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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 17:23:22 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/21/t1258763099hplu71jfq96ug8u.htm/, Retrieved Sun, 28 Apr 2024 05:04:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58502, Retrieved Sun, 28 Apr 2024 05:04:37 +0000

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Estimated Impact

201

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

-       [Multiple Regression] [Shwws7_v1] [2009-11-21 00:23:22] [93b66894f6318f3da4fcda772f2ffa6f] [Current]

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105.02	100.34	103.73	102.99	102.86	102.1
104.43	100.34	105.02	103.73	102.99	102.86
104.63	100.35	104.43	105.02	103.73	102.99
104.93	100.43	104.63	104.43	105.02	103.73
105.87	100.47	104.93	104.63	104.43	105.02
105.66	100.67	105.87	104.93	104.63	104.43
106.76	100.75	105.66	105.87	104.93	104.63
106	100.78	106.76	105.66	105.87	104.93
107.22	100.79	106	106.76	105.66	105.87
107.33	100.67	107.22	106	106.76	105.66
107.11	100.64	107.33	107.22	106	106.76
108.86	100.64	107.11	107.33	107.22	106
107.72	100.76	108.86	107.11	107.33	107.22
107.88	100.79	107.72	108.86	107.11	107.33
108.38	100.79	107.88	107.72	108.86	107.11
107.72	100.9	108.38	107.88	107.72	108.86
108.41	100.98	107.72	108.38	107.88	107.72
109.9	101.11	108.41	107.72	108.38	107.88
111.45	101.18	109.9	108.41	107.72	108.38
112.18	101.22	111.45	109.9	108.41	107.72
113.34	101.23	112.18	111.45	109.9	108.41
113.46	101.09	113.34	112.18	111.45	109.9
114.06	101.26	113.46	113.34	112.18	111.45
115.54	101.28	114.06	113.46	113.34	112.18
116.39	101.43	115.54	114.06	113.46	113.34
115.94	101.53	116.39	115.54	114.06	113.46
116.97	101.54	115.94	116.39	115.54	114.06
115.94	101.54	116.97	115.94	116.39	115.54
115.91	101.79	115.94	116.97	115.94	116.39
116.43	102.18	115.91	115.94	116.97	115.94
116.26	102.37	116.43	115.91	115.94	116.97
116.35	102.46	116.26	116.43	115.91	115.94
117.9	102.46	116.35	116.26	116.43	115.91
117.7	102.03	117.9	116.35	116.26	116.43
117.53	102.26	117.7	117.9	116.35	116.26
117.86	102.33	117.53	117.7	117.9	116.35
117.65	102.44	117.86	117.53	117.7	117.9
116.51	102.5	117.65	117.86	117.53	117.7
115.93	102.52	116.51	117.65	117.86	117.53
115.31	102.66	115.93	116.51	117.65	117.86
115	102.72	115.31	115.93	116.51	117.65

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58502&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]3 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=58502&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58502&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
y(t)[t] = -36.5108269184238 + 0.472806269550534`x(t)`[t] + 0.878494577498195`y(t-1)`[t] + 0.267957282415803`y(t-2)`[t] + 0.164874664652139`y(t-3)`[t] -0.413165669629023`y(t-4)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y(t)[t] =  -36.5108269184238 +  0.472806269550534`x(t)`[t] +  0.878494577498195`y(t-1)`[t] +  0.267957282415803`y(t-2)`[t] +  0.164874664652139`y(t-3)`[t] -0.413165669629023`y(t-4)`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58502&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y(t)[t] =  -36.5108269184238 +  0.472806269550534`x(t)`[t] +  0.878494577498195`y(t-1)`[t] +  0.267957282415803`y(t-2)`[t] +  0.164874664652139`y(t-3)`[t] -0.413165669629023`y(t-4)`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58502&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58502&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
y(t)[t] = -36.5108269184238 + 0.472806269550534`x(t)`[t] + 0.878494577498195`y(t-1)`[t] + 0.267957282415803`y(t-2)`[t] + 0.164874664652139`y(t-3)`[t] -0.413165669629023`y(t-4)`[t] + 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)	-36.5108269184238	51.098031	-0.7145	0.479641	0.239821
`x(t)`	0.472806269550534	0.589968	0.8014	0.428302	0.214151
`y(t-1)`	0.878494577498195	0.161326	5.4455	4e-06	2e-06
`y(t-2)`	0.267957282415803	0.211502	1.2669	0.213547	0.106773
`y(t-3)`	0.164874664652139	0.21022	0.7843	0.438143	0.219072
`y(t-4)`	-0.413165669629023	0.188817	-2.1882	0.035421	0.017711

