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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 10 Dec 2008 13:26:13 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/10/t1228941019h7pwzyjjurvlft6.htm/, Retrieved Sun, 19 May 2024 04:23:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=32098, Retrieved Sun, 19 May 2024 04:23:48 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

190

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

-       [Multiple Regression] [Paper - Regressie...] [2008-12-10 20:26:13] [4127a50d3937d4bda99dae34ed7ecdc5] [Current]
-  M D    [Multiple Regression] [Paper Regressie a...] [2009-12-19 07:59:01] [1d635fe1113b56bab3f378c464a289bc] 
-   P       [Multiple Regression] [Paper Regressie a...] [2009-12-19 09:31:23] [1d635fe1113b56bab3f378c464a289bc] 
-   P         [Multiple Regression] [Paper Regressie a...] [2009-12-19 10:57:36] [1d635fe1113b56bab3f378c464a289bc] 

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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	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32098&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32098&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32098&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	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
Consumentenvertrouwen[t] = + 111.074074074074 -21.1296296296296Dummy[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Consumentenvertrouwen[t] =  +  111.074074074074 -21.1296296296296Dummy[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32098&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Consumentenvertrouwen[t] =  +  111.074074074074 -21.1296296296296Dummy[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32098&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32098&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
Consumentenvertrouwen[t] = + 111.074074074074 -21.1296296296296Dummy[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)	111.074074074074	1.060679	104.7198	0	0
Dummy	-21.1296296296296	2.121357	-9.9604	0	0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 111.074074074074 & 1.060679 & 104.7198 & 0 & 0 \tabularnewline
Dummy & -21.1296296296296 & 2.121357 & -9.9604 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32098&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]111.074074074074[/C][C]1.060679[/C][C]104.7198[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]-21.1296296296296[/C][C]2.121357[/C][C]-9.9604[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32098&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32098&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)	111.074074074074	1.060679	104.7198	0	0
Dummy	-21.1296296296296	2.121357	-9.9604	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.765710939773387
R-squared	0.586313243288644
Adjusted R-squared	0.580403432478482
F-TEST (value)	99.2101544571356
F-TEST (DF numerator)	1
F-TEST (DF denominator)	70
p-value	4.66293670342566e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.79436440013658
Sum Squared Residuals	4252.64814814815

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.765710939773387 \tabularnewline
R-squared & 0.586313243288644 \tabularnewline
Adjusted R-squared & 0.580403432478482 \tabularnewline
F-TEST (value) & 99.2101544571356 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 70 \tabularnewline
p-value & 4.66293670342566e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.79436440013658 \tabularnewline
Sum Squared Residuals & 4252.64814814815 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32098&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.765710939773387[/C][/ROW]
[ROW][C]R-squared[/C][C]0.586313243288644[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.580403432478482[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]99.2101544571356[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]70[/C][/ROW]
[ROW][C]p-value[/C][C]4.66293670342566e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]7.79436440013658[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4252.64814814815[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32098&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32098&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	0.765710939773387
R-squared	0.586313243288644
Adjusted R-squared	0.580403432478482
F-TEST (value)	99.2101544571356
F-TEST (DF numerator)	1
F-TEST (DF denominator)	70
p-value	4.66293670342566e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.79436440013658
Sum Squared Residuals	4252.64814814815

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	108	111.074074074074	-3.07407407407365
2	99	111.074074074074	-12.0740740740742
3	108	111.074074074074	-3.07407407407408
4	104	111.074074074074	-7.07407407407408
5	111	111.074074074074	-0.0740740740740797
6	110	111.074074074074	-1.07407407407408
7	106	111.074074074074	-5.07407407407408
8	101	111.074074074074	-10.0740740740741
9	102	111.074074074074	-9.07407407407408
10	99	111.074074074074	-12.0740740740741
11	100	111.074074074074	-11.0740740740741
12	98	111.074074074074	-13.0740740740741
