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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 18 Nov 2009 13:57:24 -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/18/t12585779078fsekstwvqotd1x.htm/, Retrieved Wed, 01 May 2024 20:38:49 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57621, Retrieved Wed, 01 May 2024 20:38:49 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

216

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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS 7 4] [2009-11-14 13:58:56] [6e4e01d7eb22a9f33d58ebb35753a195]
-   PD      [Multiple Regression] [ws7 4] [2009-11-18 20:49:31] [6e4e01d7eb22a9f33d58ebb35753a195]
-    D          [Multiple Regression] [WS 75] [2009-11-18 20:57:24] [2e4ef2c1b76db9b31c0a03b96e94ad77] [Current]
-    D            [Multiple Regression] [Paper hypothese t...] [2010-12-19 12:54:13] [a9e130f95bad0a0597234e75c6380c5a] 
-    D              [Multiple Regression] [Multiple Regression] [2010-12-22 14:47:15] [a9e130f95bad0a0597234e75c6380c5a] 
-    D              [Multiple Regression] [] [2011-12-20 17:24:43] [06f5daa9a1979410bf169cb7a41fb3eb] 

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103.91	89.00	103.88	103.77
103.91	86.40	103.91	103.88
103.92	84.50	103.91	103.91
104.05	82.70	103.92	103.91
104.23	80.80	104.05	103.92
104.30	81.80	104.23	104.05
104.31	81.80	104.30	104.23
104.31	82.90	104.31	104.30
104.34	83.80	104.31	104.31
104.55	86.20	104.34	104.31
104.65	86.10	104.55	104.34
104.73	86.20	104.65	104.55
104.75	88.80	104.73	104.65
104.75	89.60	104.75	104.73
104.76	87.80	104.75	104.75
104.94	88.30	104.76	104.75
105.29	88.60	104.94	104.76
105.38	91.00	105.29	104.94
105.43	91.50	105.38	105.29
105.43	95.40	105.43	105.38
105.42	98.70	105.43	105.43
105.52	99.90	105.42	105.43
105.69	98.60	105.52	105.42
105.72	100.30	105.69	105.52
105.74	100.20	105.72	105.69
105.74	100.40	105.74	105.72
105.74	101.40	105.74	105.74
105.95	103.00	105.74	105.74
106.17	109.10	105.95	105.74
106.34	111.40	106.17	105.95
106.37	114.10	106.34	106.17
106.37	121.80	106.37	106.34
106.36	127.60	106.37	106.37
106.44	129.90	106.36	106.37
106.29	128.00	106.44	106.36
106.23	123.50	106.29	106.44
106.23	124.00	106.23	106.29
106.23	127.40	106.23	106.23
106.23	127.60	106.23	106.23
106.34	128.40	106.23	106.23
106.44	131.40	106.34	106.23
106.44	135.10	106.44	106.34
106.48	134.00	106.44	106.44
106.50	144.50	106.48	106.44
106.57	147.30	106.50	106.48
106.40	150.90	106.57	106.50
106.37	148.70	106.40	106.57
106.25	141.40	106.37	106.40
106.21	138.90	106.25	106.37
106.21	139.80	106.21	106.25
106.24	145.60	106.21	106.21
106.19	147.90	106.24	106.21
106.08	148.50	106.19	106.24
106.13	151.10	106.08	106.19
106.09	157.50	106.13	106.08

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57621&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57621&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57621&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2.85133191081378 -0.00468183176077428X[t] + 1.26651149270306Y1[t] -0.289998539166879`Y2 `[t] + 0.00645926897734081t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2.85133191081378 -0.00468183176077428X[t] +  1.26651149270306Y1[t] -0.289998539166879`Y2
`[t] +  0.00645926897734081t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57621&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2.85133191081378 -0.00468183176077428X[t] +  1.26651149270306Y1[t] -0.289998539166879`Y2
`[t] +  0.00645926897734081t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57621&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57621&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] = + 2.85133191081378 -0.00468183176077428X[t] + 1.26651149270306Y1[t] -0.289998539166879`Y2 `[t] + 0.00645926897734081t + 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)	2.85133191081378	3.467109	0.8224	0.414757	0.207379
X	-0.00468183176077428	0.001887	-2.4805	0.016529	0.008265
Y1	1.26651149270306	0.128257	9.8748	0	0
`Y2 `	-0.289998539166879	0.133035	-2.1799	0.034003	0.017002
t	0.00645926897734081	0.003566	1.8115	0.07608	0.03804

\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) & 2.85133191081378 & 3.467109 & 0.8224 & 0.414757 & 0.207379 \tabularnewline
