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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 22 Nov 2011 02:43:48 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321947851r45v2lq8j83s0f8.htm/, Retrieved Fri, 01 Nov 2024 00:06:27 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146068, Retrieved Fri, 01 Nov 2024 00:06:27 +0000

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No (this computation is public)

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Estimated Impact

182

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

-       [Multiple Regression] [] [2011-11-22 07:43:48] [204816f6f70a8d342ddc2b9d4f4a80d3] [Current]
-   P     [Multiple Regression] [] [2011-11-22 08:04:47] [80bca13c5f9401fbb753952fd2952f4a] 
-    D      [Multiple Regression] [] [2011-11-22 10:08:56] [80bca13c5f9401fbb753952fd2952f4a] 

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116,24	112,42	120,58
116,03	112	120,17
115,94	111,72	120,02
114,19	111,67	120,49
115,74	111,55	120,38
115,4	111,33	120,09
115,2	111,06	119,62
114,82	110,97	118,93
114,33	110,81	119,09
111,84	110,62	118,59
113,16	110,71	117,87
112,52	110,51	117,74
112,39	110,5	117,61
112,24	110,37	117,55
112,1	110,38	117,06
109,85	110,26	117,08
111,89	110,28	117,21
111,88	110,25	117,58
111,48	110,09	117,27
110,98	110,01	117,14
110,42	109,75	116,52
107,9	109,57	116,16
109,46	109,59	114,79
109,23	109,45	114,97
109,02	109,21	114,66
109,04	109	114,3
109,49	108,83	114,48
107,23	108,62	114,96
109	108,56	115,44
109,12	108,41	116,38
109,24	108,27	116,5
108,92	108,03	116,2
109,53	107,67	116,37
107,06	107,31	116,46
109,11	107,14	115,07
109,26	107,02	115,03
109,99	106,79	115,15
110,17	106,49	114,71
110,28	106,14	114,67
109,13	105,94	115,49
110,15	105,87	114,65
109,39	105,71	114,92
108,45	105,48	114,17
108,23	105,31	112,8
107,44	105,09	112,28
104,86	104,88	112,05
106,23	104,76	111,03
105,85	104,62	110,4
104,95	104,49	109,08
104,46	104,29	107,89

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146068&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146068&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146068&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
prijsindex[t] = + 228.733517073554 -1.64854689736647gezondheid[t] + 0.59891940633533tabak[t] -0.34359909436074t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
prijsindex[t] =  +  228.733517073554 -1.64854689736647gezondheid[t] +  0.59891940633533tabak[t] -0.34359909436074t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146068&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]prijsindex[t] =  +  228.733517073554 -1.64854689736647gezondheid[t] +  0.59891940633533tabak[t] -0.34359909436074t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146068&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146068&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
prijsindex[t] = + 228.733517073554 -1.64854689736647gezondheid[t] + 0.59891940633533tabak[t] -0.34359909436074t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	228.733517073554	43.507568	5.2573	4e-06	2e-06
gezondheid	-1.64854689736647	0.361864	-4.5557	3.8e-05	1.9e-05
tabak	0.59891940633533	0.116059	5.1605	5e-06	3e-06
t	-0.34359909436074	0.062871	-5.4651	2e-06	1e-06

\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) & 228.733517073554 & 43.507568 & 5.2573 & 4e-06 & 2e-06 \tabularnewline
gezondheid & -1.64854689736647 & 0.361864 & -4.5557 & 3.8e-05 & 1.9e-05 \tabularnewline
tabak & 0.59891940633533 & 0.116059 & 5.1605 & 5e-06 & 3e-06 \tabularnewline
t & -0.34359909436074 & 0.062871 & -5.4651 & 2e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146068&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]228.733517073554[/C][C]43.507568[/C][C]5.2573[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]gezondheid[/C][C]-1.64854689736647[/C][C]0.361864[/C][C]-4.5557[/C][C]3.8e-05[/C][C]1.9e-05[/C][/ROW]
[ROW][C]tabak[/C][C]0.59891940633533[/C][C]0.116059[/C][C]5.1605[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]t[/C][C]-0.34359909436074[/C][C]0.062871[/C][C]-5.4651[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146068&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146068&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	228.733517073554	43.507568	5.2573	4e-06	2e-06
gezondheid	-1.64854689736647	0.361864	-4.5557	3.8e-05	1.9e-05
tabak	0.59891940633533	0.116059	5.1605	5e-06	3e-06
t	-0.34359909436074	0.062871	-5.4651	2e-06	1e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.951120874863822
R-squared	0.904630918601722
Adjusted R-squared	0.898411195901834
F-TEST (value)	145.445538692272
F-TEST (DF numerator)	3
F-TEST (DF denominator)	46
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.968187319599203
Sum Squared Residuals	43.1197875483037

