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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 08 Mar 2008 06:40:54 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Mar/08/t12049839076t3dgtgn3c3py8w.htm/, Retrieved Sat, 20 Apr 2024 11:01:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=14692, Retrieved Sat, 20 Apr 2024 11:01:01 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

1019

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [HPC Retail Sales] [2008-03-08 13:40:54] [2f569257101cf136a49d983584bfc44f] [Current]
- RMPD    [Exponential Smoothing] [test kuleuven] [2008-04-17 13:27:08] [74be16979710d4c4e7c6647856088456] 
- RM D    [Univariate Data Series] [test kuleuven] [2008-04-17 14:03:28] [74be16979710d4c4e7c6647856088456] 
-   PD    [Multiple Regression] [Multiple regressi...] [2008-05-09 12:49:41] [74be16979710d4c4e7c6647856088456] 
F  MPD    [Multiple Regression] [Workshop8 Methode 2] [2010-11-25 20:03:35] [c289bfbb56808c5d93a0f55b5d39f5bd] 
-  MPD    [Multiple Regression] [] [2010-11-26 10:01:22] [7789b9488494790f41ddb7f073cada1b] 
F  MPD    [Multiple Regression] [ws 8: Regression ...] [2010-11-26 10:04:32] [05ab9592748364013445d860bb938e43] 
-    D      [Multiple Regression] [review ws8] [2010-12-04 09:33:17] [4a7069087cf9e0eda253aeed7d8c30d6] 
F  MPD    [Multiple Regression] [] [2010-11-26 10:13:34] [8a9a6f7c332640af31ddca253a8ded58] 
-    D      [Multiple Regression] [] [2010-11-30 10:40:00] [fb3a7008aea9486db3846dc25434607b] 
F    D      [Multiple Regression] [ws 8] [2010-11-30 13:11:23] [af8eb90b4bf1bcfcc4325c143dbee260] 
-    D      [Multiple Regression] [multiple regressi...] [2010-12-18 17:21:06] [74deae64b71f9d77c839af86f7c687b5] 
- RM        [Multiple Regression] [] [2011-11-29 22:37:55] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- RM        [Multiple Regression] [] [2011-11-29 22:37:55] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- RM        [Multiple Regression] [] [2011-11-29 22:37:55] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- RM        [Multiple Regression] [] [2011-11-29 22:37:55] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- RM        [Multiple Regression] [] [2011-11-29 22:37:55] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- RM        [Multiple Regression] [] [2011-11-29 22:37:55] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- RM        [Multiple Regression] [] [2011-11-29 22:37:55] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- R  D      [Multiple Regression] [] [2011-11-29 22:39:27] [46d7ccc24e5d35a2decd922dfb3b3a39] 
-  M          [Multiple Regression] [] [2011-11-29 22:54:43] [46d7ccc24e5d35a2decd922dfb3b3a39] 
-  M D    [Multiple Regression] [WS8 fout] [2010-11-26 10:40:54] [1fd136673b2a4fecb5c545b9b4a05d64] 
-  M D    [Multiple Regression] [W8 Regressiemodel] [2010-11-26 11:00:47] [56d90b683fcd93137645f9226b43c62b] 
-   PD      [Multiple Regression] [W8 Regressiemodel] [2010-11-26 12:03:41] [56d90b683fcd93137645f9226b43c62b] 
-    D        [Multiple Regression] [Paper Multiple Re...] [2010-12-11 10:24:16] [56d90b683fcd93137645f9226b43c62b] 
-    D        [Multiple Regression] [Paper MR Time Series] [2010-12-11 10:40:35] [56d90b683fcd93137645f9226b43c62b] 
- RMPD    [Multiple Regression] [] [2010-11-26 11:40:42] [d39e5c40c631ed6c22677d2e41dbfc7d] 
-    D      [Multiple Regression] [] [2010-12-15 20:30:41] [d39e5c40c631ed6c22677d2e41dbfc7d] 
-    D      [Multiple Regression] [] [2010-12-15 20:39:35] [d39e5c40c631ed6c22677d2e41dbfc7d] 
-    D        [Multiple Regression] [] [2010-12-17 12:00:00] [d39e5c40c631ed6c22677d2e41dbfc7d] 
-    D          [Multiple Regression] [] [2010-12-17 12:30:50] [d39e5c40c631ed6c22677d2e41dbfc7d] 
-    D          [Multiple Regression] [] [2010-12-17 12:34:10] [d39e5c40c631ed6c22677d2e41dbfc7d] 
-   PD        [Multiple Regression] [paper multiple alles] [2010-12-26 17:24:50] [eeb33d252044f8583501f5ba0605ad6d] 
-   PD        [Multiple Regression] [paper Regression ...] [2010-12-26 17:36:00] [eeb33d252044f8583501f5ba0605ad6d] 
-   PD        [Multiple Regression] [paper Regression ...] [2010-12-26 17:37:56] [eeb33d252044f8583501f5ba0605ad6d] 
-   PD        [Multiple Regression] [paper Regression ...] [2010-12-26 17:42:43] [eeb33d252044f8583501f5ba0605ad6d] 
- R         [Multiple Regression] [] [2011-11-30 14:02:14] [a90f9476280cfbc76b67d22532268624] 
- R         [Multiple Regression] [] [2011-11-30 14:02:14] [a90f9476280cfbc76b67d22532268624] 
- R         [Multiple Regression] [] [2011-11-30 14:02:14] [a90f9476280cfbc76b67d22532268624] 
- RMP         [Exponential Smoothing] [] [2011-11-30 14:16:57] [a90f9476280cfbc76b67d22532268624] 
- RMP         [Spectral Analysis] [] [2011-11-30 14:18:26] [a90f9476280cfbc76b67d22532268624] 
- R         [Multiple Regression] [] [2011-11-30 14:02:14] [a90f9476280cfbc76b67d22532268624] 
- R         [Multiple Regression] [regression analys...] [2011-11-30 18:56:32] [d5821fed422662f85834b1edae505ce2] 
-  MPD    [Multiple Regression] [] [2010-11-26 13:22:56] [7789b9488494790f41ddb7f073cada1b] 
F           [Multiple Regression] [] [2010-11-26 15:58:19] [8a9a6f7c332640af31ddca253a8ded58] 
F             [Multiple Regression] [ws 8] [2010-11-30 13:16:29] [af8eb90b4bf1bcfcc4325c143dbee260] 
-             [Multiple Regression] [autoregressief model] [2010-12-21 09:48:19] [74deae64b71f9d77c839af86f7c687b5] 
- R           [Multiple Regression] [] [2011-11-29 23:29:15] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- R           [Multiple Regression] [] [2011-11-29 23:29:15] [46d7ccc24e5d35a2decd922dfb3b3a39] 

[Truncated]

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Dataseries X:

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Summary of compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14692&T=0

[TABLE]
[ROW][C]Summary of compuational 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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14692&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14692&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 compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
