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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 00:41:25 -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/20/t1258702963azcaalt7gwjjguj.htm/, Retrieved Tue, 16 Apr 2024 16:00:32 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57977, Retrieved Tue, 16 Apr 2024 16:00:32 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

170

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 07:41:25] [2b679e8ec54382eeb0ec0b6bb527570a] [Current]

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Dataseries X:

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100.03	2
100.25	1.8
99.6	2.7
100.16	2.3
100.49	1.9
99.72	2
100.14	2.3
98.48	2.8
100.38	2.4
101.45	2.3
98.42	2.7
98.6	2.7
100.06	2.9
98.62	3
100.84	2.2
100.02	2.3
97.95	2.8
98.32	2.8
98.27	2.8
97.22	2.2
99.28	2.6
100.38	2.8
99.02	2.5
100.32	2.4
99.81	2.3
100.6	1.9
101.19	1.7
100.47	2
101.77	2.1
102.32	1.7
102.39	1.8
101.16	1.8
100.63	1.8
101.48	1.3
101.44	1.3
100.09	1.3
100.7	1.2
100.78	1.4
99.81	2.2
98.45	2.9
98.49	3.1
97.48	3.5
97.91	3.6
96.94	4.4
98.53	4.1
96.82	5.1
95.76	5.8
95.27	5.9
97.32	5.4
96.68	5.5
97.87	4.8
97.42	3.2
97.94	2.7
99.52	2.1
100.99	1.9
99.92	0.6
101.97	0.7
101.58	-0.2
99.54	-1
100.83	-1.7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57977&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]5 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=57977&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57977&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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 100.937861512697 -0.903708260706107X[t] + 1.14037328685191M1[t] + 0.906224956423668M2[t] + 1.38222495642367M3[t] + 0.661557469496567M4[t] + 0.667483304282443M5[t] + 0.721112478211832M6[t] + 1.2433349738542M7[t] -0.0611100174305318M8[t] + 1.31674165214122M9[t] + 1.44651915649886M10[t] -0.0594808435011405M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  100.937861512697 -0.903708260706107X[t] +  1.14037328685191M1[t] +  0.906224956423668M2[t] +  1.38222495642367M3[t] +  0.661557469496567M4[t] +  0.667483304282443M5[t] +  0.721112478211832M6[t] +  1.2433349738542M7[t] -0.0611100174305318M8[t] +  1.31674165214122M9[t] +  1.44651915649886M10[t] -0.0594808435011405M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57977&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  100.937861512697 -0.903708260706107X[t] +  1.14037328685191M1[t] +  0.906224956423668M2[t] +  1.38222495642367M3[t] +  0.661557469496567M4[t] +  0.667483304282443M5[t] +  0.721112478211832M6[t] +  1.2433349738542M7[t] -0.0611100174305318M8[t] +  1.31674165214122M9[t] +  1.44651915649886M10[t] -0.0594808435011405M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57977&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57977&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] = + 100.937861512697 -0.903708260706107X[t] + 1.14037328685191M1[t] + 0.906224956423668M2[t] + 1.38222495642367M3[t] + 0.661557469496567M4[t] + 0.667483304282443M5[t] + 0.721112478211832M6[t] + 1.2433349738542M7[t] -0.0611100174305318M8[t] + 1.31674165214122M9[t] + 1.44651915649886M10[t] -0.0594808435011405M11[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)	100.937861512697	0.503342	200.5355	0	0
X	-0.903708260706107	0.09529	-9.4837	0	0
M1	1.14037328685191	0.654832	1.7415	0.088143	0.044071
M2	0.906224956423668	0.654488	1.3846	0.172703	0.086352
M3	1.38222495642367	0.654488	2.1119	0.040037	0.020018
M4	0.661557469496567	0.653213	1.0128	0.316353	0.158177
M5	0.667483304282443	0.653099	1.022	0.312001	0.156001
M6	0.721112478211832	0.652612	1.105	0.2748	0.1374
M7	1.2433349738542	0.652887	1.9044	0.062994	0.031497
M8	-0.0611100174305318	0.652387	-0.0937	0.925768	0.462884
M9	1.31674165214122	0.652264	2.0187	0.049242	0.024621
M10	1.44651915649886	0.652122	2.2182	0.031412	0.015706
M11	-0.0594808435011405	0.652122	-0.0912	0.927712	0.463856

