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

Fri, 19 Nov 2010 12:05:06 +0000

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/2010/Nov/19/t1290168240jpyrpbhf36rasls.htm/, Retrieved Thu, 06 Nov 2025 03:15:25 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97931, Retrieved Thu, 06 Nov 2025 03:15:25 +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

377

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

-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [maand] [2010-11-19 12:05:06] [df17410ebb98883e83037e1662207ccb] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

6	101.82	107.34	93.63	99.85	101.76
6	101.68	107.34	93.63	99.91	102.37
6	101.68	107.34	93.63	99.87	102.38
6	102.45	107.34	96.13	99.86	102.86
6	102.45	107.34	96.13	100.10	102.87
6	102.45	107.34	96.13	100.10	102.92
6	102.45	107.34	96.13	100.12	102.95
6	102.45	107.34	96.13	99.95	103.02
6	102.45	112.60	96.13	99.94	104.08
6	102.52	112.60	96.13	100.18	104.16
6	102.52	112.60	96.13	100.31	104.24
6	102.85	112.60	96.13	100.65	104.33
7	102.85	112.61	96.13	100.65	104.73
7	102.85	112.61	96.13	100.69	104.86
7	103.25	112.61	96.13	101.26	105.03
7	103.25	112.61	98.73	101.26	105.62
7	103.25	112.61	98.73	101.38	105.63
7	103.25	112.61	98.73	101.38	105.63
7	104.45	112.61	98.73	101.38	105.94
7	104.45	112.61	98.73	101.44	106.61
7	104.45	118.65	98.73	101.40	107.69
7	104.80	118.65	98.73	101.40	107.78
7	104.80	118.65	98.73	100.58	107.93
7	105.29	118.65	98.73	100.58	108.48
8	105.29	114.29	98.73	100.58	108.14
8	105.29	114.29	98.73	100.59	108.48
8	105.29	114.29	98.73	100.81	108.48
8	106.04	114.29	101.67	100.75	108.89
8	105.94	114.29	101.67	100.75	108.93
8	105.94	114.29	101.67	100.96	109.21
8	105.94	114.29	101.67	101.31	109.47
8	106.28	114.29	101.67	101.64	109.80
8	106.48	123.33	101.67	101.46	111.73
8	107.19	123.33	101.67	101.73	111.85
8	108.14	123.33	101.67	101.73	112.12
8	108.22	123.33	101.67	101.64	112.15
9	108.22	123.33	101.67	101.77	112.17
9	108.61	123.33	101.67	101.74	112.67
9	108.61	123.33	101.67	101.89	112.80
9	108.61	123.33	107.94	101.89	113.44
9	108.61	123.33	107.94	101.93	113.53
9	109.06	123.33	107.94	101.93	114.53
9	109.06	123.33	107.94	102.32	114.51
9	112.93	123.33	107.94	102.41	115.05
9	115.84	129.03	107.94	103.58	116.67
9	118.57	128.76	107.94	104.12	117.07
9	118.57	128.76	107.94	104.10	116.92
9	118.86	128.76	107.94	104.15	117.00
10	118.98	128.76	107.94	104.15	117.02
10	119.27	128.76	107.94	104.16	117.35
10	119.39	128.76	107.94	102.94	117.36
10	119.49	128.76	110.30	103.07	117.82
10	119.59	128.76	110.30	103.04	117.88
10	120.12	128.76	110.30	103.06	118.24
10	120.14	128.76	110.30	103.05	118.50
10	120.14	128.76	110.30	102.95	118.80
10	120.14	132.63	110.30	102.95	119.76
10	120.14	132.63	110.30	103.05	120.09

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97931&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]9 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=97931&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97931&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	9 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
And.dienstenrecr.&cultuur[t] = + 43.1196518887856 + 1.06911178353784maand[t] + 0.095509296468249Bioscoop[t] + 0.293827546929862Schouwburgabonnement[t] + 0.271011310340615Eendagsattracties[t] -0.140287440271954HuurvaneenDVD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
And.dienstenrecr.&cultuur[t] =  +  43.1196518887856 +  1.06911178353784maand[t] +  0.095509296468249Bioscoop[t] +  0.293827546929862Schouwburgabonnement[t] +  0.271011310340615Eendagsattracties[t] -0.140287440271954HuurvaneenDVD[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97931&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]And.dienstenrecr.&cultuur[t] =  +  43.1196518887856 +  1.06911178353784maand[t] +  0.095509296468249Bioscoop[t] +  0.293827546929862Schouwburgabonnement[t] +  0.271011310340615Eendagsattracties[t] -0.140287440271954HuurvaneenDVD[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97931&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97931&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
And.dienstenrecr.&cultuur[t] = + 43.1196518887856 + 1.06911178353784maand[t] + 0.095509296468249Bioscoop[t] + 0.293827546929862Schouwburgabonnement[t] + 0.271011310340615Eendagsattracties[t] -0.140287440271954HuurvaneenDVD[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)	43.1196518887856	11.20716	3.8475	0.000328	0.000164
maand	1.06911178353784	0.142107	7.5233	0	0
Bioscoop	0.095509296468249	0.030258	3.1565	0.002656	0.001328
Schouwburgabonnement	0.293827546929862	0.024122	12.1808	0	0
Eendagsattracties	0.271011310340615	0.040701	6.6585	0	0
HuurvaneenDVD	-0.140287440271954	0.134669	-1.0417	0.302359	0.15118