HPC[t] = + 15515.9166666667 -2132.84102182539M1[t] -2859.02430555556M2[t] -1507.77901785714M3[t] -2251.39087301588M4[t] -1687.43129960317M5[t] -2357.90029761905M6[t] -2422.36929563492M7[t] -2104.69543650794M8[t] -2896.45014880952M9[t] -2143.91914682540M10[t] -2478.67385912699M11[t] + 77.7547123015873t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HPC[t] =  +  15515.9166666667 -2132.84102182539M1[t] -2859.02430555556M2[t] -1507.77901785714M3[t] -2251.39087301588M4[t] -1687.43129960317M5[t] -2357.90029761905M6[t] -2422.36929563492M7[t] -2104.69543650794M8[t] -2896.45014880952M9[t] -2143.91914682540M10[t] -2478.67385912699M11[t] +  77.7547123015873t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14692&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HPC[t] =  +  15515.9166666667 -2132.84102182539M1[t] -2859.02430555556M2[t] -1507.77901785714M3[t] -2251.39087301588M4[t] -1687.43129960317M5[t] -2357.90029761905M6[t] -2422.36929563492M7[t] -2104.69543650794M8[t] -2896.45014880952M9[t] -2143.91914682540M10[t] -2478.67385912699M11[t] +  77.7547123015873t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14692&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14692&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
HPC[t] = + 15515.9166666667 -2132.84102182539M1[t] -2859.02430555556M2[t] -1507.77901785714M3[t] -2251.39087301588M4[t] -1687.43129960317M5[t] -2357.90029761905M6[t] -2422.36929563492M7[t] -2104.69543650794M8[t] -2896.45014880952M9[t] -2143.91914682540M10[t] -2478.67385912699M11[t] + 77.7547123015873t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	15515.9166666667	129.27789	120.0199	0	0
M1	-2132.84102182539	159.023856	-13.4121	0	0
M2	-2859.02430555556	158.904073	-17.9921	0	0
M3	-1507.77901785714	158.79562	-9.4951	0	0
M4	-2251.39087301588	158.69852	-14.1866	0	0
M5	-1687.43129960317	158.612794	-10.6387	0	0
M6	-2357.90029761905	158.538461	-14.8727	0	0
M7	-2422.36929563492	158.475536	-15.2854	0	0
M8	-2104.69543650794	158.424034	-13.2852	0	0
M9	-2896.45014880952	158.383965	-18.2875	0	0
M10	-2143.91914682540	158.355338	-13.5387	0	0
M11	-2478.67385912699	158.338159	-15.6543	0	0
t	77.7547123015873	1.346645	57.7396	0	0

\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) & 15515.9166666667 & 129.27789 & 120.0199 & 0 & 0 \tabularnewline
M1 & -2132.84102182539 & 159.023856 & -13.4121 & 0 & 0 \tabularnewline
M2 & -2859.02430555556 & 158.904073 & -17.9921 & 0 & 0 \tabularnewline
M3 & -1507.77901785714 & 158.79562 & -9.4951 & 0 & 0 \tabularnewline
M4 & -2251.39087301588 & 158.69852 & -14.1866 & 0 & 0 \tabularnewline
M5 & -1687.43129960317 & 158.612794 & -10.6387 & 0 & 0 \tabularnewline
M6 & -2357.90029761905 & 158.538461 & -14.8727 & 0 & 0 \tabularnewline
M7 & -2422.36929563492 & 158.475536 & -15.2854 & 0 & 0 \tabularnewline
M8 & -2104.69543650794 & 158.424034 & -13.2852 & 0 & 0 \tabularnewline
M9 & -2896.45014880952 & 158.383965 & -18.2875 & 0 & 0 \tabularnewline
M10 & -2143.91914682540 & 158.355338 & -13.5387 & 0 & 0 \tabularnewline
M11 & -2478.67385912699 & 158.338159 & -15.6543 & 0 & 0 \tabularnewline
t & 77.7547123015873 & 1.346645 & 57.7396 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14692&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]15515.9166666667[/C][C]129.27789[/C][C]120.0199[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-2132.84102182539[/C][C]159.023856[/C][C]-13.4121[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]-2859.02430555556[/C][C]158.904073[/C][C]-17.9921[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]-1507.77901785714[/C][C]158.79562[/C][C]-9.4951[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]-2251.39087301588[/C][C]158.69852[/C][C]-14.1866[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-1687.43129960317[/C][C]158.612794[/C][C]-10.6387[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-2357.90029761905[/C][C]158.538461[/C][C]-14.8727[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]-2422.36929563492[/C][C]158.475536[/C][C]-15.2854[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-2104.69543650794[/C][C]158.424034[/C][C]-13.2852[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-2896.45014880952[/C][C]158.383965[/C][C]-18.2875[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-2143.91914682540[/C][C]158.355338[/C][C]-13.5387[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-2478.67385912699[/C][C]158.338159[/C][C]-15.6543[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]77.7547123015873[/C][C]1.346645[/C][C]57.7396[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14692&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14692&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	15515.9166666667	129.27789	120.0199	0	0
M1	-2132.84102182539	159.023856	-13.4121	0	0
M2	-2859.02430555556	158.904073	-17.9921	0	0
M3	-1507.77901785714	158.79562	-9.4951	0	0
M4	-2251.39087301588	158.69852	-14.1866	0	0
M5	-1687.43129960317	158.612794	-10.6387	0	0
M6	-2357.90029761905	158.538461	-14.8727	0	0
M7	-2422.36929563492	158.475536	-15.2854	0	0
M8	-2104.69543650794	158.424034	-13.2852	0	0
M9	-2896.45014880952	158.383965	-18.2875	0	0
M10	-2143.91914682540	158.355338	-13.5387	0	0
M11	-2478.67385912699	158.338159	-15.6543	0	0
t	77.7547123015873	1.346645	57.7396	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.99130582514897
R-squared	0.98268723897428
Adjusted R-squared	0.979761138519229
F-TEST (value)	335.835100014381
F-TEST (DF numerator)	12
F-TEST (DF denominator)	71
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	296.212858097257
Sum Squared Residuals	6229686.06845236

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99130582514897 \tabularnewline
R-squared & 0.98268723897428 \tabularnewline
Adjusted R-squared & 0.979761138519229 \tabularnewline
F-TEST (value) & 335.835100014381 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 71 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 296.212858097257 \tabularnewline
Sum Squared Residuals & 6229686.06845236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14692&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99130582514897[/C][/ROW]
