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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 08 Mar 2008 06:40:54 -0700
Cite this page as followsStatistical 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 Thu, 06 Nov 2025 03:21:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=14692, Retrieved Thu, 06 Nov 2025 03:21:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact1377

Tree of Dependent Computations

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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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13328
12873
14000
13477
14237
13674
13529
14058
12975
14326
14008
16193
14483
14011
15057
14884
15414
14440
14900
15074
14442
15307
14938
17193
15528
14765
15838
15723
16150
15486
15986
15983
15692
16490
15686
18897
16316
15636
17163
16534
16518
16375
16290
16352
15943
16362
16393
19051
16747
16320
17910
16961
17480
17049
16879
17473
16998
17307
17418
20169
17871
17226
19062
17804
19100
18522
18060
18869
18127
18871
18890
21263
19547
18450
20254
19240
20216
19420
19415
20018
18652
19978
19509
21971

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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:  
QR Code


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

Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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:  
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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15515.9166666667129.27789120.019900
M1-2132.84102182539159.023856-13.412100
M2-2859.02430555556158.904073-17.992100
M3-1507.77901785714158.79562-9.495100
M4-2251.39087301588158.69852-14.186600
M5-1687.43129960317158.612794-10.638700
M6-2357.90029761905158.538461-14.872700
M7-2422.36929563492158.475536-15.285400
M8-2104.69543650794158.424034-13.285200
M9-2896.45014880952158.383965-18.287500
M10-2143.91914682540158.355338-13.538700
M11-2478.67385912699158.338159-15.654300
t77.75471230158731.34664557.739600

\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:  
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)15515.9166666667129.27789120.019900
M1-2132.84102182539159.023856-13.412100
M2-2859.02430555556158.904073-17.992100
M3-1507.77901785714158.79562-9.495100
M4-2251.39087301588158.69852-14.186600
M5-1687.43129960317158.612794-10.638700
M6-2357.90029761905158.538461-14.872700
M7-2422.36929563492158.475536-15.285400
M8-2104.69543650794158.424034-13.285200
M9-2896.45014880952158.383965-18.287500
M10-2143.91914682540158.355338-13.538700
M11-2478.67385912699158.338159-15.654300
t77.75471230158731.34664557.739600






Multiple Linear Regression - Regression Statistics
Multiple R0.99130582514897
R-squared0.98268723897428
Adjusted R-squared0.979761138519229
F-TEST (value)335.835100014381
F-TEST (DF numerator)12
F-TEST (DF denominator)71
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation296.212858097257
Sum Squared Residuals6229686.06845236

\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:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.99130582514897
R-squared0.98268723897428
Adjusted R-squared0.979761138519229
F-TEST (value)335.835100014381
F-TEST (DF numerator)12
F-TEST (DF denominator)71
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation296.212858097257
Sum Squared Residuals6229686.06845236