13	92	89.9444444444444	2.05555555555555
14	87	89.9444444444444	-2.94444444444445
15	79	89.9444444444444	-10.9444444444444
16	87	89.9444444444444	-2.94444444444445
17	87	89.9444444444444	-2.94444444444445
18	88	89.9444444444444	-1.94444444444445
19	83	89.9444444444444	-6.94444444444445
20	85	89.9444444444444	-4.94444444444445
21	92	89.9444444444444	2.05555555555555
22	84	89.9444444444444	-5.94444444444445
23	92	89.9444444444444	2.05555555555555
24	98	89.9444444444444	8.05555555555555
25	103	111.074074074074	-8.07407407407408
26	104	111.074074074074	-7.07407407407408
27	109	111.074074074074	-2.07407407407408
28	107	111.074074074074	-4.07407407407408
29	106	111.074074074074	-5.07407407407408
30	113	111.074074074074	1.92592592592592
31	107	111.074074074074	-4.07407407407408
32	114	111.074074074074	2.92592592592592
33	108	111.074074074074	-3.07407407407408
34	104	111.074074074074	-7.07407407407408
35	105	111.074074074074	-6.07407407407408
36	109	111.074074074074	-2.07407407407408
37	109	111.074074074074	-2.07407407407408
38	112	111.074074074074	0.92592592592592
39	118	111.074074074074	6.92592592592592
40	111	111.074074074074	-0.0740740740740797
41	99	89.9444444444444	9.05555555555555
42	92	89.9444444444444	2.05555555555555
43	92	89.9444444444444	2.05555555555555
44	98	89.9444444444444	8.05555555555555
45	87	89.9444444444444	-2.94444444444445
46	97	89.9444444444444	7.05555555555555
47	102	111.074074074074	-9.07407407407408
48	105	111.074074074074	-6.07407407407408
49	111	111.074074074074	-0.0740740740740797
50	110	111.074074074074	-1.07407407407408
51	109	111.074074074074	-2.07407407407408
52	111	111.074074074074	-0.0740740740740797
53	113	111.074074074074	1.92592592592592
54	114	111.074074074074	2.92592592592592
55	120	111.074074074074	8.92592592592592
56	114	111.074074074074	2.92592592592592
57	120	111.074074074074	8.92592592592592
58	122	111.074074074074	10.9259259259259
59	123	111.074074074074	11.9259259259259
60	115	111.074074074074	3.92592592592592
61	123	111.074074074074	11.9259259259259
62	124	111.074074074074	12.9259259259259
63	124	111.074074074074	12.9259259259259
64	132	111.074074074074	20.9259259259259
65	126	111.074074074074	14.9259259259259
66	126	111.074074074074	14.9259259259259
67	122	111.074074074074	10.9259259259259
68	120	111.074074074074	8.92592592592592
69	114	111.074074074074	2.92592592592592
70	116	111.074074074074	4.92592592592592
71	100	111.074074074074	-11.0740740740741
72	97	111.074074074074	-14.0740740740741

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 108 & 111.074074074074 & -3.07407407407365 \tabularnewline
2 & 99 & 111.074074074074 & -12.0740740740742 \tabularnewline
3 & 108 & 111.074074074074 & -3.07407407407408 \tabularnewline
4 & 104 & 111.074074074074 & -7.07407407407408 \tabularnewline
5 & 111 & 111.074074074074 & -0.0740740740740797 \tabularnewline
6 & 110 & 111.074074074074 & -1.07407407407408 \tabularnewline
7 & 106 & 111.074074074074 & -5.07407407407408 \tabularnewline
8 & 101 & 111.074074074074 & -10.0740740740741 \tabularnewline
9 & 102 & 111.074074074074 & -9.07407407407408 \tabularnewline
10 & 99 & 111.074074074074 & -12.0740740740741 \tabularnewline
11 & 100 & 111.074074074074 & -11.0740740740741 \tabularnewline
12 & 98 & 111.074074074074 & -13.0740740740741 \tabularnewline
13 & 92 & 89.9444444444444 & 2.05555555555555 \tabularnewline
14 & 87 & 89.9444444444444 & -2.94444444444445 \tabularnewline
15 & 79 & 89.9444444444444 & -10.9444444444444 \tabularnewline
16 & 87 & 89.9444444444444 & -2.94444444444445 \tabularnewline
17 & 87 & 89.9444444444444 & -2.94444444444445 \tabularnewline
18 & 88 & 89.9444444444444 & -1.94444444444445 \tabularnewline
19 & 83 & 89.9444444444444 & -6.94444444444445 \tabularnewline
20 & 85 & 89.9444444444444 & -4.94444444444445 \tabularnewline
21 & 92 & 89.9444444444444 & 2.05555555555555 \tabularnewline
22 & 84 & 89.9444444444444 & -5.94444444444445 \tabularnewline
23 & 92 & 89.9444444444444 & 2.05555555555555 \tabularnewline
24 & 98 & 89.9444444444444 & 8.05555555555555 \tabularnewline
25 & 103 & 111.074074074074 & -8.07407407407408 \tabularnewline
26 & 104 & 111.074074074074 & -7.07407407407408 \tabularnewline
27 & 109 & 111.074074074074 & -2.07407407407408 \tabularnewline
28 & 107 & 111.074074074074 & -4.07407407407408 \tabularnewline
29 & 106 & 111.074074074074 & -5.07407407407408 \tabularnewline
30 & 113 & 111.074074074074 & 1.92592592592592 \tabularnewline
31 & 107 & 111.074074074074 & -4.07407407407408 \tabularnewline
32 & 114 & 111.074074074074 & 2.92592592592592 \tabularnewline
33 & 108 & 111.074074074074 & -3.07407407407408 \tabularnewline
34 & 104 & 111.074074074074 & -7.07407407407408 \tabularnewline
35 & 105 & 111.074074074074 & -6.07407407407408 \tabularnewline
36 & 109 & 111.074074074074 & -2.07407407407408 \tabularnewline
37 & 109 & 111.074074074074 & -2.07407407407408 \tabularnewline
38 & 112 & 111.074074074074 & 0.92592592592592 \tabularnewline
39 & 118 & 111.074074074074 & 6.92592592592592 \tabularnewline
40 & 111 & 111.074074074074 & -0.0740740740740797 \tabularnewline
41 & 99 & 89.9444444444444 & 9.05555555555555 \tabularnewline