[ROW][C]R-squared[/C][C]0.98268723897428[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.979761138519229[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]335.835100014381[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]71[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]296.212858097257[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6229686.06845236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14692&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14692&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 R	0.99130582514897
R-squared	0.98268723897428
Adjusted R-squared	0.979761138519229
F-TEST (value)	335.835100014381
F-TEST (DF numerator)	12
F-TEST (DF denominator)	71
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	296.212858097257
Sum Squared Residuals	6229686.06845236

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13328	13460.8303571428	-132.830357142841
2	12873	12812.4017857143	60.59821428571
3	14000	14241.4017857143	-241.401785714278
4	13477	13575.5446428571	-98.544642857145
5	14237	14217.2589285714	19.7410714285763
6	13674	13624.5446428571	49.4553571428511
7	13529	13637.8303571429	-108.830357142853
8	14058	14033.2589285714	24.7410714285705
9	12975	13319.2589285714	-344.25892857143
10	14326	14149.5446428571	176.455357142857
11	14008	13892.5446428571	115.455357142854
12	16193	16448.9732142857	-255.973214285714
13	14483	14393.8869047619	89.1130952380873
14	14011	13745.4583333333	265.541666666667
15	15057	15174.4583333333	-117.458333333334
16	14884	14508.6011904762	375.398809523809
17	15414	15150.3154761905	263.684523809523
18	14440	14557.6011904762	-117.60119047619
19	14900	14570.8869047619	329.113095238095
20	15074	14966.3154761905	107.684523809524
21	14442	14252.3154761905	189.684523809524
22	15307	15082.6011904762	224.398809523809
23	14938	14825.6011904762	112.398809523810
24	17193	17382.0297619048	-189.029761904763
25	15528	15326.9434523810	201.056547619046
26	14765	14678.5148809524	86.4851190476194
27	15838	16107.5148809524	-269.514880952383
28	15723	15441.6577380952	281.342261904762
29	16150	16083.3720238095	66.6279761904752
30	15486	15490.6577380952	-4.65773809523746
31	15986	15503.9434523810	482.056547619047
32	15983	15899.3720238095	83.6279761904762
33	15692	15185.3720238095	506.627976190476
34	16490	16015.6577380952	474.342261904761
35	15686	15758.6577380952	-72.6577380952376
36	18897	18315.0863095238	581.913690476189
37	16316	16260	55.9999999999982
38	15636	15611.5714285714	24.4285714285718
39	17163	17040.5714285714	122.428571428570
40	16534	16374.7142857143	159.285714285714
41	16518	17016.4285714286	-498.428571428572
42	16375	16423.7142857143	-48.7142857142847
43	16290	16437	-147.000000000001
44	16352	16832.4285714286	-480.428571428571
45	15943	16118.4285714286	-175.428571428571
46	16362	16948.7142857143	-586.714285714285
47	16393	16691.7142857143	-298.714285714285
48	19051	19248.1428571429	-197.142857142859
49	16747	17193.0565476190	-446.056547619049
50	16320	16544.6279761905	-224.627976190475
51	17910	17973.6279761905	-63.6279761904773
52	16961	17307.7708333333	-346.770833333333
53	17480	17949.4851190476	-469.48511904762
54	17049	17356.7708333333	-307.770833333332
55	16879	17370.0565476190	-491.056547619048
56	17473	17765.4851190476	-292.485119047618
57	16998	17051.4851190476	-53.4851190476179
58	17307	17881.7708333333	-574.770833333332
59	17418	17624.7708333333	-206.770833333332
60	20169	20181.1994047619	-12.1994047619047
61	17871	18126.1130952381	-255.113095238096
62	17226	17477.6845238095	-251.684523809522
63	19062	18906.6845238095	155.315476190475
64	17804	18240.8273809524	-436.82738095238
65	19100	18882.5416666667	217.458333333333
66	18522	18289.8273809524	232.172619047621
67	18060	18303.1130952381	-243.113095238096
68	18869	18698.5416666667	170.458333333333
69	18127	17984.5416666667	142.458333333333
70	18871	18814.8273809524	56.1726190476184
71	18890	18557.8273809524	332.172619047620
72	21263	21114.2559523810	148.744047619049
73	19547	19059.1696428571	487.830357142856
74	18450	18410.7410714286	39.2589285714303
75	20254	19839.7410714286	414.258928571429
76	19240	19173.8839285714	66.1160714285725
77	20216	19815.5982142857	400.401785714284
78	19420	19222.8839285714	197.116071428572
79	19415	19236.1696428571	178.830357142856
80	20018	19631.5982142857	386.401785714287
81	18652	18917.5982142857	-265.598214285715
82	19978	19747.8839285714	230.116071428572
83	19509	19490.8839285714	18.1160714285717
84	21971	22047.3125	-76.3124999999989

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13328 & 13460.8303571428 & -132.830357142841 \tabularnewline
2 & 12873 & 12812.4017857143 & 60.59821428571 \tabularnewline
3 & 14000 & 14241.4017857143 & -241.401785714278 \tabularnewline
4 & 13477 & 13575.5446428571 & -98.544642857145 \tabularnewline
5 & 14237 & 14217.2589285714 & 19.7410714285763 \tabularnewline
6 & 13674 & 13624.5446428571 & 49.4553571428511 \tabularnewline
7 & 13529 & 13637.8303571429 & -108.830357142853 \tabularnewline
8 & 14058 & 14033.2589285714 & 24.7410714285705 \tabularnewline
9 & 12975 & 13319.2589285714 & -344.25892857143 \tabularnewline
10 & 14326 & 14149.5446428571 & 176.455357142857 \tabularnewline
11 & 14008 & 13892.5446428571 & 115.455357142854 \tabularnewline
12 & 16193 & 16448.9732142857 & -255.973214285714 \tabularnewline
13 & 14483 & 14393.8869047619 & 89.1130952380873 \tabularnewline
14 & 14011 & 13745.4583333333 & 265.541666666667 \tabularnewline
15 & 15057 & 15174.4583333333 & -117.458333333334 \tabularnewline
16 & 14884 & 14508.6011904762 & 375.398809523809 \tabularnewline
17 & 15414 & 15150.3154761905 & 263.684523809523 \tabularnewline
18 & 14440 & 14557.6011904762 & -117.60119047619 \tabularnewline
19 & 14900 & 14570.8869047619 & 329.113095238095 \tabularnewline
20 & 15074 & 14966.3154761905 & 107.684523809524 \tabularnewline
21 & 14442 & 14252.3154761905 & 189.684523809524 \tabularnewline
22 & 15307 & 15082.6011904762 & 224.398809523809 \tabularnewline
23 & 14938 & 14825.6011904762 & 112.398809523810 \tabularnewline
24 & 17193 & 17382.0297619048 & -189.029761904763 \tabularnewline
25 & 15528 & 15326.9434523810 & 201.056547619046 \tabularnewline
26 & 14765 & 14678.5148809524 & 86.4851190476194 \tabularnewline
27 & 15838 & 16107.5148809524 & -269.514880952383 \tabularnewline