X & -0.00468183176077428 & 0.001887 & -2.4805 & 0.016529 & 0.008265 \tabularnewline
Y1 & 1.26651149270306 & 0.128257 & 9.8748 & 0 & 0 \tabularnewline
`Y2
` & -0.289998539166879 & 0.133035 & -2.1799 & 0.034003 & 0.017002 \tabularnewline
t & 0.00645926897734081 & 0.003566 & 1.8115 & 0.07608 & 0.03804 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57621&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]2.85133191081378[/C][C]3.467109[/C][C]0.8224[/C][C]0.414757[/C][C]0.207379[/C][/ROW]
[ROW][C]X[/C][C]-0.00468183176077428[/C][C]0.001887[/C][C]-2.4805[/C][C]0.016529[/C][C]0.008265[/C][/ROW]
[ROW][C]Y1[/C][C]1.26651149270306[/C][C]0.128257[/C][C]9.8748[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y2
`[/C][C]-0.289998539166879[/C][C]0.133035[/C][C]-2.1799[/C][C]0.034003[/C][C]0.017002[/C][/ROW]
[ROW][C]t[/C][C]0.00645926897734081[/C][C]0.003566[/C][C]1.8115[/C][C]0.07608[/C][C]0.03804[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57621&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57621&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)	2.85133191081378	3.467109	0.8224	0.414757	0.207379
X	-0.00468183176077428	0.001887	-2.4805	0.016529	0.008265
Y1	1.26651149270306	0.128257	9.8748	0	0
`Y2 `	-0.289998539166879	0.133035	-2.1799	0.034003	0.017002
t	0.00645926897734081	0.003566	1.8115	0.07608	0.03804

Multiple Linear Regression - Regression Statistics
Multiple R	0.995797803529525
R-squared	0.991613265514226
Adjusted R-squared	0.990942326755364
F-TEST (value)	1477.94899671055
F-TEST (DF numerator)	4
F-TEST (DF denominator)	50
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0808895041753397
Sum Squared Residuals	0.327155594286615

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.995797803529525 \tabularnewline
R-squared & 0.991613265514226 \tabularnewline
Adjusted R-squared & 0.990942326755364 \tabularnewline
F-TEST (value) & 1477.94899671055 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0808895041753397 \tabularnewline
Sum Squared Residuals & 0.327155594286615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57621&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.995797803529525[/C][/ROW]
[ROW][C]R-squared[/C][C]0.991613265514226[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.990942326755364[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1477.94899671055[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/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]0.0808895041753397[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.327155594286615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57621&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57621&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	0.995797803529525
R-squared	0.991613265514226
Adjusted R-squared	0.990942326755364
F-TEST (value)	1477.94899671055
F-TEST (DF numerator)	4
F-TEST (DF denominator)	50
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0808895041753397
Sum Squared Residuals	0.327155594286615

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	103.91	103.913173605729	-0.00317360572922217
2	103.91	103.937901142757	-0.0279011427570598
3	103.92	103.944555935905	-0.0245559359048573
4	104.05	103.972107616979	0.0778923830213673
5	104.23	104.149208874961	0.0807911250388404
6	104.3	104.341258570773	-0.0412585707725991
7	104.31	104.384173907189	-0.0741739071891
8	104.31	104.377848378415	-0.0678483784149466
9	104.34	104.377194013416	-0.0371940134159191
10	104.55	104.410412230948	0.139587769051499
11	104.65	104.674607140395	-0.0246071403945386
12	104.73	104.746349682241	-0.0163496822410774
13	104.75	104.81295725414	-0.0629572541399611
14	104.75	104.817801404429	-0.0678014044293888
15	104.76	104.826887999793	-0.0668879997927819
16	104.94	104.843671467817	0.09632853218322
17	105.29	105.073798270561	0.216201729439249
18	105.38	105.460100428708	-0.0801004287082893
19	105.43	105.47670532744	-0.0467053274400829
20	105.43	105.502131158661	-0.0721311586605553
21	105.42	105.478640455869	-0.058640455868999
22	105.52	105.466816411806	0.0531835881936207
23	105.69	105.608913196735	0.0810868032653062
24	105.72	105.793720451562	-0.073720451561553
25	105.74	105.789343496838	-0.0493434968376984