\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) & 43.1196518887856 & 11.20716 & 3.8475 & 0.000328 & 0.000164 \tabularnewline
maand & 1.06911178353784 & 0.142107 & 7.5233 & 0 & 0 \tabularnewline
Bioscoop & 0.095509296468249 & 0.030258 & 3.1565 & 0.002656 & 0.001328 \tabularnewline
Schouwburgabonnement & 0.293827546929862 & 0.024122 & 12.1808 & 0 & 0 \tabularnewline
Eendagsattracties & 0.271011310340615 & 0.040701 & 6.6585 & 0 & 0 \tabularnewline
HuurvaneenDVD & -0.140287440271954 & 0.134669 & -1.0417 & 0.302359 & 0.15118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97931&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]43.1196518887856[/C][C]11.20716[/C][C]3.8475[/C][C]0.000328[/C][C]0.000164[/C][/ROW]
[ROW][C]maand[/C][C]1.06911178353784[/C][C]0.142107[/C][C]7.5233[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bioscoop[/C][C]0.095509296468249[/C][C]0.030258[/C][C]3.1565[/C][C]0.002656[/C][C]0.001328[/C][/ROW]
[ROW][C]Schouwburgabonnement[/C][C]0.293827546929862[/C][C]0.024122[/C][C]12.1808[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Eendagsattracties[/C][C]0.271011310340615[/C][C]0.040701[/C][C]6.6585[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]HuurvaneenDVD[/C][C]-0.140287440271954[/C][C]0.134669[/C][C]-1.0417[/C][C]0.302359[/C][C]0.15118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97931&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97931&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)	43.1196518887856	11.20716	3.8475	0.000328	0.000164
maand	1.06911178353784	0.142107	7.5233	0	0
Bioscoop	0.095509296468249	0.030258	3.1565	0.002656	0.001328
Schouwburgabonnement	0.293827546929862	0.024122	12.1808	0	0
Eendagsattracties	0.271011310340615	0.040701	6.6585	0	0
HuurvaneenDVD	-0.140287440271954	0.134669	-1.0417	0.302359	0.15118

Multiple Linear Regression - Regression Statistics
Multiple R	0.996983707496076
R-squared	0.99397651301262
Adjusted R-squared	0.993397331571527
F-TEST (value)	1716.17466045675
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.452997679974222
Sum Squared Residuals	10.6707586992254

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.996983707496076 \tabularnewline
R-squared & 0.99397651301262 \tabularnewline
Adjusted R-squared & 0.993397331571527 \tabularnewline
F-TEST (value) & 1716.17466045675 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.452997679974222 \tabularnewline
Sum Squared Residuals & 10.6707586992254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97931&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.996983707496076[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99397651301262[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.993397331571527[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1716.17466045675[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.452997679974222[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.6707586992254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97931&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97931&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	0.996983707496076
R-squared	0.99397651301262
Adjusted R-squared	0.993397331571527
F-TEST (value)	1716.17466045675
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.452997679974222
Sum Squared Residuals	10.6707586992254