28 & 15723 & 15441.6577380952 & 281.342261904762 \tabularnewline
29 & 16150 & 16083.3720238095 & 66.6279761904752 \tabularnewline
30 & 15486 & 15490.6577380952 & -4.65773809523746 \tabularnewline
31 & 15986 & 15503.9434523810 & 482.056547619047 \tabularnewline
32 & 15983 & 15899.3720238095 & 83.6279761904762 \tabularnewline
33 & 15692 & 15185.3720238095 & 506.627976190476 \tabularnewline
34 & 16490 & 16015.6577380952 & 474.342261904761 \tabularnewline
35 & 15686 & 15758.6577380952 & -72.6577380952376 \tabularnewline
36 & 18897 & 18315.0863095238 & 581.913690476189 \tabularnewline
37 & 16316 & 16260 & 55.9999999999982 \tabularnewline
38 & 15636 & 15611.5714285714 & 24.4285714285718 \tabularnewline
39 & 17163 & 17040.5714285714 & 122.428571428570 \tabularnewline
40 & 16534 & 16374.7142857143 & 159.285714285714 \tabularnewline
41 & 16518 & 17016.4285714286 & -498.428571428572 \tabularnewline
42 & 16375 & 16423.7142857143 & -48.7142857142847 \tabularnewline
43 & 16290 & 16437 & -147.000000000001 \tabularnewline
44 & 16352 & 16832.4285714286 & -480.428571428571 \tabularnewline
45 & 15943 & 16118.4285714286 & -175.428571428571 \tabularnewline
46 & 16362 & 16948.7142857143 & -586.714285714285 \tabularnewline
47 & 16393 & 16691.7142857143 & -298.714285714285 \tabularnewline
48 & 19051 & 19248.1428571429 & -197.142857142859 \tabularnewline
49 & 16747 & 17193.0565476190 & -446.056547619049 \tabularnewline
50 & 16320 & 16544.6279761905 & -224.627976190475 \tabularnewline
51 & 17910 & 17973.6279761905 & -63.6279761904773 \tabularnewline
52 & 16961 & 17307.7708333333 & -346.770833333333 \tabularnewline
53 & 17480 & 17949.4851190476 & -469.48511904762 \tabularnewline
54 & 17049 & 17356.7708333333 & -307.770833333332 \tabularnewline
55 & 16879 & 17370.0565476190 & -491.056547619048 \tabularnewline
56 & 17473 & 17765.4851190476 & -292.485119047618 \tabularnewline
57 & 16998 & 17051.4851190476 & -53.4851190476179 \tabularnewline
58 & 17307 & 17881.7708333333 & -574.770833333332 \tabularnewline
59 & 17418 & 17624.7708333333 & -206.770833333332 \tabularnewline
60 & 20169 & 20181.1994047619 & -12.1994047619047 \tabularnewline
61 & 17871 & 18126.1130952381 & -255.113095238096 \tabularnewline
62 & 17226 & 17477.6845238095 & -251.684523809522 \tabularnewline
63 & 19062 & 18906.6845238095 & 155.315476190475 \tabularnewline
64 & 17804 & 18240.8273809524 & -436.82738095238 \tabularnewline
65 & 19100 & 18882.5416666667 & 217.458333333333 \tabularnewline
66 & 18522 & 18289.8273809524 & 232.172619047621 \tabularnewline
67 & 18060 & 18303.1130952381 & -243.113095238096 \tabularnewline
68 & 18869 & 18698.5416666667 & 170.458333333333 \tabularnewline
69 & 18127 & 17984.5416666667 & 142.458333333333 \tabularnewline
70 & 18871 & 18814.8273809524 & 56.1726190476184 \tabularnewline
71 & 18890 & 18557.8273809524 & 332.172619047620 \tabularnewline
72 & 21263 & 21114.2559523810 & 148.744047619049 \tabularnewline
73 & 19547 & 19059.1696428571 & 487.830357142856 \tabularnewline
74 & 18450 & 18410.7410714286 & 39.2589285714303 \tabularnewline
75 & 20254 & 19839.7410714286 & 414.258928571429 \tabularnewline
76 & 19240 & 19173.8839285714 & 66.1160714285725 \tabularnewline
77 & 20216 & 19815.5982142857 & 400.401785714284 \tabularnewline
78 & 19420 & 19222.8839285714 & 197.116071428572 \tabularnewline
79 & 19415 & 19236.1696428571 & 178.830357142856 \tabularnewline
80 & 20018 & 19631.5982142857 & 386.401785714287 \tabularnewline
81 & 18652 & 18917.5982142857 & -265.598214285715 \tabularnewline
82 & 19978 & 19747.8839285714 & 230.116071428572 \tabularnewline
83 & 19509 & 19490.8839285714 & 18.1160714285717 \tabularnewline
84 & 21971 & 22047.3125 & -76.3124999999989 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14692&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]13328[/C][C]13460.8303571428[/C][C]-132.830357142841[/C][/ROW]
[ROW][C]2[/C][C]12873[/C][C]12812.4017857143[/C][C]60.59821428571[/C][/ROW]
[ROW][C]3[/C][C]14000[/C][C]14241.4017857143[/C][C]-241.401785714278[/C][/ROW]
[ROW][C]4[/C][C]13477[/C][C]13575.5446428571[/C][C]-98.544642857145[/C][/ROW]
[ROW][C]5[/C][C]14237[/C][C]14217.2589285714[/C][C]19.7410714285763[/C][/ROW]
[ROW][C]6[/C][C]13674[/C][C]13624.5446428571[/C][C]49.4553571428511[/C][/ROW]
[ROW][C]7[/C][C]13529[/C][C]13637.8303571429[/C][C]-108.830357142853[/C][/ROW]
[ROW][C]8[/C][C]14058[/C][C]14033.2589285714[/C][C]24.7410714285705[/C][/ROW]
[ROW][C]9[/C][C]12975[/C][C]13319.2589285714[/C][C]-344.25892857143[/C][/ROW]
[ROW][C]10[/C][C]14326[/C][C]14149.5446428571[/C][C]176.455357142857[/C][/ROW]
[ROW][C]11[/C][C]14008[/C][C]13892.5446428571[/C][C]115.455357142854[/C][/ROW]
[ROW][C]12[/C][C]16193[/C][C]16448.9732142857[/C][C]-255.973214285714[/C][/ROW]
[ROW][C]13[/C][C]14483[/C][C]14393.8869047619[/C][C]89.1130952380873[/C][/ROW]
[ROW][C]14[/C][C]14011[/C][C]13745.4583333333[/C][C]265.541666666667[/C][/ROW]
[ROW][C]15[/C][C]15057[/C][C]15174.4583333333[/C][C]-117.458333333334[/C][/ROW]
[ROW][C]16[/C][C]14884[/C][C]14508.6011904762[/C][C]375.398809523809[/C][/ROW]
[ROW][C]17[/C][C]15414[/C][C]15150.3154761905[/C][C]263.684523809523[/C][/ROW]
[ROW][C]18[/C][C]14440[/C][C]14557.6011904762[/C][C]-117.60119047619[/C][/ROW]
[ROW][C]19[/C][C]14900[/C][C]14570.8869047619[/C][C]329.113095238095[/C][/ROW]
[ROW][C]20[/C][C]15074[/C][C]14966.3154761905[/C][C]107.684523809524[/C][/ROW]
[ROW][C]21[/C][C]14442[/C][C]14252.3154761905[/C][C]189.684523809524[/C][/ROW]
[ROW][C]22[/C][C]15307[/C][C]15082.6011904762[/C][C]224.398809523809[/C][/ROW]
[ROW][C]23[/C][C]14938[/C][C]14825.6011904762[/C][C]112.398809523810[/C][/ROW]
[ROW][C]24[/C][C]17193[/C][C]17382.0297619048[/C][C]-189.029761904763[/C][/ROW]
[ROW][C]25[/C][C]15528[/C][C]15326.9434523810[/C][C]201.056547619046[/C][/ROW]
[ROW][C]26[/C][C]14765[/C][C]14678.5148809524[/C][C]86.4851190476194[/C][/ROW]
[ROW][C]27[/C][C]15838[/C][C]16107.5148809524[/C][C]-269.514880952383[/C][/ROW]
[ROW][C]28[/C][C]15723[/C][C]15441.6577380952[/C][C]281.342261904762[/C][/ROW]
[ROW][C]29[/C][C]16150[/C][C]16083.3720238095[/C][C]66.6279761904752[/C][/ROW]
[ROW][C]30[/C][C]15486[/C][C]15490.6577380952[/C][C]-4.65773809523746[/C][/ROW]
[ROW][C]31[/C][C]15986[/C][C]15503.9434523810[/C][C]482.056547619047[/C][/ROW]