26	105.74	105.811496673142	-0.0714966731419339
27	105.74	105.807474139575	-0.0674741395751641
28	105.95	105.806442477735	0.143557522264742
29	106.17	106.050309986440	0.119690013560471
30	106.34	106.263733877537	0.0762661224632882
31	106.37	106.409059475903	-0.03905947590277
32	106.37	106.368164233445	0.00183576655512804
33	106.36	106.338768922035	0.0212310779652797
34	106.44	106.321794863035	0.118205136964755
35	106.29	106.441370517166	-0.151370517165961
36	106.23	106.255721422028	-0.0257214220279908
37	106.23	106.227348866438	0.00265113356221255
38	106.23	106.235289819779	-0.00528981977850895
39	106.23	106.240812722404	-0.0108127224036950
40	106.34	106.243526525972	0.096473474027583
41	106.44	106.375256563865	0.0647434361352238
42	106.44	106.459144365289	-0.0191443652891944
43	106.48	106.441753795287	0.0382462047133056
44	106.5	106.449714290484	0.0502857095159608
45	106.57	106.456794718819	0.113205281181402
46	106.4	106.529255227163	-0.129255227163008
47	106.37	106.310407674513	0.0595923254871302
48	106.25	106.362348722221	-0.11234872222114
49	106.21	106.237231147651	-0.0272311476510567
50	106.21	106.223616133036	-0.013616133035597
51	106.24	106.214520719367	0.0254792806328769
52	106.19	106.248207120076	-0.0582071200757735
53	106.08	106.179831759186	-0.0998317591864931
54	106.13	106.049301928347	0.080698071653169
55	106.09	106.121022887999	-0.0310228879987143

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.91 & 103.913173605729 & -0.00317360572922217 \tabularnewline
2 & 103.91 & 103.937901142757 & -0.0279011427570598 \tabularnewline
3 & 103.92 & 103.944555935905 & -0.0245559359048573 \tabularnewline
4 & 104.05 & 103.972107616979 & 0.0778923830213673 \tabularnewline
5 & 104.23 & 104.149208874961 & 0.0807911250388404 \tabularnewline
6 & 104.3 & 104.341258570773 & -0.0412585707725991 \tabularnewline
7 & 104.31 & 104.384173907189 & -0.0741739071891 \tabularnewline
8 & 104.31 & 104.377848378415 & -0.0678483784149466 \tabularnewline
9 & 104.34 & 104.377194013416 & -0.0371940134159191 \tabularnewline
10 & 104.55 & 104.410412230948 & 0.139587769051499 \tabularnewline
11 & 104.65 & 104.674607140395 & -0.0246071403945386 \tabularnewline
12 & 104.73 & 104.746349682241 & -0.0163496822410774 \tabularnewline
13 & 104.75 & 104.81295725414 & -0.0629572541399611 \tabularnewline
14 & 104.75 & 104.817801404429 & -0.0678014044293888 \tabularnewline
15 & 104.76 & 104.826887999793 & -0.0668879997927819 \tabularnewline
16 & 104.94 & 104.843671467817 & 0.09632853218322 \tabularnewline
17 & 105.29 & 105.073798270561 & 0.216201729439249 \tabularnewline
18 & 105.38 & 105.460100428708 & -0.0801004287082893 \tabularnewline
19 & 105.43 & 105.47670532744 & -0.0467053274400829 \tabularnewline
20 & 105.43 & 105.502131158661 & -0.0721311586605553 \tabularnewline
21 & 105.42 & 105.478640455869 & -0.058640455868999 \tabularnewline
22 & 105.52 & 105.466816411806 & 0.0531835881936207 \tabularnewline
23 & 105.69 & 105.608913196735 & 0.0810868032653062 \tabularnewline
24 & 105.72 & 105.793720451562 & -0.073720451561553 \tabularnewline
25 & 105.74 & 105.789343496838 & -0.0493434968376984 \tabularnewline
26 & 105.74 & 105.811496673142 & -0.0714966731419339 \tabularnewline
27 & 105.74 & 105.807474139575 & -0.0674741395751641 \tabularnewline
28 & 105.95 & 105.806442477735 & 0.143557522264742 \tabularnewline
29 & 106.17 & 106.050309986440 & 0.119690013560471 \tabularnewline
30 & 106.34 & 106.263733877537 & 0.0762661224632882 \tabularnewline
31 & 106.37 & 106.409059475903 & -0.03905947590277 \tabularnewline
32 & 106.37 & 106.368164233445 & 0.00183576655512804 \tabularnewline
33 & 106.36 & 106.338768922035 & 0.0212310779652797 \tabularnewline
34 & 106.44 & 106.321794863035 & 0.118205136964755 \tabularnewline
35 & 106.29 & 106.441370517166 & -0.151370517165961 \tabularnewline
36 & 106.23 & 106.255721422028 & -0.0257214220279908 \tabularnewline
37 & 106.23 & 106.227348866438 & 0.00265113356221255 \tabularnewline
38 & 106.23 & 106.235289819779 & -0.00528981977850895 \tabularnewline
39 & 106.23 & 106.240812722404 & -0.0108127224036950 \tabularnewline
40 & 106.34 & 106.243526525972 & 0.096473474027583 \tabularnewline
41 & 106.44 & 106.375256563865 & 0.0647434361352238 \tabularnewline
