Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

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

Sun, 17 Nov 2013 15:20:35 -0500

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/2013/Nov/17/t1384719663rywjv9mv55fpj4q.htm/, Retrieved Mon, 29 Apr 2024 05:33:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=225870, Retrieved Mon, 29 Apr 2024 05:33:58 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2013-11-17 20:20:35] [6e8e5acb4eeea99a4ca6102a2b150481] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

1,66	0,67	0,38	0,25	0,07	0,16
0,34	0,10	0,07	0,63	0,45	0,08
0,27	0,24	0,91	0,46	0,76	0,75
1,55	0,18	0,72	0,18	0,27	0,81
0,39	0,60	0,58	0,44	0,06	0,02
0,28	0,77	0,05	0,79	0,80	0,66
-0,06	0,25	0,87	0,23	0,22	0,03
0,24	0,88	0,89	0,92	0,82	0,22
2,03	0,22	0,30	0,32	0,60	0,31
1,80	0,10	0,44	0,28	0,60	0,87
0,39	0,08	0,65	0,40	0,78	0,28
-0,68	0,85	0,28	0,91	0,90	0,73
1,00	0,66	0,43	0,87	0,70	0,20
1,69	0,81	0,72	0,73	0,38	0,36
1,25	0,45	0,96	0,46	0,51	0,08
0,93	0,41	0,75	0,86	0,62	0,24
0,22	0,56	0,86	0,48	0,81	0,94
0,46	0,38	0,47	0,27	0,96	0,48
-1,05	0,32	0,05	0,37	0,07	0,58
1,25	0,74	0,01	0,95	0,31	0,03
1,88	0,32	0,05	0,02	0,56	0,15
1,31	0,88	0,82	0,60	0,22	0,22
1,37	0,85	0,99	0,57	0,50	0,48
-1,22	0,03	0,10	0,73	0,85	0,27
-0,14	0,11	0,18	0,21	0,47	0,68
0,29	0,16	0,83	0,70	0,83	0,16
0,60	0,63	0,06	0,02	0,93	0,67
0,84	0,90	0,39	0,67	0,32	0,70
-2,13	0,30	0,17	0,80	0,39	0,35
1,30	0,54	0,39	0,67	0,64	0,09
1,25	0,53	0,31	0,64	0,86	0,93
1,54	0,15	0,30	0,85	0,68	0,20
1,58	0,38	0,26	0,88	0,89	0,18
1,20	0,94	0,01	0,22	0,63	0,41
1,81	0,86	0,95	0,88	0,42	0,39
-0,63	0,53	0,05	0,89	0,87	0,89
0,74	1,00	0,47	0,84	0,64	0,03
1,69	0,74	0,65	0,31	0,33	0,14
0,80	0,03	0,17	0,13	0,99	0,77

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225870&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225870&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225870&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	7 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
succes[t] = + 0.753939 + 0.944317kleding[t] + 0.608782socialevaardigheden[t] -1.21886zelfzekerheid[t] + 0.325952testosteron[t] -0.73407verzorgdheid[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
succes[t] =  +  0.753939 +  0.944317kleding[t] +  0.608782socialevaardigheden[t] -1.21886zelfzekerheid[t] +  0.325952testosteron[t] -0.73407verzorgdheid[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225870&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]succes[t] =  +  0.753939 +  0.944317kleding[t] +  0.608782socialevaardigheden[t] -1.21886zelfzekerheid[t] +  0.325952testosteron[t] -0.73407verzorgdheid[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225870&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225870&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
succes[t] = + 0.753939 + 0.944317kleding[t] + 0.608782socialevaardigheden[t] -1.21886zelfzekerheid[t] + 0.325952testosteron[t] -0.73407verzorgdheid[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)	0.753939	0.54733	1.377	0.177634	0.0888169
kleding	0.944317	0.541851	1.743	0.0906882	0.0453441
socialevaardigheden	0.608782	0.464679	1.31	0.199205	0.0996023
zelfzekerheid	-1.21886	0.59353	-2.054	0.0480101	0.0240051
testosteron	0.325952	0.630507	0.517	0.608626	0.304313
verzorgdheid	-0.73407	0.555646	-1.321	0.195552	0.0977761