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.951120874863822 \tabularnewline
R-squared & 0.904630918601722 \tabularnewline
Adjusted R-squared & 0.898411195901834 \tabularnewline
F-TEST (value) & 145.445538692272 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.968187319599203 \tabularnewline
Sum Squared Residuals & 43.1197875483037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146068&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.951120874863822[/C][/ROW]
[ROW][C]R-squared[/C][C]0.904630918601722[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.898411195901834[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]145.445538692272[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/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]0.968187319599203[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]43.1197875483037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146068&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146068&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.951120874863822
R-squared	0.904630918601722
Adjusted R-squared	0.898411195901834
F-TEST (value)	145.445538692272
F-TEST (DF numerator)	3
F-TEST (DF denominator)	46
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.968187319599203
Sum Squared Residuals	43.1197875483037

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	116.24	115.277977793169	0.962022206831092
2	116.03	115.381211439105	0.6487885608954
3	115.94	115.409367565056	0.530632434943825
4	114.19	115.429687936541	-1.23968793654136
5	115.74	115.218033335168	0.521966664832281
6	115.4	115.06342793039	0.336572069609652
7	115.2	114.883444377341	0.316555622659053
8	114.82	114.274960113372	0.545039886628172
9	114.33	114.290955627603	0.0390443723966369
10	111.84	113.961120740575	-2.12112074057458
11	113.16	113.037930452889	0.122069547110556
12	112.52	112.946181215178	-0.426181215178381
13	112.39	112.541208066968	-0.151208066967719
14	112.24	112.375984904884	-0.135984904884497
15	112.1	111.722429832446	0.3775701675542
16	109.85	111.588634753896	-1.73863475389572
17	111.89	111.289924244411	0.600075755588755
18	111.88	111.217381737316	0.662618262684419
19	111.48	110.95188513057	0.528114869430492
20	110.98	110.662310265174	0.317689734825508
21	110.42	110.376003332201	0.0439966677988616
22	107.9	110.113531693086	-2.21353169308565
23	109.46	108.916442074098	0.543557925901819
24	109.23	108.911445038509	0.318554961490908
25	109.02	108.777832183552	0.242167816447625
26	109.04	108.564816951358	0.475183048642145
27	109.49	108.60927632269	0.880723677310208
28	107.23	108.899353391817	-1.66935339181694
29	109	108.942148426339	0.0578515736608395
30	109.12	109.408815608539	-0.288815608538604
31	109.24	109.367883408569	-0.127883408569423
32	108.92	109.240259747676	-0.320259747676023
33	109.53	109.591953835444	-0.0619538354442193
34	107.06	109.895734370706	-2.83573437070558
35	109.11	108.999890274091	0.110109725908963
36	109.26	108.830160031161	0.429839968839133
37	109.99	108.937597051955	1.05240294804535
38	110.17	108.825037488016	1.34496251198369
39	110.28	109.03447303148	1.24552696851958
40	109.13	109.511697229788	-0.38169722978795
41	110.15	108.780404116921	1.36959588307883
42	109.39	108.86228076585	0.527719234150375
43	108.45	108.448657903132	0.00134209686834286
44	108.23	107.564792194644	0.665207805356186
45	107.44	107.272435326409	0.167564673590667
46	104.86	107.137279617038	-2.27727961703844
47	106.23	106.3806083559	-0.150608355899617
48	105.85	105.890486601179	-0.0404866011789387
49	104.95	104.970624987113	-0.0206249871132063
50	104.46	104.244021178687	0.21597882131329

