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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 27 Nov 2009 02:57:05 -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/27/t1259316197o3y34jd8w0qw77j.htm/, Retrieved Sat, 27 Apr 2024 09:40:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60521, Retrieved Sat, 27 Apr 2024 09:40:21 +0000

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Estimated Impact

135

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Workshop 7] [2009-11-19 16:33:52] [85be98bd9ebcfd4d73e77f8552419c9a]
-   P         [Multiple Regression] [] [2009-11-27 09:57:05] [2be55e40556dba706f1211fd5a4ae809] [Current]

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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	'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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60521&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60521&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60521&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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 3.14254545454545 -1.92272727272727X[t] -0.0985454545454535M1[t] + 0.178000000000001M2[t] + 0.0820000000000008M3[t] + 0.00200000000000018M4[t] -0.103999999999999M5[t] -0.0739999999999996M6[t] -0.0859999999999994M7[t] -0.111999999999999M8[t] -0.0719999999999994M9[t] -0.0599999999999995M10[t] -0.0119999999999993M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  3.14254545454545 -1.92272727272727X[t] -0.0985454545454535M1[t] +  0.178000000000001M2[t] +  0.0820000000000008M3[t] +  0.00200000000000018M4[t] -0.103999999999999M5[t] -0.0739999999999996M6[t] -0.0859999999999994M7[t] -0.111999999999999M8[t] -0.0719999999999994M9[t] -0.0599999999999995M10[t] -0.0119999999999993M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60521&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  3.14254545454545 -1.92272727272727X[t] -0.0985454545454535M1[t] +  0.178000000000001M2[t] +  0.0820000000000008M3[t] +  0.00200000000000018M4[t] -0.103999999999999M5[t] -0.0739999999999996M6[t] -0.0859999999999994M7[t] -0.111999999999999M8[t] -0.0719999999999994M9[t] -0.0599999999999995M10[t] -0.0119999999999993M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60521&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60521&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] = + 3.14254545454545 -1.92272727272727X[t] -0.0985454545454535M1[t] + 0.178000000000001M2[t] + 0.0820000000000008M3[t] + 0.00200000000000018M4[t] -0.103999999999999M5[t] -0.0739999999999996M6[t] -0.0859999999999994M7[t] -0.111999999999999M8[t] -0.0719999999999994M9[t] -0.0599999999999995M10[t] -0.0119999999999993M11[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)	3.14254545454545	0.432843	7.2602	0	0
X	-1.92272727272727	0.322622	-5.9597	0	0
M1	-0.0985454545454535	0.608722	-0.1619	0.872087	0.436044
M2	0.178000000000001	0.605293	0.2941	0.769997	0.384999
M3	0.0820000000000008	0.605293	0.1355	0.892817	0.446409
M4	0.00200000000000018	0.605293	0.0033	0.997378	0.498689
M5	-0.103999999999999	0.605293	-0.1718	0.864318	0.432159
M6	-0.0739999999999996	0.605293	-0.1223	0.903218	0.451609
M7	-0.0859999999999994	0.605293	-0.1421	0.887624	0.443812
M8	-0.111999999999999	0.605293	-0.185	0.853998	0.426999
M9	-0.0719999999999994	0.605293	-0.119	0.905821	0.452911
M10	-0.0599999999999995	0.605293	-0.0991	0.92146	0.46073
M11	-0.0119999999999993	0.605293	-0.0198	0.984267	0.492133

\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) & 3.14254545454545 & 0.432843 & 7.2602 & 0 & 0 \tabularnewline
X & -1.92272727272727 & 0.322622 & -5.9597 & 0 & 0 \tabularnewline
M1 & -0.0985454545454535 & 0.608722 & -0.1619 & 0.872087 & 0.436044 \tabularnewline
M2 & 0.178000000000001 & 0.605293 & 0.2941 & 0.769997 & 0.384999 \tabularnewline
M3 & 0.0820000000000008 & 0.605293 & 0.1355 & 0.892817 & 0.446409 \tabularnewline
M4 & 0.00200000000000018 & 0.605293 & 0.0033 & 0.997378 & 0.498689 \tabularnewline
M5 & -0.103999999999999 & 0.605293 & -0.1718 & 0.864318 & 0.432159 \tabularnewline
