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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 13:05:51 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352138801sa8d07doe3wrklq.htm/, Retrieved Fri, 03 Feb 2023 10:26:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186185, Retrieved Fri, 03 Feb 2023 10:26:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact57
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 18:05:51] [bea181a9b0bafb448dbedad686e1d59e] [Current]
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Dataseries X:
6,8	225	0,442	0,672	9,2
6,3	180	0,435	0,797	11,7
6,4	190	0,456	0,761	15,8
6,2	180	0,416	0,651	8,6
6,9	205	0,449	0,9	23,2
6,4	225	0,431	0,78	27,4
6,3	185	0,487	0,771	9,3
6,8	235	0,469	0,75	16
6,9	235	0,435	0,818	4,7
6,7	210	0,48	0,825	12,5
6,9	245	0,516	0,632	20,1
6,9	245	0,493	0,757	9,1
6,3	185	0,374	0,709	8,1
6,1	185	0,424	0,782	8,6
6,2	180	0,441	0,775	20,3
6,8	220	0,503	0,88	25
6,5	194	0,503	0,833	19,2
7,6	225	0,425	0,571	3,3
6,3	210	0,371	0,816	11,2
7,1	240	0,504	0,714	10,5
6,8	225	0,4	0,765	10,1
7,3	263	0,482	0,655	7,2
6,4	210	0,475	0,244	13,6
6,8	235	0,428	0,728	9
7,2	230	0,559	0,721	24,6
6,4	190	0,441	0,757	12,6
6,6	220	0,492	0,747	5,6
6,8	210	0,402	0,739	8,7
6,1	180	0,415	0,713	7,7
6,5	235	0,492	0,742	24,1
6,4	185	0,484	0,861	11,7
6	175	0,387	0,721	7,7
6	192	0,436	0,785	9,6
7,3	263	0,482	0,655	7,2
6,1	180	0,34	0,821	12,3
6,7	240	0,516	0,728	8,9
6,4	210	0,475	0,846	13,6
5,8	160	0,412	0,813	11,2
6,9	230	0,411	0,595	2,8
7	245	0,407	0,573	3,2
7,3	228	0,445	0,726	9,4
5,9	155	0,291	0,707	11,9
6,2	200	0,449	0,804	15,4
6,8	235	0,546	0,784	7,4
7	235	0,48	0,744	18,9
5,9	105	0,359	0,839	7,9
6,1	180	0,528	0,79	12,2
5,7	185	0,352	0,701	11
7,1	245	0,414	0,778	2,8
5,8	180	0,425	0,872	11,8
7,4	240	0,599	0,713	17,1
6,8	225	0,482	0,701	11,6
6,8	215	0,457	0,734	5,8
7	230	0,435	0,764	8,3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186185&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186185&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186185&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Points[t] = + 4.14870670628904 -3.69049908336127Height[t] + 0.00945845788151719Weight[t] + 47.9401991647774Fieldgoals[t] + 11.3710192606295Freethrows[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Points[t] =  +  4.14870670628904 -3.69049908336127Height[t] +  0.00945845788151719Weight[t] +  47.9401991647774Fieldgoals[t] +  11.3710192606295Freethrows[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186185&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Points[t] =  +  4.14870670628904 -3.69049908336127Height[t] +  0.00945845788151719Weight[t] +  47.9401991647774Fieldgoals[t] +  11.3710192606295Freethrows[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186185&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186185&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
Points[t] = + 4.14870670628904 -3.69049908336127Height[t] + 0.00945845788151719Weight[t] + 47.9401991647774Fieldgoals[t] + 11.3710192606295Freethrows[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.1487067062890414.8550060.27930.7812050.390603
Height-3.690499083361272.97078-1.24230.2200510.110026
Weight0.009458457881517190.0462970.20430.8389660.419483
Fieldgoals47.940199164777415.7091313.05170.0036680.001834
Freethrows11.37101926062957.8685361.44510.1547880.077394

\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) & 4.14870670628904 & 14.855006 & 0.2793 & 0.781205 & 0.390603 \tabularnewline
Height & -3.69049908336127 & 2.97078 & -1.2423 & 0.220051 & 0.110026 \tabularnewline
Weight & 0.00945845788151719 & 0.046297 & 0.2043 & 0.838966 & 0.419483 \tabularnewline
Fieldgoals & 47.9401991647774 & 15.709131 & 3.0517 & 0.003668 & 0.001834 \tabularnewline