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 116.24 & 115.277977793169 & 0.962022206831092 \tabularnewline
2 & 116.03 & 115.381211439105 & 0.6487885608954 \tabularnewline
3 & 115.94 & 115.409367565056 & 0.530632434943825 \tabularnewline
4 & 114.19 & 115.429687936541 & -1.23968793654136 \tabularnewline
5 & 115.74 & 115.218033335168 & 0.521966664832281 \tabularnewline
6 & 115.4 & 115.06342793039 & 0.336572069609652 \tabularnewline
7 & 115.2 & 114.883444377341 & 0.316555622659053 \tabularnewline
8 & 114.82 & 114.274960113372 & 0.545039886628172 \tabularnewline
9 & 114.33 & 114.290955627603 & 0.0390443723966369 \tabularnewline
10 & 111.84 & 113.961120740575 & -2.12112074057458 \tabularnewline
11 & 113.16 & 113.037930452889 & 0.122069547110556 \tabularnewline
12 & 112.52 & 112.946181215178 & -0.426181215178381 \tabularnewline
13 & 112.39 & 112.541208066968 & -0.151208066967719 \tabularnewline
14 & 112.24 & 112.375984904884 & -0.135984904884497 \tabularnewline
15 & 112.1 & 111.722429832446 & 0.3775701675542 \tabularnewline
16 & 109.85 & 111.588634753896 & -1.73863475389572 \tabularnewline
17 & 111.89 & 111.289924244411 & 0.600075755588755 \tabularnewline
18 & 111.88 & 111.217381737316 & 0.662618262684419 \tabularnewline
19 & 111.48 & 110.95188513057 & 0.528114869430492 \tabularnewline
20 & 110.98 & 110.662310265174 & 0.317689734825508 \tabularnewline
21 & 110.42 & 110.376003332201 & 0.0439966677988616 \tabularnewline
22 & 107.9 & 110.113531693086 & -2.21353169308565 \tabularnewline
23 & 109.46 & 108.916442074098 & 0.543557925901819 \tabularnewline
24 & 109.23 & 108.911445038509 & 0.318554961490908 \tabularnewline
25 & 109.02 & 108.777832183552 & 0.242167816447625 \tabularnewline
26 & 109.04 & 108.564816951358 & 0.475183048642145 \tabularnewline
27 & 109.49 & 108.60927632269 & 0.880723677310208 \tabularnewline
28 & 107.23 & 108.899353391817 & -1.66935339181694 \tabularnewline
29 & 109 & 108.942148426339 & 0.0578515736608395 \tabularnewline
30 & 109.12 & 109.408815608539 & -0.288815608538604 \tabularnewline
31 & 109.24 & 109.367883408569 & -0.127883408569423 \tabularnewline
32 & 108.92 & 109.240259747676 & -0.320259747676023 \tabularnewline
33 & 109.53 & 109.591953835444 & -0.0619538354442193 \tabularnewline
34 & 107.06 & 109.895734370706 & -2.83573437070558 \tabularnewline
35 & 109.11 & 108.999890274091 & 0.110109725908963 \tabularnewline
36 & 109.26 & 108.830160031161 & 0.429839968839133 \tabularnewline
37 & 109.99 & 108.937597051955 & 1.05240294804535 \tabularnewline
38 & 110.17 & 108.825037488016 & 1.34496251198369 \tabularnewline
39 & 110.28 & 109.03447303148 & 1.24552696851958 \tabularnewline
40 & 109.13 & 109.511697229788 & -0.38169722978795 \tabularnewline
41 & 110.15 & 108.780404116921 & 1.36959588307883 \tabularnewline
42 & 109.39 & 108.86228076585 & 0.527719234150375 \tabularnewline
43 & 108.45 & 108.448657903132 & 0.00134209686834286 \tabularnewline
44 & 108.23 & 107.564792194644 & 0.665207805356186 \tabularnewline
45 & 107.44 & 107.272435326409 & 0.167564673590667 \tabularnewline
46 & 104.86 & 107.137279617038 & -2.27727961703844 \tabularnewline
47 & 106.23 & 106.3806083559 & -0.150608355899617 \tabularnewline
48 & 105.85 & 105.890486601179 & -0.0404866011789387 \tabularnewline
49 & 104.95 & 104.970624987113 & -0.0206249871132063 \tabularnewline