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11332813460.8303571428-132.830357142841
21287312812.401785714360.59821428571
31400014241.4017857143-241.401785714278
41347713575.5446428571-98.544642857145
51423714217.258928571419.7410714285763
61367413624.544642857149.4553571428511
71352913637.8303571429-108.830357142853
81405814033.258928571424.7410714285705
91297513319.2589285714-344.25892857143
101432614149.5446428571176.455357142857
111400813892.5446428571115.455357142854
121619316448.9732142857-255.973214285714
131448314393.886904761989.1130952380873
141401113745.4583333333265.541666666667
151505715174.4583333333-117.458333333334
161488414508.6011904762375.398809523809
171541415150.3154761905263.684523809523
181444014557.6011904762-117.60119047619
191490014570.8869047619329.113095238095
201507414966.3154761905107.684523809524
211444214252.3154761905189.684523809524
221530715082.6011904762224.398809523809
231493814825.6011904762112.398809523810
241719317382.0297619048-189.029761904763
251552815326.9434523810201.056547619046
261476514678.514880952486.4851190476194
271583816107.5148809524-269.514880952383
281572315441.6577380952281.342261904762
291615016083.372023809566.6279761904752
301548615490.6577380952-4.65773809523746
311598615503.9434523810482.056547619047
321598315899.372023809583.6279761904762
331569215185.3720238095506.627976190476
341649016015.6577380952474.342261904761
351568615758.6577380952-72.6577380952376
361889718315.0863095238581.913690476189
37163161626055.9999999999982
381563615611.571428571424.4285714285718
391716317040.5714285714122.428571428570
401653416374.7142857143159.285714285714
411651817016.4285714286-498.428571428572
421637516423.7142857143-48.7142857142847
431629016437-147.000000000001
441635216832.4285714286-480.428571428571
451594316118.4285714286-175.428571428571
461636216948.7142857143-586.714285714285
471639316691.7142857143-298.714285714285
481905119248.1428571429-197.142857142859
491674717193.0565476190-446.056547619049
501632016544.6279761905-224.627976190475
511791017973.6279761905-63.6279761904773
521696117307.7708333333-346.770833333333
531748017949.4851190476-469.48511904762
541704917356.7708333333-307.770833333332
551687917370.0565476190-491.056547619048
561747317765.4851190476-292.485119047618
571699817051.4851190476-53.4851190476179
581730717881.7708333333-574.770833333332
591741817624.7708333333-206.770833333332
602016920181.1994047619-12.1994047619047
611787118126.1130952381-255.113095238096
621722617477.6845238095-251.684523809522
631906218906.6845238095155.315476190475
641780418240.8273809524-436.82738095238
651910018882.5416666667217.458333333333
661852218289.8273809524232.172619047621
671806018303.1130952381-243.113095238096
681886918698.5416666667170.458333333333
691812717984.5416666667142.458333333333
701887118814.827380952456.1726190476184
711889018557.8273809524332.172619047620
722126321114.2559523810148.744047619049
731954719059.1696428571487.830357142856
741845018410.741071428639.2589285714303
752025419839.7410714286414.258928571429
761924019173.883928571466.1160714285725
772021619815.5982142857400.401785714284
781942019222.8839285714197.116071428572
791941519236.1696428571178.830357142856
802001819631.5982142857386.401785714287
811865218917.5982142857-265.598214285715
821997819747.8839285714230.116071428572
831950919490.883928571418.1160714285717
842197122047.3125-76.3124999999989