42 & 106.44 & 106.459144365289 & -0.0191443652891944 \tabularnewline
43 & 106.48 & 106.441753795287 & 0.0382462047133056 \tabularnewline
44 & 106.5 & 106.449714290484 & 0.0502857095159608 \tabularnewline
45 & 106.57 & 106.456794718819 & 0.113205281181402 \tabularnewline
46 & 106.4 & 106.529255227163 & -0.129255227163008 \tabularnewline
47 & 106.37 & 106.310407674513 & 0.0595923254871302 \tabularnewline
48 & 106.25 & 106.362348722221 & -0.11234872222114 \tabularnewline
49 & 106.21 & 106.237231147651 & -0.0272311476510567 \tabularnewline
50 & 106.21 & 106.223616133036 & -0.013616133035597 \tabularnewline
51 & 106.24 & 106.214520719367 & 0.0254792806328769 \tabularnewline
52 & 106.19 & 106.248207120076 & -0.0582071200757735 \tabularnewline
53 & 106.08 & 106.179831759186 & -0.0998317591864931 \tabularnewline
54 & 106.13 & 106.049301928347 & 0.080698071653169 \tabularnewline
55 & 106.09 & 106.121022887999 & -0.0310228879987143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57621&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]103.91[/C][C]103.913173605729[/C][C]-0.00317360572922217[/C][/ROW]
[ROW][C]2[/C][C]103.91[/C][C]103.937901142757[/C][C]-0.0279011427570598[/C][/ROW]
[ROW][C]3[/C][C]103.92[/C][C]103.944555935905[/C][C]-0.0245559359048573[/C][/ROW]
[ROW][C]4[/C][C]104.05[/C][C]103.972107616979[/C][C]0.0778923830213673[/C][/ROW]
[ROW][C]5[/C][C]104.23[/C][C]104.149208874961[/C][C]0.0807911250388404[/C][/ROW]
[ROW][C]6[/C][C]104.3[/C][C]104.341258570773[/C][C]-0.0412585707725991[/C][/ROW]
[ROW][C]7[/C][C]104.31[/C][C]104.384173907189[/C][C]-0.0741739071891[/C][/ROW]
[ROW][C]8[/C][C]104.31[/C][C]104.377848378415[/C][C]-0.0678483784149466[/C][/ROW]
[ROW][C]9[/C][C]104.34[/C][C]104.377194013416[/C][C]-0.0371940134159191[/C][/ROW]
[ROW][C]10[/C][C]104.55[/C][C]104.410412230948[/C][C]0.139587769051499[/C][/ROW]
[ROW][C]11[/C][C]104.65[/C][C]104.674607140395[/C][C]-0.0246071403945386[/C][/ROW]
[ROW][C]12[/C][C]104.73[/C][C]104.746349682241[/C][C]-0.0163496822410774[/C][/ROW]
[ROW][C]13[/C][C]104.75[/C][C]104.81295725414[/C][C]-0.0629572541399611[/C][/ROW]
[ROW][C]14[/C][C]104.75[/C][C]104.817801404429[/C][C]-0.0678014044293888[/C][/ROW]
[ROW][C]15[/C][C]104.76[/C][C]104.826887999793[/C][C]-0.0668879997927819[/C][/ROW]
[ROW][C]16[/C][C]104.94[/C][C]104.843671467817[/C][C]0.09632853218322[/C][/ROW]
[ROW][C]17[/C][C]105.29[/C][C]105.073798270561[/C][C]0.216201729439249[/C][/ROW]
[ROW][C]18[/C][C]105.38[/C][C]105.460100428708[/C][C]-0.0801004287082893[/C][/ROW]
[ROW][C]19[/C][C]105.43[/C][C]105.47670532744[/C][C]-0.0467053274400829[/C][/ROW]
[ROW][C]20[/C][C]105.43[/C][C]105.502131158661[/C][C]-0.0721311586605553[/C][/ROW]
[ROW][C]21[/C][C]105.42[/C][C]105.478640455869[/C][C]-0.058640455868999[/C][/ROW]
[ROW][C]22[/C][C]105.52[/C][C]105.466816411806[/C][C]0.0531835881936207[/C][/ROW]
[ROW][C]23[/C][C]105.69[/C][C]105.608913196735[/C][C]0.0810868032653062[/C][/ROW]
[ROW][C]24[/C][C]105.72[/C][C]105.793720451562[/C][C]-0.073720451561553[/C][/ROW]
[ROW][C]25[/C][C]105.74[/C][C]105.789343496838[/C][C]-0.0493434968376984[/C][/ROW]
[ROW][C]26[/C][C]105.74[/C][C]105.811496673142[/C][C]-0.0714966731419339[/C][/ROW]
[ROW][C]27[/C][C]105.74[/C][C]105.807474139575[/C][C]-0.0674741395751641[/C][/ROW]
[ROW][C]28[/C][C]105.95[/C][C]105.806442477735[/C][C]0.143557522264742[/C][/ROW]
[ROW][C]29[/C][C]106.17[/C][C]106.050309986440[/C][C]0.119690013560471[/C][/ROW]
[ROW][C]30[/C][C]106.34[/C][C]106.263733877537[/C][C]0.0762661224632882[/C][/ROW]
[ROW][C]31[/C][C]106.37[/C][C]106.409059475903[/C][C]-0.03905947590277[/C][/ROW]
[ROW][C]32[/C][C]106.37[/C][C]106.368164233445[/C][C]0.00183576655512804[/C][/ROW]
[ROW][C]33[/C][C]106.36[/C][C]106.338768922035[/C][C]0.0212310779652797[/C][/ROW]
[ROW][C]34[/C][C]106.44[/C][C]106.321794863035[/C][C]0.118205136964755[/C][/ROW]
[ROW][C]35[/C][C]106.29[/C][C]106.441370517166[/C][C]-0.151370517165961[/C][/ROW]
[ROW][C]36[/C][C]106.23[/C][C]106.255721422028[/C][C]-0.0257214220279908[/C][/ROW]
[ROW][C]37[/C][C]106.23[/C][C]106.227348866438[/C][C]0.00265113356221255[/C][/ROW]
[ROW][C]38[/C][C]106.23[/C][C]106.235289819779[/C][C]-0.00528981977850895[/C][/ROW]