50 & 104.46 & 104.244021178687 & 0.21597882131329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146068&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]116.24[/C][C]115.277977793169[/C][C]0.962022206831092[/C][/ROW]
[ROW][C]2[/C][C]116.03[/C][C]115.381211439105[/C][C]0.6487885608954[/C][/ROW]
[ROW][C]3[/C][C]115.94[/C][C]115.409367565056[/C][C]0.530632434943825[/C][/ROW]
[ROW][C]4[/C][C]114.19[/C][C]115.429687936541[/C][C]-1.23968793654136[/C][/ROW]
[ROW][C]5[/C][C]115.74[/C][C]115.218033335168[/C][C]0.521966664832281[/C][/ROW]
[ROW][C]6[/C][C]115.4[/C][C]115.06342793039[/C][C]0.336572069609652[/C][/ROW]
[ROW][C]7[/C][C]115.2[/C][C]114.883444377341[/C][C]0.316555622659053[/C][/ROW]
[ROW][C]8[/C][C]114.82[/C][C]114.274960113372[/C][C]0.545039886628172[/C][/ROW]
[ROW][C]9[/C][C]114.33[/C][C]114.290955627603[/C][C]0.0390443723966369[/C][/ROW]
[ROW][C]10[/C][C]111.84[/C][C]113.961120740575[/C][C]-2.12112074057458[/C][/ROW]
[ROW][C]11[/C][C]113.16[/C][C]113.037930452889[/C][C]0.122069547110556[/C][/ROW]
[ROW][C]12[/C][C]112.52[/C][C]112.946181215178[/C][C]-0.426181215178381[/C][/ROW]
[ROW][C]13[/C][C]112.39[/C][C]112.541208066968[/C][C]-0.151208066967719[/C][/ROW]
[ROW][C]14[/C][C]112.24[/C][C]112.375984904884[/C][C]-0.135984904884497[/C][/ROW]
[ROW][C]15[/C][C]112.1[/C][C]111.722429832446[/C][C]0.3775701675542[/C][/ROW]
[ROW][C]16[/C][C]109.85[/C][C]111.588634753896[/C][C]-1.73863475389572[/C][/ROW]
[ROW][C]17[/C][C]111.89[/C][C]111.289924244411[/C][C]0.600075755588755[/C][/ROW]
[ROW][C]18[/C][C]111.88[/C][C]111.217381737316[/C][C]0.662618262684419[/C][/ROW]
[ROW][C]19[/C][C]111.48[/C][C]110.95188513057[/C][C]0.528114869430492[/C][/ROW]
[ROW][C]20[/C][C]110.98[/C][C]110.662310265174[/C][C]0.317689734825508[/C][/ROW]
[ROW][C]21[/C][C]110.42[/C][C]110.376003332201[/C][C]0.0439966677988616[/C][/ROW]
[ROW][C]22[/C][C]107.9[/C][C]110.113531693086[/C][C]-2.21353169308565[/C][/ROW]
[ROW][C]23[/C][C]109.46[/C][C]108.916442074098[/C][C]0.543557925901819[/C][/ROW]
[ROW][C]24[/C][C]109.23[/C][C]108.911445038509[/C][C]0.318554961490908[/C][/ROW]
[ROW][C]25[/C][C]109.02[/C][C]108.777832183552[/C][C]0.242167816447625[/C][/ROW]
[ROW][C]26[/C][C]109.04[/C][C]108.564816951358[/C][C]0.475183048642145[/C][/ROW]
[ROW][C]27[/C][C]109.49[/C][C]108.60927632269[/C][C]0.880723677310208[/C][/ROW]
[ROW][C]28[/C][C]107.23[/C][C]108.899353391817[/C][C]-1.66935339181694[/C][/ROW]
[ROW][C]29[/C][C]109[/C][C]108.942148426339[/C][C]0.0578515736608395[/C][/ROW]
[ROW][C]30[/C][C]109.12[/C][C]109.408815608539[/C][C]-0.288815608538604[/C][/ROW]
[ROW][C]31[/C][C]109.24[/C][C]109.367883408569[/C][C]-0.127883408569423[/C][/ROW]
[ROW][C]32[/C][C]108.92[/C][C]109.240259747676[/C][C]-0.320259747676023[/C][/ROW]
[ROW][C]33[/C][C]109.53[/C][C]109.591953835444[/C][C]-0.0619538354442193[/C][/ROW]
[ROW][C]34[/C][C]107.06[/C][C]109.895734370706[/C][C]-2.83573437070558[/C][/ROW]
[ROW][C]35[/C][C]109.11[/C][C]108.999890274091[/C][C]0.110109725908963[/C][/ROW]
[ROW][C]36[/C][C]109.26[/C][C]108.830160031161[/C][C]0.429839968839133[/C][/ROW]
[ROW][C]37[/C][C]109.99[/C][C]108.937597051955[/C][C]1.05240294804535[/C][/ROW]
[ROW][C]38[/C][C]110.17[/C][C]108.825037488016[/C][C]1.34496251198369[/C][/ROW]
[ROW][C]39[/C][C]110.28[/C][C]109.03447303148[/C][C]1.24552696851958[/C][/ROW]