[ROW][C]32[/C][C]15983[/C][C]15899.3720238095[/C][C]83.6279761904762[/C][/ROW]
[ROW][C]33[/C][C]15692[/C][C]15185.3720238095[/C][C]506.627976190476[/C][/ROW]
[ROW][C]34[/C][C]16490[/C][C]16015.6577380952[/C][C]474.342261904761[/C][/ROW]
[ROW][C]35[/C][C]15686[/C][C]15758.6577380952[/C][C]-72.6577380952376[/C][/ROW]
[ROW][C]36[/C][C]18897[/C][C]18315.0863095238[/C][C]581.913690476189[/C][/ROW]
[ROW][C]37[/C][C]16316[/C][C]16260[/C][C]55.9999999999982[/C][/ROW]
[ROW][C]38[/C][C]15636[/C][C]15611.5714285714[/C][C]24.4285714285718[/C][/ROW]
[ROW][C]39[/C][C]17163[/C][C]17040.5714285714[/C][C]122.428571428570[/C][/ROW]
[ROW][C]40[/C][C]16534[/C][C]16374.7142857143[/C][C]159.285714285714[/C][/ROW]
[ROW][C]41[/C][C]16518[/C][C]17016.4285714286[/C][C]-498.428571428572[/C][/ROW]
[ROW][C]42[/C][C]16375[/C][C]16423.7142857143[/C][C]-48.7142857142847[/C][/ROW]
[ROW][C]43[/C][C]16290[/C][C]16437[/C][C]-147.000000000001[/C][/ROW]
[ROW][C]44[/C][C]16352[/C][C]16832.4285714286[/C][C]-480.428571428571[/C][/ROW]
[ROW][C]45[/C][C]15943[/C][C]16118.4285714286[/C][C]-175.428571428571[/C][/ROW]
[ROW][C]46[/C][C]16362[/C][C]16948.7142857143[/C][C]-586.714285714285[/C][/ROW]
[ROW][C]47[/C][C]16393[/C][C]16691.7142857143[/C][C]-298.714285714285[/C][/ROW]
[ROW][C]48[/C][C]19051[/C][C]19248.1428571429[/C][C]-197.142857142859[/C][/ROW]
[ROW][C]49[/C][C]16747[/C][C]17193.0565476190[/C][C]-446.056547619049[/C][/ROW]
[ROW][C]50[/C][C]16320[/C][C]16544.6279761905[/C][C]-224.627976190475[/C][/ROW]
[ROW][C]51[/C][C]17910[/C][C]17973.6279761905[/C][C]-63.6279761904773[/C][/ROW]
[ROW][C]52[/C][C]16961[/C][C]17307.7708333333[/C][C]-346.770833333333[/C][/ROW]
[ROW][C]53[/C][C]17480[/C][C]17949.4851190476[/C][C]-469.48511904762[/C][/ROW]
[ROW][C]54[/C][C]17049[/C][C]17356.7708333333[/C][C]-307.770833333332[/C][/ROW]
[ROW][C]55[/C][C]16879[/C][C]17370.0565476190[/C][C]-491.056547619048[/C][/ROW]
[ROW][C]56[/C][C]17473[/C][C]17765.4851190476[/C][C]-292.485119047618[/C][/ROW]
[ROW][C]57[/C][C]16998[/C][C]17051.4851190476[/C][C]-53.4851190476179[/C][/ROW]
[ROW][C]58[/C][C]17307[/C][C]17881.7708333333[/C][C]-574.770833333332[/C][/ROW]
[ROW][C]59[/C][C]17418[/C][C]17624.7708333333[/C][C]-206.770833333332[/C][/ROW]
[ROW][C]60[/C][C]20169[/C][C]20181.1994047619[/C][C]-12.1994047619047[/C][/ROW]
[ROW][C]61[/C][C]17871[/C][C]18126.1130952381[/C][C]-255.113095238096[/C][/ROW]
[ROW][C]62[/C][C]17226[/C][C]17477.6845238095[/C][C]-251.684523809522[/C][/ROW]
[ROW][C]63[/C][C]19062[/C][C]18906.6845238095[/C][C]155.315476190475[/C][/ROW]
[ROW][C]64[/C][C]17804[/C][C]18240.8273809524[/C][C]-436.82738095238[/C][/ROW]
[ROW][C]65[/C][C]19100[/C][C]18882.5416666667[/C][C]217.458333333333[/C][/ROW]
[ROW][C]66[/C][C]18522[/C][C]18289.8273809524[/C][C]232.172619047621[/C][/ROW]
[ROW][C]67[/C][C]18060[/C][C]18303.1130952381[/C][C]-243.113095238096[/C][/ROW]
[ROW][C]68[/C][C]18869[/C][C]18698.5416666667[/C][C]170.458333333333[/C][/ROW]
[ROW][C]69[/C][C]18127[/C][C]17984.5416666667[/C][C]142.458333333333[/C][/ROW]
[ROW][C]70[/C][C]18871[/C][C]18814.8273809524[/C][C]56.1726190476184[/C][/ROW]
[ROW][C]71[/C][C]18890[/C][C]18557.8273809524[/C][C]332.172619047620[/C][/ROW]
[ROW][C]72[/C][C]21263[/C][C]21114.2559523810[/C][C]148.744047619049[/C][/ROW]
[ROW][C]73[/C][C]19547[/C][C]19059.1696428571[/C][C]487.830357142856[/C][/ROW]
[ROW][C]74[/C][C]18450[/C][C]18410.7410714286[/C][C]39.2589285714303[/C][/ROW]
[ROW][C]75[/C][C]20254[/C][C]19839.7410714286[/C][C]414.258928571429[/C][/ROW]
[ROW][C]76[/C][C]19240[/C][C]19173.8839285714[/C][C]66.1160714285725[/C][/ROW]
[ROW][C]77[/C][C]20216[/C][C]19815.5982142857[/C][C]400.401785714284[/C][/ROW]
[ROW][C]78[/C][C]19420[/C][C]19222.8839285714[/C][C]197.116071428572[/C][/ROW]
[ROW][C]79[/C][C]19415[/C][C]19236.1696428571[/C][C]178.830357142856[/C][/ROW]
[ROW][C]80[/C][C]20018[/C][C]19631.5982142857[/C][C]386.401785714287[/C][/ROW]
[ROW][C]81[/C][C]18652[/C][C]18917.5982142857[/C][C]-265.598214285715[/C][/ROW]
[ROW][C]82[/C][C]19978[/C][C]19747.8839285714[/C][C]230.116071428572[/C][/ROW]
[ROW][C]83[/C][C]19509[/C][C]19490.8839285714[/C][C]18.1160714285717[/C][/ROW]
[ROW][C]84[/C][C]21971[/C][C]22047.3125[/C][C]-76.3124999999989[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14692&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14692&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13328	13460.8303571428	-132.830357142841
2	12873	12812.4017857143	60.59821428571
3	14000	14241.4017857143	-241.401785714278
4	13477	13575.5446428571	-98.544642857145
5	14237	14217.2589285714	19.7410714285763
6	13674	13624.5446428571	49.4553571428511
7	13529	13637.8303571429	-108.830357142853
8	14058	14033.2589285714	24.7410714285705
9	12975	13319.2589285714	-344.25892857143
10	14326	14149.5446428571	176.455357142857
11	14008	13892.5446428571	115.455357142854
12	16193	16448.9732142857	-255.973214285714
13	14483	14393.8869047619	89.1130952380873
14	14011	13745.4583333333	265.541666666667
15	15057	15174.4583333333	-117.458333333334
16	14884	14508.6011904762	375.398809523809
17	15414	15150.3154761905	263.684523809523
18	14440	14557.6011904762	-117.60119047619
19	14900	14570.8869047619	329.113095238095
20	15074	14966.3154761905	107.684523809524
21	14442	14252.3154761905	189.684523809524
22	15307	15082.6011904762	224.398809523809
23	14938	14825.6011904762	112.398809523810
24	17193	17382.0297619048	-189.029761904763
25	15528	15326.9434523810	201.056547619046
26	14765	14678.5148809524	86.4851190476194
27	15838	16107.5148809524	-269.514880952383
28	15723	15441.6577380952	281.342261904762
29	16150	16083.3720238095	66.6279761904752
30	15486	15490.6577380952	-4.65773809523746
31	15986	15503.9434523810	482.056547619047
32	15983	15899.3720238095	83.6279761904762
33	15692	15185.3720238095	506.627976190476
34	16490	16015.6577380952	474.342261904761
35	15686	15758.6577380952	-72.6577380952376
36	18897	18315.0863095238	581.913690476189
37	16316	16260	55.9999999999982
38	15636	15611.5714285714	24.4285714285718
39	17163	17040.5714285714	122.428571428570
40	16534	16374.7142857143	159.285714285714
41	16518	17016.4285714286	-498.428571428572
42	16375	16423.7142857143	-48.7142857142847