\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) & -36.5108269184238 & 51.098031 & -0.7145 & 0.479641 & 0.239821 \tabularnewline
`x(t)` & 0.472806269550534 & 0.589968 & 0.8014 & 0.428302 & 0.214151 \tabularnewline
`y(t-1)` & 0.878494577498195 & 0.161326 & 5.4455 & 4e-06 & 2e-06 \tabularnewline
`y(t-2)` & 0.267957282415803 & 0.211502 & 1.2669 & 0.213547 & 0.106773 \tabularnewline
`y(t-3)` & 0.164874664652139 & 0.21022 & 0.7843 & 0.438143 & 0.219072 \tabularnewline
`y(t-4)` & -0.413165669629023 & 0.188817 & -2.1882 & 0.035421 & 0.017711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58502&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]-36.5108269184238[/C][C]51.098031[/C][C]-0.7145[/C][C]0.479641[/C][C]0.239821[/C][/ROW]
[ROW][C]`x(t)`[/C][C]0.472806269550534[/C][C]0.589968[/C][C]0.8014[/C][C]0.428302[/C][C]0.214151[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]0.878494577498195[/C][C]0.161326[/C][C]5.4455[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]`y(t-2)`[/C][C]0.267957282415803[/C][C]0.211502[/C][C]1.2669[/C][C]0.213547[/C][C]0.106773[/C][/ROW]
[ROW][C]`y(t-3)`[/C][C]0.164874664652139[/C][C]0.21022[/C][C]0.7843[/C][C]0.438143[/C][C]0.219072[/C][/ROW]
[ROW][C]`y(t-4)`[/C][C]-0.413165669629023[/C][C]0.188817[/C][C]-2.1882[/C][C]0.035421[/C][C]0.017711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58502&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58502&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)	-36.5108269184238	51.098031	-0.7145	0.479641	0.239821
`x(t)`	0.472806269550534	0.589968	0.8014	0.428302	0.214151
`y(t-1)`	0.878494577498195	0.161326	5.4455	4e-06	2e-06
`y(t-2)`	0.267957282415803	0.211502	1.2669	0.213547	0.106773
`y(t-3)`	0.164874664652139	0.21022	0.7843	0.438143	0.219072
`y(t-4)`	-0.413165669629023	0.188817	-2.1882	0.035421	0.017711

Multiple Linear Regression - Regression Statistics
Multiple R	0.988327860167978
R-squared	0.976791959184214
Adjusted R-squared	0.97347652478196
F-TEST (value)	294.619600532529
F-TEST (DF numerator)	5
F-TEST (DF denominator)	35
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.772889638581292
Sum Squared Residuals	20.9075437699212

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.988327860167978 \tabularnewline
R-squared & 0.976791959184214 \tabularnewline
Adjusted R-squared & 0.97347652478196 \tabularnewline
F-TEST (value) & 294.619600532529 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 35 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.772889638581292 \tabularnewline
Sum Squared Residuals & 20.9075437699212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58502&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.988327860167978[/C][/ROW]
[ROW][C]R-squared[/C][C]0.976791959184214[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.97347652478196[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]294.619600532529[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]35[/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]0.772889638581292[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]20.9075437699212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58502&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58502&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	0.988327860167978
R-squared	0.976791959184214
Adjusted R-squared	0.97347652478196
F-TEST (value)	294.619600532529
F-TEST (DF numerator)	5
F-TEST (DF denominator)	35
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.772889638581292
Sum Squared Residuals	20.9075437699212