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	102.165616119899	-0.405616119898775
2	102.37	102.143827571977	0.226172428023504
3	102.38	102.149439069587	0.230560930412621
4	102.86	102.901912378122	-0.0419123781221807
5	102.87	102.868243392457	0.00175660754309176
6	102.92	102.868243392457	0.0517566075430885
7	102.95	102.865437643651	0.0845623563485303
8	103.02	102.889286508498	0.130713491502291
9	104.08	104.436222279751	-0.356222279751498
10	104.16	104.409238944839	-0.249238944839006
11	104.24	104.391001577604	-0.151001577603655
12	104.33	104.374821915746	-0.0448219157457087
13	104.73	105.446871974753	-0.716871974752847
14	104.86	105.441260477142	-0.581260477141975
15	105.03	105.399500354774	-0.369500354774259
16	105.62	106.104129761660	-0.484129761659856
17	105.63	106.087295268827	-0.457295268827233
18	105.63	106.087295268827	-0.457295268827233
19	105.94	106.201906424589	-0.261906424589129
20	106.61	106.193489178173	0.41651082182719
21	107.69	107.97381905924	-0.283819059240057
22	107.78	108.007247313004	-0.227247313003940
23	107.93	108.122283014027	-0.192283014026938
24	108.48	108.169082569296	0.310917430703617
25	108.14	107.95710624822	0.182893751779967
26	108.48	107.955703373817	0.524296626182691
27	108.48	107.924840136957	0.55515986304252
28	108.89	108.801662608126	0.0883373918736056
29	108.93	108.792111678480	0.137888321520438
30	109.21	108.762651316022	0.447348683977534
31	109.47	108.713550711927	0.756449288072724
32	109.8	108.699729017437	1.10027098256326
33	111.73	111.400283640225	0.329716359774716
34	111.85	111.430217631844	0.419782368155679
35	112.12	111.520951463489	0.599048536510853
36	112.15	111.541218076831	0.608781923168917
37	112.17	112.592092493134	-0.422092493133577
38	112.67	112.633549741964	0.0364502580356471
39	112.8	112.612506625924	0.187493374076436
40	113.44	114.311747541759	-0.871747541759218
41	113.53	114.306136044148	-0.776136044148335
42	114.53	114.349115227559	0.180884772440952
43	114.51	114.294403125853	0.215596874147016
44	115.05	114.651398233561	0.398601766439361
45	116.67	116.440010998665	0.229989001334733
46	117.07	116.545662722606	0.524337277394328
47	116.92	116.548468471411	0.371531528588895
48	117	116.569151795373	0.4308482046267
49	117.02	117.649724694487	-0.629724694487338
50	117.35	117.676019516060	-0.326019516060413
51	117.36	117.858631308768	-0.498631308768381
52	117.82	118.489531563584	-0.66953156358371
53	117.88	118.503291116439	-0.62329111643869
54	118.24	118.551105294761	-0.311105294761424
55	118.5	118.554418355094	-0.0544183550935037
56	118.8	118.568447099121	0.231552900879299
57	119.76	119.705559705739	0.0544402942607398
58	120.09	119.691530961712	0.398469038287933