43	16290	16437	-147.000000000001
44	16352	16832.4285714286	-480.428571428571
45	15943	16118.4285714286	-175.428571428571
46	16362	16948.7142857143	-586.714285714285
47	16393	16691.7142857143	-298.714285714285
48	19051	19248.1428571429	-197.142857142859
49	16747	17193.0565476190	-446.056547619049
50	16320	16544.6279761905	-224.627976190475
51	17910	17973.6279761905	-63.6279761904773
52	16961	17307.7708333333	-346.770833333333
53	17480	17949.4851190476	-469.48511904762
54	17049	17356.7708333333	-307.770833333332
55	16879	17370.0565476190	-491.056547619048
56	17473	17765.4851190476	-292.485119047618
57	16998	17051.4851190476	-53.4851190476179
58	17307	17881.7708333333	-574.770833333332
59	17418	17624.7708333333	-206.770833333332
60	20169	20181.1994047619	-12.1994047619047
61	17871	18126.1130952381	-255.113095238096
62	17226	17477.6845238095	-251.684523809522
63	19062	18906.6845238095	155.315476190475
64	17804	18240.8273809524	-436.82738095238
65	19100	18882.5416666667	217.458333333333
66	18522	18289.8273809524	232.172619047621
67	18060	18303.1130952381	-243.113095238096
68	18869	18698.5416666667	170.458333333333
69	18127	17984.5416666667	142.458333333333
70	18871	18814.8273809524	56.1726190476184
71	18890	18557.8273809524	332.172619047620
72	21263	21114.2559523810	148.744047619049
73	19547	19059.1696428571	487.830357142856
74	18450	18410.7410714286	39.2589285714303
75	20254	19839.7410714286	414.258928571429
76	19240	19173.8839285714	66.1160714285725
77	20216	19815.5982142857	400.401785714284
78	19420	19222.8839285714	197.116071428572
79	19415	19236.1696428571	178.830357142856
80	20018	19631.5982142857	386.401785714287
81	18652	18917.5982142857	-265.598214285715
82	19978	19747.8839285714	230.116071428572
83	19509	19490.8839285714	18.1160714285717
84	21971	22047.3125	-76.3124999999989

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0480796323752217	0.0961592647504433	0.951920367624778
17	0.0132432466795157	0.0264864933590315	0.986756753320484
18	0.0432107824099886	0.0864215648199773	0.956789217590011
19	0.0315084969136549	0.0630169938273099	0.968491503086345
20	0.0152933905266358	0.0305867810532717	0.984706609473364
21	0.0158812131725257	0.0317624263450514	0.984118786827474
22	0.0096668261431016	0.0193336522862032	0.990333173856898
23	0.00630198435563797	0.0126039687112759	0.993698015644362
24	0.00319094541650994	0.00638189083301988	0.99680905458349
25	0.00143352618919697	0.00286705237839393	0.998566473810803
26	0.00212110096484131	0.00424220192968263	0.997878899035159
27	0.00238615697093017	0.00477231394186035	0.99761384302907
28	0.00131420023077466	0.00262840046154933	0.998685799769225
29	0.00104463247716715	0.00208926495433430	0.998955367522833
30	0.000514169460455889	0.00102833892091178	0.999485830539544
31	0.000776122767264067	0.00155224553452813	0.999223877232736
32	0.000452014186878448	0.000904028373756897	0.999547985813122
33	0.0023910490053108	0.0047820980106216	0.99760895099469
34	0.00481588271422685	0.0096317654284537	0.995184117285773
35	0.00686617241902962	0.0137323448380592	0.99313382758097
36	0.102272467142474	0.204544934284947	0.897727532857526
37	0.113338559214991	0.226677118429981	0.88666144078501
38	0.159719499815923	0.319438999631846	0.840280500184077
39	0.139186574074145	0.278373148148289	0.860813425925855
40	0.291585373792782	0.583170747585564	0.708414626207218
41	0.584474474166785	0.83105105166643	0.415525525833215
42	0.564639796808961	0.870720406382078	0.435360203191039
43	0.702621746976863	0.594756506046275	0.297378253023137
44	0.782353228856718	0.435293542286564	0.217646771143282
45	0.797662920721426	0.404674158557148	0.202337079278574
46	0.898354404500992	0.203291190998015	0.101645595499008
47	0.876890356415658	0.246219287168684	0.123109643584342
48	0.858653848981129	0.282692302037743	0.141346151018871
49	0.857155395926084	0.285689208147832	0.142844604073916
50	0.83488474687285	0.330230506254299	0.165115253127149
51	0.785695729980796	0.428608540038408	0.214304270019204
52	0.77295482680727	0.454090346385459	0.227045173192730
53	0.809578338257665	0.38084332348467	0.190421661742335
54	0.770831147074807	0.458337705850385	0.229168852925192
55	0.753727922744181	0.492544154511638	0.246272077255819
56	0.730345718803357	0.539308562393286	0.269654281196643
57	0.701912347381598	0.596175305236803	0.298087652618402
58	0.7867422343356	0.4265155313288	0.2132577656644
59	0.731444268566858	0.537111462866284	0.268555731433142
60	0.669796407678478	0.660407184643043	0.330203592321522
61	0.800781167812374	0.398437664375251	0.199218832187626
62	0.744179576169956	0.511640847660089	0.255820423830044
63	0.704109506968415	0.59178098606317	0.295890493031585
64	0.776403232124108	0.447193535751784	0.223596767875892
65	0.730615425479785	0.538769149040429	0.269384574520215
66	0.628788517161013	0.742422965677975	0.371211482838987
67	0.724631049343983	0.550737901312033	0.275368950656017
68	0.75285278661892	0.494294426762161	0.247147213381080

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0480796323752217 & 0.0961592647504433 & 0.951920367624778 \tabularnewline
17 & 0.0132432466795157 & 0.0264864933590315 & 0.986756753320484 \tabularnewline
18 & 0.0432107824099886 & 0.0864215648199773 & 0.956789217590011 \tabularnewline
19 & 0.0315084969136549 & 0.0630169938273099 & 0.968491503086345 \tabularnewline
20 & 0.0152933905266358 & 0.0305867810532717 & 0.984706609473364 \tabularnewline
21 & 0.0158812131725257 & 0.0317624263450514 & 0.984118786827474 \tabularnewline
22 & 0.0096668261431016 & 0.0193336522862032 & 0.990333173856898 \tabularnewline
23 & 0.00630198435563797 & 0.0126039687112759 & 0.993698015644362 \tabularnewline
24 & 0.00319094541650994 & 0.00638189083301988 & 0.99680905458349 \tabularnewline
25 & 0.00143352618919697 & 0.00286705237839393 & 0.998566473810803 \tabularnewline
26 & 0.00212110096484131 & 0.00424220192968263 & 0.997878899035159 \tabularnewline
27 & 0.00238615697093017 & 0.00477231394186035 & 0.99761384302907 \tabularnewline
28 & 0.00131420023077466 & 0.00262840046154933 & 0.998685799769225 \tabularnewline
29 & 0.00104463247716715 & 0.00208926495433430 & 0.998955367522833 \tabularnewline
30 & 0.000514169460455889 & 0.00102833892091178 & 0.999485830539544 \tabularnewline