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	105.02	104.428510345164	0.591489654835985
2	104.43	105.467484536611	-1.03748453661099
3	104.63	105.367861407690	-0.737861407689779
4	104.93	105.330235750004	-0.400235750003897
5	105.87	105.036028064552	0.833971935447653
6	105.66	106.313504084047	-0.653504084047054
7	106.76	106.385553835277	0.374446164722843
8	106	107.340843513189	-1.34084351318868
9	107.22	106.549669298615	0.67033070138529
10	107.33	107.629175317920	-0.299175317919881
11	107.11	107.458746436178	-0.348746436177889
12	108.86	107.810105929988	1.04989407001231
13	107.72	108.859331686988	-1.13933168698845
14	107.88	108.259236651072	-0.379236651072023
15	108.38	108.473751591977	-0.0937515919773426
16	107.72	108.096883696009	-0.376883696009296
17	108.41	108.186269227354	0.223730772646137
18	109.9	108.693374319660	1.20662568033983
19	111.45	109.904918090383	1.54508190961698
20	112.18	112.071206147652	0.108793852348092
21	113.34	113.093147977953	0.246852022046739
22	113.46	113.881556508741	-0.421556508741203
23	114.06	113.858135088738	0.201864911262031
24	115.54	114.316486506685	1.22351349331492
25	116.39	115.388866574253	1.00113342574692
26	115.94	116.628789288493	-0.68878928849279
27	116.97	116.462073583275	0.507926416724715
28	115.94	116.775000494915	-0.835000494914678
29	115.91	115.838964230089	0.0710357699106803
30	116.43	116.076753292926	0.35324670707444
31	116.26	116.019983401657	0.240016598342837
32	116.35	116.473144074377	-0.123144074376571
33	117.9	116.604785644049	1.29521435595131
34	117.7	117.544386857484	0.155613142516366
35	117.53	117.977844055381	-0.447844055380741
36	117.86	118.026375779536	-0.166375779535631
37	117.65	117.649353220894	0.000646779105519066
38	116.51	117.636268079925	-1.12626807992506
39	115.93	116.712616160833	-0.782616160832944
40	115.31	115.792842531113	-0.482842531112532
41	115	115.019936718354	-0.0199367183541721