\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) & 0.753939 & 0.54733 & 1.377 & 0.177634 & 0.0888169 \tabularnewline
kleding & 0.944317 & 0.541851 & 1.743 & 0.0906882 & 0.0453441 \tabularnewline
socialevaardigheden & 0.608782 & 0.464679 & 1.31 & 0.199205 & 0.0996023 \tabularnewline
zelfzekerheid & -1.21886 & 0.59353 & -2.054 & 0.0480101 & 0.0240051 \tabularnewline
testosteron & 0.325952 & 0.630507 & 0.517 & 0.608626 & 0.304313 \tabularnewline
verzorgdheid & -0.73407 & 0.555646 & -1.321 & 0.195552 & 0.0977761 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225870&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]0.753939[/C][C]0.54733[/C][C]1.377[/C][C]0.177634[/C][C]0.0888169[/C][/ROW]
[ROW][C]kleding[/C][C]0.944317[/C][C]0.541851[/C][C]1.743[/C][C]0.0906882[/C][C]0.0453441[/C][/ROW]
[ROW][C]socialevaardigheden[/C][C]0.608782[/C][C]0.464679[/C][C]1.31[/C][C]0.199205[/C][C]0.0996023[/C][/ROW]
[ROW][C]zelfzekerheid[/C][C]-1.21886[/C][C]0.59353[/C][C]-2.054[/C][C]0.0480101[/C][C]0.0240051[/C][/ROW]
[ROW][C]testosteron[/C][C]0.325952[/C][C]0.630507[/C][C]0.517[/C][C]0.608626[/C][C]0.304313[/C][/ROW]
[ROW][C]verzorgdheid[/C][C]-0.73407[/C][C]0.555646[/C][C]-1.321[/C][C]0.195552[/C][C]0.0977761[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225870&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225870&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)	0.753939	0.54733	1.377	0.177634	0.0888169
kleding	0.944317	0.541851	1.743	0.0906882	0.0453441
socialevaardigheden	0.608782	0.464679	1.31	0.199205	0.0996023
zelfzekerheid	-1.21886	0.59353	-2.054	0.0480101	0.0240051
testosteron	0.325952	0.630507	0.517	0.608626	0.304313
verzorgdheid	-0.73407	0.555646	-1.321	0.195552	0.0977761

Multiple Linear Regression - Regression Statistics
Multiple R	0.458655
R-squared	0.210364
Adjusted R-squared	0.0907223
F-TEST (value)	1.75828
F-TEST (DF numerator)	5
F-TEST (DF denominator)	33
p-value	0.148931
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.905438
Sum Squared Residuals	27.054

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.458655 \tabularnewline
R-squared & 0.210364 \tabularnewline
Adjusted R-squared & 0.0907223 \tabularnewline
F-TEST (value) & 1.75828 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 33 \tabularnewline
p-value & 0.148931 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.905438 \tabularnewline
Sum Squared Residuals & 27.054 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225870&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.458655[/C][/ROW]
[ROW][C]R-squared[/C][C]0.210364[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0907223[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.75828[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]33[/C][/ROW]
[ROW][C]p-value[/C][C]0.148931[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.905438[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]27.054[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225870&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225870&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	0.458655
R-squared	0.210364
Adjusted R-squared	0.0907223
F-TEST (value)	1.75828
F-TEST (DF numerator)	5
F-TEST (DF denominator)	33
p-value	0.148931
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.905438
Sum Squared Residuals	27.054