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 102.165616119899 & -0.405616119898775 \tabularnewline
2 & 102.37 & 102.143827571977 & 0.226172428023504 \tabularnewline
3 & 102.38 & 102.149439069587 & 0.230560930412621 \tabularnewline
4 & 102.86 & 102.901912378122 & -0.0419123781221807 \tabularnewline
5 & 102.87 & 102.868243392457 & 0.00175660754309176 \tabularnewline
6 & 102.92 & 102.868243392457 & 0.0517566075430885 \tabularnewline
7 & 102.95 & 102.865437643651 & 0.0845623563485303 \tabularnewline
8 & 103.02 & 102.889286508498 & 0.130713491502291 \tabularnewline
9 & 104.08 & 104.436222279751 & -0.356222279751498 \tabularnewline
10 & 104.16 & 104.409238944839 & -0.249238944839006 \tabularnewline
11 & 104.24 & 104.391001577604 & -0.151001577603655 \tabularnewline
12 & 104.33 & 104.374821915746 & -0.0448219157457087 \tabularnewline
13 & 104.73 & 105.446871974753 & -0.716871974752847 \tabularnewline
14 & 104.86 & 105.441260477142 & -0.581260477141975 \tabularnewline
15 & 105.03 & 105.399500354774 & -0.369500354774259 \tabularnewline
16 & 105.62 & 106.104129761660 & -0.484129761659856 \tabularnewline
17 & 105.63 & 106.087295268827 & -0.457295268827233 \tabularnewline
18 & 105.63 & 106.087295268827 & -0.457295268827233 \tabularnewline
19 & 105.94 & 106.201906424589 & -0.261906424589129 \tabularnewline
20 & 106.61 & 106.193489178173 & 0.41651082182719 \tabularnewline
21 & 107.69 & 107.97381905924 & -0.283819059240057 \tabularnewline
22 & 107.78 & 108.007247313004 & -0.227247313003940 \tabularnewline
23 & 107.93 & 108.122283014027 & -0.192283014026938 \tabularnewline
24 & 108.48 & 108.169082569296 & 0.310917430703617 \tabularnewline
25 & 108.14 & 107.95710624822 & 0.182893751779967 \tabularnewline
26 & 108.48 & 107.955703373817 & 0.524296626182691 \tabularnewline
27 & 108.48 & 107.924840136957 & 0.55515986304252 \tabularnewline
28 & 108.89 & 108.801662608126 & 0.0883373918736056 \tabularnewline
29 & 108.93 & 108.792111678480 & 0.137888321520438 \tabularnewline
30 & 109.21 & 108.762651316022 & 0.447348683977534 \tabularnewline
31 & 109.47 & 108.713550711927 & 0.756449288072724 \tabularnewline
32 & 109.8 & 108.699729017437 & 1.10027098256326 \tabularnewline
33 & 111.73 & 111.400283640225 & 0.329716359774716 \tabularnewline
34 & 111.85 & 111.430217631844 & 0.419782368155679 \tabularnewline
35 & 112.12 & 111.520951463489 & 0.599048536510853 \tabularnewline
36 & 112.15 & 111.541218076831 & 0.608781923168917 \tabularnewline
37 & 112.17 & 112.592092493134 & -0.422092493133577 \tabularnewline
38 & 112.67 & 112.633549741964 & 0.0364502580356471 \tabularnewline
39 & 112.8 & 112.612506625924 & 0.187493374076436 \tabularnewline
40 & 113.44 & 114.311747541759 & -0.871747541759218 \tabularnewline
41 & 113.53 & 114.306136044148 & -0.776136044148335 \tabularnewline
42 & 114.53 & 114.349115227559 & 0.180884772440952 \tabularnewline
43 & 114.51 & 114.294403125853 & 0.215596874147016 \tabularnewline
44 & 115.05 & 114.651398233561 & 0.398601766439361 \tabularnewline
45 & 116.67 & 116.440010998665 & 0.229989001334733 \tabularnewline
46 & 117.07 & 116.545662722606 & 0.524337277394328 \tabularnewline
47 & 116.92 & 116.548468471411 & 0.371531528588895 \tabularnewline
48 & 117 & 116.569151795373 & 0.4308482046267 \tabularnewline
49 & 117.02 & 117.649724694487 & -0.629724694487338 \tabularnewline
50 & 117.35 & 117.676019516060 & -0.326019516060413 \tabularnewline
51 & 117.36 & 117.858631308768 & -0.498631308768381 \tabularnewline
52 & 117.82 & 118.489531563584 & -0.66953156358371 \tabularnewline
53 & 117.88 & 118.503291116439 & -0.62329111643869 \tabularnewline
54 & 118.24 & 118.551105294761 & -0.311105294761424 \tabularnewline
55 & 118.5 & 118.554418355094 & -0.0544183550935037 \tabularnewline
56 & 118.8 & 118.568447099121 & 0.231552900879299 \tabularnewline
57 & 119.76 & 119.705559705739 & 0.0544402942607398 \tabularnewline
58 & 120.09 & 119.691530961712 & 0.398469038287933 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97931&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]101.76[/C][C]102.165616119899[/C][C]-0.405616119898775[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]102.143827571977[/C][C]0.226172428023504[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]102.149439069587[/C][C]0.230560930412621[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]102.901912378122[/C][C]-0.0419123781221807[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]102.868243392457[/C][C]0.00175660754309176[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.868243392457[/C][C]0.0517566075430885[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.865437643651[/C][C]0.0845623563485303[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]102.889286508498[/C][C]0.130713491502291[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.436222279751[/C][C]-0.356222279751498[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.409238944839[/C][C]-0.249238944839006[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.391001577604[/C][C]-0.151001577603655[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.374821915746[/C][C]-0.0448219157457087[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]105.446871974753[/C][C]-0.716871974752847[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.441260477142[/C][C]-0.581260477141975[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.399500354774[/C][C]-0.369500354774259[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]106.104129761660[/C][C]-0.484129761659856[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]106.087295268827[/C][C]-0.457295268827233[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.087295268827[/C][C]-0.457295268827233[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.201906424589[/C][C]-0.261906424589129[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.193489178173[/C][C]0.41651082182719[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.97381905924[/C][C]-0.283819059240057[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.007247313004[/C][C]-0.227247313003940[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