[ROW][C]39[/C][C]106.23[/C][C]106.240812722404[/C][C]-0.0108127224036950[/C][/ROW]
[ROW][C]40[/C][C]106.34[/C][C]106.243526525972[/C][C]0.096473474027583[/C][/ROW]
[ROW][C]41[/C][C]106.44[/C][C]106.375256563865[/C][C]0.0647434361352238[/C][/ROW]
[ROW][C]42[/C][C]106.44[/C][C]106.459144365289[/C][C]-0.0191443652891944[/C][/ROW]
[ROW][C]43[/C][C]106.48[/C][C]106.441753795287[/C][C]0.0382462047133056[/C][/ROW]
[ROW][C]44[/C][C]106.5[/C][C]106.449714290484[/C][C]0.0502857095159608[/C][/ROW]
[ROW][C]45[/C][C]106.57[/C][C]106.456794718819[/C][C]0.113205281181402[/C][/ROW]
[ROW][C]46[/C][C]106.4[/C][C]106.529255227163[/C][C]-0.129255227163008[/C][/ROW]
[ROW][C]47[/C][C]106.37[/C][C]106.310407674513[/C][C]0.0595923254871302[/C][/ROW]
[ROW][C]48[/C][C]106.25[/C][C]106.362348722221[/C][C]-0.11234872222114[/C][/ROW]
[ROW][C]49[/C][C]106.21[/C][C]106.237231147651[/C][C]-0.0272311476510567[/C][/ROW]
[ROW][C]50[/C][C]106.21[/C][C]106.223616133036[/C][C]-0.013616133035597[/C][/ROW]
[ROW][C]51[/C][C]106.24[/C][C]106.214520719367[/C][C]0.0254792806328769[/C][/ROW]
[ROW][C]52[/C][C]106.19[/C][C]106.248207120076[/C][C]-0.0582071200757735[/C][/ROW]
[ROW][C]53[/C][C]106.08[/C][C]106.179831759186[/C][C]-0.0998317591864931[/C][/ROW]
[ROW][C]54[/C][C]106.13[/C][C]106.049301928347[/C][C]0.080698071653169[/C][/ROW]
[ROW][C]55[/C][C]106.09[/C][C]106.121022887999[/C][C]-0.0310228879987143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57621&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57621&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	103.91	103.913173605729	-0.00317360572922217
2	103.91	103.937901142757	-0.0279011427570598
3	103.92	103.944555935905	-0.0245559359048573
4	104.05	103.972107616979	0.0778923830213673
5	104.23	104.149208874961	0.0807911250388404
6	104.3	104.341258570773	-0.0412585707725991
7	104.31	104.384173907189	-0.0741739071891
8	104.31	104.377848378415	-0.0678483784149466
9	104.34	104.377194013416	-0.0371940134159191
10	104.55	104.410412230948	0.139587769051499
11	104.65	104.674607140395	-0.0246071403945386
12	104.73	104.746349682241	-0.0163496822410774
13	104.75	104.81295725414	-0.0629572541399611
14	104.75	104.817801404429	-0.0678014044293888
15	104.76	104.826887999793	-0.0668879997927819
16	104.94	104.843671467817	0.09632853218322
17	105.29	105.073798270561	0.216201729439249
18	105.38	105.460100428708	-0.0801004287082893
19	105.43	105.47670532744	-0.0467053274400829
20	105.43	105.502131158661	-0.0721311586605553
21	105.42	105.478640455869	-0.058640455868999
22	105.52	105.466816411806	0.0531835881936207
23	105.69	105.608913196735	0.0810868032653062
24	105.72	105.793720451562	-0.073720451561553
25	105.74	105.789343496838	-0.0493434968376984
26	105.74	105.811496673142	-0.0714966731419339
27	105.74	105.807474139575	-0.0674741395751641
28	105.95	105.806442477735	0.143557522264742
29	106.17	106.050309986440	0.119690013560471
30	106.34	106.263733877537	0.0762661224632882
31	106.37	106.409059475903	-0.03905947590277
32	106.37	106.368164233445	0.00183576655512804
33	106.36	106.338768922035	0.0212310779652797
34	106.44	106.321794863035	0.118205136964755
35	106.29	106.441370517166	-0.151370517165961
36	106.23	106.255721422028	-0.0257214220279908
37	106.23	106.227348866438	0.00265113356221255
38	106.23	106.235289819779	-0.00528981977850895
39	106.23	106.240812722404	-0.0108127224036950
40	106.34	106.243526525972	0.096473474027583
41	106.44	106.375256563865	0.0647434361352238
42	106.44	106.459144365289	-0.0191443652891944
43	106.48	106.441753795287	0.0382462047133056
44	106.5	106.449714290484	0.0502857095159608
45	106.57	106.456794718819	0.113205281181402
46	106.4	106.529255227163	-0.129255227163008
47	106.37	106.310407674513	0.0595923254871302
48	106.25	106.362348722221	-0.11234872222114
49	106.21	106.237231147651	-0.0272311476510567
50	106.21	106.223616133036	-0.013616133035597
51	106.24	106.214520719367	0.0254792806328769
52	106.19	106.248207120076	-0.0582071200757735
53	106.08	106.179831759186	-0.0998317591864931
54	106.13	106.049301928347	0.080698071653169
55	106.09	106.121022887999	-0.0310228879987143

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0465051922971893	0.0930103845943787	0.95349480770281