\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) & 100.937861512697 & 0.503342 & 200.5355 & 0 & 0 \tabularnewline
X & -0.903708260706107 & 0.09529 & -9.4837 & 0 & 0 \tabularnewline
M1 & 1.14037328685191 & 0.654832 & 1.7415 & 0.088143 & 0.044071 \tabularnewline
M2 & 0.906224956423668 & 0.654488 & 1.3846 & 0.172703 & 0.086352 \tabularnewline
M3 & 1.38222495642367 & 0.654488 & 2.1119 & 0.040037 & 0.020018 \tabularnewline
M4 & 0.661557469496567 & 0.653213 & 1.0128 & 0.316353 & 0.158177 \tabularnewline
M5 & 0.667483304282443 & 0.653099 & 1.022 & 0.312001 & 0.156001 \tabularnewline
M6 & 0.721112478211832 & 0.652612 & 1.105 & 0.2748 & 0.1374 \tabularnewline
M7 & 1.2433349738542 & 0.652887 & 1.9044 & 0.062994 & 0.031497 \tabularnewline
M8 & -0.0611100174305318 & 0.652387 & -0.0937 & 0.925768 & 0.462884 \tabularnewline
M9 & 1.31674165214122 & 0.652264 & 2.0187 & 0.049242 & 0.024621 \tabularnewline
M10 & 1.44651915649886 & 0.652122 & 2.2182 & 0.031412 & 0.015706 \tabularnewline
M11 & -0.0594808435011405 & 0.652122 & -0.0912 & 0.927712 & 0.463856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57977&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]100.937861512697[/C][C]0.503342[/C][C]200.5355[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.903708260706107[/C][C]0.09529[/C][C]-9.4837[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1.14037328685191[/C][C]0.654832[/C][C]1.7415[/C][C]0.088143[/C][C]0.044071[/C][/ROW]
[ROW][C]M2[/C][C]0.906224956423668[/C][C]0.654488[/C][C]1.3846[/C][C]0.172703[/C][C]0.086352[/C][/ROW]
[ROW][C]M3[/C][C]1.38222495642367[/C][C]0.654488[/C][C]2.1119[/C][C]0.040037[/C][C]0.020018[/C][/ROW]
[ROW][C]M4[/C][C]0.661557469496567[/C][C]0.653213[/C][C]1.0128[/C][C]0.316353[/C][C]0.158177[/C][/ROW]
[ROW][C]M5[/C][C]0.667483304282443[/C][C]0.653099[/C][C]1.022[/C][C]0.312001[/C][C]0.156001[/C][/ROW]
[ROW][C]M6[/C][C]0.721112478211832[/C][C]0.652612[/C][C]1.105[/C][C]0.2748[/C][C]0.1374[/C][/ROW]
[ROW][C]M7[/C][C]1.2433349738542[/C][C]0.652887[/C][C]1.9044[/C][C]0.062994[/C][C]0.031497[/C][/ROW]
[ROW][C]M8[/C][C]-0.0611100174305318[/C][C]0.652387[/C][C]-0.0937[/C][C]0.925768[/C][C]0.462884[/C][/ROW]
[ROW][C]M9[/C][C]1.31674165214122[/C][C]0.652264[/C][C]2.0187[/C][C]0.049242[/C][C]0.024621[/C][/ROW]
[ROW][C]M10[/C][C]1.44651915649886[/C][C]0.652122[/C][C]2.2182[/C][C]0.031412[/C][C]0.015706[/C][/ROW]
[ROW][C]M11[/C][C]-0.0594808435011405[/C][C]0.652122[/C][C]-0.0912[/C][C]0.927712[/C][C]0.463856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57977&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57977&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)	100.937861512697	0.503342	200.5355	0	0
X	-0.903708260706107	0.09529	-9.4837	0	0
M1	1.14037328685191	0.654832	1.7415	0.088143	0.044071
M2	0.906224956423668	0.654488	1.3846	0.172703	0.086352
M3	1.38222495642367	0.654488	2.1119	0.040037	0.020018
M4	0.661557469496567	0.653213	1.0128	0.316353	0.158177
M5	0.667483304282443	0.653099	1.022	0.312001	0.156001
M6	0.721112478211832	0.652612	1.105	0.2748	0.1374
M7	1.2433349738542	0.652887	1.9044	0.062994	0.031497
M8	-0.0611100174305318	0.652387	-0.0937	0.925768	0.462884
M9	1.31674165214122	0.652264	2.0187	0.049242	0.024621
M10	1.44651915649886	0.652122	2.2182	0.031412	0.015706
M11	-0.0594808435011405	0.652122	-0.0912	0.927712	0.463856