.122283014027[/C][C]-0.192283014026938[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.169082569296[/C][C]0.310917430703617[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.95710624822[/C][C]0.182893751779967[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]107.955703373817[/C][C]0.524296626182691[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]107.924840136957[/C][C]0.55515986304252[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.801662608126[/C][C]0.0883373918736056[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]108.792111678480[/C][C]0.137888321520438[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]108.762651316022[/C][C]0.447348683977534[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]108.713550711927[/C][C]0.756449288072724[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]108.699729017437[/C][C]1.10027098256326[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.400283640225[/C][C]0.329716359774716[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.430217631844[/C][C]0.419782368155679[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.520951463489[/C][C]0.599048536510853[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]111.541218076831[/C][C]0.608781923168917[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.592092493134[/C][C]-0.422092493133577[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.633549741964[/C][C]0.0364502580356471[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.612506625924[/C][C]0.187493374076436[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]114.311747541759[/C][C]-0.871747541759218[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]114.306136044148[/C][C]-0.776136044148335[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.349115227559[/C][C]0.180884772440952[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.294403125853[/C][C]0.215596874147016[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.651398233561[/C][C]0.398601766439361[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.440010998665[/C][C]0.229989001334733[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.545662722606[/C][C]0.524337277394328[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.548468471411[/C][C]0.371531528588895[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]116.569151795373[/C][C]0.4308482046267[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.649724694487[/C][C]-0.629724694487338[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.676019516060[/C][C]-0.326019516060413[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.858631308768[/C][C]-0.498631308768381[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.489531563584[/C][C]-0.66953156358371[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.503291116439[/C][C]-0.62329111643869[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.551105294761[/C][C]-0.311105294761424[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.554418355094[/C][C]-0.0544183550935037[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.568447099121[/C][C]0.231552900879299[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.705559705739[/C][C]0.0544402942607398[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.691530961712[/C][C]0.398469038287933[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97931&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97931&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	101.76	102.165616119899	-0.405616119898775
2	102.37	102.143827571977	0.226172428023504
3	102.38	102.149439069587	0.230560930412621
4	102.86	102.901912378122	-0.0419123781221807
5	102.87	102.868243392457	0.00175660754309176
6	102.92	102.868243392457	0.0517566075430885
7	102.95	102.865437643651	0.0845623563485303
8	103.02	102.889286508498	0.130713491502291
9	104.08	104.436222279751	-0.356222279751498
10	104.16	104.409238944839	-0.249238944839006
11	104.24	104.391001577604	-0.151001577603655
12	104.33	104.374821915746	-0.0448219157457087
13	104.73	105.446871974753	-0.716871974752847
14	104.86	105.441260477142	-0.581260477141975
15	105.03	105.399500354774	-0.369500354774259
16	105.62	106.104129761660	-0.484129761659856
17	105.63	106.087295268827	-0.457295268827233
18	105.63	106.087295268827	-0.457295268827233
19	105.94	106.201906424589	-0.261906424589129
20	106.61	106.193489178173	0.41651082182719
21	107.69	107.97381905924	-0.283819059240057
22	107.78	108.007247313004	-0.227247313003940
23	107.93	108.122283014027	-0.192283014026938
24	108.48	108.169082569296	0.310917430703617
25	108.14	107.95710624822	0.182893751779967
26	108.48	107.955703373817	0.524296626182691
27	108.48	107.924840136957	0.55515986304252
28	108.89	108.801662608126	0.0883373918736056
29	108.93	108.792111678480	0.137888321520438
30	109.21	108.762651316022	0.447348683977534
31	109.47	108.713550711927	0.756449288072724
32	109.8	108.699729017437	1.10027098256326
33	111.73	111.400283640225	0.329716359774716
34	111.85	111.430217631844	0.419782368155679
35	112.12	111.520951463489	0.599048536510853
36	112.15	111.541218076831	0.608781923168917
37	112.17	112.592092493134	-0.422092493133577
38	112.67	112.633549741964	0.0364502580356471
39	112.8	112.612506625924	0.187493374076436
40	113.44	114.311747541759	-0.871747541759218
41	113.53	114.306136044148	-0.776136044148335
42	114.53	114.349115227559	0.180884772440952
43	114.51	114.294403125853	0.215596874147016
44	115.05	114.651398233561	0.398601766439361
45	116.67	116.440010998665	0.229989001334733
46	117.07	116.545662722606	0.524337277394328
47	116.92	116.548468471411	0.371531528588895
48	117	116.569151795373	0.4308482046267
49	117.02	117.649724694487	-0.629724694487338
50	117.35	117.676019516060	-0.326019516060413
51	117.36	117.858631308768	-0.498631308768381
52	117.82	118.489531563584	-0.66953156358371
53	117.88	118.503291116439	-0.62329111643869
54	118.24	118.551105294761	-0.311105294761424
55	118.5	118.554418355094	-0.0544183550935037
56	118.8	118.568447099121	0.231552900879299
57	119.76	119.705559705739	0.0544402942607398
58	120.09	119.691530961712	0.398469038287933

