428199277685[/C][/ROW]
[ROW][C]104[/C][C]0.90537114238227[/C][C]0.18925771523546[/C][C]0.0946288576177299[/C][/ROW]
[ROW][C]105[/C][C]0.884109852335829[/C][C]0.231780295328342[/C][C]0.115890147664171[/C][/ROW]
[ROW][C]106[/C][C]0.895160028463767[/C][C]0.209679943072465[/C][C]0.104839971536233[/C][/ROW]
[ROW][C]107[/C][C]0.886239145772133[/C][C]0.227521708455734[/C][C]0.113760854227867[/C][/ROW]
[ROW][C]108[/C][C]0.890608538782965[/C][C]0.21878292243407[/C][C]0.109391461217035[/C][/ROW]
[ROW][C]109[/C][C]0.907531169368706[/C][C]0.184937661262588[/C][C]0.0924688306312939[/C][/ROW]
[ROW][C]110[/C][C]0.883652464193199[/C][C]0.232695071613602[/C][C]0.116347535806801[/C][/ROW]
[ROW][C]111[/C][C]0.857004682337797[/C][C]0.285990635324406[/C][C]0.142995317662203[/C][/ROW]
[ROW][C]112[/C][C]0.847008350816519[/C][C]0.305983298366963[/C][C]0.152991649183481[/C][/ROW]
[ROW][C]113[/C][C]0.853332632871919[/C][C]0.293334734256163[/C][C]0.146667367128081[/C][/ROW]
[ROW][C]114[/C][C]0.899361432248031[/C][C]0.201277135503938[/C][C]0.100638567751969[/C][/ROW]
[ROW][C]115[/C][C]0.935560176655384[/C][C]0.128879646689233[/C][C]0.0644398233446164[/C][/ROW]
[ROW][C]116[/C][C]0.92284403657076[/C][C]0.154311926858479[/C][C]0.0771559634292396[/C][/ROW]
[ROW][C]117[/C][C]0.898246741891823[/C][C]0.203506516216354[/C][C]0.101753258108177[/C][/ROW]
[ROW][C]118[/C][C]0.868057238657562[/C][C]0.263885522684875[/C][C]0.131942761342438[/C][/ROW]
[ROW][C]119[/C][C]0.833357081328817[/C][C]0.333285837342367[/C][C]0.166642918671183[/C][/ROW]
[ROW][C]120[/C][C]0.7975853146028[/C][C]0.4048293707944[/C][C]0.2024146853972[/C][/ROW]
[ROW][C]121[/C][C]0.74993610915939[/C][C]0.500127781681221[/C][C]0.25006389084061[/C][/ROW]
[ROW][C]122[/C][C]0.725296573234415[/C][C]0.54940685353117[/C][C]0.274703426765585[/C][/ROW]
[ROW][C]123[/C][C]0.709431790589272[/C][C]0.581136418821456[/C][C]0.290568209410728[/C][/ROW]
[ROW][C]124[/C][C]0.654974238634274[/C][C]0.690051522731452[/C][C]0.345025761365726[/C][/ROW]
[ROW][C]125[/C][C]0.623703059976675[/C][C]0.752593880046649[/C][C]0.376296940023325[/C][/ROW]
[ROW][C]126[/C][C]0.604951407030637[/C][C]0.790097185938726[/C][C]0.395048592969363[/C][/ROW]
[ROW][C]127[/C][C]0.535646786445117[/C][C]0.928706427109766[/C][C]0.464353213554883[/C][/ROW]
[ROW][C]128[/C][C]0.532163670982796[/C][C]0.935672658034408[/C][C]0.467836329017204[/C][/ROW]
[ROW][C]129[/C][C]0.628619448703267[/C][C]0.742761102593467[/C][C]0.371380551296733[/C][/ROW]
[ROW][C]130[/C][C]0.590948549295025[/C][C]0.81810290140995[/C][C]0.409051450704975[/C][/ROW]
[ROW][C]131[/C][C]0.52438076165464[/C][C]0.95123847669072[/C][C]0.47561923834536[/C][/ROW]
[ROW][C]132[/C][C]0.529522055622193[/C][C]0.940955888755615[/C][C]0.470477944377807[/C][/ROW]
[ROW][C]133[/C][C]0.449119912872687[/C][C]0.898239825745373[/C][C]0.550880087127313[/C][/ROW]
[ROW][C]134[/C][C]0.383764236292879[/C][C]0.767528472585759[/C][C]0.616235763707121[/C][/ROW]
[ROW][C]135[/C][C]0.318638676326513[/C][C]0.637277352653027[/C][C]0.681361323673487[/C][/ROW]
[ROW][C]136[/C][C]0.468979457132666[/C][C]0.937958914265332[/C][C]0.531020542867334[/C][/ROW]
[ROW][C]137[/C][C]0.612817906967112[/C][C]0.774364186065777[/C][C]0.387182093032888[/C][/ROW]
[ROW][C]138[/C][C]0.561338118437131[/C][C]0.877323763125738[/C][C]0.438661881562869[/C][/ROW]
[ROW][C]139[/C][C]0.488347393961583[/C][C]0.976694787923165[/C][C]0.511652606038417[/C][/ROW]
[ROW][C]140[/C][C]0.42171084650682[/C][C]0.843421693013639[/C][C]0.57828915349318[/C][/ROW]
[ROW][C]141[/C][C]0.742058487986422[/C][C]0.515883024027155[/C][C]0.257941512013578[/C][/ROW]
[ROW][C]142[/C][C]0.613434169267491[/C][C]0.773131661465018[/C][C]0.386565830732509[/C][/ROW]
[ROW][C]143[/C][C]0.683392697850592[/C][C]0.633214604298816[/C][C]0.316607302149408[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147127&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147127&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