42 & 92 & 89.9444444444444 & 2.05555555555555 \tabularnewline
43 & 92 & 89.9444444444444 & 2.05555555555555 \tabularnewline
44 & 98 & 89.9444444444444 & 8.05555555555555 \tabularnewline
45 & 87 & 89.9444444444444 & -2.94444444444445 \tabularnewline
46 & 97 & 89.9444444444444 & 7.05555555555555 \tabularnewline
47 & 102 & 111.074074074074 & -9.07407407407408 \tabularnewline
48 & 105 & 111.074074074074 & -6.07407407407408 \tabularnewline
49 & 111 & 111.074074074074 & -0.0740740740740797 \tabularnewline
50 & 110 & 111.074074074074 & -1.07407407407408 \tabularnewline
51 & 109 & 111.074074074074 & -2.07407407407408 \tabularnewline
52 & 111 & 111.074074074074 & -0.0740740740740797 \tabularnewline
53 & 113 & 111.074074074074 & 1.92592592592592 \tabularnewline
54 & 114 & 111.074074074074 & 2.92592592592592 \tabularnewline
55 & 120 & 111.074074074074 & 8.92592592592592 \tabularnewline
56 & 114 & 111.074074074074 & 2.92592592592592 \tabularnewline
57 & 120 & 111.074074074074 & 8.92592592592592 \tabularnewline
58 & 122 & 111.074074074074 & 10.9259259259259 \tabularnewline
59 & 123 & 111.074074074074 & 11.9259259259259 \tabularnewline
60 & 115 & 111.074074074074 & 3.92592592592592 \tabularnewline
61 & 123 & 111.074074074074 & 11.9259259259259 \tabularnewline
62 & 124 & 111.074074074074 & 12.9259259259259 \tabularnewline
63 & 124 & 111.074074074074 & 12.9259259259259 \tabularnewline
64 & 132 & 111.074074074074 & 20.9259259259259 \tabularnewline
65 & 126 & 111.074074074074 & 14.9259259259259 \tabularnewline
66 & 126 & 111.074074074074 & 14.9259259259259 \tabularnewline
67 & 122 & 111.074074074074 & 10.9259259259259 \tabularnewline
68 & 120 & 111.074074074074 & 8.92592592592592 \tabularnewline
69 & 114 & 111.074074074074 & 2.92592592592592 \tabularnewline
70 & 116 & 111.074074074074 & 4.92592592592592 \tabularnewline
71 & 100 & 111.074074074074 & -11.0740740740741 \tabularnewline
72 & 97 & 111.074074074074 & -14.0740740740741 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32098&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]108[/C][C]111.074074074074[/C][C]-3.07407407407365[/C][/ROW]
[ROW][C]2[/C][C]99[/C][C]111.074074074074[/C][C]-12.0740740740742[/C][/ROW]
[ROW][C]3[/C][C]108[/C][C]111.074074074074[/C][C]-3.07407407407408[/C][/ROW]
[ROW][C]4[/C][C]104[/C][C]111.074074074074[/C][C]-7.07407407407408[/C][/ROW]
[ROW][C]5[/C][C]111[/C][C]111.074074074074[/C][C]-0.0740740740740797[/C][/ROW]
[ROW][C]6[/C][C]110[/C][C]111.074074074074[/C][C]-1.07407407407408[/C][/ROW]
[ROW][C]7[/C][C]106[/C][C]111.074074074074[/C][C]-5.07407407407408[/C][/ROW]
[ROW][C]8[/C][C]101[/C][C]111.074074074074[/C][C]-10.0740740740741[/C][/ROW]
[ROW][C]9[/C][C]102[/C][C]111.074074074074[/C][C]-9.07407407407408[/C][/ROW]
[ROW][C]10[/C][C]99[/C][C]111.074074074074[/C][C]-12.0740740740741[/C][/ROW]
[ROW][C]11[/C][C]100[/C][C]111.074074074074[/C][C]-11.0740740740741[/C][/ROW]
[ROW][C]12[/C][C]98[/C][C]111.074074074074[/C][C]-13.0740740740741[/C][/ROW]
[ROW][C]13[/C][C]92[/C][C]89.9444444444444[/C][C]2.05555555555555[/C][/ROW]
[ROW][C]14[/C][C]87[/C][C]89.9444444444444[/C][C]-2.94444444444445[/C][/ROW]
[ROW][C]15[/C][C]79[/C][C]89.9444444444444[/C][C]-10.9444444444444[/C][/ROW]
[ROW][C]16[/C][C]87[/C][C]89.9444444444444[/C][C]-2.94444444444445[/C][/ROW]
[ROW][C]17[/C][C]87[/C][C]89.9444444444444[/C][C]-2.94444444444445[/C][/ROW]
[ROW][C]18[/C][C]88[/C][C]89.9444444444444[/C][C]-1.94444444444445[/C][/ROW]
[ROW][C]19[/C][C]83[/C][C]89.9444444444444[/C][C]-6.94444444444445[/C][/ROW]
[ROW][C]20[/C][C]85[/C][C]89.9444444444444[/C][C]-4.94444444444445[/C][/ROW]
[ROW][C]21[/C][C]92[/C][C]89.9444444444444[/C][C]2.05555555555555[/C][/ROW]
[ROW][C]22[/C][C]84[/C][C]89.9444444444444[/C][C]-5.94444444444445[/C][/ROW]
[ROW][C]23[/C][C]92[/C][C]89.9444444444444[/C][C]2.05555555555555[/C][/ROW]
[ROW][C]24[/C][C]98[/C][C]89.9444444444444[/C][C]8.05555555555555[/C][/ROW]
[ROW][C]25[/C][C]103[/C][C]111.074074074074[/C][C]-8.07407407407408[/C][/ROW]
[ROW][C]26[/C][C]104[/C][C]111.074074074074[/C][C]-7.07407407407408[/C][/ROW]
[ROW][C]27[/C][C]109[/C][C]111.074074074074[/C][C]-2.07407407407408[/C][/ROW]
[ROW][C]28[/C][C]107[/C][C]111.074074074074[/C][C]-4.07407407407408[/C][/ROW]
[ROW][C]29[/C][C]106[/C][C]111.074074074074[/C][C]-5.07407407407408[/C][/ROW]
[ROW][C]30[/C][C]113[/C][C]111.074074074074[/C][C]1.92592592592592[/C][/ROW]
[ROW][C]31[/C][C]107[/C][C]111.074074074074[/C][C]-4.07407407407408[/C][/ROW]
[ROW][C]32[/C][C]114[/C][C]111.074074074074[/C][C]2.92592592592592[/C][/ROW]
[ROW][C]33[/C][C]108[/C][C]111.074074074074[/C][C]-3.07407407407408[/C][/ROW]
[ROW][C]34[/C][C]104[/C][C]111.074074074074[/C][C]-7.07407407407408[/C][/ROW]
[ROW][C]35[/C][C]105[/C][C]111.074074074074[/C][C]-6.07407407407408[/C][/ROW]
[ROW][C]36[/C][C]109[/C][C]111.074074074074[/C][C]-2.07407407407408[/C][/ROW]
[ROW][C]37[/C][C]109[/C][C]111.074074074074[/C][C]-2.07407407407408[/C][/ROW]
[ROW][C]38[/C][C]112[/C][C]111.074074074074[/C][C]0.92592592592592[/C][/ROW]
[ROW][C]39[/C][C]118[/C][C]111.074074074074[/C][C]6.92592592592592[/C][/ROW]
[ROW][C]40[/C][C]111[/C][C]111.074074074074[/C][C]-0.0740740740740797[/C][/ROW]