Multiple Linear Regression - Regression Statistics
Multiple R	0.828776724306222
R-squared	0.68687085875175
Adjusted R-squared	0.606922992901134
F-TEST (value)	8.5914846061704
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	2.56587797675678e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.03087971742847
Sum Squared Residuals	49.9475106148534

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.828776724306222 \tabularnewline
R-squared & 0.68687085875175 \tabularnewline
Adjusted R-squared & 0.606922992901134 \tabularnewline
F-TEST (value) & 8.5914846061704 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 2.56587797675678e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.03087971742847 \tabularnewline
Sum Squared Residuals & 49.9475106148534 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57977&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.828776724306222[/C][/ROW]
[ROW][C]R-squared[/C][C]0.68687085875175[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.606922992901134[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.5914846061704[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]2.56587797675678e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.03087971742847[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]49.9475106148534[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57977&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57977&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	0.828776724306222
R-squared	0.68687085875175
Adjusted R-squared	0.606922992901134
F-TEST (value)	8.5914846061704
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	2.56587797675678e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.03087971742847
Sum Squared Residuals	49.9475106148534

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.03	100.270818278137	-0.24081827813664
2	100.25	100.217411599850	0.0325884001503753
3	99.6	99.880074165214	-0.280074165214128
4	100.16	99.5208899825695	0.639110017430531
5	100.49	99.8882991216378	0.60170087836221
6	99.72	99.8515574694966	-0.131557469496564
7	100.14	100.102667486927	0.0373325130729031
8	98.48	98.3463683652893	0.133631634710691
9	100.38	100.085703339144	0.294296660856486
10	101.45	100.305851669572	1.14414833042825
11	98.42	98.4383683652893	-0.0183683652893126
12	98.6	98.4978492087905	0.102150791209539
13	100.06	99.4574808435011	0.602519156498856
14	98.62	99.1329616870023	-0.512961687002286
15	100.84	100.331928295567	0.508071704432827
16	100.02	99.5208899825695	0.499110017430531
17	97.95	99.0749616870023	-1.12496168700228
18	98.32	99.1285908609317	-0.808590860931683
19	98.27	99.650813356574	-1.38081335657405
20	97.22	98.888593321713	-1.66859332171298
21	99.28	99.9049616870023	-0.624961687002287
22	100.38	99.8539975392187	0.526002460781295
23	99.02	98.6191100174305	0.40088998256946
24	100.32	98.7689616870023	1.55103831299771
25	99.81	99.9997057999248	-0.189705799924808
26	100.6	100.127040773779	0.472959226220986
27	101.19	100.783782425920	0.406217574079767
28	100.47	99.7920024607813	0.677997539218702
29	101.77	99.7075574694966	2.06244253050343
30	102.32	100.122669947708	2.1973300522916
31	102.39	100.554521617280	1.83547838271985
32	101.16	99.2500766259954	1.90992337400458
33	100.63	100.627928295567	0.00207170443282080
34	101.48	101.209559930278	0.270440069722142
35	101.44	99.7035599302779	1.73644006972213
36	100.09	99.763040773779	0.326959226220998
37	100.7	100.993784886702	-0.293784886701526
38	100.78	100.578894904132	0.201105095867939
39	99.81	100.331928295567	-0.521928295567174
40	98.45	98.9786650261458	-0.528665026145798
41	98.49	98.8038492087905	-0.31384920879046
42	97.48	98.4959950784374	-1.01599507843740
43	97.91	98.9278467480092	-1.01784674800916
44	96.94	96.9004351481595	0.0395648518404578
45	98.53	98.5493992959431	-0.0193992959431265
46	96.82	97.7754685395947	-0.95546853959466
47	95.76	95.6368727571004	0.123127242899625
48	95.27	95.605982774531	-0.335982774530914
49	97.32	97.1982101917359	0.121789808264117
50	96.68	96.873691035237	-0.193691035237014
51	97.87	97.9822868177313	-0.112286817731292
52	97.42	98.707552547934	-1.28755254793397
53	97.94	99.1653325130729	-1.2253325130729
54	99.52	99.761186643426	-0.241186643425955
55	100.99	100.464150791210	0.525849208790454
56	99.92	100.334526538843	-0.414526538842747
57	101.97	101.622007382344	0.347992617656106
58	101.58	102.565122321337	-0.985122321337025
59	99.54	101.782088929902	-2.24208892990191
60	100.83	102.474165555897	-1.64416555589733