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 105.02 & 104.428510345164 & 0.591489654835985 \tabularnewline
2 & 104.43 & 105.467484536611 & -1.03748453661099 \tabularnewline
3 & 104.63 & 105.367861407690 & -0.737861407689779 \tabularnewline
4 & 104.93 & 105.330235750004 & -0.400235750003897 \tabularnewline
5 & 105.87 & 105.036028064552 & 0.833971935447653 \tabularnewline
6 & 105.66 & 106.313504084047 & -0.653504084047054 \tabularnewline
7 & 106.76 & 106.385553835277 & 0.374446164722843 \tabularnewline
8 & 106 & 107.340843513189 & -1.34084351318868 \tabularnewline
9 & 107.22 & 106.549669298615 & 0.67033070138529 \tabularnewline
10 & 107.33 & 107.629175317920 & -0.299175317919881 \tabularnewline
11 & 107.11 & 107.458746436178 & -0.348746436177889 \tabularnewline
12 & 108.86 & 107.810105929988 & 1.04989407001231 \tabularnewline
13 & 107.72 & 108.859331686988 & -1.13933168698845 \tabularnewline
14 & 107.88 & 108.259236651072 & -0.379236651072023 \tabularnewline
15 & 108.38 & 108.473751591977 & -0.0937515919773426 \tabularnewline
16 & 107.72 & 108.096883696009 & -0.376883696009296 \tabularnewline
17 & 108.41 & 108.186269227354 & 0.223730772646137 \tabularnewline
18 & 109.9 & 108.693374319660 & 1.20662568033983 \tabularnewline
19 & 111.45 & 109.904918090383 & 1.54508190961698 \tabularnewline
20 & 112.18 & 112.071206147652 & 0.108793852348092 \tabularnewline
21 & 113.34 & 113.093147977953 & 0.246852022046739 \tabularnewline
22 & 113.46 & 113.881556508741 & -0.421556508741203 \tabularnewline
23 & 114.06 & 113.858135088738 & 0.201864911262031 \tabularnewline
24 & 115.54 & 114.316486506685 & 1.22351349331492 \tabularnewline
25 & 116.39 & 115.388866574253 & 1.00113342574692 \tabularnewline
26 & 115.94 & 116.628789288493 & -0.68878928849279 \tabularnewline
27 & 116.97 & 116.462073583275 & 0.507926416724715 \tabularnewline
28 & 115.94 & 116.775000494915 & -0.835000494914678 \tabularnewline
29 & 115.91 & 115.838964230089 & 0.0710357699106803 \tabularnewline
30 & 116.43 & 116.076753292926 & 0.35324670707444 \tabularnewline
31 & 116.26 & 116.019983401657 & 0.240016598342837 \tabularnewline
32 & 116.35 & 116.473144074377 & -0.123144074376571 \tabularnewline
33 & 117.9 & 116.604785644049 & 1.29521435595131 \tabularnewline
34 & 117.7 & 117.544386857484 & 0.155613142516366 \tabularnewline
35 & 117.53 & 117.977844055381 & -0.447844055380741 \tabularnewline
36 & 117.86 & 118.026375779536 & -0.166375779535631 \tabularnewline
37 & 117.65 & 117.649353220894 & 0.000646779105519066 \tabularnewline
38 & 116.51 & 117.636268079925 & -1.12626807992506 \tabularnewline
39 & 115.93 & 116.712616160833 & -0.782616160832944 \tabularnewline
40 & 115.31 & 115.792842531113 & -0.482842531112532 \tabularnewline
41 & 115 & 115.019936718354 & -0.0199367183541721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58502&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]105.02[/C][C]104.428510345164[/C][C]0.591489654835985[/C][/ROW]
[ROW][C]2[/C][C]104.43[/C][C]105.467484536611[/C][C]-1.03748453661099[/C][/ROW]
[ROW][C]3[/C][C]104.63[/C][C]105.367861407690[/C][C]-0.737861407689779[/C][/ROW]
[ROW][C]4[/C][C]104.93[/C][C]105.330235750004[/C][C]-0.400235750003897[/C][/ROW]
[ROW][C]5[/C][C]105.87[/C][C]105.036028064552[/C][C]0.833971935447653[/C][/ROW]
[ROW][C]6[/C][C]105.66[/C][C]106.313504084047[/C][C]-0.653504084047054[/C][/ROW]
[ROW][C]7[/C][C]106.76[/C][C]106.385553835277[/C][C]0.374446164722843[/C][/ROW]
[ROW][C]8[/C][C]106[/C][C]107.340843513189[/C][C]-1.34084351318868[/C][/ROW]
[ROW][C]9[/C][C]107.22[/C][C]106.549669298615[/C][C]0.67033070138529[/C][/ROW]
[ROW][C]10[/C][C]107.33[/C][C]107.629175317920[/C][C]-0.299175317919881[/C][/ROW]
[ROW][C]11[/C][C]107.11[/C][C]107.458746436178[/C][C]-0.348746436177889[/C][/ROW]
[ROW][C]12[/C][C]108.86[/C][C]107.810105929988[/C][C]1.04989407001231[/C][/ROW]
[ROW][C]13[/C][C]107.72[/C][C]108.859331686988[/C][C]-1.13933168698845[/C][/ROW]
[ROW][C]14[/C][C]107.88[/C][C]108.259236651072[/C][C]-0.379236651072023[/C][/ROW]
[ROW][C]15[/C][C]108.38[/C][C]108.473751591977[/C][C]-0.0937515919773426[/C][/ROW]