M6 & -0.0739999999999996 & 0.605293 & -0.1223 & 0.903218 & 0.451609 \tabularnewline
M7 & -0.0859999999999994 & 0.605293 & -0.1421 & 0.887624 & 0.443812 \tabularnewline
M8 & -0.111999999999999 & 0.605293 & -0.185 & 0.853998 & 0.426999 \tabularnewline
M9 & -0.0719999999999994 & 0.605293 & -0.119 & 0.905821 & 0.452911 \tabularnewline
M10 & -0.0599999999999995 & 0.605293 & -0.0991 & 0.92146 & 0.46073 \tabularnewline
M11 & -0.0119999999999993 & 0.605293 & -0.0198 & 0.984267 & 0.492133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60521&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]3.14254545454545[/C][C]0.432843[/C][C]7.2602[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1.92272727272727[/C][C]0.322622[/C][C]-5.9597[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.0985454545454535[/C][C]0.608722[/C][C]-0.1619[/C][C]0.872087[/C][C]0.436044[/C][/ROW]
[ROW][C]M2[/C][C]0.178000000000001[/C][C]0.605293[/C][C]0.2941[/C][C]0.769997[/C][C]0.384999[/C][/ROW]
[ROW][C]M3[/C][C]0.0820000000000008[/C][C]0.605293[/C][C]0.1355[/C][C]0.892817[/C][C]0.446409[/C][/ROW]
[ROW][C]M4[/C][C]0.00200000000000018[/C][C]0.605293[/C][C]0.0033[/C][C]0.997378[/C][C]0.498689[/C][/ROW]
[ROW][C]M5[/C][C]-0.103999999999999[/C][C]0.605293[/C][C]-0.1718[/C][C]0.864318[/C][C]0.432159[/C][/ROW]
[ROW][C]M6[/C][C]-0.0739999999999996[/C][C]0.605293[/C][C]-0.1223[/C][C]0.903218[/C][C]0.451609[/C][/ROW]
[ROW][C]M7[/C][C]-0.0859999999999994[/C][C]0.605293[/C][C]-0.1421[/C][C]0.887624[/C][C]0.443812[/C][/ROW]
[ROW][C]M8[/C][C]-0.111999999999999[/C][C]0.605293[/C][C]-0.185[/C][C]0.853998[/C][C]0.426999[/C][/ROW]
[ROW][C]M9[/C][C]-0.0719999999999994[/C][C]0.605293[/C][C]-0.119[/C][C]0.905821[/C][C]0.452911[/C][/ROW]
[ROW][C]M10[/C][C]-0.0599999999999995[/C][C]0.605293[/C][C]-0.0991[/C][C]0.92146[/C][C]0.46073[/C][/ROW]
[ROW][C]M11[/C][C]-0.0119999999999993[/C][C]0.605293[/C][C]-0.0198[/C][C]0.984267[/C][C]0.492133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60521&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60521&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)	3.14254545454545	0.432843	7.2602	0	0
X	-1.92272727272727	0.322622	-5.9597	0	0
M1	-0.0985454545454535	0.608722	-0.1619	0.872087	0.436044
M2	0.178000000000001	0.605293	0.2941	0.769997	0.384999
M3	0.0820000000000008	0.605293	0.1355	0.892817	0.446409
M4	0.00200000000000018	0.605293	0.0033	0.997378	0.498689
M5	-0.103999999999999	0.605293	-0.1718	0.864318	0.432159
M6	-0.0739999999999996	0.605293	-0.1223	0.903218	0.451609
M7	-0.0859999999999994	0.605293	-0.1421	0.887624	0.443812
M8	-0.111999999999999	0.605293	-0.185	0.853998	0.426999
M9	-0.0719999999999994	0.605293	-0.119	0.905821	0.452911
M10	-0.0599999999999995	0.605293	-0.0991	0.92146	0.46073
M11	-0.0119999999999993	0.605293	-0.0198	0.984267	0.492133

Multiple Linear Regression - Regression Statistics
Multiple R	0.660747900887518
R-squared	0.436587788527261
Adjusted R-squared	0.292737862193795
F-TEST (value)	3.03502267714191
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.00316967686042147
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.957051484257931
Sum Squared Residuals	43.0495345454545

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.660747900887518 \tabularnewline
R-squared & 0.436587788527261 \tabularnewline
Adjusted R-squared & 0.292737862193795 \tabularnewline
F-TEST (value) & 3.03502267714191 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.00316967686042147 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.957051484257931 \tabularnewline
Sum Squared Residuals & 43.0495345454545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60521&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.660747900887518[/C][/ROW]
[ROW][C]R-squared[/C][C]0.436587788527261[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.292737862193795[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.03502267714191[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.00316967686042147[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.957051484257931[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]43.0495345454545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60521&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60521&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	0.660747900887518