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.66	1.21862	0.441382
2	0.34	0.211054	0.128946
3	0.27	0.67106	-0.40106
4	1.55	0.636254	0.913746
5	0.39	1.1422	-0.752198
6	0.28	0.324875	-0.0448753
7	-0.06	1.28901	-1.34901
8	0.24	1.11118	-0.871185
9	2.03	0.722296	1.3077
10	1.8	0.331883	1.46812
11	0.39	0.78635	-0.39635
12	-0.68	0.375387	-1.05539
13	1	0.659906	0.340094
14	1.69	0.926985	0.763015
15	1.25	1.31015	-0.0601451
16	0.93	0.575386	0.354614
17	0.22	0.79525	-0.57525
18	0.46	1.03037	-0.570374
19	-1.05	0.232636	-1.28264
20	1.25	0.379924	0.870076
21	1.88	1.1346	0.745395
22	1.31	1.26304	0.0469649
23	1.37	1.27517	0.0948272
24	-1.22	0.0322364	-1.25224
25	-0.14	0.365463	-0.505463
26	0.29	0.710203	-0.420203
27	0.6	1.17232	-0.572317
28	0.84	0.615066	0.224934
29	-2.13	0.0358327	-2.16583
30	1.3	0.8272	0.4728
31	1.25	0.26071	0.98929
32	1.54	0.11702	1.42298
33	1.58	0.356427	1.22357
34	1.2	1.28392	-0.083916
35	1.81	0.922407	0.887593
36	-0.63	-0.169667	-0.460333
37	0.74	1.14713	-0.407125
38	1.69	1.47539	0.214611
39	0.8	0.484768	0.315232