Freethrows & 11.3710192606295 & 7.868536 & 1.4451 & 0.154788 & 0.077394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186185&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]4.14870670628904[/C][C]14.855006[/C][C]0.2793[/C][C]0.781205[/C][C]0.390603[/C][/ROW]
[ROW][C]Height[/C][C]-3.69049908336127[/C][C]2.97078[/C][C]-1.2423[/C][C]0.220051[/C][C]0.110026[/C][/ROW]
[ROW][C]Weight[/C][C]0.00945845788151719[/C][C]0.046297[/C][C]0.2043[/C][C]0.838966[/C][C]0.419483[/C][/ROW]
[ROW][C]Fieldgoals[/C][C]47.9401991647774[/C][C]15.709131[/C][C]3.0517[/C][C]0.003668[/C][C]0.001834[/C][/ROW]
[ROW][C]Freethrows[/C][C]11.3710192606295[/C][C]7.868536[/C][C]1.4451[/C][C]0.154788[/C][C]0.077394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186185&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186185&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.1487067062890414.8550060.27930.7812050.390603
Height-3.690499083361272.97078-1.24230.2200510.110026
Weight0.009458457881517190.0462970.20430.8389660.419483
Fieldgoals47.940199164777415.7091313.05170.0036680.001834
Freethrows11.37101926062957.8685361.44510.1547880.077394







Multiple Linear Regression - Regression Statistics
Multiple R0.471434652205528
R-squared0.222250631300147
Adjusted R-squared0.158760886916485
F-TEST (value)3.50057530484154
F-TEST (DF numerator)4
F-TEST (DF denominator)49
p-value0.0136396816766932
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.41074474747539
Sum Squared Residuals1434.5317773943

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.471434652205528 \tabularnewline
R-squared & 0.222250631300147 \tabularnewline
Adjusted R-squared & 0.158760886916485 \tabularnewline
F-TEST (value) & 3.50057530484154 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 49 \tabularnewline
p-value & 0.0136396816766932 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.41074474747539 \tabularnewline
Sum Squared Residuals & 1434.5317773943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186185&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.471434652205528[/C][/ROW]
[ROW][C]R-squared[/C][C]0.222250631300147[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.158760886916485[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.50057530484154[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]49[/C][/ROW]
[ROW][C]p-value[/C][C]0.0136396816766932[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.41074474747539[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1434.5317773943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186185&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186185&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.471434652205528
R-squared0.222250631300147
Adjusted R-squared0.158760886916485
F-TEST (value)3.50057530484154
F-TEST (DF numerator)4
F-TEST (DF denominator)49
p-value0.0136396816766932
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.41074474747539
Sum Squared Residuals1434.5317773943







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.210.0123589367484-0.812358936748392
211.712.517773887186-0.817773887185997
315.812.84069604674272.9593039532573
48.610.3157911993394-1.71579119933944
523.212.382313656358910.8176863436411
627.412.189286459428315.2107135405717
79.314.7623100323856-5.46231003238564
81612.28826839534163.71173160465835
94.711.0624810251259-6.36248102512589
1012.513.8010254919996-1.30102549199961
1120.112.9252121538117.17478784618905
129.113.2439649805998-4.14396498059976
138.18.64006433260676-0.540064332606764
148.612.6052585135438-4.00525851354384
1520.312.92430256677697.37569743322306
162515.25459080260329.74540919739684
1719.215.58138271744253.61861728255749
183.35.09650333893457-1.79650333893457
1911.29.949404243037721.25059575696228
2010.512.4969612371254-1.9969612371254
2110.19.056375363066281.04362463693372
227.210.2508314337258-3.0508314337258
2313.68.061912030758375.53808796924163
24910.0725578058519-1.07255780585193