9	0.0157527027918181	0.0315054055836363	0.984247297208182
10	0.0317969130893807	0.0635938261787614	0.96820308691062
11	0.0389038082890316	0.0778076165780632	0.961096191710968
12	0.0349181746922135	0.069836349384427	0.965081825307786
13	0.0163647054213892	0.0327294108427784	0.98363529457861
14	0.00904133244438548	0.0180826648887710	0.990958667555615
15	0.0102999955086691	0.0205999910173382	0.98970000449133
16	0.00682084438361271	0.0136416887672254	0.993179155616387
17	0.0988442277214873	0.197688455442975	0.901155772278513
18	0.0650645450744863	0.130129090148973	0.934935454925514
19	0.282858853391818	0.565717706783637	0.717141146608182
20	0.248402935746490	0.496805871492981	0.75159706425351
21	0.210577172043988	0.421154344087977	0.789422827956011
22	0.160419036506981	0.320838073013962	0.839580963493019
23	0.127177751855763	0.254355503711527	0.872822248144237
24	0.150412330852787	0.300824661705574	0.849587669147213
25	0.128239490420713	0.256478980841425	0.871760509579287
26	0.169209118201504	0.338418236403009	0.830790881798496
27	0.352493072034138	0.704986144068276	0.647506927965862
28	0.305721588575387	0.611443177150774	0.694278411424613
29	0.241953669835749	0.483907339671498	0.758046330164251
30	0.213860805114185	0.427721610228369	0.786139194885815
31	0.158781175513222	0.317562351026445	0.841218824486778
32	0.113115162979296	0.226230325958593	0.886884837020704
33	0.0854094935325607	0.170818987065121	0.91459050646744
34	0.0730768732463889	0.146153746492778	0.926923126753611
35	0.536687753852254	0.926624492295492	0.463312246147746
36	0.573330132740862	0.853339734518275	0.426669867259138
37	0.602238744260342	0.795522511479317	0.397761255739658
38	0.678537219326828	0.642925561346344	0.321462780673172
39	0.859139732360346	0.281720535279308	0.140860267639654
40	0.893333420997179	0.213333158005643	0.106666579002821
41	0.880633123871401	0.238733752257198	0.119366876128599
42	0.962619955809162	0.0747600883816756	0.0373800441908378
43	0.935881921972426	0.128236156055147	0.0641180780275735
44	0.978036619935195	0.0439267601296100	0.0219633800648050
45	0.981675428664667	0.0366491426706665	0.0183245713353332
46	0.959295031950771	0.0814099360984575	0.0407049680492287
47	0.906090927913122	0.187818144173756	0.093909072086878

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0465051922971893 & 0.0930103845943787 & 0.95349480770281 \tabularnewline
9 & 0.0157527027918181 & 0.0315054055836363 & 0.984247297208182 \tabularnewline
10 & 0.0317969130893807 & 0.0635938261787614 & 0.96820308691062 \tabularnewline
11 & 0.0389038082890316 & 0.0778076165780632 & 0.961096191710968 \tabularnewline
12 & 0.0349181746922135 & 0.069836349384427 & 0.965081825307786 \tabularnewline
13 & 0.0163647054213892 & 0.0327294108427784 & 0.98363529457861 \tabularnewline
14 & 0.00904133244438548 & 0.0180826648887710 & 0.990958667555615 \tabularnewline
15 & 0.0102999955086691 & 0.0205999910173382 & 0.98970000449133 \tabularnewline
16 & 0.00682084438361271 & 0.0136416887672254 & 0.993179155616387 \tabularnewline
17 & 0.0988442277214873 & 0.197688455442975 & 0.901155772278513 \tabularnewline
18 & 0.0650645450744863 & 0.130129090148973 & 0.934935454925514 \tabularnewline
19 & 0.282858853391818 & 0.565717706783637 & 0.717141146608182 \tabularnewline
20 & 0.248402935746490 & 0.496805871492981 & 0.75159706425351 \tabularnewline
21 & 0.210577172043988 & 0.421154344087977 & 0.789422827956011 \tabularnewline
22 & 0.160419036506981 & 0.320838073013962 & 0.839580963493019 \tabularnewline
23 & 0.127177751855763 & 0.254355503711527 & 0.872822248144237 \tabularnewline
24 & 0.150412330852787 & 0.300824661705574 & 0.849587669147213 \tabularnewline
25 & 0.128239490420713 & 0.256478980841425 & 0.871760509579287 \tabularnewline
26 & 0.169209118201504 & 0.338418236403009 & 0.830790881798496 \tabularnewline
27 & 0.352493072034138 & 0.704986144068276 & 0.647506927965862 \tabularnewline
28 & 0.305721588575387 & 0.611443177150774 & 0.694278411424613 \tabularnewline
29 & 0.241953669835749 & 0.483907339671498 & 0.758046330164251 \tabularnewline
30 & 0.213860805114185 & 0.427721610228369 & 0.786139194885815 \tabularnewline
31 & 0.158781175513222 & 0.317562351026445 & 0.841218824486778 \tabularnewline