[ROW][C]41[/C][C]99[/C][C]89.9444444444444[/C][C]9.05555555555555[/C][/ROW]
[ROW][C]42[/C][C]92[/C][C]89.9444444444444[/C][C]2.05555555555555[/C][/ROW]
[ROW][C]43[/C][C]92[/C][C]89.9444444444444[/C][C]2.05555555555555[/C][/ROW]
[ROW][C]44[/C][C]98[/C][C]89.9444444444444[/C][C]8.05555555555555[/C][/ROW]
[ROW][C]45[/C][C]87[/C][C]89.9444444444444[/C][C]-2.94444444444445[/C][/ROW]
[ROW][C]46[/C][C]97[/C][C]89.9444444444444[/C][C]7.05555555555555[/C][/ROW]
[ROW][C]47[/C][C]102[/C][C]111.074074074074[/C][C]-9.07407407407408[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]111.074074074074[/C][C]-6.07407407407408[/C][/ROW]
[ROW][C]49[/C][C]111[/C][C]111.074074074074[/C][C]-0.0740740740740797[/C][/ROW]
[ROW][C]50[/C][C]110[/C][C]111.074074074074[/C][C]-1.07407407407408[/C][/ROW]
[ROW][C]51[/C][C]109[/C][C]111.074074074074[/C][C]-2.07407407407408[/C][/ROW]
[ROW][C]52[/C][C]111[/C][C]111.074074074074[/C][C]-0.0740740740740797[/C][/ROW]
[ROW][C]53[/C][C]113[/C][C]111.074074074074[/C][C]1.92592592592592[/C][/ROW]
[ROW][C]54[/C][C]114[/C][C]111.074074074074[/C][C]2.92592592592592[/C][/ROW]
[ROW][C]55[/C][C]120[/C][C]111.074074074074[/C][C]8.92592592592592[/C][/ROW]
[ROW][C]56[/C][C]114[/C][C]111.074074074074[/C][C]2.92592592592592[/C][/ROW]
[ROW][C]57[/C][C]120[/C][C]111.074074074074[/C][C]8.92592592592592[/C][/ROW]
[ROW][C]58[/C][C]122[/C][C]111.074074074074[/C][C]10.9259259259259[/C][/ROW]
[ROW][C]59[/C][C]123[/C][C]111.074074074074[/C][C]11.9259259259259[/C][/ROW]
[ROW][C]60[/C][C]115[/C][C]111.074074074074[/C][C]3.92592592592592[/C][/ROW]
[ROW][C]61[/C][C]123[/C][C]111.074074074074[/C][C]11.9259259259259[/C][/ROW]
[ROW][C]62[/C][C]124[/C][C]111.074074074074[/C][C]12.9259259259259[/C][/ROW]
[ROW][C]63[/C][C]124[/C][C]111.074074074074[/C][C]12.9259259259259[/C][/ROW]
[ROW][C]64[/C][C]132[/C][C]111.074074074074[/C][C]20.9259259259259[/C][/ROW]
[ROW][C]65[/C][C]126[/C][C]111.074074074074[/C][C]14.9259259259259[/C][/ROW]
[ROW][C]66[/C][C]126[/C][C]111.074074074074[/C][C]14.9259259259259[/C][/ROW]
[ROW][C]67[/C][C]122[/C][C]111.074074074074[/C][C]10.9259259259259[/C][/ROW]
[ROW][C]68[/C][C]120[/C][C]111.074074074074[/C][C]8.92592592592592[/C][/ROW]
[ROW][C]69[/C][C]114[/C][C]111.074074074074[/C][C]2.92592592592592[/C][/ROW]
[ROW][C]70[/C][C]116[/C][C]111.074074074074[/C][C]4.92592592592592[/C][/ROW]
[ROW][C]71[/C][C]100[/C][C]111.074074074074[/C][C]-11.0740740740741[/C][/ROW]
[ROW][C]72[/C][C]97[/C][C]111.074074074074[/C][C]-14.0740740740741[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32098&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32098&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	108	111.074074074074	-3.07407407407365
2	99	111.074074074074	-12.0740740740742
3	108	111.074074074074	-3.07407407407408
4	104	111.074074074074	-7.07407407407408
5	111	111.074074074074	-0.0740740740740797
6	110	111.074074074074	-1.07407407407408
7	106	111.074074074074	-5.07407407407408
8	101	111.074074074074	-10.0740740740741
9	102	111.074074074074	-9.07407407407408
10	99	111.074074074074	-12.0740740740741
11	100	111.074074074074	-11.0740740740741
12	98	111.074074074074	-13.0740740740741
13	92	89.9444444444444	2.05555555555555
14	87	89.9444444444444	-2.94444444444445
15	79	89.9444444444444	-10.9444444444444
16	87	89.9444444444444	-2.94444444444445
17	87	89.9444444444444	-2.94444444444445
18	88	89.9444444444444	-1.94444444444445
19	83	89.9444444444444	-6.94444444444445
20	85	89.9444444444444	-4.94444444444445
21	92	89.9444444444444	2.05555555555555
22	84	89.9444444444444	-5.94444444444445
23	92	89.9444444444444	2.05555555555555
24	98	89.9444444444444	8.05555555555555
25	103	111.074074074074	-8.07407407407408
26	104	111.074074074074	-7.07407407407408
27	109	111.074074074074	-2.07407407407408
28	107	111.074074074074	-4.07407407407408
29	106	111.074074074074	-5.07407407407408
30	113	111.074074074074	1.92592592592592
31	107	111.074074074074	-4.07407407407408
32	114	111.074074074074	2.92592592592592
33	108	111.074074074074	-3.07407407407408
34	104	111.074074074074	-7.07407407407408
35	105	111.074074074074	-6.07407407407408
36	109	111.074074074074	-2.07407407407408
37	109	111.074074074074	-2.07407407407408
38	112	111.074074074074	0.92592592592592
39	118	111.074074074074	6.92592592592592
40	111	111.074074074074	-0.0740740740740797
41	99	89.9444444444444	9.05555555555555
42	92	89.9444444444444	2.05555555555555
43	92	89.9444444444444	2.05555555555555
44	98	89.9444444444444	8.05555555555555
45	87	89.9444444444444	-2.94444444444445
46	97	89.9444444444444	7.05555555555555
47	102	111.074074074074	-9.07407407407408
48	105	111.074074074074	-6.07407407407408
49	111	111.074074074074	-0.0740740740740797
50	110	111.074074074074	-1.07407407407408
51	109	111.074074074074	-2.07407407407408
52	111	111.074074074074	-0.0740740740740797
53	113	111.074074074074	1.92592592592592
54	114	111.074074074074	2.92592592592592
55	120	111.074074074074	8.92592592592592
56	114	111.074074074074	2.92592592592592
57	120	111.074074074074	8.92592592592592
58	122	111.074074074074	10.9259259259259
59	123	111.074074074074	11.9259259259259
60	115	111.074074074074	3.92592592592592
61	123	111.074074074074	11.9259259259259