[ROW][C]16[/C][C]107.72[/C][C]108.096883696009[/C][C]-0.376883696009296[/C][/ROW]
[ROW][C]17[/C][C]108.41[/C][C]108.186269227354[/C][C]0.223730772646137[/C][/ROW]
[ROW][C]18[/C][C]109.9[/C][C]108.693374319660[/C][C]1.20662568033983[/C][/ROW]
[ROW][C]19[/C][C]111.45[/C][C]109.904918090383[/C][C]1.54508190961698[/C][/ROW]
[ROW][C]20[/C][C]112.18[/C][C]112.071206147652[/C][C]0.108793852348092[/C][/ROW]
[ROW][C]21[/C][C]113.34[/C][C]113.093147977953[/C][C]0.246852022046739[/C][/ROW]
[ROW][C]22[/C][C]113.46[/C][C]113.881556508741[/C][C]-0.421556508741203[/C][/ROW]
[ROW][C]23[/C][C]114.06[/C][C]113.858135088738[/C][C]0.201864911262031[/C][/ROW]
[ROW][C]24[/C][C]115.54[/C][C]114.316486506685[/C][C]1.22351349331492[/C][/ROW]
[ROW][C]25[/C][C]116.39[/C][C]115.388866574253[/C][C]1.00113342574692[/C][/ROW]
[ROW][C]26[/C][C]115.94[/C][C]116.628789288493[/C][C]-0.68878928849279[/C][/ROW]
[ROW][C]27[/C][C]116.97[/C][C]116.462073583275[/C][C]0.507926416724715[/C][/ROW]
[ROW][C]28[/C][C]115.94[/C][C]116.775000494915[/C][C]-0.835000494914678[/C][/ROW]
[ROW][C]29[/C][C]115.91[/C][C]115.838964230089[/C][C]0.0710357699106803[/C][/ROW]
[ROW][C]30[/C][C]116.43[/C][C]116.076753292926[/C][C]0.35324670707444[/C][/ROW]
[ROW][C]31[/C][C]116.26[/C][C]116.019983401657[/C][C]0.240016598342837[/C][/ROW]
[ROW][C]32[/C][C]116.35[/C][C]116.473144074377[/C][C]-0.123144074376571[/C][/ROW]
[ROW][C]33[/C][C]117.9[/C][C]116.604785644049[/C][C]1.29521435595131[/C][/ROW]
[ROW][C]34[/C][C]117.7[/C][C]117.544386857484[/C][C]0.155613142516366[/C][/ROW]
[ROW][C]35[/C][C]117.53[/C][C]117.977844055381[/C][C]-0.447844055380741[/C][/ROW]
[ROW][C]36[/C][C]117.86[/C][C]118.026375779536[/C][C]-0.166375779535631[/C][/ROW]
[ROW][C]37[/C][C]117.65[/C][C]117.649353220894[/C][C]0.000646779105519066[/C][/ROW]
[ROW][C]38[/C][C]116.51[/C][C]117.636268079925[/C][C]-1.12626807992506[/C][/ROW]
[ROW][C]39[/C][C]115.93[/C][C]116.712616160833[/C][C]-0.782616160832944[/C][/ROW]
[ROW][C]40[/C][C]115.31[/C][C]115.792842531113[/C][C]-0.482842531112532[/C][/ROW]
[ROW][C]41[/C][C]115[/C][C]115.019936718354[/C][C]-0.0199367183541721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58502&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58502&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	105.02	104.428510345164	0.591489654835985
2	104.43	105.467484536611	-1.03748453661099
3	104.63	105.367861407690	-0.737861407689779
4	104.93	105.330235750004	-0.400235750003897
5	105.87	105.036028064552	0.833971935447653
6	105.66	106.313504084047	-0.653504084047054
7	106.76	106.385553835277	0.374446164722843
8	106	107.340843513189	-1.34084351318868
9	107.22	106.549669298615	0.67033070138529
10	107.33	107.629175317920	-0.299175317919881
11	107.11	107.458746436178	-0.348746436177889
12	108.86	107.810105929988	1.04989407001231
13	107.72	108.859331686988	-1.13933168698845
14	107.88	108.259236651072	-0.379236651072023
15	108.38	108.473751591977	-0.0937515919773426
16	107.72	108.096883696009	-0.376883696009296
17	108.41	108.186269227354	0.223730772646137
18	109.9	108.693374319660	1.20662568033983
19	111.45	109.904918090383	1.54508190961698
20	112.18	112.071206147652	0.108793852348092
21	113.34	113.093147977953	0.246852022046739
22	113.46	113.881556508741	-0.421556508741203
23	114.06	113.858135088738	0.201864911262031
24	115.54	114.316486506685	1.22351349331492
25	116.39	115.388866574253	1.00113342574692
26	115.94	116.628789288493	-0.68878928849279
27	116.97	116.462073583275	0.507926416724715
28	115.94	116.775000494915	-0.835000494914678
29	115.91	115.838964230089	0.0710357699106803
30	116.43	116.076753292926	0.35324670707444
31	116.26	116.019983401657	0.240016598342837
32	116.35	116.473144074377	-0.123144074376571
33	117.9	116.604785644049	1.29521435595131
34	117.7	117.544386857484	0.155613142516366
35	117.53	117.977844055381	-0.447844055380741
36	117.86	118.026375779536	-0.166375779535631
37	117.65	117.649353220894	0.000646779105519066
38	116.51	117.636268079925	-1.12626807992506
39	115.93	116.712616160833	-0.782616160832944
40	115.31	115.792842531113	-0.482842531112532
41	115	115.019936718354	-0.0199367183541721