\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:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11332813460.8303571428-132.830357142841
21287312812.401785714360.59821428571
31400014241.4017857143-241.401785714278
41347713575.5446428571-98.544642857145
51423714217.258928571419.7410714285763
61367413624.544642857149.4553571428511
71352913637.8303571429-108.830357142853
81405814033.258928571424.7410714285705
91297513319.2589285714-344.25892857143
101432614149.5446428571176.455357142857
111400813892.5446428571115.455357142854
121619316448.9732142857-255.973214285714
131448314393.886904761989.1130952380873
141401113745.4583333333265.541666666667
151505715174.4583333333-117.458333333334
161488414508.6011904762375.398809523809
171541415150.3154761905263.684523809523
181444014557.6011904762-117.60119047619
191490014570.8869047619329.113095238095
201507414966.3154761905107.684523809524
211444214252.3154761905189.684523809524
221530715082.6011904762224.398809523809
231493814825.6011904762112.398809523810
241719317382.0297619048-189.029761904763
251552815326.9434523810201.056547619046
261476514678.514880952486.4851190476194
271583816107.5148809524-269.514880952383
281572315441.6577380952281.342261904762
291615016083.372023809566.6279761904752
301548615490.6577380952-4.65773809523746
311598615503.9434523810482.056547619047
321598315899.372023809583.6279761904762
331569215185.3720238095506.627976190476
341649016015.6577380952474.342261904761
351568615758.6577380952-72.6577380952376
361889718315.0863095238581.913690476189
37163161626055.9999999999982
381563615611.571428571424.4285714285718
391716317040.5714285714122.428571428570
401653416374.7142857143159.285714285714
411651817016.4285714286-498.428571428572
421637516423.7142857143-48.7142857142847
431629016437-147.000000000001
441635216832.4285714286-480.428571428571
451594316118.4285714286-175.428571428571
461636216948.7142857143-586.714285714285
471639316691.7142857143-298.714285714285
481905119248.1428571429-197.142857142859
491674717193.0565476190-446.056547619049
501632016544.6279761905-224.627976190475
511791017973.6279761905-63.6279761904773
521696117307.7708333333-346.770833333333
531748017949.4851190476-469.48511904762
541704917356.7708333333-307.770833333332
551687917370.0565476190-491.056547619048
561747317765.4851190476-292.485119047618
571699817051.4851190476-53.4851190476179
581730717881.7708333333-574.770833333332
591741817624.7708333333-206.770833333332
602016920181.1994047619-12.1994047619047
611787118126.1130952381-255.113095238096
621722617477.6845238095-251.684523809522
631906218906.6845238095155.315476190475
641780418240.8273809524-436.82738095238
651910018882.5416666667217.458333333333
661852218289.8273809524232.172619047621
671806018303.1130952381-243.113095238096
681886918698.5416666667170.458333333333
691812717984.5416666667142.458333333333
701887118814.827380952456.1726190476184
711889018557.8273809524332.172619047620
722126321114.2559523810148.744047619049
731954719059.1696428571487.830357142856
741845018410.741071428639.2589285714303
752025419839.7410714286414.258928571429
761924019173.883928571466.1160714285725
772021619815.5982142857400.401785714284
781942019222.8839285714197.116071428572
791941519236.1696428571178.830357142856
802001819631.5982142857386.401785714287
811865218917.5982142857-265.598214285715
821997819747.8839285714230.116071428572
831950919490.883928571418.1160714285717
842197122047.3125-76.3124999999989






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.04807963237522170.09615926475044330.951920367624778
170.01324324667951570.02648649335903150.986756753320484
180.04321078240998860.08642156481997730.956789217590011
190.03150849691365490.06301699382730990.968491503086345
200.01529339052663580.03058678105327170.984706609473364
210.01588121317252570.03176242634505140.984118786827474
220.00966682614310160.01933365228620320.990333173856898
230.006301984355637970.01260396871127590.993698015644362
240.003190945416509940.006381890833019880.99680905458349
250.001433526189196970.002867052378393930.998566473810803
260.002121100964841310.004242201929682630.997878899035159
270.002386156970930170.004772313941860350.99761384302907
280.001314200230774660.002628400461549330.998685799769225
290.001044632477167150.002089264954334300.998955367522833
300.0005141694604558890.001028338920911780.999485830539544
310.0007761227672640670.001552245534528130.999223877232736
320.0004520141868784480.0009040283737568970.999547985813122
330.00239104900531080.00478209801062160.99760895099469
340.004815882714226850.00963176542845370.995184117285773
350.006866172419029620.01373234483805920.99313382758097
360.1022724671424740.2045449342849470.897727532857526
370.1133385592149910.2266771184299810.88666144078501
380.1597194998159230.3194389996318460.840280500184077
390.1391865740741450.2783731481482890.860813425925855
400.2915853737927820.5831707475855640.708414626207218
410.5844744741667850.831051051666430.415525525833215
420.5646397968089610.8707204063820780.435360203191039
430.7026217469768630.5947565060462750.297378253023137
440.7823532288567180.4352935422865640.217646771143282
450.7976629207214260.4046741585571480.202337079278574
460.8983544045009920.2032911909980150.101645595499008
470.8768903564156580.2462192871686840.123109643584342
480.8586538489811290.2826923020377430.141346151018871
490.8571553959260840.2856892081478320.142844604073916
500.834884746872850.3302305062542990.165115253127149
510.7856957299807960.4286085400384080.214304270019204
520.772954826807270.4540903463854590.227045173192730
530.8095783382576650.380843323484670.190421661742335
540.7708311470748070.4583377058503850.229168852925192
550.7537279227441810.4925441545116380.246272077255819
560.7303457188033570.5393085623932860.269654281196643
570.7019123473815980.5961753052368030.298087652618402
580.78674223433560.42651553132880.2132577656644
590.7314442685668580.5371114628662840.268555731433142
600.6697964076784780.6604071846430430.330203592321522
610.8007811678123740.3984376643752510.199218832187626
620.7441795761699560.5116408476600890.255820423830044
630.7041095069684150.591780986063170.295890493031585
640.7764032321241080.4471935357517840.223596767875892
650.7306154254797850.5387691490404290.269384574520215
660.6287885171610130.7424229656779750.371211482838987
670.7246310493439830.5507379013120330.275368950656017
680.752852786618920.4942944267621610.247147213381080