62	124	111.074074074074	12.9259259259259
63	124	111.074074074074	12.9259259259259
64	132	111.074074074074	20.9259259259259
65	126	111.074074074074	14.9259259259259
66	126	111.074074074074	14.9259259259259
67	122	111.074074074074	10.9259259259259
68	120	111.074074074074	8.92592592592592
69	114	111.074074074074	2.92592592592592
70	116	111.074074074074	4.92592592592592
71	100	111.074074074074	-11.0740740740741
72	97	111.074074074074	-14.0740740740741

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.290886754298317	0.581773508596634	0.709113245701683
6	0.188607897139438	0.377215794278876	0.811392102860562
7	0.0977543346945003	0.195508669389001	0.9022456653055
8	0.086155172814742	0.172310345629484	0.913844827185258
9	0.0598001297792977	0.119600259558595	0.940199870220702
10	0.0680440786003055	0.136088157200611	0.931955921399694
11	0.0594455884320059	0.118891176864012	0.940554411567994
12	0.071889624242719	0.143779248485438	0.928110375757281
13	0.0417454839360989	0.0834909678721979	0.958254516063901
14	0.0287392839156515	0.057478567831303	0.971260716084348
15	0.0545732452872916	0.109146490574583	0.945426754712708
16	0.033727142142175	0.06745428428435	0.966272857857825
17	0.0201839056178297	0.0403678112356593	0.97981609438217
18	0.0119276234810040	0.0238552469620080	0.988072376518996
19	0.00912587006045215	0.0182517401209043	0.990874129939548
20	0.00567056036110966	0.0113411207222193	0.99432943963889
21	0.00527469995162996	0.0105493999032599	0.99472530004837
22	0.00391354682446992	0.00782709364893983	0.99608645317553
23	0.00344851720430606	0.00689703440861213	0.996551482795694
24	0.00980813465708063	0.0196162693141613	0.99019186534292
25	0.00741042720184144	0.0148208544036829	0.992589572798159
26	0.00540105595786691	0.0108021119157338	0.994598944042133
27	0.00459211257406406	0.00918422514812812	0.995407887425936
28	0.00330742496821655	0.0066148499364331	0.996692575031783
29	0.00234606395863053	0.00469212791726107	0.99765393604137
30	0.00332707427518605	0.0066541485503721	0.996672925724814
31	0.00236756555197643	0.00473513110395286	0.997632434448024
32	0.00340142220488332	0.00680284440976664	0.996598577795117
33	0.00242070300900563	0.00484140601801126	0.997579296990994
34	0.00215781666847740	0.00431563333695479	0.997842183331523
35	0.00182484522739411	0.00364969045478822	0.998175154772606
36	0.00140108396512042	0.00280216793024083	0.99859891603488
37	0.00107550775818040	0.00215101551636080	0.99892449224182
38	0.000999518981300081	0.00199903796260016	0.9990004810187
39	0.00254659017431202	0.00509318034862404	0.997453409825688
40	0.00196847147371695	0.00393694294743391	0.998031528526283
41	0.00358462408976365	0.00716924817952729	0.996415375910236
42	0.00230160090254899	0.00460320180509798	0.99769839909745
43	0.00143865369312538	0.00287730738625075	0.998561346306875
44	0.00169532384248639	0.00339064768497277	0.998304676157514
45	0.00128976389493788	0.00257952778987576	0.998710236105062
46	0.00110214250077499	0.00220428500154997	0.998897857499225
47	0.00206534833513455	0.00413069667026911	0.997934651664866
48	0.00255562473637469	0.00511124947274938	0.997444375263625
49	0.00209597986055727	0.00419195972111453	0.997904020139443
50	0.00178292218042142	0.00356584436084284	0.998217077819579
51	0.00167921400467411	0.00335842800934823	0.998320785995326
52	0.00147540862713327	0.00295081725426654	0.998524591372867
53	0.00127766948684567	0.00255533897369134	0.998722330513154
54	0.00111088901340006	0.00222177802680012	0.9988891109866
55	0.00164849193332294	0.00329698386664587	0.998351508066677
56	0.00130531442047431	0.00261062884094862	0.998694685579526
57	0.00153362570019993	0.00306725140039985	0.9984663742998
58	0.00212614593569359	0.00425229187138718	0.997873854064306
59	0.00296835699321475	0.0059367139864295	0.997031643006785
60	0.00190148631215023	0.00380297262430047	0.99809851368785
61	0.00217303578684728	0.00434607157369456	0.997826964213153
62	0.00263024289297798	0.00526048578595595	0.997369757107022
63	0.00293209512668572	0.00586419025337145	0.997067904873314
64	0.0187989488548523	0.0375978977097045	0.981201051145148
65	0.0305156748490222	0.0610313496980444	0.969484325150978
66	0.0603901885262018	0.120780377052404	0.939609811473798
67	0.077767762332227	0.155535524664454	0.922232237667773

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.290886754298317 & 0.581773508596634 & 0.709113245701683 \tabularnewline
6 & 0.188607897139438 & 0.377215794278876 & 0.811392102860562 \tabularnewline
7 & 0.0977543346945003 & 0.195508669389001 & 0.9022456653055 \tabularnewline
8 & 0.086155172814742 & 0.172310345629484 & 0.913844827185258 \tabularnewline
9 & 0.0598001297792977 & 0.119600259558595 & 0.940199870220702 \tabularnewline
10 & 0.0680440786003055 & 0.136088157200611 & 0.931955921399694 \tabularnewline
11 & 0.0594455884320059 & 0.118891176864012 & 0.940554411567994 \tabularnewline
12 & 0.071889624242719 & 0.143779248485438 & 0.928110375757281 \tabularnewline
13 & 0.0417454839360989 & 0.0834909678721979 & 0.958254516063901 \tabularnewline
14 & 0.0287392839156515 & 0.057478567831303 & 0.971260716084348 \tabularnewline
15 & 0.0545732452872916 & 0.109146490574583 & 0.945426754712708 \tabularnewline