[ROW][C]40[/C][C]109.13[/C][C]109.511697229788[/C][C]-0.38169722978795[/C][/ROW]
[ROW][C]41[/C][C]110.15[/C][C]108.780404116921[/C][C]1.36959588307883[/C][/ROW]
[ROW][C]42[/C][C]109.39[/C][C]108.86228076585[/C][C]0.527719234150375[/C][/ROW]
[ROW][C]43[/C][C]108.45[/C][C]108.448657903132[/C][C]0.00134209686834286[/C][/ROW]
[ROW][C]44[/C][C]108.23[/C][C]107.564792194644[/C][C]0.665207805356186[/C][/ROW]
[ROW][C]45[/C][C]107.44[/C][C]107.272435326409[/C][C]0.167564673590667[/C][/ROW]
[ROW][C]46[/C][C]104.86[/C][C]107.137279617038[/C][C]-2.27727961703844[/C][/ROW]
[ROW][C]47[/C][C]106.23[/C][C]106.3806083559[/C][C]-0.150608355899617[/C][/ROW]
[ROW][C]48[/C][C]105.85[/C][C]105.890486601179[/C][C]-0.0404866011789387[/C][/ROW]
[ROW][C]49[/C][C]104.95[/C][C]104.970624987113[/C][C]-0.0206249871132063[/C][/ROW]
[ROW][C]50[/C][C]104.46[/C][C]104.244021178687[/C][C]0.21597882131329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146068&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146068&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	116.24	115.277977793169	0.962022206831092
2	116.03	115.381211439105	0.6487885608954
3	115.94	115.409367565056	0.530632434943825
4	114.19	115.429687936541	-1.23968793654136
5	115.74	115.218033335168	0.521966664832281
6	115.4	115.06342793039	0.336572069609652
7	115.2	114.883444377341	0.316555622659053
8	114.82	114.274960113372	0.545039886628172
9	114.33	114.290955627603	0.0390443723966369
10	111.84	113.961120740575	-2.12112074057458
11	113.16	113.037930452889	0.122069547110556
12	112.52	112.946181215178	-0.426181215178381
13	112.39	112.541208066968	-0.151208066967719
14	112.24	112.375984904884	-0.135984904884497
15	112.1	111.722429832446	0.3775701675542
16	109.85	111.588634753896	-1.73863475389572
17	111.89	111.289924244411	0.600075755588755
18	111.88	111.217381737316	0.662618262684419
19	111.48	110.95188513057	0.528114869430492
20	110.98	110.662310265174	0.317689734825508
21	110.42	110.376003332201	0.0439966677988616
22	107.9	110.113531693086	-2.21353169308565
23	109.46	108.916442074098	0.543557925901819
24	109.23	108.911445038509	0.318554961490908
25	109.02	108.777832183552	0.242167816447625
26	109.04	108.564816951358	0.475183048642145
27	109.49	108.60927632269	0.880723677310208
28	107.23	108.899353391817	-1.66935339181694
29	109	108.942148426339	0.0578515736608395
30	109.12	109.408815608539	-0.288815608538604
31	109.24	109.367883408569	-0.127883408569423
32	108.92	109.240259747676	-0.320259747676023
33	109.53	109.591953835444	-0.0619538354442193
34	107.06	109.895734370706	-2.83573437070558
35	109.11	108.999890274091	0.110109725908963
36	109.26	108.830160031161	0.429839968839133
37	109.99	108.937597051955	1.05240294804535
38	110.17	108.825037488016	1.34496251198369
39	110.28	109.03447303148	1.24552696851958
40	109.13	109.511697229788	-0.38169722978795
41	110.15	108.780404116921	1.36959588307883
42	109.39	108.86228076585	0.527719234150375
43	108.45	108.448657903132	0.00134209686834286
44	108.23	107.564792194644	0.665207805356186
45	107.44	107.272435326409	0.167564673590667
46	104.86	107.137279617038	-2.27727961703844
47	106.23	106.3806083559	-0.150608355899617
48	105.85	105.890486601179	-0.0404866011789387
49	104.95	104.970624987113	-0.0206249871132063
50	104.46	104.244021178687	0.21597882131329