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.03 & 100.270818278137 & -0.24081827813664 \tabularnewline
2 & 100.25 & 100.217411599850 & 0.0325884001503753 \tabularnewline
3 & 99.6 & 99.880074165214 & -0.280074165214128 \tabularnewline
4 & 100.16 & 99.5208899825695 & 0.639110017430531 \tabularnewline
5 & 100.49 & 99.8882991216378 & 0.60170087836221 \tabularnewline
6 & 99.72 & 99.8515574694966 & -0.131557469496564 \tabularnewline
7 & 100.14 & 100.102667486927 & 0.0373325130729031 \tabularnewline
8 & 98.48 & 98.3463683652893 & 0.133631634710691 \tabularnewline
9 & 100.38 & 100.085703339144 & 0.294296660856486 \tabularnewline
10 & 101.45 & 100.305851669572 & 1.14414833042825 \tabularnewline
11 & 98.42 & 98.4383683652893 & -0.0183683652893126 \tabularnewline
12 & 98.6 & 98.4978492087905 & 0.102150791209539 \tabularnewline
13 & 100.06 & 99.4574808435011 & 0.602519156498856 \tabularnewline
14 & 98.62 & 99.1329616870023 & -0.512961687002286 \tabularnewline
15 & 100.84 & 100.331928295567 & 0.508071704432827 \tabularnewline
16 & 100.02 & 99.5208899825695 & 0.499110017430531 \tabularnewline
17 & 97.95 & 99.0749616870023 & -1.12496168700228 \tabularnewline
18 & 98.32 & 99.1285908609317 & -0.808590860931683 \tabularnewline
19 & 98.27 & 99.650813356574 & -1.38081335657405 \tabularnewline
20 & 97.22 & 98.888593321713 & -1.66859332171298 \tabularnewline
21 & 99.28 & 99.9049616870023 & -0.624961687002287 \tabularnewline
22 & 100.38 & 99.8539975392187 & 0.526002460781295 \tabularnewline
23 & 99.02 & 98.6191100174305 & 0.40088998256946 \tabularnewline
24 & 100.32 & 98.7689616870023 & 1.55103831299771 \tabularnewline
25 & 99.81 & 99.9997057999248 & -0.189705799924808 \tabularnewline
26 & 100.6 & 100.127040773779 & 0.472959226220986 \tabularnewline
27 & 101.19 & 100.783782425920 & 0.406217574079767 \tabularnewline
28 & 100.47 & 99.7920024607813 & 0.677997539218702 \tabularnewline
29 & 101.77 & 99.7075574694966 & 2.06244253050343 \tabularnewline
30 & 102.32 & 100.122669947708 & 2.1973300522916 \tabularnewline
31 & 102.39 & 100.554521617280 & 1.83547838271985 \tabularnewline
32 & 101.16 & 99.2500766259954 & 1.90992337400458 \tabularnewline
33 & 100.63 & 100.627928295567 & 0.00207170443282080 \tabularnewline
34 & 101.48 & 101.209559930278 & 0.270440069722142 \tabularnewline
35 & 101.44 & 99.7035599302779 & 1.73644006972213 \tabularnewline
36 & 100.09 & 99.763040773779 & 0.326959226220998 \tabularnewline
37 & 100.7 & 100.993784886702 & -0.293784886701526 \tabularnewline
38 & 100.78 & 100.578894904132 & 0.201105095867939 \tabularnewline
39 & 99.81 & 100.331928295567 & -0.521928295567174 \tabularnewline
40 & 98.45 & 98.9786650261458 & -0.528665026145798 \tabularnewline