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.66 & 1.21862 & 0.441382 \tabularnewline
2 & 0.34 & 0.211054 & 0.128946 \tabularnewline
3 & 0.27 & 0.67106 & -0.40106 \tabularnewline
4 & 1.55 & 0.636254 & 0.913746 \tabularnewline
5 & 0.39 & 1.1422 & -0.752198 \tabularnewline
6 & 0.28 & 0.324875 & -0.0448753 \tabularnewline
7 & -0.06 & 1.28901 & -1.34901 \tabularnewline
8 & 0.24 & 1.11118 & -0.871185 \tabularnewline
9 & 2.03 & 0.722296 & 1.3077 \tabularnewline
10 & 1.8 & 0.331883 & 1.46812 \tabularnewline
11 & 0.39 & 0.78635 & -0.39635 \tabularnewline
12 & -0.68 & 0.375387 & -1.05539 \tabularnewline
13 & 1 & 0.659906 & 0.340094 \tabularnewline
14 & 1.69 & 0.926985 & 0.763015 \tabularnewline
15 & 1.25 & 1.31015 & -0.0601451 \tabularnewline
16 & 0.93 & 0.575386 & 0.354614 \tabularnewline
17 & 0.22 & 0.79525 & -0.57525 \tabularnewline
18 & 0.46 & 1.03037 & -0.570374 \tabularnewline
19 & -1.05 & 0.232636 & -1.28264 \tabularnewline
20 & 1.25 & 0.379924 & 0.870076 \tabularnewline
21 & 1.88 & 1.1346 & 0.745395 \tabularnewline
22 & 1.31 & 1.26304 & 0.0469649 \tabularnewline
23 & 1.37 & 1.27517 & 0.0948272 \tabularnewline
24 & -1.22 & 0.0322364 & -1.25224 \tabularnewline
25 & -0.14 & 0.365463 & -0.505463 \tabularnewline
26 & 0.29 & 0.710203 & -0.420203 \tabularnewline
27 & 0.6 & 1.17232 & -0.572317 \tabularnewline
28 & 0.84 & 0.615066 & 0.224934 \tabularnewline
29 & -2.13 & 0.0358327 & -2.16583 \tabularnewline
30 & 1.3 & 0.8272 & 0.4728 \tabularnewline
31 & 1.25 & 0.26071 & 0.98929 \tabularnewline
32 & 1.54 & 0.11702 & 1.42298 \tabularnewline
33 & 1.58 & 0.356427 & 1.22357 \tabularnewline
34 & 1.2 & 1.28392 & -0.083916 \tabularnewline
35 & 1.81 & 0.922407 & 0.887593 \tabularnewline
36 & -0.63 & -0.169667 & -0.460333 \tabularnewline
37 & 0.74 & 1.14713 & -0.407125 \tabularnewline
38 & 1.69 & 1.47539 & 0.214611 \tabularnewline
39 & 0.8 & 0.484768 & 0.315232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225870&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]1.66[/C][C]1.21862[/C][C]0.441382[/C][/ROW]
[ROW][C]2[/C][C]0.34[/C][C]0.211054[/C][C]0.128946[/C][/ROW]
[ROW][C]3[/C][C]0.27[/C][C]0.67106[/C][C]-0.40106[/C][/ROW]
[ROW][C]4[/C][C]1.55[/C][C]0.636254[/C][C]0.913746[/C][/ROW]
[ROW][C]5[/C][C]0.39[/C][C]1.1422[/C][C]-0.752198[/C][/ROW]
[ROW][C]6[/C][C]0.28[/C][C]0.324875[/C][C]-0.0448753[/C][/ROW]
[ROW][C]7[/C][C]-0.06[/C][C]1.28901[/C][C]-1.34901[/C][/ROW]
[ROW][C]8[/C][C]0.24[/C][C]1.11118[/C][C]-0.871185[/C][/ROW]
[ROW][C]9[/C][C]2.03[/C][C]0.722296[/C][C]1.3077[/C][/ROW]
[ROW][C]10[/C][C]1.8[/C][C]0.331883[/C][C]1.46812[/C][/ROW]
[ROW][C]11[/C][C]0.39[/C][C]0.78635[/C][C]-0.39635[/C][/ROW]
[ROW][C]12[/C][C]-0.68[/C][C]0.375387[/C][C]-1.05539[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.659906[/C][C]0.340094[/C][/ROW]