32 & 0.113115162979296 & 0.226230325958593 & 0.886884837020704 \tabularnewline
33 & 0.0854094935325607 & 0.170818987065121 & 0.91459050646744 \tabularnewline
34 & 0.0730768732463889 & 0.146153746492778 & 0.926923126753611 \tabularnewline
35 & 0.536687753852254 & 0.926624492295492 & 0.463312246147746 \tabularnewline
36 & 0.573330132740862 & 0.853339734518275 & 0.426669867259138 \tabularnewline
37 & 0.602238744260342 & 0.795522511479317 & 0.397761255739658 \tabularnewline
38 & 0.678537219326828 & 0.642925561346344 & 0.321462780673172 \tabularnewline
39 & 0.859139732360346 & 0.281720535279308 & 0.140860267639654 \tabularnewline
40 & 0.893333420997179 & 0.213333158005643 & 0.106666579002821 \tabularnewline
41 & 0.880633123871401 & 0.238733752257198 & 0.119366876128599 \tabularnewline
42 & 0.962619955809162 & 0.0747600883816756 & 0.0373800441908378 \tabularnewline
43 & 0.935881921972426 & 0.128236156055147 & 0.0641180780275735 \tabularnewline
44 & 0.978036619935195 & 0.0439267601296100 & 0.0219633800648050 \tabularnewline
45 & 0.981675428664667 & 0.0366491426706665 & 0.0183245713353332 \tabularnewline
46 & 0.959295031950771 & 0.0814099360984575 & 0.0407049680492287 \tabularnewline
47 & 0.906090927913122 & 0.187818144173756 & 0.093909072086878 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57621&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]8[/C][C]0.0465051922971893[/C][C]0.0930103845943787[/C][C]0.95349480770281[/C][/ROW]
[ROW][C]9[/C][C]0.0157527027918181[/C][C]0.0315054055836363[/C][C]0.984247297208182[/C][/ROW]
[ROW][C]10[/C][C]0.0317969130893807[/C][C]0.0635938261787614[/C][C]0.96820308691062[/C][/ROW]
[ROW][C]11[/C][C]0.0389038082890316[/C][C]0.0778076165780632[/C][C]0.961096191710968[/C][/ROW]
[ROW][C]12[/C][C]0.0349181746922135[/C][C]0.069836349384427[/C][C]0.965081825307786[/C][/ROW]
[ROW][C]13[/C][C]0.0163647054213892[/C][C]0.0327294108427784[/C][C]0.98363529457861[/C][/ROW]
[ROW][C]14[/C][C]0.00904133244438548[/C][C]0.0180826648887710[/C][C]0.990958667555615[/C][/ROW]
[ROW][C]15[/C][C]0.0102999955086691[/C][C]0.0205999910173382[/C][C]0.98970000449133[/C][/ROW]
[ROW][C]16[/C][C]0.00682084438361271[/C][C]0.0136416887672254[/C][C]0.993179155616387[/C][/ROW]
[ROW][C]17[/C][C]0.0988442277214873[/C][C]0.197688455442975[/C][C]0.901155772278513[/C][/ROW]
[ROW][C]18[/C][C]0.0650645450744863[/C][C]0.130129090148973[/C][C]0.934935454925514[/C][/ROW]
[ROW][C]19[/C][C]0.282858853391818[/C][C]0.565717706783637[/C][C]0.717141146608182[/C][/ROW]
[ROW][C]20[/C][C]0.248402935746490[/C][C]0.496805871492981[/C][C]0.75159706425351[/C][/ROW]
[ROW][C]21[/C][C]0.210577172043988[/C][C]0.421154344087977[/C][C]0.789422827956011[/C][/ROW]
[ROW][C]22[/C][C]0.160419036506981[/C][C]0.320838073013962[/C][C]0.839580963493019[/C][/ROW]
[ROW][C]23[/C][C]0.127177751855763[/C][C]0.254355503711527[/C][C]0.872822248144237[/C][/ROW]
[ROW][C]24[/C][C]0.150412330852787[/C][C]0.300824661705574[/C][C]0.849587669147213[/C][/ROW]
[ROW][C]25[/C][C]0.128239490420713[/C][C]0.256478980841425[/C][C]0.871760509579287[/C][/ROW]
[ROW][C]26[/C][C]0.169209118201504[/C][C]0.338418236403009[/C][C]0.830790881798496[/C][/ROW]
[ROW][C]27[/C][C]0.352493072034138[/C][C]0.704986144068276[/C][C]0.647506927965862[/C][/ROW]
[ROW][C]28[/C][C]0.305721588575387[/C][C]0.611443177150774[/C][C]0.694278411424613[/C][/ROW]
[ROW][C]29[/C][C]0.241953669835749[/C][C]0.483907339671498[/C][C]0.758046330164251[/C][/ROW]
[ROW][C]30[/C][C]0.213860805114185[/C][C]0.427721610228369[/C][C]0.786139194885815[/C][/ROW]
[ROW][C]31[/C][C]0.158781175513222[/C][C]0.317562351026445[/C][C]0.841218824486778[/C][/ROW]
[ROW][C]32[/C][C]0.113115162979296[/C][C]0.226230325958593[/C][C]0.886884837020704[/C][/ROW]
[ROW][C]33[/C][C]0.0854094935325607[/C][C]0.170818987065121[/C][C]0.91459050646744[/C][/ROW]
[ROW][C]34[/C][C]0.0730768732463889[/C][C]0.146153746492778[/C][C]0.926923126753611[/C][/ROW]
[ROW][C]35[/C][C]0.536687753852254[/C][C]0.926624492295492[/C][C]0.463312246147746[/C][/ROW]
[ROW][C]36[/C][C]0.573330132740862[/C][C]0.853339734518275[/C][C]0.426669867259138[/C][/ROW]
[ROW][C]37[/C][C]0.602238744260342[/C][C]0.795522511479317[/C][C]0.397761255739658[/C][/ROW]