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.00502072491588504	0.0100414498317701	0.994979275084115
10	0.00669688092433359	0.0133937618486672	0.993303119075666
11	0.00174015818935123	0.00348031637870245	0.998259841810649
12	0.00741959649460561	0.0148391929892112	0.992580403505394
13	0.00279602970144358	0.00559205940288716	0.997203970298556
14	0.00110194305715356	0.00220388611430711	0.998898056942847
15	0.000476665719152708	0.000953331438305415	0.999523334280847
16	0.00064992572400615	0.0012998514480123	0.999350074275994
17	0.000320809571281239	0.000641619142562478	0.999679190428719
18	0.000172828219287652	0.000345656438575304	0.999827171780712
19	0.000119458066808872	0.000238916133617744	0.99988054193319
20	0.00186151063345025	0.0037230212669005	0.99813848936655
21	0.00171554117483303	0.00343108234966607	0.998284458825167
22	0.00179872864038976	0.00359745728077952	0.99820127135961
23	0.00384167320135373	0.00768334640270746	0.996158326798646
24	0.00965278179992464	0.0193055635998493	0.990347218200075
25	0.00793658829058407	0.0158731765811681	0.992063411709416
26	0.0101081415990090	0.0202162831980180	0.989891858400991
27	0.0092300943078133	0.0184601886156266	0.990769905692187
28	0.0122062673198159	0.0244125346396317	0.987793732680184
29	0.0124905485051112	0.0249810970102224	0.987509451494889
30	0.0114283557611547	0.0228567115223094	0.988571644238845
31	0.0190947554300832	0.0381895108601665	0.980905244569917
32	0.0535432595351537	0.107086519070307	0.946456740464846
33	0.0592902142808539	0.118580428561708	0.940709785719146
34	0.0416617970909572	0.0833235941819145	0.958338202909043
35	0.0409780145515126	0.0819560291030251	0.959021985448487
36	0.0371994480109550	0.0743988960219099	0.962800551989045
37	0.146917170391747	0.293834340783493	0.853082829608253
38	0.133094861081878	0.266189722163757	0.866905138918121
39	0.107505251252865	0.215010502505729	0.892494748747135
40	0.392622786231283	0.785245572462565	0.607377213768717
41	0.769805673907981	0.460388652184037	0.230194326092019
42	0.692911364555103	0.614177270889794	0.307088635444897
43	0.60160505552487	0.796789888950261	0.398394944475131
44	0.845896298592991	0.308207402814018	0.154103701407009
45	0.969961559340197	0.0600768813196053	0.0300384406598027
46	0.95948545337447	0.081029093251062	0.040514546625531
47	0.933478182701915	0.133043634596170	0.0665218172980848
48	0.857549983752715	0.28490003249457	0.142450016247285
49	0.777445777511192	0.445108444977617	0.222554222488808