[ROW][C]14[/C][C]1.69[/C][C]0.926985[/C][C]0.763015[/C][/ROW]
[ROW][C]15[/C][C]1.25[/C][C]1.31015[/C][C]-0.0601451[/C][/ROW]
[ROW][C]16[/C][C]0.93[/C][C]0.575386[/C][C]0.354614[/C][/ROW]
[ROW][C]17[/C][C]0.22[/C][C]0.79525[/C][C]-0.57525[/C][/ROW]
[ROW][C]18[/C][C]0.46[/C][C]1.03037[/C][C]-0.570374[/C][/ROW]
[ROW][C]19[/C][C]-1.05[/C][C]0.232636[/C][C]-1.28264[/C][/ROW]
[ROW][C]20[/C][C]1.25[/C][C]0.379924[/C][C]0.870076[/C][/ROW]
[ROW][C]21[/C][C]1.88[/C][C]1.1346[/C][C]0.745395[/C][/ROW]
[ROW][C]22[/C][C]1.31[/C][C]1.26304[/C][C]0.0469649[/C][/ROW]
[ROW][C]23[/C][C]1.37[/C][C]1.27517[/C][C]0.0948272[/C][/ROW]
[ROW][C]24[/C][C]-1.22[/C][C]0.0322364[/C][C]-1.25224[/C][/ROW]
[ROW][C]25[/C][C]-0.14[/C][C]0.365463[/C][C]-0.505463[/C][/ROW]
[ROW][C]26[/C][C]0.29[/C][C]0.710203[/C][C]-0.420203[/C][/ROW]
[ROW][C]27[/C][C]0.6[/C][C]1.17232[/C][C]-0.572317[/C][/ROW]
[ROW][C]28[/C][C]0.84[/C][C]0.615066[/C][C]0.224934[/C][/ROW]
[ROW][C]29[/C][C]-2.13[/C][C]0.0358327[/C][C]-2.16583[/C][/ROW]
[ROW][C]30[/C][C]1.3[/C][C]0.8272[/C][C]0.4728[/C][/ROW]
[ROW][C]31[/C][C]1.25[/C][C]0.26071[/C][C]0.98929[/C][/ROW]
[ROW][C]32[/C][C]1.54[/C][C]0.11702[/C][C]1.42298[/C][/ROW]
[ROW][C]33[/C][C]1.58[/C][C]0.356427[/C][C]1.22357[/C][/ROW]
[ROW][C]34[/C][C]1.2[/C][C]1.28392[/C][C]-0.083916[/C][/ROW]
[ROW][C]35[/C][C]1.81[/C][C]0.922407[/C][C]0.887593[/C][/ROW]
[ROW][C]36[/C][C]-0.63[/C][C]-0.169667[/C][C]-0.460333[/C][/ROW]
[ROW][C]37[/C][C]0.74[/C][C]1.14713[/C][C]-0.407125[/C][/ROW]
[ROW][C]38[/C][C]1.69[/C][C]1.47539[/C][C]0.214611[/C][/ROW]
[ROW][C]39[/C][C]0.8[/C][C]0.484768[/C][C]0.315232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225870&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225870&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	1.66	1.21862	0.441382
2	0.34	0.211054	0.128946
3	0.27	0.67106	-0.40106
4	1.55	0.636254	0.913746
5	0.39	1.1422	-0.752198
6	0.28	0.324875	-0.0448753
7	-0.06	1.28901	-1.34901
8	0.24	1.11118	-0.871185
9	2.03	0.722296	1.3077
10	1.8	0.331883	1.46812
11	0.39	0.78635	-0.39635
12	-0.68	0.375387	-1.05539
13	1	0.659906	0.340094
14	1.69	0.926985	0.763015
15	1.25	1.31015	-0.0601451
16	0.93	0.575386	0.354614
17	0.22	0.79525	-0.57525
18	0.46	1.03037	-0.570374
19	-1.05	0.232636	-1.28264
20	1.25	0.379924	0.870076
21	1.88	1.1346	0.745395
22	1.31	1.26304	0.0469649
23	1.37	1.27517	0.0948272
24	-1.22	0.0322364	-1.25224
25	-0.14	0.365463	-0.505463
26	0.29	0.710203	-0.420203
27	0.6	1.17232	-0.572317
28	0.84	0.615066	0.224934
29	-2.13	0.0358327	-2.16583
30	1.3	0.8272	0.4728
31	1.25	0.26071	0.98929
32	1.54	0.11702	1.42298
33	1.58	0.356427	1.22357
34	1.2	1.28392	-0.083916
35	1.81	0.922407	0.887593
36	-0.63	-0.169667	-0.460333
37	0.74	1.14713	-0.407125
38	1.69	1.47539	0.214611
39	0.8	0.484768	0.315232