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0451356385640813	0.0902712771281626	0.954864361435919
10	0.469658800596047	0.939317601192095	0.530341199403953
11	0.322464364694623	0.644928729389246	0.677535635305377
12	0.586354081304861	0.827291837390278	0.413645918695139
13	0.617367253407676	0.765265493184648	0.382632746592324
14	0.549024418588971	0.901951162822058	0.450975581411029
15	0.491586015042808	0.983172030085615	0.508413984957192
16	0.52726990914329	0.94546018171342	0.47273009085671
17	0.454372253413406	0.908744506826811	0.545627746586594
18	0.530564460671435	0.93887107865713	0.469435539328565
19	0.774769323378153	0.450461353243694	0.225230676621847
20	0.714751562752236	0.570496874495529	0.285248437247764
21	0.636052677801393	0.727894644397214	0.363947322198607
22	0.744564310449359	0.510871379101283	0.255435689550641
23	0.741856886082402	0.516286227835196	0.258143113917598
24	0.712630855883681	0.574738288232638	0.287369144116319
25	0.652447153598367	0.695105692803266	0.347552846401633
26	0.835529227657114	0.328941544685773	0.164470772342886
27	0.752259579220707	0.495480841558585	0.247740420779293
28	0.898193462986668	0.203613074026664	0.101806537013332
29	0.92261473484101	0.154770530317978	0.077385265158989
30	0.862340262045868	0.275319475908263	0.137659737954132
31	0.761940899738316	0.476118200523367	0.238059100261684
32	0.757208981921663	0.485582036156673	0.242791018078337