2524.614.74963483886139.85036516113873
2612.612.07610898222850.523891017771477
275.613.9530028667991-8.35300286679914
288.78.71473239239671-0.0147323923967117
297.711.3419041026698-3.64190410266982
3024.114.40707454705499.69292545294513
3111.715.2728312600118-3.57283126001183
327.710.4123042990696-2.71230429906963
339.613.6499130748098-4.04991307480981
347.210.2508314337258-3.0508314337258
3512.38.97445924545953.3255407545405
368.914.7076375300961-5.80763753009605
3713.614.9072656256573-1.30726562565732
3811.213.2531659986165-2.05316599861648
392.87.32888666064327-4.52888666064327
403.26.65979040013695-3.45979040013695
419.48.953340406280630.446659593719372
4211.95.830731660307976.06926833969203
4315.413.82675287628381.57324712371624
447.416.3662783858909-8.96627838589092
4518.912.00928465391826.89071534608183
467.910.1187168518401-2.21871685184007
4712.217.6347150913581-5.43471509135815
48119.708711246913381.29128875308661
492.88.95738083438331-6.15738083438331
5011.814.7364478817661-2.93644788176607
5117.115.93275941351021.16724058648976
5211.612.2597264618977-0.65972646189774
535.811.3418805395639-5.54188053956391
548.310.0321037873082-1.73210378730819

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.2 & 10.0123589367484 & -0.812358936748392 \tabularnewline
2 & 11.7 & 12.517773887186 & -0.817773887185997 \tabularnewline
3 & 15.8 & 12.8406960467427 & 2.9593039532573 \tabularnewline
4 & 8.6 & 10.3157911993394 & -1.71579119933944 \tabularnewline
5 & 23.2 & 12.3823136563589 & 10.8176863436411 \tabularnewline
6 & 27.4 & 12.1892864594283 & 15.2107135405717 \tabularnewline
7 & 9.3 & 14.7623100323856 & -5.46231003238564 \tabularnewline
8 & 16 & 12.2882683953416 & 3.71173160465835 \tabularnewline
9 & 4.7 & 11.0624810251259 & -6.36248102512589 \tabularnewline
10 & 12.5 & 13.8010254919996 & -1.30102549199961 \tabularnewline
11 & 20.1 & 12.925212153811 & 7.17478784618905 \tabularnewline
12 & 9.1 & 13.2439649805998 & -4.14396498059976 \tabularnewline
13 & 8.1 & 8.64006433260676 & -0.540064332606764 \tabularnewline
14 & 8.6 & 12.6052585135438 & -4.00525851354384 \tabularnewline
15 & 20.3 & 12.9243025667769 & 7.37569743322306 \tabularnewline
16 & 25 & 15.2545908026032 & 9.74540919739684 \tabularnewline
17 & 19.2 & 15.5813827174425 & 3.61861728255749 \tabularnewline
18 & 3.3 & 5.09650333893457 & -1.79650333893457 \tabularnewline
19 & 11.2 & 9.94940424303772 & 1.25059575696228 \tabularnewline
20 & 10.5 & 12.4969612371254 & -1.9969612371254 \tabularnewline
21 & 10.1 & 9.05637536306628 & 1.04362463693372 \tabularnewline
22 & 7.2 & 10.2508314337258 & -3.0508314337258 \tabularnewline
23 & 13.6 & 8.06191203075837 & 5.53808796924163 \tabularnewline
24 & 9 & 10.0725578058519 & -1.07255780585193 \tabularnewline
25 & 24.6 & 14.7496348388613 & 9.85036516113873 \tabularnewline
26 & 12.6 & 12.0761089822285 & 0.523891017771477 \tabularnewline
27 & 5.6 & 13.9530028667991 & -8.35300286679914 \tabularnewline
28 & 8.7 & 8.71473239239671 & -0.0147323923967117 \tabularnewline
29 & 7.7 & 11.3419041026698 & -3.64190410266982 \tabularnewline
30 & 24.1 & 14.4070745470549 & 9.69292545294513 \tabularnewline
31 & 11.7 & 15.2728312600118 & -3.57283126001183 \tabularnewline
32 & 7.7 & 10.4123042990696 & -2.71230429906963 \tabularnewline
33 & 9.6 & 13.6499130748098 & -4.04991307480981 \tabularnewline
34 & 7.2 & 10.2508314337258 & -3.0508314337258 \tabularnewline
35 & 12.3 & 8.9744592454595 & 3.3255407545405 \tabularnewline
36 & 8.9 & 14.7076375300961 & -5.80763753009605 \tabularnewline
37 & 13.6 & 14.9072656256573 & -1.30726562565732 \tabularnewline
38 & 11.2 & 13.2531659986165 & -2.05316599861648 \tabularnewline
39 & 2.8 & 7.32888666064327 & -4.52888666064327 \tabularnewline
40 & 3.2 & 6.65979040013695 & -3.45979040013695 \tabularnewline
41 & 9.4 & 8.95334040628063 & 0.446659593719372 \tabularnewline