R-squared	0.436587788527261
Adjusted R-squared	0.292737862193795
F-TEST (value)	3.03502267714191
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.00316967686042147
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.957051484257931
Sum Squared Residuals	43.0495345454545

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2.11	3.04400000000000	-0.933999999999997
2	2.09	3.32054545454545	-1.23054545454545
3	2.05	3.22454545454545	-1.17454545454545
4	2.08	3.14454545454545	-1.06454545454545
5	2.06	3.03854545454545	-0.978545454545454
6	2.06	3.06854545454545	-1.00854545454545
7	2.08	3.05654545454545	-0.976545454545455
8	2.07	3.03054545454545	-0.960545454545454
9	2.06	3.07054545454545	-1.01054545454545
10	2.07	3.08254545454545	-1.01254545454545
11	2.06	3.13054545454545	-1.07054545454545
12	2.09	3.14254545454545	-1.05254545454545
13	2.07	3.044	-0.974
14	2.09	3.32054545454545	-1.23054545454545
15	2.28	3.22454545454545	-0.944545454545455
16	2.33	3.14454545454545	-0.814545454545454
17	2.35	3.03854545454545	-0.688545454545454
18	2.52	3.06854545454545	-0.548545454545455
19	2.63	3.05654545454545	-0.426545454545455
20	2.58	3.03054545454545	-0.450545454545454
21	2.7	3.07054545454545	-0.370545454545454
22	2.81	3.08254545454545	-0.272545454545454
23	2.97	3.13054545454545	-0.160545454545454
24	3.04	3.14254545454545	-0.102545454545454
25	3.28	3.044	0.235999999999999
26	3.33	3.32054545454546	0.00945454545454505
27	3.5	3.22454545454545	0.275454545454545
28	3.56	3.14454545454545	0.415454545454546
29	3.57	3.03854545454545	0.531454545454545
30	3.69	3.06854545454545	0.621454545454545
31	3.82	3.05654545454545	0.763454545454545
32	3.79	3.03054545454545	0.759454545454545
33	3.96	3.07054545454545	0.889454545454545
34	4.06	3.08254545454545	0.977454545454545
35	4.05	3.13054545454545	0.919454545454545
36	4.03	3.14254545454545	0.887454545454546
37	3.94	3.044	0.896
38	4.02	3.32054545454546	0.699454545454544
39	3.88	3.22454545454545	0.655454545454545
40	4.02	3.14454545454545	0.875454545454545
41	4.03	3.03854545454545	0.991454545454546
42	4.09	3.06854545454545	1.02145454545455
43	3.99	3.05654545454545	0.933454545454546
44	4.01	3.03054545454545	0.979454545454545
45	4.01	3.07054545454545	0.939454545454545
46	4.19	3.08254545454545	1.10745454545455
47	4.3	3.13054545454545	1.16945454545455
48	4.27	3.14254545454545	1.12745454545455
49	3.82	3.044	0.776
50	3.15	1.39781818181818	1.75218181818182
51	2.49	1.30181818181818	1.18818181818182
52	1.81	1.22181818181818	0.588181818181819
53	1.26	1.11581818181818	0.144181818181818
54	1.06	1.14581818181818	-0.0858181818181818
55	0.84	1.13381818181818	-0.293818181818182
56	0.78	1.10781818181818	-0.327818181818182
57	0.7	1.14781818181818	-0.447818181818182
58	0.36	1.15981818181818	-0.799818181818182
59	0.35	1.20781818181818	-0.857818181818182
60	0.36	1.21981818181818	-0.859818181818181

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2.11 & 3.04400000000000 & -0.933999999999997 \tabularnewline
2 & 2.09 & 3.32054545454545 & -1.23054545454545 \tabularnewline
3 & 2.05 & 3.22454545454545 & -1.17454545454545 \tabularnewline
4 & 2.08 & 3.14454545454545 & -1.06454545454545 \tabularnewline
5 & 2.06 & 3.03854545454545 & -0.978545454545454 \tabularnewline
6 & 2.06 & 3.06854545454545 & -1.00854545454545 \tabularnewline
7 & 2.08 & 3.05654545454545 & -0.976545454545455 \tabularnewline
8 & 2.07 & 3.03054545454545 & -0.960545454545454 \tabularnewline
9 & 2.06 & 3.07054545454545 & -1.01054545454545 \tabularnewline
10 & 2.07 & 3.08254545454545 & -1.01254545454545 \tabularnewline
11 & 2.06 & 3.13054545454545 & -1.07054545454545 \tabularnewline
12 & 2.09 & 3.14254545454545 & -1.05254545454545 \tabularnewline
13 & 2.07 & 3.044 & -0.974 \tabularnewline
14 & 2.09 & 3.32054545454545 & -1.23054545454545 \tabularnewline