16 & 0.033727142142175 & 0.06745428428435 & 0.966272857857825 \tabularnewline
17 & 0.0201839056178297 & 0.0403678112356593 & 0.97981609438217 \tabularnewline
18 & 0.0119276234810040 & 0.0238552469620080 & 0.988072376518996 \tabularnewline
19 & 0.00912587006045215 & 0.0182517401209043 & 0.990874129939548 \tabularnewline
20 & 0.00567056036110966 & 0.0113411207222193 & 0.99432943963889 \tabularnewline
21 & 0.00527469995162996 & 0.0105493999032599 & 0.99472530004837 \tabularnewline
22 & 0.00391354682446992 & 0.00782709364893983 & 0.99608645317553 \tabularnewline
23 & 0.00344851720430606 & 0.00689703440861213 & 0.996551482795694 \tabularnewline
24 & 0.00980813465708063 & 0.0196162693141613 & 0.99019186534292 \tabularnewline
25 & 0.00741042720184144 & 0.0148208544036829 & 0.992589572798159 \tabularnewline
26 & 0.00540105595786691 & 0.0108021119157338 & 0.994598944042133 \tabularnewline
27 & 0.00459211257406406 & 0.00918422514812812 & 0.995407887425936 \tabularnewline
28 & 0.00330742496821655 & 0.0066148499364331 & 0.996692575031783 \tabularnewline
29 & 0.00234606395863053 & 0.00469212791726107 & 0.99765393604137 \tabularnewline
30 & 0.00332707427518605 & 0.0066541485503721 & 0.996672925724814 \tabularnewline
31 & 0.00236756555197643 & 0.00473513110395286 & 0.997632434448024 \tabularnewline
32 & 0.00340142220488332 & 0.00680284440976664 & 0.996598577795117 \tabularnewline
33 & 0.00242070300900563 & 0.00484140601801126 & 0.997579296990994 \tabularnewline
34 & 0.00215781666847740 & 0.00431563333695479 & 0.997842183331523 \tabularnewline
35 & 0.00182484522739411 & 0.00364969045478822 & 0.998175154772606 \tabularnewline
36 & 0.00140108396512042 & 0.00280216793024083 & 0.99859891603488 \tabularnewline
37 & 0.00107550775818040 & 0.00215101551636080 & 0.99892449224182 \tabularnewline
38 & 0.000999518981300081 & 0.00199903796260016 & 0.9990004810187 \tabularnewline
39 & 0.00254659017431202 & 0.00509318034862404 & 0.997453409825688 \tabularnewline
40 & 0.00196847147371695 & 0.00393694294743391 & 0.998031528526283 \tabularnewline
41 & 0.00358462408976365 & 0.00716924817952729 & 0.996415375910236 \tabularnewline
42 & 0.00230160090254899 & 0.00460320180509798 & 0.99769839909745 \tabularnewline
43 & 0.00143865369312538 & 0.00287730738625075 & 0.998561346306875 \tabularnewline
44 & 0.00169532384248639 & 0.00339064768497277 & 0.998304676157514 \tabularnewline
45 & 0.00128976389493788 & 0.00257952778987576 & 0.998710236105062 \tabularnewline
46 & 0.00110214250077499 & 0.00220428500154997 & 0.998897857499225 \tabularnewline
47 & 0.00206534833513455 & 0.00413069667026911 & 0.997934651664866 \tabularnewline
48 & 0.00255562473637469 & 0.00511124947274938 & 0.997444375263625 \tabularnewline
49 & 0.00209597986055727 & 0.00419195972111453 & 0.997904020139443 \tabularnewline
50 & 0.00178292218042142 & 0.00356584436084284 & 0.998217077819579 \tabularnewline
51 & 0.00167921400467411 & 0.00335842800934823 & 0.998320785995326 \tabularnewline
52 & 0.00147540862713327 & 0.00295081725426654 & 0.998524591372867 \tabularnewline
53 & 0.00127766948684567 & 0.00255533897369134 & 0.998722330513154 \tabularnewline
54 & 0.00111088901340006 & 0.00222177802680012 & 0.9988891109866 \tabularnewline
55 & 0.00164849193332294 & 0.00329698386664587 & 0.998351508066677 \tabularnewline
56 & 0.00130531442047431 & 0.00261062884094862 & 0.998694685579526 \tabularnewline
57 & 0.00153362570019993 & 0.00306725140039985 & 0.9984663742998 \tabularnewline
58 & 0.00212614593569359 & 0.00425229187138718 & 0.997873854064306 \tabularnewline
59 & 0.00296835699321475 & 0.0059367139864295 & 0.997031643006785 \tabularnewline
60 & 0.00190148631215023 & 0.00380297262430047 & 0.99809851368785 \tabularnewline
61 & 0.00217303578684728 & 0.00434607157369456 & 0.997826964213153 \tabularnewline
62 & 0.00263024289297798 & 0.00526048578595595 & 0.997369757107022 \tabularnewline
63 & 0.00293209512668572 & 0.00586419025337145 & 0.997067904873314 \tabularnewline
64 & 0.0187989488548523 & 0.0375978977097045 & 0.981201051145148 \tabularnewline
65 & 0.0305156748490222 & 0.0610313496980444 & 0.969484325150978 \tabularnewline
66 & 0.0603901885262018 & 0.120780377052404 & 0.939609811473798 \tabularnewline
67 & 0.077767762332227 & 0.155535524664454 & 0.922232237667773 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32098&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]5[/C][C]0.290886754298317[/C][C]0.581773508596634[/C][C]0.709113245701683[/C][/ROW]
[ROW][C]6[/C][C]0.188607897139438[/C][C]0.377215794278876[/C][C]0.811392102860562[/C][/ROW]
[ROW][C]7[/C][C]0.0977543346945003[/C][C]0.195508669389001[/C][C]0.9022456653055[/C][/ROW]
[ROW][C]8[/C][C]0.086155172814742[/C][C]0.172310345629484[/C][C]0.913844827185258[/C][/ROW]
[ROW][C]9[/C][C]0.0598001297792977[/C][C]0.119600259558595[/C][C]0.940199870220702[/C][/ROW]
[ROW][C]10[/C][C]0.0680440786003055[/C][C]0.136088157200611[/C][C]0.931955921399694[/C][/ROW]
[ROW][C]11[/C][C]0.0594455884320059[/C][C]0.118891176864012[/C][C]0.940554411567994[/C][/ROW]
[ROW][C]12[/C][C]0.071889624242719[/C][C]0.143779248485438[/C][C]0.928110375757281[/C][/ROW]