42 & 11.9 & 5.83073166030797 & 6.06926833969203 \tabularnewline
43 & 15.4 & 13.8267528762838 & 1.57324712371624 \tabularnewline
44 & 7.4 & 16.3662783858909 & -8.96627838589092 \tabularnewline
45 & 18.9 & 12.0092846539182 & 6.89071534608183 \tabularnewline
46 & 7.9 & 10.1187168518401 & -2.21871685184007 \tabularnewline
47 & 12.2 & 17.6347150913581 & -5.43471509135815 \tabularnewline
48 & 11 & 9.70871124691338 & 1.29128875308661 \tabularnewline
49 & 2.8 & 8.95738083438331 & -6.15738083438331 \tabularnewline
50 & 11.8 & 14.7364478817661 & -2.93644788176607 \tabularnewline
51 & 17.1 & 15.9327594135102 & 1.16724058648976 \tabularnewline
52 & 11.6 & 12.2597264618977 & -0.65972646189774 \tabularnewline
53 & 5.8 & 11.3418805395639 & -5.54188053956391 \tabularnewline
54 & 8.3 & 10.0321037873082 & -1.73210378730819 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186185&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]9.2[/C][C]10.0123589367484[/C][C]-0.812358936748392[/C][/ROW]
[ROW][C]2[/C][C]11.7[/C][C]12.517773887186[/C][C]-0.817773887185997[/C][/ROW]
[ROW][C]3[/C][C]15.8[/C][C]12.8406960467427[/C][C]2.9593039532573[/C][/ROW]
[ROW][C]4[/C][C]8.6[/C][C]10.3157911993394[/C][C]-1.71579119933944[/C][/ROW]
[ROW][C]5[/C][C]23.2[/C][C]12.3823136563589[/C][C]10.8176863436411[/C][/ROW]
[ROW][C]6[/C][C]27.4[/C][C]12.1892864594283[/C][C]15.2107135405717[/C][/ROW]
[ROW][C]7[/C][C]9.3[/C][C]14.7623100323856[/C][C]-5.46231003238564[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]12.2882683953416[/C][C]3.71173160465835[/C][/ROW]
[ROW][C]9[/C][C]4.7[/C][C]11.0624810251259[/C][C]-6.36248102512589[/C][/ROW]
[ROW][C]10[/C][C]12.5[/C][C]13.8010254919996[/C][C]-1.30102549199961[/C][/ROW]
[ROW][C]11[/C][C]20.1[/C][C]12.925212153811[/C][C]7.17478784618905[/C][/ROW]
[ROW][C]12[/C][C]9.1[/C][C]13.2439649805998[/C][C]-4.14396498059976[/C][/ROW]
[ROW][C]13[/C][C]8.1[/C][C]8.64006433260676[/C][C]-0.540064332606764[/C][/ROW]
[ROW][C]14[/C][C]8.6[/C][C]12.6052585135438[/C][C]-4.00525851354384[/C][/ROW]
[ROW][C]15[/C][C]20.3[/C][C]12.9243025667769[/C][C]7.37569743322306[/C][/ROW]
[ROW][C]16[/C][C]25[/C][C]15.2545908026032[/C][C]9.74540919739684[/C][/ROW]
[ROW][C]17[/C][C]19.2[/C][C]15.5813827174425[/C][C]3.61861728255749[/C][/ROW]
[ROW][C]18[/C][C]3.3[/C][C]5.09650333893457[/C][C]-1.79650333893457[/C][/ROW]
[ROW][C]19[/C][C]11.2[/C][C]9.94940424303772[/C][C]1.25059575696228[/C][/ROW]
[ROW][C]20[/C][C]10.5[/C][C]12.4969612371254[/C][C]-1.9969612371254[/C][/ROW]
[ROW][C]21[/C][C]10.1[/C][C]9.05637536306628[/C][C]1.04362463693372[/C][/ROW]
[ROW][C]22[/C][C]7.2[/C][C]10.2508314337258[/C][C]-3.0508314337258[/C][/ROW]
[ROW][C]23[/C][C]13.6[/C][C]8.06191203075837[/C][C]5.53808796924163[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]10.0725578058519[/C][C]-1.07255780585193[/C][/ROW]
[ROW][C]25[/C][C]24.6[/C][C]14.7496348388613[/C][C]9.85036516113873[/C][/ROW]
[ROW][C]26[/C][C]12.6[/C][C]12.0761089822285[/C][C]0.523891017771477[/C][/ROW]
[ROW][C]27[/C][C]5.6[/C][C]13.9530028667991[/C][C]-8.35300286679914[/C][/ROW]
[ROW][C]28[/C][C]8.7[/C][C]8.71473239239671[/C][C]-0.0147323923967117[/C][/ROW]
[ROW][C]29[/C][C]7.7[/C][C]11.3419041026698[/C][C]-3.64190410266982[/C][/ROW]
[ROW][C]30[/C][C]24.1[/C][C]14.4070745470549[/C][C]9.69292545294513[/C][/ROW]
[ROW][C]31[/C][C]11.7[/C][C]15.2728312600118[/C][C]-3.57283126001183[/C][/ROW]
[ROW][C]32[/C][C]7.7[/C][C]10.4123042990696[/C][C]-2.71230429906963[/C][/ROW]
[ROW][C]33[/C][C]9.6[/C][C]13.6499130748098[/C][C]-4.04991307480981[/C][/ROW]
[ROW][C]34[/C][C]7.2[/C][C]10.2508314337258[/C][C]-3.0508314337258[/C][/ROW]
[ROW][C]35[/C][C]12.3[/C][C]8.9744592454595[/C][C]3.3255407545405[/C][/ROW]
[ROW][C]36[/C][C]8.9[/C][C]14.7076375300961[/C][C]-5.80763753009605[/C][/ROW]
[ROW][C]37[/C][C]13.6[/C][C]14.9072656256573[/C][C]-1.30726562565732[/C][/ROW]
[ROW][C]38[/C][C]11.2[/C][C]13.2531659986165[/C][C]-2.05316599861648[/C][/ROW]