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.00502072491588504 & 0.0100414498317701 & 0.994979275084115 \tabularnewline
10 & 0.00669688092433359 & 0.0133937618486672 & 0.993303119075666 \tabularnewline
11 & 0.00174015818935123 & 0.00348031637870245 & 0.998259841810649 \tabularnewline
12 & 0.00741959649460561 & 0.0148391929892112 & 0.992580403505394 \tabularnewline
13 & 0.00279602970144358 & 0.00559205940288716 & 0.997203970298556 \tabularnewline
14 & 0.00110194305715356 & 0.00220388611430711 & 0.998898056942847 \tabularnewline
15 & 0.000476665719152708 & 0.000953331438305415 & 0.999523334280847 \tabularnewline
16 & 0.00064992572400615 & 0.0012998514480123 & 0.999350074275994 \tabularnewline
17 & 0.000320809571281239 & 0.000641619142562478 & 0.999679190428719 \tabularnewline
18 & 0.000172828219287652 & 0.000345656438575304 & 0.999827171780712 \tabularnewline
19 & 0.000119458066808872 & 0.000238916133617744 & 0.99988054193319 \tabularnewline
20 & 0.00186151063345025 & 0.0037230212669005 & 0.99813848936655 \tabularnewline
21 & 0.00171554117483303 & 0.00343108234966607 & 0.998284458825167 \tabularnewline
22 & 0.00179872864038976 & 0.00359745728077952 & 0.99820127135961 \tabularnewline
23 & 0.00384167320135373 & 0.00768334640270746 & 0.996158326798646 \tabularnewline
24 & 0.00965278179992464 & 0.0193055635998493 & 0.990347218200075 \tabularnewline
25 & 0.00793658829058407 & 0.0158731765811681 & 0.992063411709416 \tabularnewline
26 & 0.0101081415990090 & 0.0202162831980180 & 0.989891858400991 \tabularnewline
27 & 0.0092300943078133 & 0.0184601886156266 & 0.990769905692187 \tabularnewline
28 & 0.0122062673198159 & 0.0244125346396317 & 0.987793732680184 \tabularnewline
29 & 0.0124905485051112 & 0.0249810970102224 & 0.987509451494889 \tabularnewline
30 & 0.0114283557611547 & 0.0228567115223094 & 0.988571644238845 \tabularnewline
31 & 0.0190947554300832 & 0.0381895108601665 & 0.980905244569917 \tabularnewline
32 & 0.0535432595351537 & 0.107086519070307 & 0.946456740464846 \tabularnewline
33 & 0.0592902142808539 & 0.118580428561708 & 0.940709785719146 \tabularnewline
34 & 0.0416617970909572 & 0.0833235941819145 & 0.958338202909043 \tabularnewline
35 & 0.0409780145515126 & 0.0819560291030251 & 0.959021985448487 \tabularnewline
36 & 0.0371994480109550 & 0.0743988960219099 & 0.962800551989045 \tabularnewline
37 & 0.146917170391747 & 0.293834340783493 & 0.853082829608253 \tabularnewline
38 & 0.133094861081878 & 0.266189722163757 & 0.866905138918121 \tabularnewline
39 & 0.107505251252865 & 0.215010502505729 & 0.892494748747135 \tabularnewline
40 & 0.392622786231283 & 0.785245572462565 & 0.607377213768717 \tabularnewline
41 & 0.769805673907981 & 0.460388652184037 & 0.230194326092019 \tabularnewline
42 & 0.692911364555103 & 0.614177270889794 & 0.307088635444897 \tabularnewline
43 & 0.60160505552487 & 0.796789888950261 & 0.398394944475131 \tabularnewline
44 & 0.845896298592991 & 0.308207402814018 & 0.154103701407009 \tabularnewline
45 & 0.969961559340197 & 0.0600768813196053 & 0.0300384406598027 \tabularnewline
46 & 0.95948545337447 & 0.081029093251062 & 0.040514546625531 \tabularnewline
47 & 0.933478182701915 & 0.133043634596170 & 0.0665218172980848 \tabularnewline
48 & 0.857549983752715 & 0.28490003249457 & 0.142450016247285 \tabularnewline
49 & 0.777445777511192 & 0.445108444977617 & 0.222554222488808 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97931&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]0.00502072491588504[/C][C]0.0100414498317701[/C][C]0.994979275084115[/C][/ROW]
[ROW][C]10[/C][C]0.00669688092433359[/C][C]0.0133937618486672[/C][C]0.993303119075666[/C][/ROW]
[ROW][C]11[/C][C]0.00174015818935123[/C][C]0.00348031637870245[/C][C]0.998259841810649[/C][/ROW]
[ROW][C]12[/C][C]0.00741959649460561[/C][C]0.0148391929892112[/C][C]0.992580403505394[/C][/ROW]
[ROW][C]13[/C][C]0.00279602970144358[/C][C]0.00559205940288716[/C][C]0.997203970298556[/C][/ROW]
[ROW][C]14[/C][C]0.00110194305715356[/C][C]0.00220388611430711[/C][C]0.998898056942847[/C][/ROW]
[ROW][C]15[/C][C]0.000476665719152708[/C][C]0.000953331438305415[/C][C]0.999523334280847[/C][/ROW]
[ROW][C]16[/C][C]0.00064992572400615[/C][C]0.0012998514480123[/C][C]0.999350074275994[/C][/ROW]
[ROW][C]17[/C][C]0.000320809571281239[/C][C]0.000641619142562478[/C][C]0.999679190428719[/C][/ROW]
[ROW][C]18[/C][C]0.000172828219287652[/C][C]0.000345656438575304[/C][C]0.999827171780712[/C][/ROW]
[ROW][C]19[/C][C]0.000119458066808872[/C][C]0.000238916133617744[/C][C]0.99988054193319[/C][/ROW]
[ROW][C]20[/C][C]0.00186151063345025[/C][C]0.0037230212669005[/C][C]0.99813848936655[/C][/ROW]
[ROW][C]21[/C][C]0.00171554117483303[/C][C]0.00343108234966607[/C][C]0.998284458825167[/C][/ROW]
[ROW][C]22[/C][C]0.00179872864038976[/C][C]0.00359745728077952[/C][C]0.99820127135961[/C][/ROW]
[ROW][C]23[/C][C]0.00384167320135373[/C][C]0.00768334640270746[/C][C]0.996158326798646[/C][/ROW]
[ROW][C]24[/C][C]0.00965278179992464[/C][C]0.0193055635998493[/C][C]0.990347218200075[/C][/ROW]
[ROW][C]25[/C][C]0.00793658829058407[/C][C]0.0158731765811681[/C][C]0.992063411709416[/C][/ROW]
[ROW][C]26[/C][C]0.0101081415990090[/C][C]0.0202162831980180[/C][C]0.989891858400991[/C][/ROW]
[ROW][C]27[/C][C]0.0092300943078133[/C][C]0.0184601886156266[/C][C]0.990769905692187[/C][/ROW]
[ROW][C]28[/C][C]0.0122062673198159[/C][C]0.0244125346396317[/C][C]0.987793732680184[/C][/ROW]
[ROW][C]29[/C][C]0.0124905485051112[/C][C]0.0249810970102224[/C][C]0.987509451494889[/C][/ROW]
[ROW][C]30[/C][C]0.0114283557611547[/C][C]0.0228567115223094[/C][C]0.988571644238845[/C][/ROW]