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0451356385640813 & 0.0902712771281626 & 0.954864361435919 \tabularnewline
10 & 0.469658800596047 & 0.939317601192095 & 0.530341199403953 \tabularnewline
11 & 0.322464364694623 & 0.644928729389246 & 0.677535635305377 \tabularnewline
12 & 0.586354081304861 & 0.827291837390278 & 0.413645918695139 \tabularnewline
13 & 0.617367253407676 & 0.765265493184648 & 0.382632746592324 \tabularnewline
14 & 0.549024418588971 & 0.901951162822058 & 0.450975581411029 \tabularnewline
15 & 0.491586015042808 & 0.983172030085615 & 0.508413984957192 \tabularnewline
16 & 0.52726990914329 & 0.94546018171342 & 0.47273009085671 \tabularnewline
17 & 0.454372253413406 & 0.908744506826811 & 0.545627746586594 \tabularnewline
18 & 0.530564460671435 & 0.93887107865713 & 0.469435539328565 \tabularnewline
19 & 0.774769323378153 & 0.450461353243694 & 0.225230676621847 \tabularnewline
20 & 0.714751562752236 & 0.570496874495529 & 0.285248437247764 \tabularnewline
21 & 0.636052677801393 & 0.727894644397214 & 0.363947322198607 \tabularnewline
22 & 0.744564310449359 & 0.510871379101283 & 0.255435689550641 \tabularnewline
23 & 0.741856886082402 & 0.516286227835196 & 0.258143113917598 \tabularnewline
24 & 0.712630855883681 & 0.574738288232638 & 0.287369144116319 \tabularnewline
25 & 0.652447153598367 & 0.695105692803266 & 0.347552846401633 \tabularnewline
26 & 0.835529227657114 & 0.328941544685773 & 0.164470772342886 \tabularnewline
27 & 0.752259579220707 & 0.495480841558585 & 0.247740420779293 \tabularnewline
28 & 0.898193462986668 & 0.203613074026664 & 0.101806537013332 \tabularnewline
29 & 0.92261473484101 & 0.154770530317978 & 0.077385265158989 \tabularnewline
30 & 0.862340262045868 & 0.275319475908263 & 0.137659737954132 \tabularnewline
31 & 0.761940899738316 & 0.476118200523367 & 0.238059100261684 \tabularnewline
32 & 0.757208981921663 & 0.485582036156673 & 0.242791018078337 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58502&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]9[/C][C]0.0451356385640813[/C][C]0.0902712771281626[/C][C]0.954864361435919[/C][/ROW]
[ROW][C]10[/C][C]0.469658800596047[/C][C]0.939317601192095[/C][C]0.530341199403953[/C][/ROW]
[ROW][C]11[/C][C]0.322464364694623[/C][C]0.644928729389246[/C][C]0.677535635305377[/C][/ROW]
[ROW][C]12[/C][C]0.586354081304861[/C][C]0.827291837390278[/C][C]0.413645918695139[/C][/ROW]
[ROW][C]13[/C][C]0.617367253407676[/C][C]0.765265493184648[/C][C]0.382632746592324[/C][/ROW]
[ROW][C]14[/C][C]0.549024418588971[/C][C]0.901951162822058[/C][C]0.450975581411029[/C][/ROW]
[ROW][C]15[/C][C]0.491586015042808[/C][C]0.983172030085615[/C][C]0.508413984957192[/C][/ROW]
[ROW][C]16[/C][C]0.52726990914329[/C][C]0.94546018171342[/C][C]0.47273009085671[/C][/ROW]
[ROW][C]17[/C][C]0.454372253413406[/C][C]0.908744506826811[/C][C]0.545627746586594[/C][/ROW]
[ROW][C]18[/C][C]0.530564460671435[/C][C]0.93887107865713[/C][C]0.469435539328565[/C][/ROW]
[ROW][C]19[/C][C]0.774769323378153[/C][C]0.450461353243694[/C][C]0.225230676621847[/C][/ROW]
[ROW][C]20[/C][C]0.714751562752236[/C][C]0.570496874495529[/C][C]0.285248437247764[/C][/ROW]
[ROW][C]21[/C][C]0.636052677801393[/C][C]0.727894644397214[/C][C]0.363947322198607[/C][/ROW]
[ROW][C]22[/C][C]0.744564310449359[/C][C]0.510871379101283[/C][C]0.255435689550641[/C][/ROW]
[ROW][C]23[/C][C]0.741856886082402[/C][C]0.516286227835196[/C][C]0.258143113917598[/C][/ROW]
[ROW][C]24[/C][C]0.712630855883681[/C][C]0.574738288232638[/C][C]0.287369144116319[/C][/ROW]
[ROW][C]25[/C][C]0.652447153598367[/C][C]0.695105692803266[/C][C]0.347552846401633[/C][/ROW]
[ROW][C]26[/C][C]0.835529227657114[/C][C]0.328941544685773[/C][C]0.164470772342886[/C][/ROW]
[ROW][C]27[/C][C]0.752259579220707[/C][C]0.495480841558585[/C][C]0.247740420779293[/C][/ROW]
[ROW][C]28[/C][C]0.898193462986668[/C][C]0.203613074026664[/C][C]0.101806537013332[/C][/ROW]
[ROW][C]29[/C][C]0.92261473484101[/C][C]0.154770530317978[/C][C]0.077385265158989[/C][/ROW]
[ROW][C]30[/C][C]0.862340262045868[/C][C]0.275319475908263[/C][C]0.137659737954132[/C][/ROW]
[ROW][C]31[/C][C]0.761940899738316[/C][C]0.476118200523367[/C][C]0.238059100261684[/C][/ROW]
[ROW][C]32[/C][C]0.757208981921663[/C][C]0.485582036156673[/C][C]0.242791018078337[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58502&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58502&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
9	0.0451356385640813	0.0902712771281626	0.954864361435919
10	0.469658800596047	0.939317601192095	0.530341199403953
11	0.322464364694623	0.644928729389246	0.677535635305377
12	0.586354081304861	0.827291837390278	0.413645918695139
13	0.617367253407676	0.765265493184648	0.382632746592324
14	0.549024418588971	0.901951162822058	0.450975581411029
15	0.491586015042808	0.983172030085615	0.508413984957192
16	0.52726990914329	0.94546018171342	0.47273009085671
17	0.454372253413406	0.908744506826811	0.545627746586594
18	0.530564460671435	0.93887107865713	0.469435539328565
19	0.774769323378153	0.450461353243694	0.225230676621847
20	0.714751562752236	0.570496874495529	0.285248437247764
21	0.636052677801393	0.727894644397214	0.363947322198607
22	0.744564310449359	0.510871379101283	0.255435689550641
23	0.741856886082402	0.516286227835196	0.258143113917598
24	0.712630855883681	0.574738288232638	0.287369144116319
25	0.652447153598367	0.695105692803266	0.347552846401633
26	0.835529227657114	0.328941544685773	0.164470772342886
27	0.752259579220707	0.495480841558585	0.247740420779293
28	0.898193462986668	0.203613074026664	0.101806537013332
29	0.92261473484101	0.154770530317978	0.077385265158989
30	0.862340262045868	0.275319475908263	0.137659737954132
31	0.761940899738316	0.476118200523367	0.238059100261684
32	0.757208981921663	0.485582036156673	0.242791018078337

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	1	0.0416666666666667	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 & 1 & 0.0416666666666667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58502&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]1[/C][C]0.0416666666666667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58502&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58502&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	1	0.0416666666666667	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code