[ROW][C]38[/C][C]0.678537219326828[/C][C]0.642925561346344[/C][C]0.321462780673172[/C][/ROW]
[ROW][C]39[/C][C]0.859139732360346[/C][C]0.281720535279308[/C][C]0.140860267639654[/C][/ROW]
[ROW][C]40[/C][C]0.893333420997179[/C][C]0.213333158005643[/C][C]0.106666579002821[/C][/ROW]
[ROW][C]41[/C][C]0.880633123871401[/C][C]0.238733752257198[/C][C]0.119366876128599[/C][/ROW]
[ROW][C]42[/C][C]0.962619955809162[/C][C]0.0747600883816756[/C][C]0.0373800441908378[/C][/ROW]
[ROW][C]43[/C][C]0.935881921972426[/C][C]0.128236156055147[/C][C]0.0641180780275735[/C][/ROW]
[ROW][C]44[/C][C]0.978036619935195[/C][C]0.0439267601296100[/C][C]0.0219633800648050[/C][/ROW]
[ROW][C]45[/C][C]0.981675428664667[/C][C]0.0366491426706665[/C][C]0.0183245713353332[/C][/ROW]
[ROW][C]46[/C][C]0.959295031950771[/C][C]0.0814099360984575[/C][C]0.0407049680492287[/C][/ROW]
[ROW][C]47[/C][C]0.906090927913122[/C][C]0.187818144173756[/C][C]0.093909072086878[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57621&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57621&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
8	0.0465051922971893	0.0930103845943787	0.95349480770281
9	0.0157527027918181	0.0315054055836363	0.984247297208182
10	0.0317969130893807	0.0635938261787614	0.96820308691062
11	0.0389038082890316	0.0778076165780632	0.961096191710968
12	0.0349181746922135	0.069836349384427	0.965081825307786
13	0.0163647054213892	0.0327294108427784	0.98363529457861
14	0.00904133244438548	0.0180826648887710	0.990958667555615
15	0.0102999955086691	0.0205999910173382	0.98970000449133
16	0.00682084438361271	0.0136416887672254	0.993179155616387
17	0.0988442277214873	0.197688455442975	0.901155772278513
18	0.0650645450744863	0.130129090148973	0.934935454925514
19	0.282858853391818	0.565717706783637	0.717141146608182
20	0.248402935746490	0.496805871492981	0.75159706425351
21	0.210577172043988	0.421154344087977	0.789422827956011
22	0.160419036506981	0.320838073013962	0.839580963493019
23	0.127177751855763	0.254355503711527	0.872822248144237
24	0.150412330852787	0.300824661705574	0.849587669147213
25	0.128239490420713	0.256478980841425	0.871760509579287
26	0.169209118201504	0.338418236403009	0.830790881798496
27	0.352493072034138	0.704986144068276	0.647506927965862
28	0.305721588575387	0.611443177150774	0.694278411424613
29	0.241953669835749	0.483907339671498	0.758046330164251
30	0.213860805114185	0.427721610228369	0.786139194885815
31	0.158781175513222	0.317562351026445	0.841218824486778
32	0.113115162979296	0.226230325958593	0.886884837020704
33	0.0854094935325607	0.170818987065121	0.91459050646744
34	0.0730768732463889	0.146153746492778	0.926923126753611
35	0.536687753852254	0.926624492295492	0.463312246147746
36	0.573330132740862	0.853339734518275	0.426669867259138
37	0.602238744260342	0.795522511479317	0.397761255739658
38	0.678537219326828	0.642925561346344	0.321462780673172
39	0.859139732360346	0.281720535279308	0.140860267639654
40	0.893333420997179	0.213333158005643	0.106666579002821
41	0.880633123871401	0.238733752257198	0.119366876128599
42	0.962619955809162	0.0747600883816756	0.0373800441908378
43	0.935881921972426	0.128236156055147	0.0641180780275735
44	0.978036619935195	0.0439267601296100	0.0219633800648050
45	0.981675428664667	0.0366491426706665	0.0183245713353332
46	0.959295031950771	0.0814099360984575	0.0407049680492287
47	0.906090927913122	0.187818144173756	0.093909072086878

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	7	0.175	NOK
10% type I error level	13	0.325	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 & 0 & 0 & OK \tabularnewline
5% type I error level & 7 & 0.175 & NOK \tabularnewline
10% type I error level & 13 & 0.325 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57621&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]7[/C][C]0.175[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.325[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57621&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57621&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	7	0.175	NOK
10% type I error level	13	0.325	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 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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