[ROW][C]39[/C][C]2.8[/C][C]7.32888666064327[/C][C]-4.52888666064327[/C][/ROW]
[ROW][C]40[/C][C]3.2[/C][C]6.65979040013695[/C][C]-3.45979040013695[/C][/ROW]
[ROW][C]41[/C][C]9.4[/C][C]8.95334040628063[/C][C]0.446659593719372[/C][/ROW]
[ROW][C]42[/C][C]11.9[/C][C]5.83073166030797[/C][C]6.06926833969203[/C][/ROW]
[ROW][C]43[/C][C]15.4[/C][C]13.8267528762838[/C][C]1.57324712371624[/C][/ROW]
[ROW][C]44[/C][C]7.4[/C][C]16.3662783858909[/C][C]-8.96627838589092[/C][/ROW]
[ROW][C]45[/C][C]18.9[/C][C]12.0092846539182[/C][C]6.89071534608183[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]10.1187168518401[/C][C]-2.21871685184007[/C][/ROW]
[ROW][C]47[/C][C]12.2[/C][C]17.6347150913581[/C][C]-5.43471509135815[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]9.70871124691338[/C][C]1.29128875308661[/C][/ROW]
[ROW][C]49[/C][C]2.8[/C][C]8.95738083438331[/C][C]-6.15738083438331[/C][/ROW]
[ROW][C]50[/C][C]11.8[/C][C]14.7364478817661[/C][C]-2.93644788176607[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]15.9327594135102[/C][C]1.16724058648976[/C][/ROW]
[ROW][C]52[/C][C]11.6[/C][C]12.2597264618977[/C][C]-0.65972646189774[/C][/ROW]
[ROW][C]53[/C][C]5.8[/C][C]11.3418805395639[/C][C]-5.54188053956391[/C][/ROW]
[ROW][C]54[/C][C]8.3[/C][C]10.0321037873082[/C][C]-1.73210378730819[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186185&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186185&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.210.0123589367484-0.812358936748392
211.712.517773887186-0.817773887185997
315.812.84069604674272.9593039532573
48.610.3157911993394-1.71579119933944
523.212.382313656358910.8176863436411
627.412.189286459428315.2107135405717
79.314.7623100323856-5.46231003238564
81612.28826839534163.71173160465835
94.711.0624810251259-6.36248102512589
1012.513.8010254919996-1.30102549199961
1120.112.9252121538117.17478784618905
129.113.2439649805998-4.14396498059976
138.18.64006433260676-0.540064332606764
148.612.6052585135438-4.00525851354384
1520.312.92430256677697.37569743322306
162515.25459080260329.74540919739684
1719.215.58138271744253.61861728255749
183.35.09650333893457-1.79650333893457
1911.29.949404243037721.25059575696228
2010.512.4969612371254-1.9969612371254
2110.19.056375363066281.04362463693372
227.210.2508314337258-3.0508314337258
2313.68.061912030758375.53808796924163
24910.0725578058519-1.07255780585193
2524.614.74963483886139.85036516113873
2612.612.07610898222850.523891017771477
275.613.9530028667991-8.35300286679914
288.78.71473239239671-0.0147323923967117
297.711.3419041026698-3.64190410266982
3024.114.40707454705499.69292545294513
3111.715.2728312600118-3.57283126001183
327.710.4123042990696-2.71230429906963
339.613.6499130748098-4.04991307480981
347.210.2508314337258-3.0508314337258
3512.38.97445924545953.3255407545405
368.914.7076375300961-5.80763753009605
3713.614.9072656256573-1.30726562565732
3811.213.2531659986165-2.05316599861648
392.87.32888666064327-4.52888666064327
403.26.65979040013695-3.45979040013695
419.48.953340406280630.446659593719372
4211.95.830731660307976.06926833969203
4315.413.82675287628381.57324712371624
447.416.3662783858909-8.96627838589092
4518.912.00928465391826.89071534608183
467.910.1187168518401-2.21871685184007
4712.217.6347150913581-5.43471509135815
48119.708711246913381.29128875308661
492.88.95738083438331-6.15738083438331
5011.814.7364478817661-2.93644788176607
5117.115.93275941351021.16724058648976
5211.612.2597264618977-0.65972646189774
535.811.3418805395639-5.54188053956391
548.310.0321037873082-1.73210378730819







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3466744649593050.693348929918610.653325535040695
90.9696735048959310.06065299020813860.0303264951040693
100.9423893189351020.1152213621297950.0576106810648975
110.9646045531360960.07079089372780730.0353954468639036