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.252279365343596	0.504558730687193	0.747720634656404
8	0.257535916525211	0.515071833050422	0.742464083474789
9	0.165250079839447	0.330500159678895	0.834749920160553
10	0.597137091364419	0.805725817271162	0.402862908635581
11	0.490177372049311	0.980354744098622	0.509822627950689
12	0.373876706738414	0.747753413476828	0.626123293261586
13	0.27141314936732	0.54282629873464	0.72858685063268
14	0.189133325666527	0.378266651333054	0.810866674333473
15	0.134404478912709	0.268808957825418	0.865595521087291
16	0.241572400301101	0.483144800602202	0.758427599698899
17	0.236234266486315	0.47246853297263	0.763765733513685
18	0.191100682015632	0.382201364031264	0.808899317984368
19	0.142398923655597	0.284797847311193	0.857601076344403
20	0.0990170818358778	0.198034163671756	0.900982918164122
21	0.0652672400286423	0.130534480057285	0.934732759971358
22	0.240917549854989	0.481835099709977	0.759082450145012
23	0.198026463225538	0.396052926451076	0.801973536774462
24	0.147900330269864	0.295800660539727	0.852099669730136
25	0.110389234707855	0.22077846941571	0.889610765292145
26	0.0933847232889185	0.186769446577837	0.906615276711082
27	0.132901376672446	0.265802753344892	0.867098623327554
28	0.138210351471773	0.276420702943545	0.861789648528227
29	0.117638521133407	0.235277042266814	0.882361478866593
30	0.0857386113276426	0.171477222655285	0.914261388672357
31	0.0593485461881727	0.118697092376345	0.940651453811827
32	0.0387096404468252	0.0774192808936504	0.961290359553175
33	0.027648968466228	0.0552979369324559	0.972351031533772
34	0.423401529239863	0.846803058479725	0.576598470760137
35	0.53061630790316	0.938767384193681	0.469383692096841
36	0.669543044501492	0.660913910997016	0.330456955498508
37	0.823323170956819	0.353353658086362	0.176676829043181
38	0.968744321670148	0.0625113566597047	0.0312556783298523
39	0.943492475170835	0.11301504965833	0.0565075248291652
40	0.918207450596129	0.163585098807743	0.0817925494038714
41	0.86648670987582	0.267026580248361	0.13351329012418
42	0.784372700053894	0.431254599892213	0.215627299946106
43	0.659125323219397	0.681749353561207	0.340874676780603