15 & 2.28 & 3.22454545454545 & -0.944545454545455 \tabularnewline
16 & 2.33 & 3.14454545454545 & -0.814545454545454 \tabularnewline
17 & 2.35 & 3.03854545454545 & -0.688545454545454 \tabularnewline
18 & 2.52 & 3.06854545454545 & -0.548545454545455 \tabularnewline
19 & 2.63 & 3.05654545454545 & -0.426545454545455 \tabularnewline
20 & 2.58 & 3.03054545454545 & -0.450545454545454 \tabularnewline
21 & 2.7 & 3.07054545454545 & -0.370545454545454 \tabularnewline
22 & 2.81 & 3.08254545454545 & -0.272545454545454 \tabularnewline
23 & 2.97 & 3.13054545454545 & -0.160545454545454 \tabularnewline
24 & 3.04 & 3.14254545454545 & -0.102545454545454 \tabularnewline
25 & 3.28 & 3.044 & 0.235999999999999 \tabularnewline
26 & 3.33 & 3.32054545454546 & 0.00945454545454505 \tabularnewline
27 & 3.5 & 3.22454545454545 & 0.275454545454545 \tabularnewline
28 & 3.56 & 3.14454545454545 & 0.415454545454546 \tabularnewline
29 & 3.57 & 3.03854545454545 & 0.531454545454545 \tabularnewline
30 & 3.69 & 3.06854545454545 & 0.621454545454545 \tabularnewline
31 & 3.82 & 3.05654545454545 & 0.763454545454545 \tabularnewline
32 & 3.79 & 3.03054545454545 & 0.759454545454545 \tabularnewline
33 & 3.96 & 3.07054545454545 & 0.889454545454545 \tabularnewline
34 & 4.06 & 3.08254545454545 & 0.977454545454545 \tabularnewline
35 & 4.05 & 3.13054545454545 & 0.919454545454545 \tabularnewline
36 & 4.03 & 3.14254545454545 & 0.887454545454546 \tabularnewline
37 & 3.94 & 3.044 & 0.896 \tabularnewline
38 & 4.02 & 3.32054545454546 & 0.699454545454544 \tabularnewline
39 & 3.88 & 3.22454545454545 & 0.655454545454545 \tabularnewline
40 & 4.02 & 3.14454545454545 & 0.875454545454545 \tabularnewline
41 & 4.03 & 3.03854545454545 & 0.991454545454546 \tabularnewline
42 & 4.09 & 3.06854545454545 & 1.02145454545455 \tabularnewline
43 & 3.99 & 3.05654545454545 & 0.933454545454546 \tabularnewline
44 & 4.01 & 3.03054545454545 & 0.979454545454545 \tabularnewline
45 & 4.01 & 3.07054545454545 & 0.939454545454545 \tabularnewline
46 & 4.19 & 3.08254545454545 & 1.10745454545455 \tabularnewline
47 & 4.3 & 3.13054545454545 & 1.16945454545455 \tabularnewline
48 & 4.27 & 3.14254545454545 & 1.12745454545455 \tabularnewline
49 & 3.82 & 3.044 & 0.776 \tabularnewline
50 & 3.15 & 1.39781818181818 & 1.75218181818182 \tabularnewline
51 & 2.49 & 1.30181818181818 & 1.18818181818182 \tabularnewline
52 & 1.81 & 1.22181818181818 & 0.588181818181819 \tabularnewline
53 & 1.26 & 1.11581818181818 & 0.144181818181818 \tabularnewline
54 & 1.06 & 1.14581818181818 & -0.0858181818181818 \tabularnewline
55 & 0.84 & 1.13381818181818 & -0.293818181818182 \tabularnewline
56 & 0.78 & 1.10781818181818 & -0.327818181818182 \tabularnewline
57 & 0.7 & 1.14781818181818 & -0.447818181818182 \tabularnewline
58 & 0.36 & 1.15981818181818 & -0.799818181818182 \tabularnewline
59 & 0.35 & 1.20781818181818 & -0.857818181818182 \tabularnewline
60 & 0.36 & 1.21981818181818 & -0.859818181818181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60521&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]2.11[/C][C]3.04400000000000[/C][C]-0.933999999999997[/C][/ROW]
[ROW][C]2[/C][C]2.09[/C][C]3.32054545454545[/C][C]-1.23054545454545[/C][/ROW]
[ROW][C]3[/C][C]2.05[/C][C]3.22454545454545[/C][C]-1.17454545454545[/C][/ROW]
[ROW][C]4[/C][C]2.08[/C][C]3.14454545454545[/C][C]-1.06454545454545[/C][/ROW]
[ROW][C]5[/C][C]2.06[/C][C]3.03854545454545[/C][C]-0.978545454545454[/C][/ROW]
[ROW][C]6[/C][C]2.06[/C][C]3.06854545454545[/C][C]-1.00854545454545[/C][/ROW]
[ROW][C]7[/C][C]2.08[/C][C]3.05654545454545[/C][C]-0.976545454545455[/C][/ROW]
[ROW][C]8[/C][C]2.07[/C][C]3.03054545454545[/C][C]-0.960545454545454[/C][/ROW]
[ROW][C]9[/C][C]2.06[/C][C]3.07054545454545[/C][C]-1.01054545454545[/C][/ROW]
[ROW][C]10[/C][C]2.07[/C][C]3.08254545454545[/C][C]-1.01254545454545[/C][/ROW]
[ROW][C]11[/C][C]2.06[/C][C]3.13054545454545[/C][C]-1.07054545454545[/C][/ROW]
[ROW][C]12[/C][C]2.09[/C][C]3.14254545454545[/C][C]-1.05254545454545[/C][/ROW]
[ROW][C]13[/C][C]2.07[/C][C]3.044[/C][C]-0.974[/C][/ROW]