[ROW][C]31[/C][C]0.0190947554300832[/C][C]0.0381895108601665[/C][C]0.980905244569917[/C][/ROW]
[ROW][C]32[/C][C]0.0535432595351537[/C][C]0.107086519070307[/C][C]0.946456740464846[/C][/ROW]
[ROW][C]33[/C][C]0.0592902142808539[/C][C]0.118580428561708[/C][C]0.940709785719146[/C][/ROW]
[ROW][C]34[/C][C]0.0416617970909572[/C][C]0.0833235941819145[/C][C]0.958338202909043[/C][/ROW]
[ROW][C]35[/C][C]0.0409780145515126[/C][C]0.0819560291030251[/C][C]0.959021985448487[/C][/ROW]
[ROW][C]36[/C][C]0.0371994480109550[/C][C]0.0743988960219099[/C][C]0.962800551989045[/C][/ROW]
[ROW][C]37[/C][C]0.146917170391747[/C][C]0.293834340783493[/C][C]0.853082829608253[/C][/ROW]
[ROW][C]38[/C][C]0.133094861081878[/C][C]0.266189722163757[/C][C]0.866905138918121[/C][/ROW]
[ROW][C]39[/C][C]0.107505251252865[/C][C]0.215010502505729[/C][C]0.892494748747135[/C][/ROW]
[ROW][C]40[/C][C]0.392622786231283[/C][C]0.785245572462565[/C][C]0.607377213768717[/C][/ROW]
[ROW][C]41[/C][C]0.769805673907981[/C][C]0.460388652184037[/C][C]0.230194326092019[/C][/ROW]
[ROW][C]42[/C][C]0.692911364555103[/C][C]0.614177270889794[/C][C]0.307088635444897[/C][/ROW]
[ROW][C]43[/C][C]0.60160505552487[/C][C]0.796789888950261[/C][C]0.398394944475131[/C][/ROW]
[ROW][C]44[/C][C]0.845896298592991[/C][C]0.308207402814018[/C][C]0.154103701407009[/C][/ROW]
[ROW][C]45[/C][C]0.969961559340197[/C][C]0.0600768813196053[/C][C]0.0300384406598027[/C][/ROW]
[ROW][C]46[/C][C]0.95948545337447[/C][C]0.081029093251062[/C][C]0.040514546625531[/C][/ROW]
[ROW][C]47[/C][C]0.933478182701915[/C][C]0.133043634596170[/C][C]0.0665218172980848[/C][/ROW]
[ROW][C]48[/C][C]0.857549983752715[/C][C]0.28490003249457[/C][C]0.142450016247285[/C][/ROW]
[ROW][C]49[/C][C]0.777445777511192[/C][C]0.445108444977617[/C][C]0.222554222488808[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97931&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97931&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	0.00502072491588504	0.0100414498317701	0.994979275084115
10	0.00669688092433359	0.0133937618486672	0.993303119075666
11	0.00174015818935123	0.00348031637870245	0.998259841810649
12	0.00741959649460561	0.0148391929892112	0.992580403505394
13	0.00279602970144358	0.00559205940288716	0.997203970298556
14	0.00110194305715356	0.00220388611430711	0.998898056942847
15	0.000476665719152708	0.000953331438305415	0.999523334280847
16	0.00064992572400615	0.0012998514480123	0.999350074275994
17	0.000320809571281239	0.000641619142562478	0.999679190428719
18	0.000172828219287652	0.000345656438575304	0.999827171780712
19	0.000119458066808872	0.000238916133617744	0.99988054193319
20	0.00186151063345025	0.0037230212669005	0.99813848936655
21	0.00171554117483303	0.00343108234966607	0.998284458825167
22	0.00179872864038976	0.00359745728077952	0.99820127135961
23	0.00384167320135373	0.00768334640270746	0.996158326798646
24	0.00965278179992464	0.0193055635998493	0.990347218200075
25	0.00793658829058407	0.0158731765811681	0.992063411709416
26	0.0101081415990090	0.0202162831980180	0.989891858400991
27	0.0092300943078133	0.0184601886156266	0.990769905692187
28	0.0122062673198159	0.0244125346396317	0.987793732680184
29	0.0124905485051112	0.0249810970102224	0.987509451494889
30	0.0114283557611547	0.0228567115223094	0.988571644238845
31	0.0190947554300832	0.0381895108601665	0.980905244569917
32	0.0535432595351537	0.107086519070307	0.946456740464846
33	0.0592902142808539	0.118580428561708	0.940709785719146
34	0.0416617970909572	0.0833235941819145	0.958338202909043
35	0.0409780145515126	0.0819560291030251	0.959021985448487
36	0.0371994480109550	0.0743988960219099	0.962800551989045
37	0.146917170391747	0.293834340783493	0.853082829608253
38	0.133094861081878	0.266189722163757	0.866905138918121
39	0.107505251252865	0.215010502505729	0.892494748747135
40	0.392622786231283	0.785245572462565	0.607377213768717
41	0.769805673907981	0.460388652184037	0.230194326092019
42	0.692911364555103	0.614177270889794	0.307088635444897
43	0.60160505552487	0.796789888950261	0.398394944475131
44	0.845896298592991	0.308207402814018	0.154103701407009
45	0.969961559340197	0.0600768813196053	0.0300384406598027
46	0.95948545337447	0.081029093251062	0.040514546625531
47	0.933478182701915	0.133043634596170	0.0665218172980848
48	0.857549983752715	0.28490003249457	0.142450016247285
49	0.777445777511192	0.445108444977617	0.222554222488808

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	12	0.292682926829268	NOK
5% type I error level	23	0.560975609756098	NOK
10% type I error level	28	0.682926829268293	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 & 12 & 0.292682926829268 & NOK \tabularnewline
5% type I error level & 23 & 0.560975609756098 & NOK \tabularnewline
10% type I error level & 28 & 0.682926829268293 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97931&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]12[/C][C]0.292682926829268[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.560975609756098[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.682926829268293[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97931&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97931&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	12	0.292682926829268	NOK
5% type I error level	23	0.560975609756098	NOK
10% type I error level	28	0.682926829268293	NOK

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 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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