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.252279365343596 & 0.504558730687193 & 0.747720634656404 \tabularnewline
8 & 0.257535916525211 & 0.515071833050422 & 0.742464083474789 \tabularnewline
9 & 0.165250079839447 & 0.330500159678895 & 0.834749920160553 \tabularnewline
10 & 0.597137091364419 & 0.805725817271162 & 0.402862908635581 \tabularnewline
11 & 0.490177372049311 & 0.980354744098622 & 0.509822627950689 \tabularnewline
12 & 0.373876706738414 & 0.747753413476828 & 0.626123293261586 \tabularnewline
13 & 0.27141314936732 & 0.54282629873464 & 0.72858685063268 \tabularnewline
14 & 0.189133325666527 & 0.378266651333054 & 0.810866674333473 \tabularnewline
15 & 0.134404478912709 & 0.268808957825418 & 0.865595521087291 \tabularnewline
16 & 0.241572400301101 & 0.483144800602202 & 0.758427599698899 \tabularnewline
17 & 0.236234266486315 & 0.47246853297263 & 0.763765733513685 \tabularnewline
18 & 0.191100682015632 & 0.382201364031264 & 0.808899317984368 \tabularnewline
19 & 0.142398923655597 & 0.284797847311193 & 0.857601076344403 \tabularnewline
20 & 0.0990170818358778 & 0.198034163671756 & 0.900982918164122 \tabularnewline
21 & 0.0652672400286423 & 0.130534480057285 & 0.934732759971358 \tabularnewline
22 & 0.240917549854989 & 0.481835099709977 & 0.759082450145012 \tabularnewline
23 & 0.198026463225538 & 0.396052926451076 & 0.801973536774462 \tabularnewline
24 & 0.147900330269864 & 0.295800660539727 & 0.852099669730136 \tabularnewline
25 & 0.110389234707855 & 0.22077846941571 & 0.889610765292145 \tabularnewline
26 & 0.0933847232889185 & 0.186769446577837 & 0.906615276711082 \tabularnewline
27 & 0.132901376672446 & 0.265802753344892 & 0.867098623327554 \tabularnewline
28 & 0.138210351471773 & 0.276420702943545 & 0.861789648528227 \tabularnewline
29 & 0.117638521133407 & 0.235277042266814 & 0.882361478866593 \tabularnewline
30 & 0.0857386113276426 & 0.171477222655285 & 0.914261388672357 \tabularnewline
31 & 0.0593485461881727 & 0.118697092376345 & 0.940651453811827 \tabularnewline
32 & 0.0387096404468252 & 0.0774192808936504 & 0.961290359553175 \tabularnewline
33 & 0.027648968466228 & 0.0552979369324559 & 0.972351031533772 \tabularnewline
34 & 0.423401529239863 & 0.846803058479725 & 0.576598470760137 \tabularnewline
35 & 0.53061630790316 & 0.938767384193681 & 0.469383692096841 \tabularnewline
36 & 0.669543044501492 & 0.660913910997016 & 0.330456955498508 \tabularnewline
37 & 0.823323170956819 & 0.353353658086362 & 0.176676829043181 \tabularnewline
38 & 0.968744321670148 & 0.0625113566597047 & 0.0312556783298523 \tabularnewline
39 & 0.943492475170835 & 0.11301504965833 & 0.0565075248291652 \tabularnewline
40 & 0.918207450596129 & 0.163585098807743 & 0.0817925494038714 \tabularnewline
41 & 0.86648670987582 & 0.267026580248361 & 0.13351329012418 \tabularnewline
42 & 0.784372700053894 & 0.431254599892213 & 0.215627299946106 \tabularnewline
43 & 0.659125323219397 & 0.681749353561207 & 0.340874676780603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146068&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]7[/C][C]0.252279365343596[/C][C]0.504558730687193[/C][C]0.747720634656404[/C][/ROW]
[ROW][C]8[/C][C]0.257535916525211[/C][C]0.515071833050422[/C][C]0.742464083474789[/C][/ROW]
[ROW][C]9[/C][C]0.165250079839447[/C][C]0.330500159678895[/C][C]0.834749920160553[/C][/ROW]
[ROW][C]10[/C][C]0.597137091364419[/C][C]0.805725817271162[/C][C]0.402862908635581[/C][/ROW]
[ROW][C]11[/C][C]0.490177372049311[/C][C]0.980354744098622[/C][C]0.509822627950689[/C][/ROW]
[ROW][C]12[/C][C]0.373876706738414[/C][C]0.747753413476828[/C][C]0.626123293261586[/C][/ROW]
[ROW][C]13[/C][C]0.27141314936732[/C][C]0.54282629873464[/C][C]0.72858685063268[/C][/ROW]
[ROW][C]14[/C][C]0.189133325666527[/C][C]0.378266651333054[/C][C]0.810866674333473[/C][/ROW]
[ROW][C]15[/C][C]0.134404478912709[/C][C]0.268808957825418[/C][C]0.865595521087291[/C][/ROW]
[ROW][C]16[/C][C]0.241572400301101[/C][C]0.483144800602202[/C][C]0.758427599698899[/C][/ROW]
[ROW][C]17[/C][C]0.236234266486315[/C][C]0.47246853297263[/C][C]0.763765733513685[/C][/ROW]
[ROW][C]18[/C][C]0.191100682015632[/C][C]0.382201364031264[/C][C]0.808899317984368[/C][/ROW]
[ROW][C]19[/C][C]0.142398923655597[/C][C]0.284797847311193[/C][C]0.857601076344403[/C][/ROW]
[ROW][C]20[/C][C]0.0990170818358778[/C][C]0.198034163671756[/C][C]0.900982918164122[/C][/ROW]
[ROW][C]21[/C][C]0.0652672400286423[/C][C]0.130534480057285[/C][C]0.934732759971358[/C][/ROW]
[ROW][C]22[/C][C]0.240917549854989[/C][C]0.481835099709977[/C][C]0.759082450145012[/C][/ROW]
[ROW][C]23[/C][C]0.198026463225538[/C][C]0.396052926451076[/C][C]0.801973536774462[/C][/ROW]