\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:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.04807963237522170.09615926475044330.951920367624778
170.01324324667951570.02648649335903150.986756753320484
180.04321078240998860.08642156481997730.956789217590011
190.03150849691365490.06301699382730990.968491503086345
200.01529339052663580.03058678105327170.984706609473364
210.01588121317252570.03176242634505140.984118786827474
220.00966682614310160.01933365228620320.990333173856898
230.006301984355637970.01260396871127590.993698015644362
240.003190945416509940.006381890833019880.99680905458349
250.001433526189196970.002867052378393930.998566473810803
260.002121100964841310.004242201929682630.997878899035159
270.002386156970930170.004772313941860350.99761384302907
280.001314200230774660.002628400461549330.998685799769225
290.001044632477167150.002089264954334300.998955367522833
300.0005141694604558890.001028338920911780.999485830539544
310.0007761227672640670.001552245534528130.999223877232736
320.0004520141868784480.0009040283737568970.999547985813122
330.00239104900531080.00478209801062160.99760895099469
340.004815882714226850.00963176542845370.995184117285773
350.006866172419029620.01373234483805920.99313382758097
360.1022724671424740.2045449342849470.897727532857526
370.1133385592149910.2266771184299810.88666144078501
380.1597194998159230.3194389996318460.840280500184077
390.1391865740741450.2783731481482890.860813425925855
400.2915853737927820.5831707475855640.708414626207218
410.5844744741667850.831051051666430.415525525833215
420.5646397968089610.8707204063820780.435360203191039
430.7026217469768630.5947565060462750.297378253023137
440.7823532288567180.4352935422865640.217646771143282
450.7976629207214260.4046741585571480.202337079278574
460.8983544045009920.2032911909980150.101645595499008
470.8768903564156580.2462192871686840.123109643584342
480.8586538489811290.2826923020377430.141346151018871
490.8571553959260840.2856892081478320.142844604073916
500.834884746872850.3302305062542990.165115253127149
510.7856957299807960.4286085400384080.214304270019204
520.772954826807270.4540903463854590.227045173192730
530.8095783382576650.380843323484670.190421661742335
540.7708311470748070.4583377058503850.229168852925192
550.7537279227441810.4925441545116380.246272077255819
560.7303457188033570.5393085623932860.269654281196643
570.7019123473815980.5961753052368030.298087652618402
580.78674223433560.42651553132880.2132577656644
590.7314442685668580.5371114628662840.268555731433142
600.6697964076784780.6604071846430430.330203592321522
610.8007811678123740.3984376643752510.199218832187626
620.7441795761699560.5116408476600890.255820423830044
630.7041095069684150.591780986063170.295890493031585
640.7764032321241080.4471935357517840.223596767875892
650.7306154254797850.5387691490404290.269384574520215
660.6287885171610130.7424229656779750.371211482838987
670.7246310493439830.5507379013120330.275368950656017
680.752852786618920.4942944267621610.247147213381080






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.207547169811321NOK
5% type I error level170.320754716981132NOK
10% type I error level200.377358490566038NOK

\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

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.207547169811321NOK
5% type I error level170.320754716981132NOK
10% type I error level200.377358490566038NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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')
}