[ROW][C]14[/C][C]2.09[/C][C]3.32054545454545[/C][C]-1.23054545454545[/C][/ROW]
[ROW][C]15[/C][C]2.28[/C][C]3.22454545454545[/C][C]-0.944545454545455[/C][/ROW]
[ROW][C]16[/C][C]2.33[/C][C]3.14454545454545[/C][C]-0.814545454545454[/C][/ROW]
[ROW][C]17[/C][C]2.35[/C][C]3.03854545454545[/C][C]-0.688545454545454[/C][/ROW]
[ROW][C]18[/C][C]2.52[/C][C]3.06854545454545[/C][C]-0.548545454545455[/C][/ROW]
[ROW][C]19[/C][C]2.63[/C][C]3.05654545454545[/C][C]-0.426545454545455[/C][/ROW]
[ROW][C]20[/C][C]2.58[/C][C]3.03054545454545[/C][C]-0.450545454545454[/C][/ROW]
[ROW][C]21[/C][C]2.7[/C][C]3.07054545454545[/C][C]-0.370545454545454[/C][/ROW]
[ROW][C]22[/C][C]2.81[/C][C]3.08254545454545[/C][C]-0.272545454545454[/C][/ROW]
[ROW][C]23[/C][C]2.97[/C][C]3.13054545454545[/C][C]-0.160545454545454[/C][/ROW]
[ROW][C]24[/C][C]3.04[/C][C]3.14254545454545[/C][C]-0.102545454545454[/C][/ROW]
[ROW][C]25[/C][C]3.28[/C][C]3.044[/C][C]0.235999999999999[/C][/ROW]
[ROW][C]26[/C][C]3.33[/C][C]3.32054545454546[/C][C]0.00945454545454505[/C][/ROW]
[ROW][C]27[/C][C]3.5[/C][C]3.22454545454545[/C][C]0.275454545454545[/C][/ROW]
[ROW][C]28[/C][C]3.56[/C][C]3.14454545454545[/C][C]0.415454545454546[/C][/ROW]
[ROW][C]29[/C][C]3.57[/C][C]3.03854545454545[/C][C]0.531454545454545[/C][/ROW]
[ROW][C]30[/C][C]3.69[/C][C]3.06854545454545[/C][C]0.621454545454545[/C][/ROW]
[ROW][C]31[/C][C]3.82[/C][C]3.05654545454545[/C][C]0.763454545454545[/C][/ROW]
[ROW][C]32[/C][C]3.79[/C][C]3.03054545454545[/C][C]0.759454545454545[/C][/ROW]
[ROW][C]33[/C][C]3.96[/C][C]3.07054545454545[/C][C]0.889454545454545[/C][/ROW]
[ROW][C]34[/C][C]4.06[/C][C]3.08254545454545[/C][C]0.977454545454545[/C][/ROW]
[ROW][C]35[/C][C]4.05[/C][C]3.13054545454545[/C][C]0.919454545454545[/C][/ROW]
[ROW][C]36[/C][C]4.03[/C][C]3.14254545454545[/C][C]0.887454545454546[/C][/ROW]
[ROW][C]37[/C][C]3.94[/C][C]3.044[/C][C]0.896[/C][/ROW]
[ROW][C]38[/C][C]4.02[/C][C]3.32054545454546[/C][C]0.699454545454544[/C][/ROW]
[ROW][C]39[/C][C]3.88[/C][C]3.22454545454545[/C][C]0.655454545454545[/C][/ROW]
[ROW][C]40[/C][C]4.02[/C][C]3.14454545454545[/C][C]0.875454545454545[/C][/ROW]
[ROW][C]41[/C][C]4.03[/C][C]3.03854545454545[/C][C]0.991454545454546[/C][/ROW]
[ROW][C]42[/C][C]4.09[/C][C]3.06854545454545[/C][C]1.02145454545455[/C][/ROW]
[ROW][C]43[/C][C]3.99[/C][C]3.05654545454545[/C][C]0.933454545454546[/C][/ROW]
[ROW][C]44[/C][C]4.01[/C][C]3.03054545454545[/C][C]0.979454545454545[/C][/ROW]
[ROW][C]45[/C][C]4.01[/C][C]3.07054545454545[/C][C]0.939454545454545[/C][/ROW]
[ROW][C]46[/C][C]4.19[/C][C]3.08254545454545[/C][C]1.10745454545455[/C][/ROW]
[ROW][C]47[/C][C]4.3[/C][C]3.13054545454545[/C][C]1.16945454545455[/C][/ROW]
[ROW][C]48[/C][C]4.27[/C][C]3.14254545454545[/C][C]1.12745454545455[/C][/ROW]
[ROW][C]49[/C][C]3.82[/C][C]3.044[/C][C]0.776[/C][/ROW]
[ROW][C]50[/C][C]3.15[/C][C]1.39781818181818[/C][C]1.75218181818182[/C][/ROW]
[ROW][C]51[/C][C]2.49[/C][C]1.30181818181818[/C][C]1.18818181818182[/C][/ROW]
[ROW][C]52[/C][C]1.81[/C][C]1.22181818181818[/C][C]0.588181818181819[/C][/ROW]
[ROW][C]53[/C][C]1.26[/C][C]1.11581818181818[/C][C]0.144181818181818[/C][/ROW]
[ROW][C]54[/C][C]1.06[/C][C]1.14581818181818[/C][C]-0.0858181818181818[/C][/ROW]
[ROW][C]55[/C][C]0.84[/C][C]1.13381818181818[/C][C]-0.293818181818182[/C][/ROW]
[ROW][C]56[/C][C]0.78[/C][C]1.10781818181818[/C][C]-0.327818181818182[/C][/ROW]
[ROW][C]57[/C][C]0.7[/C][C]1.14781818181818[/C][C]-0.447818181818182[/C][/ROW]
[ROW][C]58[/C][C]0.36[/C][C]1.15981818181818[/C][C]-0.799818181818182[/C][/ROW]
[ROW][C]59[/C][C]0.35[/C][C]1.20781818181818[/C][C]-0.857818181818182[/C][/ROW]
[ROW][C]60[/C][C]0.36[/C][C]1.21981818181818[/C][C]-0.859818181818181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60521&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60521&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	2.11	3.04400000000000	-0.933999999999997
2	2.09	3.32054545454545	-1.23054545454545
3	2.05	3.22454545454545	-1.17454545454545
4	2.08	3.14454545454545	-1.06454545454545
5	2.06	3.03854545454545	-0.978545454545454