[ROW][C]24[/C][C]0.147900330269864[/C][C]0.295800660539727[/C][C]0.852099669730136[/C][/ROW]
[ROW][C]25[/C][C]0.110389234707855[/C][C]0.22077846941571[/C][C]0.889610765292145[/C][/ROW]
[ROW][C]26[/C][C]0.0933847232889185[/C][C]0.186769446577837[/C][C]0.906615276711082[/C][/ROW]
[ROW][C]27[/C][C]0.132901376672446[/C][C]0.265802753344892[/C][C]0.867098623327554[/C][/ROW]
[ROW][C]28[/C][C]0.138210351471773[/C][C]0.276420702943545[/C][C]0.861789648528227[/C][/ROW]
[ROW][C]29[/C][C]0.117638521133407[/C][C]0.235277042266814[/C][C]0.882361478866593[/C][/ROW]
[ROW][C]30[/C][C]0.0857386113276426[/C][C]0.171477222655285[/C][C]0.914261388672357[/C][/ROW]
[ROW][C]31[/C][C]0.0593485461881727[/C][C]0.118697092376345[/C][C]0.940651453811827[/C][/ROW]
[ROW][C]32[/C][C]0.0387096404468252[/C][C]0.0774192808936504[/C][C]0.961290359553175[/C][/ROW]
[ROW][C]33[/C][C]0.027648968466228[/C][C]0.0552979369324559[/C][C]0.972351031533772[/C][/ROW]
[ROW][C]34[/C][C]0.423401529239863[/C][C]0.846803058479725[/C][C]0.576598470760137[/C][/ROW]
[ROW][C]35[/C][C]0.53061630790316[/C][C]0.938767384193681[/C][C]0.469383692096841[/C][/ROW]
[ROW][C]36[/C][C]0.669543044501492[/C][C]0.660913910997016[/C][C]0.330456955498508[/C][/ROW]
[ROW][C]37[/C][C]0.823323170956819[/C][C]0.353353658086362[/C][C]0.176676829043181[/C][/ROW]
[ROW][C]38[/C][C]0.968744321670148[/C][C]0.0625113566597047[/C][C]0.0312556783298523[/C][/ROW]
[ROW][C]39[/C][C]0.943492475170835[/C][C]0.11301504965833[/C][C]0.0565075248291652[/C][/ROW]
[ROW][C]40[/C][C]0.918207450596129[/C][C]0.163585098807743[/C][C]0.0817925494038714[/C][/ROW]
[ROW][C]41[/C][C]0.86648670987582[/C][C]0.267026580248361[/C][C]0.13351329012418[/C][/ROW]
[ROW][C]42[/C][C]0.784372700053894[/C][C]0.431254599892213[/C][C]0.215627299946106[/C][/ROW]
[ROW][C]43[/C][C]0.659125323219397[/C][C]0.681749353561207[/C][C]0.340874676780603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146068&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146068&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.252279365343596	0.504558730687193	0.747720634656404
8	0.257535916525211	0.515071833050422	0.742464083474789
9	0.165250079839447	0.330500159678895	0.834749920160553
10	0.597137091364419	0.805725817271162	0.402862908635581
11	0.490177372049311	0.980354744098622	0.509822627950689
12	0.373876706738414	0.747753413476828	0.626123293261586
13	0.27141314936732	0.54282629873464	0.72858685063268
14	0.189133325666527	0.378266651333054	0.810866674333473
15	0.134404478912709	0.268808957825418	0.865595521087291
16	0.241572400301101	0.483144800602202	0.758427599698899
17	0.236234266486315	0.47246853297263	0.763765733513685
18	0.191100682015632	0.382201364031264	0.808899317984368
19	0.142398923655597	0.284797847311193	0.857601076344403
20	0.0990170818358778	0.198034163671756	0.900982918164122
21	0.0652672400286423	0.130534480057285	0.934732759971358
22	0.240917549854989	0.481835099709977	0.759082450145012
23	0.198026463225538	0.396052926451076	0.801973536774462
24	0.147900330269864	0.295800660539727	0.852099669730136
25	0.110389234707855	0.22077846941571	0.889610765292145
26	0.0933847232889185	0.186769446577837	0.906615276711082
27	0.132901376672446	0.265802753344892	0.867098623327554
28	0.138210351471773	0.276420702943545	0.861789648528227
29	0.117638521133407	0.235277042266814	0.882361478866593
30	0.0857386113276426	0.171477222655285	0.914261388672357
31	0.0593485461881727	0.118697092376345	0.940651453811827
32	0.0387096404468252	0.0774192808936504	0.961290359553175
33	0.027648968466228	0.0552979369324559	0.972351031533772
34	0.423401529239863	0.846803058479725	0.576598470760137
35	0.53061630790316	0.938767384193681	0.469383692096841
36	0.669543044501492	0.660913910997016	0.330456955498508
37	0.823323170956819	0.353353658086362	0.176676829043181
38	0.968744321670148	0.0625113566597047	0.0312556783298523
39	0.943492475170835	0.11301504965833	0.0565075248291652
40	0.918207450596129	0.163585098807743	0.0817925494038714
41	0.86648670987582	0.267026580248361	0.13351329012418
42	0.784372700053894	0.431254599892213	0.215627299946106
43	0.659125323219397	0.681749353561207	0.340874676780603

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.0810810810810811	OK

\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 & 3 & 0.0810810810810811 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146068&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]3[/C][C]0.0810810810810811[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146068&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146068&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.0810810810810811	OK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code