120.9706414441575440.05871711168491120.0293585558424556
130.9485521942914490.1028956114171020.051447805708551
140.9410528306511160.1178943386977680.058947169348884
150.9529798731805130.0940402536389730.0470201268194865
160.9734743027950410.05305139440991860.0265256972049593
170.963398597235980.07320280552804070.0366014027640203
180.9464511395530820.1070977208938360.053548860446918
190.9225840480574470.1548319038851070.0774159519425535
200.8972987244065810.2054025511868390.102701275593419
210.8567308431787030.2865383136425950.143269156821297
220.8186355684567930.3627288630864140.181364431543207
230.8245053131758720.3509893736482560.175494686824128
240.7677237880110380.4645524239779230.232276211988962
250.9007570906462850.198485818707430.0992429093537148
260.863912410917390.272175178165220.13608758908261
270.9255507589818670.1488984820362660.0744492410181328
280.8900777891978830.2198444216042330.109922210802116
290.8616793286356040.2766413427287920.138320671364396
300.9717370088618260.05652598227634850.0282629911381742
310.9613871122624670.07722577547506630.0386128877375331
320.9419608307722210.1160783384555580.0580391692277791
330.9234015355817140.1531969288365710.0765984644182856
340.8943144573727770.2113710852544470.105685542627223
350.8713854977528830.2572290044942340.128614502247117
360.8560701188171880.2878597623656250.143929881182812
370.7990309214399650.401938157120070.200969078560035
380.7236130247733350.5527739504533290.276386975226665
390.7032993132957910.5934013734084190.296700686704209
400.7462493250581820.5075013498836360.253750674941818
410.6460078564588940.7079842870822120.353992143541106
420.5872508467050320.8254983065899370.412749153294968
430.5520391810150540.8959216379698930.447960818984946
440.5905765613454890.8188468773090220.409423438654511
450.8524165822325410.2951668355349180.147583417767459
460.7989757279349880.4020485441300230.201024272065012

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.346674464959305 & 0.69334892991861 & 0.653325535040695 \tabularnewline
9 & 0.969673504895931 & 0.0606529902081386 & 0.0303264951040693 \tabularnewline
10 & 0.942389318935102 & 0.115221362129795 & 0.0576106810648975 \tabularnewline
11 & 0.964604553136096 & 0.0707908937278073 & 0.0353954468639036 \tabularnewline
12 & 0.970641444157544 & 0.0587171116849112 & 0.0293585558424556 \tabularnewline
13 & 0.948552194291449 & 0.102895611417102 & 0.051447805708551 \tabularnewline
14 & 0.941052830651116 & 0.117894338697768 & 0.058947169348884 \tabularnewline
15 & 0.952979873180513 & 0.094040253638973 & 0.0470201268194865 \tabularnewline
16 & 0.973474302795041 & 0.0530513944099186 & 0.0265256972049593 \tabularnewline
17 & 0.96339859723598 & 0.0732028055280407 & 0.0366014027640203 \tabularnewline
18 & 0.946451139553082 & 0.107097720893836 & 0.053548860446918 \tabularnewline
19 & 0.922584048057447 & 0.154831903885107 & 0.0774159519425535 \tabularnewline
20 & 0.897298724406581 & 0.205402551186839 & 0.102701275593419 \tabularnewline
21 & 0.856730843178703 & 0.286538313642595 & 0.143269156821297 \tabularnewline
22 & 0.818635568456793 & 0.362728863086414 & 0.181364431543207 \tabularnewline
23 & 0.824505313175872 & 0.350989373648256 & 0.175494686824128 \tabularnewline
24 & 0.767723788011038 & 0.464552423977923 & 0.232276211988962 \tabularnewline
25 & 0.900757090646285 & 0.19848581870743 & 0.0992429093537148 \tabularnewline
26 & 0.86391241091739 & 0.27217517816522 & 0.13608758908261 \tabularnewline
27 & 0.925550758981867 & 0.148898482036266 & 0.0744492410181328 \tabularnewline
28 & 0.890077789197883 & 0.219844421604233 & 0.109922210802116 \tabularnewline
29 & 0.861679328635604 & 0.276641342728792 & 0.138320671364396 \tabularnewline
30 & 0.971737008861826 & 0.0565259822763485 & 0.0282629911381742 \tabularnewline
31 & 0.961387112262467 & 0.0772257754750663 & 0.0386128877375331 \tabularnewline