6	2.06	3.06854545454545	-1.00854545454545
7	2.08	3.05654545454545	-0.976545454545455
8	2.07	3.03054545454545	-0.960545454545454
9	2.06	3.07054545454545	-1.01054545454545
10	2.07	3.08254545454545	-1.01254545454545
11	2.06	3.13054545454545	-1.07054545454545
12	2.09	3.14254545454545	-1.05254545454545
13	2.07	3.044	-0.974
14	2.09	3.32054545454545	-1.23054545454545
15	2.28	3.22454545454545	-0.944545454545455
16	2.33	3.14454545454545	-0.814545454545454
17	2.35	3.03854545454545	-0.688545454545454
18	2.52	3.06854545454545	-0.548545454545455
19	2.63	3.05654545454545	-0.426545454545455
20	2.58	3.03054545454545	-0.450545454545454
21	2.7	3.07054545454545	-0.370545454545454
22	2.81	3.08254545454545	-0.272545454545454
23	2.97	3.13054545454545	-0.160545454545454
24	3.04	3.14254545454545	-0.102545454545454
25	3.28	3.044	0.235999999999999
26	3.33	3.32054545454546	0.00945454545454505
27	3.5	3.22454545454545	0.275454545454545
28	3.56	3.14454545454545	0.415454545454546
29	3.57	3.03854545454545	0.531454545454545
30	3.69	3.06854545454545	0.621454545454545
31	3.82	3.05654545454545	0.763454545454545
32	3.79	3.03054545454545	0.759454545454545
33	3.96	3.07054545454545	0.889454545454545
34	4.06	3.08254545454545	0.977454545454545
35	4.05	3.13054545454545	0.919454545454545
36	4.03	3.14254545454545	0.887454545454546
37	3.94	3.044	0.896
38	4.02	3.32054545454546	0.699454545454544
39	3.88	3.22454545454545	0.655454545454545
40	4.02	3.14454545454545	0.875454545454545
41	4.03	3.03854545454545	0.991454545454546
42	4.09	3.06854545454545	1.02145454545455
43	3.99	3.05654545454545	0.933454545454546
44	4.01	3.03054545454545	0.979454545454545
45	4.01	3.07054545454545	0.939454545454545
46	4.19	3.08254545454545	1.10745454545455
47	4.3	3.13054545454545	1.16945454545455
48	4.27	3.14254545454545	1.12745454545455
49	3.82	3.044	0.776
50	3.15	1.39781818181818	1.75218181818182
51	2.49	1.30181818181818	1.18818181818182
52	1.81	1.22181818181818	0.588181818181819
53	1.26	1.11581818181818	0.144181818181818
54	1.06	1.14581818181818	-0.0858181818181818
55	0.84	1.13381818181818	-0.293818181818182
56	0.78	1.10781818181818	-0.327818181818182
57	0.7	1.14781818181818	-0.447818181818182
58	0.36	1.15981818181818	-0.799818181818182
59	0.35	1.20781818181818	-0.857818181818182
60	0.36	1.21981818181818	-0.859818181818181

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0101414379855108	0.0202828759710217	0.98985856201449
17	0.00545416239386613	0.0109083247877323	0.994545837606134
18	0.00698023616235786	0.0139604723247157	0.993019763837642
19	0.00972780541962902	0.0194556108392580	0.99027219458037
20	0.0102272433629200	0.0204544867258401	0.98977275663708
21	0.015033188301581	0.030066376603162	0.98496681169842
22	0.0244432946978093	0.0488865893956186	0.97555670530219
23	0.0472280181431450	0.0944560362862901	0.952771981856855
24	0.0762011134247896	0.152402226849579	0.92379888657521
25	0.175830786767908	0.351661573535815	0.824169213232092
26	0.437178900903216	0.874357801806432	0.562821099096784
27	0.647540469937673	0.704919060124655	0.352459530062327
28	0.763559657477072	0.472880685045857	0.236440342522928
29	0.81749728800422	0.365005423991561	0.182502711995780
30	0.84406717602024	0.31186564795952	0.15593282397976
31	0.855922624473336	0.288154751053327	0.144077375526664
32	0.857125352777552	0.285749294444897	0.142874647222448
33	0.858322048706325	0.283355902587350	0.141677951293675
34	0.85755183575583	0.284896328488339	0.142448164244169
35	0.84197650685106	0.316046986297881	0.158023493148940
36	0.81451407490792	0.370971850184161	0.185485925092080
37	0.777403429217733	0.445193141564534	0.222596570782267
38	0.935094832249302	0.129810335501397	0.0649051677506984
39	0.990132954922947	0.0197340901541051	0.00986704507705255
40	0.997055717447443	0.00588856510511477	0.00294428255255739
41	0.997251622734194	0.00549675453161283	0.00274837726580642
42	0.995612171744347	0.00877565651130582	0.00438782825565291
43	0.99175244547098	0.0164951090580394	0.00824755452901972
44	0.98592053094421	0.0281589381115786	0.0140794690557893