[ROW][C]13[/C][C]0.0417454839360989[/C][C]0.0834909678721979[/C][C]0.958254516063901[/C][/ROW]
[ROW][C]14[/C][C]0.0287392839156515[/C][C]0.057478567831303[/C][C]0.971260716084348[/C][/ROW]
[ROW][C]15[/C][C]0.0545732452872916[/C][C]0.109146490574583[/C][C]0.945426754712708[/C][/ROW]
[ROW][C]16[/C][C]0.033727142142175[/C][C]0.06745428428435[/C][C]0.966272857857825[/C][/ROW]
[ROW][C]17[/C][C]0.0201839056178297[/C][C]0.0403678112356593[/C][C]0.97981609438217[/C][/ROW]
[ROW][C]18[/C][C]0.0119276234810040[/C][C]0.0238552469620080[/C][C]0.988072376518996[/C][/ROW]
[ROW][C]19[/C][C]0.00912587006045215[/C][C]0.0182517401209043[/C][C]0.990874129939548[/C][/ROW]
[ROW][C]20[/C][C]0.00567056036110966[/C][C]0.0113411207222193[/C][C]0.99432943963889[/C][/ROW]
[ROW][C]21[/C][C]0.00527469995162996[/C][C]0.0105493999032599[/C][C]0.99472530004837[/C][/ROW]
[ROW][C]22[/C][C]0.00391354682446992[/C][C]0.00782709364893983[/C][C]0.99608645317553[/C][/ROW]
[ROW][C]23[/C][C]0.00344851720430606[/C][C]0.00689703440861213[/C][C]0.996551482795694[/C][/ROW]
[ROW][C]24[/C][C]0.00980813465708063[/C][C]0.0196162693141613[/C][C]0.99019186534292[/C][/ROW]
[ROW][C]25[/C][C]0.00741042720184144[/C][C]0.0148208544036829[/C][C]0.992589572798159[/C][/ROW]
[ROW][C]26[/C][C]0.00540105595786691[/C][C]0.0108021119157338[/C][C]0.994598944042133[/C][/ROW]
[ROW][C]27[/C][C]0.00459211257406406[/C][C]0.00918422514812812[/C][C]0.995407887425936[/C][/ROW]
[ROW][C]28[/C][C]0.00330742496821655[/C][C]0.0066148499364331[/C][C]0.996692575031783[/C][/ROW]
[ROW][C]29[/C][C]0.00234606395863053[/C][C]0.00469212791726107[/C][C]0.99765393604137[/C][/ROW]
[ROW][C]30[/C][C]0.00332707427518605[/C][C]0.0066541485503721[/C][C]0.996672925724814[/C][/ROW]
[ROW][C]31[/C][C]0.00236756555197643[/C][C]0.00473513110395286[/C][C]0.997632434448024[/C][/ROW]
[ROW][C]32[/C][C]0.00340142220488332[/C][C]0.00680284440976664[/C][C]0.996598577795117[/C][/ROW]
[ROW][C]33[/C][C]0.00242070300900563[/C][C]0.00484140601801126[/C][C]0.997579296990994[/C][/ROW]
[ROW][C]34[/C][C]0.00215781666847740[/C][C]0.00431563333695479[/C][C]0.997842183331523[/C][/ROW]
[ROW][C]35[/C][C]0.00182484522739411[/C][C]0.00364969045478822[/C][C]0.998175154772606[/C][/ROW]
[ROW][C]36[/C][C]0.00140108396512042[/C][C]0.00280216793024083[/C][C]0.99859891603488[/C][/ROW]
[ROW][C]37[/C][C]0.00107550775818040[/C][C]0.00215101551636080[/C][C]0.99892449224182[/C][/ROW]
[ROW][C]38[/C][C]0.000999518981300081[/C][C]0.00199903796260016[/C][C]0.9990004810187[/C][/ROW]
[ROW][C]39[/C][C]0.00254659017431202[/C][C]0.00509318034862404[/C][C]0.997453409825688[/C][/ROW]
[ROW][C]40[/C][C]0.00196847147371695[/C][C]0.00393694294743391[/C][C]0.998031528526283[/C][/ROW]
[ROW][C]41[/C][C]0.00358462408976365[/C][C]0.00716924817952729[/C][C]0.996415375910236[/C][/ROW]
[ROW][C]42[/C][C]0.00230160090254899[/C][C]0.00460320180509798[/C][C]0.99769839909745[/C][/ROW]
[ROW][C]43[/C][C]0.00143865369312538[/C][C]0.00287730738625075[/C][C]0.998561346306875[/C][/ROW]
[ROW][C]44[/C][C]0.00169532384248639[/C][C]0.00339064768497277[/C][C]0.998304676157514[/C][/ROW]
[ROW][C]45[/C][C]0.00128976389493788[/C][C]0.00257952778987576[/C][C]0.998710236105062[/C][/ROW]
[ROW][C]46[/C][C]0.00110214250077499[/C][C]0.00220428500154997[/C][C]0.998897857499225[/C][/ROW]
[ROW][C]47[/C][C]0.00206534833513455[/C][C]0.00413069667026911[/C][C]0.997934651664866[/C][/ROW]
[ROW][C]48[/C][C]0.00255562473637469[/C][C]0.00511124947274938[/C][C]0.997444375263625[/C][/ROW]
[ROW][C]49[/C][C]0.00209597986055727[/C][C]0.00419195972111453[/C][C]0.997904020139443[/C][/ROW]
[ROW][C]50[/C][C]0.00178292218042142[/C][C]0.00356584436084284[/C][C]0.998217077819579[/C][/ROW]
[ROW][C]51[/C][C]0.00167921400467411[/C][C]0.00335842800934823[/C][C]0.998320785995326[/C][/ROW]
[ROW][C]52[/C][C]0.00147540862713327[/C][C]0.00295081725426654[/C][C]0.998524591372867[/C][/ROW]
[ROW][C]53[/C][C]0.00127766948684567[/C][C]0.00255533897369134[/C][C]0.998722330513154[/C][/ROW]
[ROW][C]54[/C][C]0.00111088901340006[/C][C]0.00222177802680012[/C][C]0.9988891109866[/C][/ROW]
[ROW][C]55[/C][C]0.00164849193332294[/C][C]0.00329698386664587[/C][C]0.998351508066677[/C][/ROW]
[ROW][C]56[/C][C]0.00130531442047431[/C][C]0.00261062884094862[/C][C]0.998694685579526[/C][/ROW]
[ROW][C]57[/C][C]0.00153362570019993[/C][C]0.00306725140039985[/C][C]0.9984663742998[/C][/ROW]
[ROW][C]58[/C][C]0.00212614593569359[/C][C]0.00425229187138718[/C][C]0.997873854064306[/C][/ROW]
[ROW][C]59[/C][C]0.00296835699321475[/C][C]0.0059367139864295[/C][C]0.997031643006785[/C][/ROW]
[ROW][C]60[/C][C]0.00190148631215023[/C][C]0.00380297262430047[/C][C]0.99809851368785[/C][/ROW]
[ROW][C]61[/C][C]0.00217303578684728[/C][C]0.00434607157369456[/C][C]0.997826964213153[/C][/ROW]
[ROW][C]62[/C][C]0.00263024289297798[/C][C]0.00526048578595595[/C][C]0.997369757107022[/C][/ROW]
[ROW][C]63[/C][C]0.00293209512668572[/C][C]0.00586419025337145[/C][C]0.997067904873314[/C][/ROW]
[ROW][C]64[/C][C]0.0187989488548523[/C][C]0.0375978977097045[/C][C]0.981201051145148[/C][/ROW]
[ROW][C]65[/C][C]0.0305156748490222[/C][C]0.0610313496980444[/C][C]0.969484325150978[/C][/ROW]
[ROW][C]66[/C][C]0.0603901885262018[/C][C]0.120780377052404[/C][C]0.939609811473798[/C][/ROW]