31 & 0.000776122767264067 & 0.00155224553452813 & 0.999223877232736 \tabularnewline
32 & 0.000452014186878448 & 0.000904028373756897 & 0.999547985813122 \tabularnewline
33 & 0.0023910490053108 & 0.0047820980106216 & 0.99760895099469 \tabularnewline
34 & 0.00481588271422685 & 0.0096317654284537 & 0.995184117285773 \tabularnewline
35 & 0.00686617241902962 & 0.0137323448380592 & 0.99313382758097 \tabularnewline
36 & 0.102272467142474 & 0.204544934284947 & 0.897727532857526 \tabularnewline
37 & 0.113338559214991 & 0.226677118429981 & 0.88666144078501 \tabularnewline
38 & 0.159719499815923 & 0.319438999631846 & 0.840280500184077 \tabularnewline
39 & 0.139186574074145 & 0.278373148148289 & 0.860813425925855 \tabularnewline
40 & 0.291585373792782 & 0.583170747585564 & 0.708414626207218 \tabularnewline
41 & 0.584474474166785 & 0.83105105166643 & 0.415525525833215 \tabularnewline
42 & 0.564639796808961 & 0.870720406382078 & 0.435360203191039 \tabularnewline
43 & 0.702621746976863 & 0.594756506046275 & 0.297378253023137 \tabularnewline
44 & 0.782353228856718 & 0.435293542286564 & 0.217646771143282 \tabularnewline
45 & 0.797662920721426 & 0.404674158557148 & 0.202337079278574 \tabularnewline
46 & 0.898354404500992 & 0.203291190998015 & 0.101645595499008 \tabularnewline
47 & 0.876890356415658 & 0.246219287168684 & 0.123109643584342 \tabularnewline
48 & 0.858653848981129 & 0.282692302037743 & 0.141346151018871 \tabularnewline
49 & 0.857155395926084 & 0.285689208147832 & 0.142844604073916 \tabularnewline
50 & 0.83488474687285 & 0.330230506254299 & 0.165115253127149 \tabularnewline
51 & 0.785695729980796 & 0.428608540038408 & 0.214304270019204 \tabularnewline
52 & 0.77295482680727 & 0.454090346385459 & 0.227045173192730 \tabularnewline
53 & 0.809578338257665 & 0.38084332348467 & 0.190421661742335 \tabularnewline
54 & 0.770831147074807 & 0.458337705850385 & 0.229168852925192 \tabularnewline
55 & 0.753727922744181 & 0.492544154511638 & 0.246272077255819 \tabularnewline
56 & 0.730345718803357 & 0.539308562393286 & 0.269654281196643 \tabularnewline
57 & 0.701912347381598 & 0.596175305236803 & 0.298087652618402 \tabularnewline
58 & 0.7867422343356 & 0.4265155313288 & 0.2132577656644 \tabularnewline
59 & 0.731444268566858 & 0.537111462866284 & 0.268555731433142 \tabularnewline
60 & 0.669796407678478 & 0.660407184643043 & 0.330203592321522 \tabularnewline
61 & 0.800781167812374 & 0.398437664375251 & 0.199218832187626 \tabularnewline
62 & 0.744179576169956 & 0.511640847660089 & 0.255820423830044 \tabularnewline
63 & 0.704109506968415 & 0.59178098606317 & 0.295890493031585 \tabularnewline
64 & 0.776403232124108 & 0.447193535751784 & 0.223596767875892 \tabularnewline
65 & 0.730615425479785 & 0.538769149040429 & 0.269384574520215 \tabularnewline
66 & 0.628788517161013 & 0.742422965677975 & 0.371211482838987 \tabularnewline
67 & 0.724631049343983 & 0.550737901312033 & 0.275368950656017 \tabularnewline
68 & 0.75285278661892 & 0.494294426762161 & 0.247147213381080 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14692&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]16[/C][C]0.0480796323752217[/C][C]0.0961592647504433[/C][C]0.951920367624778[/C][/ROW]
[ROW][C]17[/C][C]0.0132432466795157[/C][C]0.0264864933590315[/C][C]0.986756753320484[/C][/ROW]
[ROW][C]18[/C][C]0.0432107824099886[/C][C]0.0864215648199773[/C][C]0.956789217590011[/C][/ROW]
[ROW][C]19[/C][C]0.0315084969136549[/C][C]0.0630169938273099[/C][C]0.968491503086345[/C][/ROW]
[ROW][C]20[/C][C]0.0152933905266358[/C][C]0.0305867810532717[/C][C]0.984706609473364[/C][/ROW]
[ROW][C]21[/C][C]0.0158812131725257[/C][C]0.0317624263450514[/C][C]0.984118786827474[/C][/ROW]
[ROW][C]22[/C][C]0.0096668261431016[/C][C]0.0193336522862032[/C][C]0.990333173856898[/C][/ROW]
[ROW][C]23[/C][C]0.00630198435563797[/C][C]0.0126039687112759[/C][C]0.993698015644362[/C][/ROW]
[ROW][C]24[/C][C]0.00319094541650994[/C][C]0.00638189083301988[/C][C]0.99680905458349[/C][/ROW]
[ROW][C]25[/C][C]0.00143352618919697[/C][C]0.00286705237839393[/C][C]0.998566473810803[/C][/ROW]
[ROW][C]26[/C][C]0.00212110096484131[/C][C]0.00424220192968263[/C][C]0.997878899035159[/C][/ROW]
[ROW][C]27[/C][C]0.00238615697093017[/C][C]0.00477231394186035[/C][C]0.99761384302907[/C][/ROW]
[ROW][C]28[/C][C]0.00131420023077466[/C][C]0.00262840046154933[/C][C]0.998685799769225[/C][/ROW]
[ROW][C]29[/C][C]0.00104463247716715[/C][C]0.00208926495433430[/C][C]0.998955367522833[/C][/ROW]
[ROW][C]30[/C][C]0.000514169460455889[/C][C]0.00102833892091178[/C][C]0.999485830539544[/C][/ROW]
[ROW][C]31[/C][C]0.000776122767264067[/C][C]0.00155224553452813[/C][C]0.999223877232736[/C][/ROW]
[ROW][C]32[/C][C]0.000452014186878448[/C][C]0.000904028373756897[/C][C]0.999547985813122[/C][/ROW]
[ROW][C]33[/C][C]0.0023910490053108[/C][C]0.0047820980106216[/C][C]0.99760895099469[/C][/ROW]
[ROW][C]34[/C][C]0.00481588271422685[/C][C]0.0096317654284537[/C][C]0.995184117285773[/C][/ROW]
[ROW][C]35[/C][C]0.00686617241902962[/C][C]0.0137323448380592[/C][C]0.99313382758097[/C][/ROW]
[ROW][C]36[/C][C]0.102272467142474[/C][C]0.204544934284947[/C][C]0.897727532857526[/C][/ROW]
[ROW][C]37[/C][C]0.113338559214991[/C][C]0.226677118429981[/C][C]0.88666144078501[/C][/ROW]
[ROW][C]38[/C][C]0.159719499815923[/C][C]0.319438999631846[/C][C]0.840280500184077[/C][/ROW]
[ROW][C]39[/C][C]0.139186574074145[/C][C]0.278373148148289[/C][C]0.860813425925855[/C][/ROW]
[ROW][C]40[/C][C]0.291585373792782[/C][C]0.583170747585564[/C][C]0.708414626207218[/C][/ROW]
[ROW][C]41[/C][C]0.584474474166785[/C][C]0.83105105166643[/C][C]0.415525525833215[/C][/ROW]
[ROW][C]42[/C][C]0.564639796808961[/C][C]0.870720406382078[/C][C]0.435360203191039[/C][/ROW]
[ROW][C]43[/C][C]0.702621746976863[/C][C]0.594756506046275[/C][C]0.297378253023137[/C][/ROW]
[ROW][C]44[/C][C]0.782353228856718[/C][C]0.435293542286564[/C][C]0.217646771143282[/C][/ROW]
[ROW][C]45[/C][C]0.797662920721426[/C][C]0.404674158557148[/C][C]0.202337079278574[/C][/ROW]
[ROW][C]46[/C][C]0.898354404500992[/C][C]0.203291190998015[/C][C]0.101645595499008[/C][/ROW]
[ROW][C]47[/C][C]0.876890356415658[/C][C]0.246219287168684[/C][C]0.123109643584342[/C][/ROW]