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0101414379855108 & 0.0202828759710217 & 0.98985856201449 \tabularnewline
17 & 0.00545416239386613 & 0.0109083247877323 & 0.994545837606134 \tabularnewline
18 & 0.00698023616235786 & 0.0139604723247157 & 0.993019763837642 \tabularnewline
19 & 0.00972780541962902 & 0.0194556108392580 & 0.99027219458037 \tabularnewline
20 & 0.0102272433629200 & 0.0204544867258401 & 0.98977275663708 \tabularnewline
21 & 0.015033188301581 & 0.030066376603162 & 0.98496681169842 \tabularnewline
22 & 0.0244432946978093 & 0.0488865893956186 & 0.97555670530219 \tabularnewline
23 & 0.0472280181431450 & 0.0944560362862901 & 0.952771981856855 \tabularnewline
24 & 0.0762011134247896 & 0.152402226849579 & 0.92379888657521 \tabularnewline
25 & 0.175830786767908 & 0.351661573535815 & 0.824169213232092 \tabularnewline
26 & 0.437178900903216 & 0.874357801806432 & 0.562821099096784 \tabularnewline
27 & 0.647540469937673 & 0.704919060124655 & 0.352459530062327 \tabularnewline
28 & 0.763559657477072 & 0.472880685045857 & 0.236440342522928 \tabularnewline
29 & 0.81749728800422 & 0.365005423991561 & 0.182502711995780 \tabularnewline
30 & 0.84406717602024 & 0.31186564795952 & 0.15593282397976 \tabularnewline
31 & 0.855922624473336 & 0.288154751053327 & 0.144077375526664 \tabularnewline
32 & 0.857125352777552 & 0.285749294444897 & 0.142874647222448 \tabularnewline
33 & 0.858322048706325 & 0.283355902587350 & 0.141677951293675 \tabularnewline
34 & 0.85755183575583 & 0.284896328488339 & 0.142448164244169 \tabularnewline
35 & 0.84197650685106 & 0.316046986297881 & 0.158023493148940 \tabularnewline
36 & 0.81451407490792 & 0.370971850184161 & 0.185485925092080 \tabularnewline
37 & 0.777403429217733 & 0.445193141564534 & 0.222596570782267 \tabularnewline
38 & 0.935094832249302 & 0.129810335501397 & 0.0649051677506984 \tabularnewline
39 & 0.990132954922947 & 0.0197340901541051 & 0.00986704507705255 \tabularnewline
40 & 0.997055717447443 & 0.00588856510511477 & 0.00294428255255739 \tabularnewline
41 & 0.997251622734194 & 0.00549675453161283 & 0.00274837726580642 \tabularnewline
42 & 0.995612171744347 & 0.00877565651130582 & 0.00438782825565291 \tabularnewline
43 & 0.99175244547098 & 0.0164951090580394 & 0.00824755452901972 \tabularnewline
44 & 0.98592053094421 & 0.0281589381115786 & 0.0140794690557893 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60521&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.0101414379855108[/C][C]0.0202828759710217[/C][C]0.98985856201449[/C][/ROW]
[ROW][C]17[/C][C]0.00545416239386613[/C][C]0.0109083247877323[/C][C]0.994545837606134[/C][/ROW]
[ROW][C]18[/C][C]0.00698023616235786[/C][C]0.0139604723247157[/C][C]0.993019763837642[/C][/ROW]
[ROW][C]19[/C][C]0.00972780541962902[/C][C]0.0194556108392580[/C][C]0.99027219458037[/C][/ROW]
[ROW][C]20[/C][C]0.0102272433629200[/C][C]0.0204544867258401[/C][C]0.98977275663708[/C][/ROW]
[ROW][C]21[/C][C]0.015033188301581[/C][C]0.030066376603162[/C][C]0.98496681169842[/C][/ROW]
[ROW][C]22[/C][C]0.0244432946978093[/C][C]0.0488865893956186[/C][C]0.97555670530219[/C][/ROW]
[ROW][C]23[/C][C]0.0472280181431450[/C][C]0.0944560362862901[/C][C]0.952771981856855[/C][/ROW]
[ROW][C]24[/C][C]0.0762011134247896[/C][C]0.152402226849579[/C][C]0.92379888657521[/C][/ROW]
[ROW][C]25[/C][C]0.175830786767908[/C][C]0.351661573535815[/C][C]0.824169213232092[/C][/ROW]
[ROW][C]26[/C][C]0.437178900903216[/C][C]0.874357801806432[/C][C]0.562821099096784[/C][/ROW]
[ROW][C]27[/C][C]0.647540469937673[/C][C]0.704919060124655[/C][C]0.352459530062327[/C][/ROW]
[ROW][C]28[/C][C]0.763559657477072[/C][C]0.472880685045857[/C][C]0.236440342522928[/C][/ROW]
[ROW][C]29[/C][C]0.81749728800422[/C][C]0.365005423991561[/C][C]0.182502711995780[/C][/ROW]
[ROW][C]30[/C][C]0.84406717602024[/C][C]0.31186564795952[/C][C]0.15593282397976[/C][/ROW]
[ROW][C]31[/C][C]0.855922624473336[/C][C]0.288154751053327[/C][C]0.144077375526664[/C][/ROW]
[ROW][C]32[/C][C]0.857125352777552[/C][C]0.285749294444897[/C][C]0.142874647222448[/C][/ROW]