[ROW][C]67[/C][C]0.077767762332227[/C][C]0.155535524664454[/C][C]0.922232237667773[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32098&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32098&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
5	0.290886754298317	0.581773508596634	0.709113245701683
6	0.188607897139438	0.377215794278876	0.811392102860562
7	0.0977543346945003	0.195508669389001	0.9022456653055
8	0.086155172814742	0.172310345629484	0.913844827185258
9	0.0598001297792977	0.119600259558595	0.940199870220702
10	0.0680440786003055	0.136088157200611	0.931955921399694
11	0.0594455884320059	0.118891176864012	0.940554411567994
12	0.071889624242719	0.143779248485438	0.928110375757281
13	0.0417454839360989	0.0834909678721979	0.958254516063901
14	0.0287392839156515	0.057478567831303	0.971260716084348
15	0.0545732452872916	0.109146490574583	0.945426754712708
16	0.033727142142175	0.06745428428435	0.966272857857825
17	0.0201839056178297	0.0403678112356593	0.97981609438217
18	0.0119276234810040	0.0238552469620080	0.988072376518996
19	0.00912587006045215	0.0182517401209043	0.990874129939548
20	0.00567056036110966	0.0113411207222193	0.99432943963889
21	0.00527469995162996	0.0105493999032599	0.99472530004837
22	0.00391354682446992	0.00782709364893983	0.99608645317553
23	0.00344851720430606	0.00689703440861213	0.996551482795694
24	0.00980813465708063	0.0196162693141613	0.99019186534292
25	0.00741042720184144	0.0148208544036829	0.992589572798159
26	0.00540105595786691	0.0108021119157338	0.994598944042133
27	0.00459211257406406	0.00918422514812812	0.995407887425936
28	0.00330742496821655	0.0066148499364331	0.996692575031783
29	0.00234606395863053	0.00469212791726107	0.99765393604137
30	0.00332707427518605	0.0066541485503721	0.996672925724814
31	0.00236756555197643	0.00473513110395286	0.997632434448024
32	0.00340142220488332	0.00680284440976664	0.996598577795117
33	0.00242070300900563	0.00484140601801126	0.997579296990994
34	0.00215781666847740	0.00431563333695479	0.997842183331523
35	0.00182484522739411	0.00364969045478822	0.998175154772606
36	0.00140108396512042	0.00280216793024083	0.99859891603488
37	0.00107550775818040	0.00215101551636080	0.99892449224182
38	0.000999518981300081	0.00199903796260016	0.9990004810187
39	0.00254659017431202	0.00509318034862404	0.997453409825688
40	0.00196847147371695	0.00393694294743391	0.998031528526283
41	0.00358462408976365	0.00716924817952729	0.996415375910236
42	0.00230160090254899	0.00460320180509798	0.99769839909745
43	0.00143865369312538	0.00287730738625075	0.998561346306875
44	0.00169532384248639	0.00339064768497277	0.998304676157514
45	0.00128976389493788	0.00257952778987576	0.998710236105062
46	0.00110214250077499	0.00220428500154997	0.998897857499225
47	0.00206534833513455	0.00413069667026911	0.997934651664866
48	0.00255562473637469	0.00511124947274938	0.997444375263625
49	0.00209597986055727	0.00419195972111453	0.997904020139443
50	0.00178292218042142	0.00356584436084284	0.998217077819579
51	0.00167921400467411	0.00335842800934823	0.998320785995326
52	0.00147540862713327	0.00295081725426654	0.998524591372867
53	0.00127766948684567	0.00255533897369134	0.998722330513154
54	0.00111088901340006	0.00222177802680012	0.9988891109866
55	0.00164849193332294	0.00329698386664587	0.998351508066677
56	0.00130531442047431	0.00261062884094862	0.998694685579526
57	0.00153362570019993	0.00306725140039985	0.9984663742998
58	0.00212614593569359	0.00425229187138718	0.997873854064306
59	0.00296835699321475	0.0059367139864295	0.997031643006785
60	0.00190148631215023	0.00380297262430047	0.99809851368785
61	0.00217303578684728	0.00434607157369456	0.997826964213153
62	0.00263024289297798	0.00526048578595595	0.997369757107022
63	0.00293209512668572	0.00586419025337145	0.997067904873314
64	0.0187989488548523	0.0375978977097045	0.981201051145148
65	0.0305156748490222	0.0610313496980444	0.969484325150978
66	0.0603901885262018	0.120780377052404	0.939609811473798
67	0.077767762332227	0.155535524664454	0.922232237667773

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	39	0.619047619047619	NOK
5% type I error level	48	0.761904761904762	NOK
10% type I error level	52	0.825396825396825	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 39 & 0.619047619047619 & NOK \tabularnewline
5% type I error level & 48 & 0.761904761904762 & NOK \tabularnewline
10% type I error level & 52 & 0.825396825396825 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32098&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]39[/C][C]0.619047619047619[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]48[/C][C]0.761904761904762[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]52[/C][C]0.825396825396825[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32098&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32098&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	39	0.619047619047619	NOK
5% type I error level	48	0.761904761904762	NOK
10% type I error level	52	0.825396825396825	NOK

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 = 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