[ROW][C]48[/C][C]0.858653848981129[/C][C]0.282692302037743[/C][C]0.141346151018871[/C][/ROW]
[ROW][C]49[/C][C]0.857155395926084[/C][C]0.285689208147832[/C][C]0.142844604073916[/C][/ROW]
[ROW][C]50[/C][C]0.83488474687285[/C][C]0.330230506254299[/C][C]0.165115253127149[/C][/ROW]
[ROW][C]51[/C][C]0.785695729980796[/C][C]0.428608540038408[/C][C]0.214304270019204[/C][/ROW]
[ROW][C]52[/C][C]0.77295482680727[/C][C]0.454090346385459[/C][C]0.227045173192730[/C][/ROW]
[ROW][C]53[/C][C]0.809578338257665[/C][C]0.38084332348467[/C][C]0.190421661742335[/C][/ROW]
[ROW][C]54[/C][C]0.770831147074807[/C][C]0.458337705850385[/C][C]0.229168852925192[/C][/ROW]
[ROW][C]55[/C][C]0.753727922744181[/C][C]0.492544154511638[/C][C]0.246272077255819[/C][/ROW]
[ROW][C]56[/C][C]0.730345718803357[/C][C]0.539308562393286[/C][C]0.269654281196643[/C][/ROW]
[ROW][C]57[/C][C]0.701912347381598[/C][C]0.596175305236803[/C][C]0.298087652618402[/C][/ROW]
[ROW][C]58[/C][C]0.7867422343356[/C][C]0.4265155313288[/C][C]0.2132577656644[/C][/ROW]
[ROW][C]59[/C][C]0.731444268566858[/C][C]0.537111462866284[/C][C]0.268555731433142[/C][/ROW]
[ROW][C]60[/C][C]0.669796407678478[/C][C]0.660407184643043[/C][C]0.330203592321522[/C][/ROW]
[ROW][C]61[/C][C]0.800781167812374[/C][C]0.398437664375251[/C][C]0.199218832187626[/C][/ROW]
[ROW][C]62[/C][C]0.744179576169956[/C][C]0.511640847660089[/C][C]0.255820423830044[/C][/ROW]
[ROW][C]63[/C][C]0.704109506968415[/C][C]0.59178098606317[/C][C]0.295890493031585[/C][/ROW]
[ROW][C]64[/C][C]0.776403232124108[/C][C]0.447193535751784[/C][C]0.223596767875892[/C][/ROW]
[ROW][C]65[/C][C]0.730615425479785[/C][C]0.538769149040429[/C][C]0.269384574520215[/C][/ROW]
[ROW][C]66[/C][C]0.628788517161013[/C][C]0.742422965677975[/C][C]0.371211482838987[/C][/ROW]
[ROW][C]67[/C][C]0.724631049343983[/C][C]0.550737901312033[/C][C]0.275368950656017[/C][/ROW]
[ROW][C]68[/C][C]0.75285278661892[/C][C]0.494294426762161[/C][C]0.247147213381080[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14692&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14692&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0480796323752217	0.0961592647504433	0.951920367624778
17	0.0132432466795157	0.0264864933590315	0.986756753320484
18	0.0432107824099886	0.0864215648199773	0.956789217590011
19	0.0315084969136549	0.0630169938273099	0.968491503086345
20	0.0152933905266358	0.0305867810532717	0.984706609473364
21	0.0158812131725257	0.0317624263450514	0.984118786827474
22	0.0096668261431016	0.0193336522862032	0.990333173856898
23	0.00630198435563797	0.0126039687112759	0.993698015644362
24	0.00319094541650994	0.00638189083301988	0.99680905458349
25	0.00143352618919697	0.00286705237839393	0.998566473810803
26	0.00212110096484131	0.00424220192968263	0.997878899035159
27	0.00238615697093017	0.00477231394186035	0.99761384302907
28	0.00131420023077466	0.00262840046154933	0.998685799769225
29	0.00104463247716715	0.00208926495433430	0.998955367522833
30	0.000514169460455889	0.00102833892091178	0.999485830539544
31	0.000776122767264067	0.00155224553452813	0.999223877232736
32	0.000452014186878448	0.000904028373756897	0.999547985813122
33	0.0023910490053108	0.0047820980106216	0.99760895099469
34	0.00481588271422685	0.0096317654284537	0.995184117285773
35	0.00686617241902962	0.0137323448380592	0.99313382758097
36	0.102272467142474	0.204544934284947	0.897727532857526
37	0.113338559214991	0.226677118429981	0.88666144078501
38	0.159719499815923	0.319438999631846	0.840280500184077
39	0.139186574074145	0.278373148148289	0.860813425925855
40	0.291585373792782	0.583170747585564	0.708414626207218
41	0.584474474166785	0.83105105166643	0.415525525833215
42	0.564639796808961	0.870720406382078	0.435360203191039
43	0.702621746976863	0.594756506046275	0.297378253023137
44	0.782353228856718	0.435293542286564	0.217646771143282
45	0.797662920721426	0.404674158557148	0.202337079278574
46	0.898354404500992	0.203291190998015	0.101645595499008
47	0.876890356415658	0.246219287168684	0.123109643584342
48	0.858653848981129	0.282692302037743	0.141346151018871
49	0.857155395926084	0.285689208147832	0.142844604073916
50	0.83488474687285	0.330230506254299	0.165115253127149
51	0.785695729980796	0.428608540038408	0.214304270019204
52	0.77295482680727	0.454090346385459	0.227045173192730
53	0.809578338257665	0.38084332348467	0.190421661742335
54	0.770831147074807	0.458337705850385	0.229168852925192
55	0.753727922744181	0.492544154511638	0.246272077255819
56	0.730345718803357	0.539308562393286	0.269654281196643
57	0.701912347381598	0.596175305236803	0.298087652618402
58	0.7867422343356	0.4265155313288	0.2132577656644
59	0.731444268566858	0.537111462866284	0.268555731433142
60	0.669796407678478	0.660407184643043	0.330203592321522
61	0.800781167812374	0.398437664375251	0.199218832187626
62	0.744179576169956	0.511640847660089	0.255820423830044
63	0.704109506968415	0.59178098606317	0.295890493031585
64	0.776403232124108	0.447193535751784	0.223596767875892
65	0.730615425479785	0.538769149040429	0.269384574520215
66	0.628788517161013	0.742422965677975	0.371211482838987
67	0.724631049343983	0.550737901312033	0.275368950656017
68	0.75285278661892	0.494294426762161	0.247147213381080

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	11	0.207547169811321	NOK
5% type I error level	17	0.320754716981132	NOK
10% type I error level	20	0.377358490566038	NOK

\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 & 11 & 0.207547169811321 & NOK \tabularnewline
5% type I error level & 17 & 0.320754716981132 & NOK \tabularnewline
10% type I error level & 20 & 0.377358490566038 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14692&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]11[/C][C]0.207547169811321[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.320754716981132[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.377358490566038[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14692&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14692&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 tests	OK/NOK
1% type I error level	11	0.207547169811321	NOK
5% type I error level	17	0.320754716981132	NOK
10% type I error level	20	0.377358490566038	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code