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.489607	0.979214	0.510393
10	0.417217	0.834435	0.582783
11	0.307266	0.614532	0.692734
12	0.382855	0.765711	0.617145
13	0.457571	0.915142	0.542429
14	0.561993	0.876015	0.438007
15	0.479456	0.958912	0.520544
16	0.405363	0.810726	0.594637
17	0.332385	0.66477	0.667615
18	0.261311	0.522622	0.738689
19	0.445234	0.890468	0.554766
20	0.446478	0.892955	0.553522
21	0.446639	0.893279	0.553361
22	0.347176	0.694352	0.652824
23	0.282644	0.565288	0.717356
24	0.315375	0.63075	0.684625
25	0.234672	0.469345	0.765328
26	0.344564	0.689129	0.655436
27	0.301599	0.603198	0.698401
28	0.327836	0.655672	0.672164
29	0.682901	0.634197	0.317099
30	0.521802	0.956397	0.478198

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.489607 & 0.979214 & 0.510393 \tabularnewline
10 & 0.417217 & 0.834435 & 0.582783 \tabularnewline
11 & 0.307266 & 0.614532 & 0.692734 \tabularnewline
12 & 0.382855 & 0.765711 & 0.617145 \tabularnewline
13 & 0.457571 & 0.915142 & 0.542429 \tabularnewline
14 & 0.561993 & 0.876015 & 0.438007 \tabularnewline
15 & 0.479456 & 0.958912 & 0.520544 \tabularnewline
16 & 0.405363 & 0.810726 & 0.594637 \tabularnewline
17 & 0.332385 & 0.66477 & 0.667615 \tabularnewline
18 & 0.261311 & 0.522622 & 0.738689 \tabularnewline
19 & 0.445234 & 0.890468 & 0.554766 \tabularnewline
20 & 0.446478 & 0.892955 & 0.553522 \tabularnewline
21 & 0.446639 & 0.893279 & 0.553361 \tabularnewline
22 & 0.347176 & 0.694352 & 0.652824 \tabularnewline
23 & 0.282644 & 0.565288 & 0.717356 \tabularnewline
24 & 0.315375 & 0.63075 & 0.684625 \tabularnewline
25 & 0.234672 & 0.469345 & 0.765328 \tabularnewline
26 & 0.344564 & 0.689129 & 0.655436 \tabularnewline
27 & 0.301599 & 0.603198 & 0.698401 \tabularnewline
28 & 0.327836 & 0.655672 & 0.672164 \tabularnewline
29 & 0.682901 & 0.634197 & 0.317099 \tabularnewline
30 & 0.521802 & 0.956397 & 0.478198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225870&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.489607[/C][C]0.979214[/C][C]0.510393[/C][/ROW]
[ROW][C]10[/C][C]0.417217[/C][C]0.834435[/C][C]0.582783[/C][/ROW]
[ROW][C]11[/C][C]0.307266[/C][C]0.614532[/C][C]0.692734[/C][/ROW]
[ROW][C]12[/C][C]0.382855[/C][C]0.765711[/C][C]0.617145[/C][/ROW]
[ROW][C]13[/C][C]0.457571[/C][C]0.915142[/C][C]0.542429[/C][/ROW]
[ROW][C]14[/C][C]0.561993[/C][C]0.876015[/C][C]0.438007[/C][/ROW]
[ROW][C]15[/C][C]0.479456[/C][C]0.958912[/C][C]0.520544[/C][/ROW]
[ROW][C]16[/C][C]0.405363[/C][C]0.810726[/C][C]0.594637[/C][/ROW]
[ROW][C]17[/C][C]0.332385[/C][C]0.66477[/C][C]0.667615[/C][/ROW]
[ROW][C]18[/C][C]0.261311[/C][C]0.522622[/C][C]0.738689[/C][/ROW]
[ROW][C]19[/C][C]0.445234[/C][C]0.890468[/C][C]0.554766[/C][/ROW]
[ROW][C]20[/C][C]0.446478[/C][C]0.892955[/C][C]0.553522[/C][/ROW]
[ROW][C]21[/C][C]0.446639[/C][C]0.893279[/C][C]0.553361[/C][/ROW]
[ROW][C]22[/C][C]0.347176[/C][C]0.694352[/C][C]0.652824[/C][/ROW]
[ROW][C]23[/C][C]0.282644[/C][C]0.565288[/C][C]0.717356[/C][/ROW]
[ROW][C]24[/C][C]0.315375[/C][C]0.63075[/C][C]0.684625[/C][/ROW]
[ROW][C]25[/C][C]0.234672[/C][C]0.469345[/C][C]0.765328[/C][/ROW]
[ROW][C]26[/C][C]0.344564[/C][C]0.689129[/C][C]0.655436[/C][/ROW]
[ROW][C]27[/C][C]0.301599[/C][C]0.603198[/C][C]0.698401[/C][/ROW]
[ROW][C]28[/C][C]0.327836[/C][C]0.655672[/C][C]0.672164[/C][/ROW]
[ROW][C]29[/C][C]0.682901[/C][C]0.634197[/C][C]0.317099[/C][/ROW]
[ROW][C]30[/C][C]0.521802[/C][C]0.956397[/C][C]0.478198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225870&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225870&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.489607	0.979214	0.510393
10	0.417217	0.834435	0.582783
11	0.307266	0.614532	0.692734
12	0.382855	0.765711	0.617145
13	0.457571	0.915142	0.542429
14	0.561993	0.876015	0.438007
15	0.479456	0.958912	0.520544
16	0.405363	0.810726	0.594637
17	0.332385	0.66477	0.667615
18	0.261311	0.522622	0.738689
19	0.445234	0.890468	0.554766
20	0.446478	0.892955	0.553522
21	0.446639	0.893279	0.553361
22	0.347176	0.694352	0.652824
23	0.282644	0.565288	0.717356
24	0.315375	0.63075	0.684625
25	0.234672	0.469345	0.765328
26	0.344564	0.689129	0.655436
27	0.301599	0.603198	0.698401
28	0.327836	0.655672	0.672164
29	0.682901	0.634197	0.317099
30	0.521802	0.956397	0.478198

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225870&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225870&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225870&T=6

As an alternative you can also use a QR Code:

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

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

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

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):

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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