190.1801474991544890.3602949983089780.819852500845511
200.802797217292110.394405565415780.19720278270789
210.9466693158501190.1066613682997610.0533306841498807
220.9259315443483680.1481369113032640.074068455651632
230.8917598425208610.2164803149582780.108240157479139
240.9715649598887190.05687008022256240.0284350401112812
250.9866795147280410.02664097054391790.013320485271959
260.999939240780780.0001215184384401366.0759219220068e-05
270.9998772324390870.0002455351218269620.000122767560913481
280.9997822807210370.0004354385579249720.000217719278962486
290.9995872246021460.0008255507957070980.000412775397853549
300.9999227498645820.0001545002708361347.72501354180669e-05
310.9998479637523290.0003040724953425160.000152036247671258
320.9997334930731830.0005330138536341850.000266506926817092
330.9995286856927420.0009426286145153140.000471314307257657
340.9992062685200250.00158746295995080.000793731479975401
350.9988245892229180.00235082155416420.0011754107770821
360.9981424392598990.003715121480202020.00185756074010101
370.997445576133180.005108847733640970.00255442386682048
380.9963896757643820.007220648471235380.00361032423561769
390.9958474863248990.008305027350201210.0041525136751006
400.9947465931271490.01050681374570140.00525340687285069
410.9929270424628660.01414591507426810.00707295753713405
420.9930704458594080.01385910828118460.0069295541405923
430.9902809657187280.01943806856254330.00971903428127163
440.988121644705430.02375671058914050.0118783552945703
450.9951540816616260.009691836676748950.00484591833837448
460.9950957223541660.009808555291667060.00490427764583353
470.9927250988220430.01454980235591410.00727490117795706
480.9902773863725780.0194452272548430.0097226136274215
490.9960105316575470.007978936684905850.00398946834245293
500.9954442201089670.009111559782066690.00455577989103335
510.9934453937597670.01310921248046540.00655460624023268
520.9914493114027720.01710137719445620.0085506885972281
530.9899700126192960.02005997476140750.0100299873807038
540.988247069502450.02350586099510060.0117529304975503
550.989957619403650.02008476119269940.0100423805963497
560.9865328617616770.02693427647664560.0134671382383228
570.9816842548738760.03663149025224750.0183157451261238
580.9769897694589480.04602046108210470.0230102305410523
590.9833634584074610.03327308318507820.0166365415925391
600.9864336499424320.02713270011513630.0135663500575681
610.9873787945366460.02524241092670750.0126212054633537
620.9858975628262580.02820487434748320.0141024371737416
630.9953881621461550.009223675707689360.00461183785384468
640.9948127839627120.01037443207457580.00518721603728791
650.9930524500241650.01389509995166910.00694754997583457
660.9988344724339060.002331055132187070.00116552756609353
670.9991373753067970.001725249386405490.000862624693202747
680.9991997679836650.001600464032670270.000800232016335137
690.9987967966682480.002406406663504510.00120320333175226
700.998662160566580.002675678866839790.00133783943341989
710.9981208780453750.003758243909250540.00187912195462527
720.998625994763750.00274801047250080.0013740052362504
730.9981742978891770.003651404221645770.00182570211082288
740.9975483585745750.004903282850850190.0024516414254251
750.9984435198498410.003112960300317690.00155648015015884
760.9978308892924150.004338221415169360.00216911070758468
770.9982737195230620.003452560953876620.00172628047693831
780.9977606228734560.004478754253087680.00223937712654384
790.9969028871826880.006194225634623160.00309711281731158
800.9969594165419480.006081166916103230.00304058345805161
810.9960171493212970.007965701357406030.00398285067870302
820.9954725556756690.009054888648662760.00452744432433138
830.9935039643698670.01299207126026620.00649603563013309
840.9914421407204520.01711571855909540.00855785927954771
850.989663692259350.02067261548129950.0103363077406498
860.9861474856218990.02770502875620250.0138525143781013
870.982113281892930.03577343621413930.0178867181070696