32 & 0.941960830772221 & 0.116078338455558 & 0.0580391692277791 \tabularnewline
33 & 0.923401535581714 & 0.153196928836571 & 0.0765984644182856 \tabularnewline
34 & 0.894314457372777 & 0.211371085254447 & 0.105685542627223 \tabularnewline
35 & 0.871385497752883 & 0.257229004494234 & 0.128614502247117 \tabularnewline
36 & 0.856070118817188 & 0.287859762365625 & 0.143929881182812 \tabularnewline
37 & 0.799030921439965 & 0.40193815712007 & 0.200969078560035 \tabularnewline
38 & 0.723613024773335 & 0.552773950453329 & 0.276386975226665 \tabularnewline
39 & 0.703299313295791 & 0.593401373408419 & 0.296700686704209 \tabularnewline
40 & 0.746249325058182 & 0.507501349883636 & 0.253750674941818 \tabularnewline
41 & 0.646007856458894 & 0.707984287082212 & 0.353992143541106 \tabularnewline
42 & 0.587250846705032 & 0.825498306589937 & 0.412749153294968 \tabularnewline
43 & 0.552039181015054 & 0.895921637969893 & 0.447960818984946 \tabularnewline
44 & 0.590576561345489 & 0.818846877309022 & 0.409423438654511 \tabularnewline
45 & 0.852416582232541 & 0.295166835534918 & 0.147583417767459 \tabularnewline
46 & 0.798975727934988 & 0.402048544130023 & 0.201024272065012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186185&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]8[/C][C]0.346674464959305[/C][C]0.69334892991861[/C][C]0.653325535040695[/C][/ROW]
[ROW][C]9[/C][C]0.969673504895931[/C][C]0.0606529902081386[/C][C]0.0303264951040693[/C][/ROW]
[ROW][C]10[/C][C]0.942389318935102[/C][C]0.115221362129795[/C][C]0.0576106810648975[/C][/ROW]
[ROW][C]11[/C][C]0.964604553136096[/C][C]0.0707908937278073[/C][C]0.0353954468639036[/C][/ROW]
[ROW][C]12[/C][C]0.970641444157544[/C][C]0.0587171116849112[/C][C]0.0293585558424556[/C][/ROW]
[ROW][C]13[/C][C]0.948552194291449[/C][C]0.102895611417102[/C][C]0.051447805708551[/C][/ROW]
[ROW][C]14[/C][C]0.941052830651116[/C][C]0.117894338697768[/C][C]0.058947169348884[/C][/ROW]
[ROW][C]15[/C][C]0.952979873180513[/C][C]0.094040253638973[/C][C]0.0470201268194865[/C][/ROW]
[ROW][C]16[/C][C]0.973474302795041[/C][C]0.0530513944099186[/C][C]0.0265256972049593[/C][/ROW]
[ROW][C]17[/C][C]0.96339859723598[/C][C]0.0732028055280407[/C][C]0.0366014027640203[/C][/ROW]
[ROW][C]18[/C][C]0.946451139553082[/C][C]0.107097720893836[/C][C]0.053548860446918[/C][/ROW]
[ROW][C]19[/C][C]0.922584048057447[/C][C]0.154831903885107[/C][C]0.0774159519425535[/C][/ROW]
[ROW][C]20[/C][C]0.897298724406581[/C][C]0.205402551186839[/C][C]0.102701275593419[/C][/ROW]
[ROW][C]21[/C][C]0.856730843178703[/C][C]0.286538313642595[/C][C]0.143269156821297[/C][/ROW]
[ROW][C]22[/C][C]0.818635568456793[/C][C]0.362728863086414[/C][C]0.181364431543207[/C][/ROW]
[ROW][C]23[/C][C]0.824505313175872[/C][C]0.350989373648256[/C][C]0.175494686824128[/C][/ROW]
[ROW][C]24[/C][C]0.767723788011038[/C][C]0.464552423977923[/C][C]0.232276211988962[/C][/ROW]
[ROW][C]25[/C][C]0.900757090646285[/C][C]0.19848581870743[/C][C]0.0992429093537148[/C][/ROW]
[ROW][C]26[/C][C]0.86391241091739[/C][C]0.27217517816522[/C][C]0.13608758908261[/C][/ROW]
[ROW][C]27[/C][C]0.925550758981867[/C][C]0.148898482036266[/C][C]0.0744492410181328[/C][/ROW]
[ROW][C]28[/C][C]0.890077789197883[/C][C]0.219844421604233[/C][C]0.109922210802116[/C][/ROW]
[ROW][C]29[/C][C]0.861679328635604[/C][C]0.276641342728792[/C][C]0.138320671364396[/C][/ROW]
[ROW][C]30[/C][C]0.971737008861826[/C][C]0.0565259822763485[/C][C]0.0282629911381742[/C][/ROW]
[ROW][C]31[/C][C]0.961387112262467[/C][C]0.0772257754750663[/C][C]0.0386128877375331[/C][/ROW]
[ROW][C]32[/C][C]0.941960830772221[/C][C]0.116078338455558[/C][C]0.0580391692277791[/C][/ROW]
[ROW][C]33[/C][C]0.923401535581714[/C][C]0.153196928836571[/C][C]0.0765984644182856[/C][/ROW]
[ROW][C]34[/C][C]0.894314457372777[/C][C]0.211371085254447[/C][C]0.105685542627223[/C][/ROW]
[ROW][C]35[/C][C]0.871385497752883[/C][C]0.257229004494234[/C][C]0.128614502247117[/C][/ROW]