[ROW][C]33[/C][C]0.858322048706325[/C][C]0.283355902587350[/C][C]0.141677951293675[/C][/ROW]
[ROW][C]34[/C][C]0.85755183575583[/C][C]0.284896328488339[/C][C]0.142448164244169[/C][/ROW]
[ROW][C]35[/C][C]0.84197650685106[/C][C]0.316046986297881[/C][C]0.158023493148940[/C][/ROW]
[ROW][C]36[/C][C]0.81451407490792[/C][C]0.370971850184161[/C][C]0.185485925092080[/C][/ROW]
[ROW][C]37[/C][C]0.777403429217733[/C][C]0.445193141564534[/C][C]0.222596570782267[/C][/ROW]
[ROW][C]38[/C][C]0.935094832249302[/C][C]0.129810335501397[/C][C]0.0649051677506984[/C][/ROW]
[ROW][C]39[/C][C]0.990132954922947[/C][C]0.0197340901541051[/C][C]0.00986704507705255[/C][/ROW]
[ROW][C]40[/C][C]0.997055717447443[/C][C]0.00588856510511477[/C][C]0.00294428255255739[/C][/ROW]
[ROW][C]41[/C][C]0.997251622734194[/C][C]0.00549675453161283[/C][C]0.00274837726580642[/C][/ROW]
[ROW][C]42[/C][C]0.995612171744347[/C][C]0.00877565651130582[/C][C]0.00438782825565291[/C][/ROW]
[ROW][C]43[/C][C]0.99175244547098[/C][C]0.0164951090580394[/C][C]0.00824755452901972[/C][/ROW]
[ROW][C]44[/C][C]0.98592053094421[/C][C]0.0281589381115786[/C][C]0.0140794690557893[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60521&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60521&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
16	0.0101414379855108	0.0202828759710217	0.98985856201449
17	0.00545416239386613	0.0109083247877323	0.994545837606134
18	0.00698023616235786	0.0139604723247157	0.993019763837642
19	0.00972780541962902	0.0194556108392580	0.99027219458037
20	0.0102272433629200	0.0204544867258401	0.98977275663708
21	0.015033188301581	0.030066376603162	0.98496681169842
22	0.0244432946978093	0.0488865893956186	0.97555670530219
23	0.0472280181431450	0.0944560362862901	0.952771981856855
24	0.0762011134247896	0.152402226849579	0.92379888657521
25	0.175830786767908	0.351661573535815	0.824169213232092
26	0.437178900903216	0.874357801806432	0.562821099096784
27	0.647540469937673	0.704919060124655	0.352459530062327
28	0.763559657477072	0.472880685045857	0.236440342522928
29	0.81749728800422	0.365005423991561	0.182502711995780
30	0.84406717602024	0.31186564795952	0.15593282397976
31	0.855922624473336	0.288154751053327	0.144077375526664
32	0.857125352777552	0.285749294444897	0.142874647222448
33	0.858322048706325	0.283355902587350	0.141677951293675
34	0.85755183575583	0.284896328488339	0.142448164244169
35	0.84197650685106	0.316046986297881	0.158023493148940
36	0.81451407490792	0.370971850184161	0.185485925092080
37	0.777403429217733	0.445193141564534	0.222596570782267
38	0.935094832249302	0.129810335501397	0.0649051677506984
39	0.990132954922947	0.0197340901541051	0.00986704507705255
40	0.997055717447443	0.00588856510511477	0.00294428255255739
41	0.997251622734194	0.00549675453161283	0.00274837726580642
42	0.995612171744347	0.00877565651130582	0.00438782825565291
43	0.99175244547098	0.0164951090580394	0.00824755452901972
44	0.98592053094421	0.0281589381115786	0.0140794690557893

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.103448275862069	NOK
5% type I error level	13	0.448275862068966	NOK
10% type I error level	14	0.482758620689655	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 & 3 & 0.103448275862069 & NOK \tabularnewline
5% type I error level & 13 & 0.448275862068966 & NOK \tabularnewline
10% type I error level & 14 & 0.482758620689655 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60521&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]3[/C][C]0.103448275862069[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.448275862068966[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.482758620689655[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60521&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60521&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	3	0.103448275862069	NOK
5% type I error level	13	0.448275862068966	NOK
10% type I error level	14	0.482758620689655	NOK

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 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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