880.9764162273462910.04716754530741770.0235837726537089
890.9697969252659650.06040614946807090.0302030747340354
900.9806548646570820.03869027068583610.019345135342918
910.9844104455966240.03117910880675280.0155895544033764
920.9787573289050760.04248534218984720.0212426710949236
930.9718248039706920.0563503920586150.0281751960293075
940.9657455727193820.06850885456123570.0342544272806178
950.9602510743045550.07949785139089090.0397489256954454
960.9482955188611770.1034089622776460.0517044811388228
970.9341010812345710.1317978375308570.0658989187654285
980.9291730851565270.1416538296869450.0708269148434726
990.9155078844904170.1689842310191650.0844921155095827
1000.8996304002772370.2007391994455260.100369599722763
1010.8932177522349760.2135644955300480.106782247765024
1020.8702599602174890.2594800795650210.129740039782511
1030.8415718007223150.316856398555370.158428199277685
1040.905371142382270.189257715235460.0946288576177299
1050.8841098523358290.2317802953283420.115890147664171
1060.8951600284637670.2096799430724650.104839971536233
1070.8862391457721330.2275217084557340.113760854227867
1080.8906085387829650.218782922434070.109391461217035
1090.9075311693687060.1849376612625880.0924688306312939
1100.8836524641931990.2326950716136020.116347535806801
1110.8570046823377970.2859906353244060.142995317662203
1120.8470083508165190.3059832983669630.152991649183481
1130.8533326328719190.2933347342561630.146667367128081
1140.8993614322480310.2012771355039380.100638567751969
1150.9355601766553840.1288796466892330.0644398233446164
1160.922844036570760.1543119268584790.0771559634292396
1170.8982467418918230.2035065162163540.101753258108177
1180.8680572386575620.2638855226848750.131942761342438
1190.8333570813288170.3332858373423670.166642918671183
1200.79758531460280.40482937079440.2024146853972
1210.749936109159390.5001277816812210.25006389084061
1220.7252965732344150.549406853531170.274703426765585
1230.7094317905892720.5811364188214560.290568209410728
1240.6549742386342740.6900515227314520.345025761365726
1250.6237030599766750.7525938800466490.376296940023325
1260.6049514070306370.7900971859387260.395048592969363
1270.5356467864451170.9287064271097660.464353213554883
1280.5321636709827960.9356726580344080.467836329017204
1290.6286194487032670.7427611025934670.371380551296733
1300.5909485492950250.818102901409950.409051450704975
1310.524380761654640.951238476690720.47561923834536
1320.5295220556221930.9409558887556150.470477944377807
1330.4491199128726870.8982398257453730.550880087127313
1340.3837642362928790.7675284725857590.616235763707121
1350.3186386763265130.6372773526530270.681361323673487
1360.4689794571326660.9379589142653320.531020542867334
1370.6128179069671120.7743641860657770.387182093032888
1380.5613381184371310.8773237631257380.438661881562869
1390.4883473939615830.9766947879231650.511652606038417
1400.421710846506820.8434216930136390.57828915349318
1410.7420584879864220.5158830240271550.257941512013578
1420.6134341692674910.7731316614650180.386565830732509
1430.6833926978505920.6332146042988160.316607302149408







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level360.288NOK
5% type I error level670.536NOK
10% type I error level720.576NOK

\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 & 36 & 0.288 & NOK \tabularnewline
5% type I error level & 67 & 0.536 & NOK \tabularnewline
10% type I error level & 72 & 0.576 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147127&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]36[/C][C]0.288[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]67[/C][C]0.536[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]72[/C][C]0.576[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147127&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147127&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 level360.288NOK
5% type I error level670.536NOK
10% type I error level720.576NOK



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