[ROW][C]36[/C][C]0.856070118817188[/C][C]0.287859762365625[/C][C]0.143929881182812[/C][/ROW]
[ROW][C]37[/C][C]0.799030921439965[/C][C]0.40193815712007[/C][C]0.200969078560035[/C][/ROW]
[ROW][C]38[/C][C]0.723613024773335[/C][C]0.552773950453329[/C][C]0.276386975226665[/C][/ROW]
[ROW][C]39[/C][C]0.703299313295791[/C][C]0.593401373408419[/C][C]0.296700686704209[/C][/ROW]
[ROW][C]40[/C][C]0.746249325058182[/C][C]0.507501349883636[/C][C]0.253750674941818[/C][/ROW]
[ROW][C]41[/C][C]0.646007856458894[/C][C]0.707984287082212[/C][C]0.353992143541106[/C][/ROW]
[ROW][C]42[/C][C]0.587250846705032[/C][C]0.825498306589937[/C][C]0.412749153294968[/C][/ROW]
[ROW][C]43[/C][C]0.552039181015054[/C][C]0.895921637969893[/C][C]0.447960818984946[/C][/ROW]
[ROW][C]44[/C][C]0.590576561345489[/C][C]0.818846877309022[/C][C]0.409423438654511[/C][/ROW]
[ROW][C]45[/C][C]0.852416582232541[/C][C]0.295166835534918[/C][C]0.147583417767459[/C][/ROW]
[ROW][C]46[/C][C]0.798975727934988[/C][C]0.402048544130023[/C][C]0.201024272065012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186185&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186185&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3466744649593050.693348929918610.653325535040695
90.9696735048959310.06065299020813860.0303264951040693
100.9423893189351020.1152213621297950.0576106810648975
110.9646045531360960.07079089372780730.0353954468639036
120.9706414441575440.05871711168491120.0293585558424556
130.9485521942914490.1028956114171020.051447805708551
140.9410528306511160.1178943386977680.058947169348884
150.9529798731805130.0940402536389730.0470201268194865
160.9734743027950410.05305139440991860.0265256972049593
170.963398597235980.07320280552804070.0366014027640203
180.9464511395530820.1070977208938360.053548860446918
190.9225840480574470.1548319038851070.0774159519425535
200.8972987244065810.2054025511868390.102701275593419
210.8567308431787030.2865383136425950.143269156821297
220.8186355684567930.3627288630864140.181364431543207
230.8245053131758720.3509893736482560.175494686824128
240.7677237880110380.4645524239779230.232276211988962
250.9007570906462850.198485818707430.0992429093537148
260.863912410917390.272175178165220.13608758908261
270.9255507589818670.1488984820362660.0744492410181328
280.8900777891978830.2198444216042330.109922210802116
290.8616793286356040.2766413427287920.138320671364396
300.9717370088618260.05652598227634850.0282629911381742
310.9613871122624670.07722577547506630.0386128877375331
320.9419608307722210.1160783384555580.0580391692277791
330.9234015355817140.1531969288365710.0765984644182856
340.8943144573727770.2113710852544470.105685542627223
350.8713854977528830.2572290044942340.128614502247117
360.8560701188171880.2878597623656250.143929881182812
370.7990309214399650.401938157120070.200969078560035
380.7236130247733350.5527739504533290.276386975226665
390.7032993132957910.5934013734084190.296700686704209
400.7462493250581820.5075013498836360.253750674941818
410.6460078564588940.7079842870822120.353992143541106
420.5872508467050320.8254983065899370.412749153294968
430.5520391810150540.8959216379698930.447960818984946
440.5905765613454890.8188468773090220.409423438654511
450.8524165822325410.2951668355349180.147583417767459
460.7989757279349880.4020485441300230.201024272065012







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level80.205128205128205NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 8 & 0.205128205128205 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186185&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.205128205128205[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186185&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186185&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level80.205128205128205NOK



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