41 & 98.49 & 98.8038492087905 & -0.31384920879046 \tabularnewline
42 & 97.48 & 98.4959950784374 & -1.01599507843740 \tabularnewline
43 & 97.91 & 98.9278467480092 & -1.01784674800916 \tabularnewline
44 & 96.94 & 96.9004351481595 & 0.0395648518404578 \tabularnewline
45 & 98.53 & 98.5493992959431 & -0.0193992959431265 \tabularnewline
46 & 96.82 & 97.7754685395947 & -0.95546853959466 \tabularnewline
47 & 95.76 & 95.6368727571004 & 0.123127242899625 \tabularnewline
48 & 95.27 & 95.605982774531 & -0.335982774530914 \tabularnewline
49 & 97.32 & 97.1982101917359 & 0.121789808264117 \tabularnewline
50 & 96.68 & 96.873691035237 & -0.193691035237014 \tabularnewline
51 & 97.87 & 97.9822868177313 & -0.112286817731292 \tabularnewline
52 & 97.42 & 98.707552547934 & -1.28755254793397 \tabularnewline
53 & 97.94 & 99.1653325130729 & -1.2253325130729 \tabularnewline
54 & 99.52 & 99.761186643426 & -0.241186643425955 \tabularnewline
55 & 100.99 & 100.464150791210 & 0.525849208790454 \tabularnewline
56 & 99.92 & 100.334526538843 & -0.414526538842747 \tabularnewline
57 & 101.97 & 101.622007382344 & 0.347992617656106 \tabularnewline
58 & 101.58 & 102.565122321337 & -0.985122321337025 \tabularnewline
59 & 99.54 & 101.782088929902 & -2.24208892990191 \tabularnewline
60 & 100.83 & 102.474165555897 & -1.64416555589733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57977&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]100.03[/C][C]100.270818278137[/C][C]-0.24081827813664[/C][/ROW]
[ROW][C]2[/C][C]100.25[/C][C]100.217411599850[/C][C]0.0325884001503753[/C][/ROW]
[ROW][C]3[/C][C]99.6[/C][C]99.880074165214[/C][C]-0.280074165214128[/C][/ROW]
[ROW][C]4[/C][C]100.16[/C][C]99.5208899825695[/C][C]0.639110017430531[/C][/ROW]
[ROW][C]5[/C][C]100.49[/C][C]99.8882991216378[/C][C]0.60170087836221[/C][/ROW]
[ROW][C]6[/C][C]99.72[/C][C]99.8515574694966[/C][C]-0.131557469496564[/C][/ROW]
[ROW][C]7[/C][C]100.14[/C][C]100.102667486927[/C][C]0.0373325130729031[/C][/ROW]
[ROW][C]8[/C][C]98.48[/C][C]98.3463683652893[/C][C]0.133631634710691[/C][/ROW]
[ROW][C]9[/C][C]100.38[/C][C]100.085703339144[/C][C]0.294296660856486[/C][/ROW]
[ROW][C]10[/C][C]101.45[/C][C]100.305851669572[/C][C]1.14414833042825[/C][/ROW]
[ROW][C]11[/C][C]98.42[/C][C]98.4383683652893[/C][C]-0.0183683652893126[/C][/ROW]
[ROW][C]12[/C][C]98.6[/C][C]98.4978492087905[/C][C]0.102150791209539[/C][/ROW]
[ROW][C]13[/C][C]100.06[/C][C]99.4574808435011[/C][C]0.602519156498856[/C][/ROW]
[ROW][C]14[/C][C]98.62[/C][C]99.1329616870023[/C][C]-0.512961687002286[/C][/ROW]
[ROW][C]15[/C][C]100.84[/C][C]100.331928295567[/C][C]0.508071704432827[/C][/ROW]
[ROW][C]16[/C][C]100.02[/C][C]99.5208899825695[/C][C]0.499110017430531[/C][/ROW]
[ROW][C]17[/C][C]97.95[/C][C]99.0749616870023[/C][C]-1.12496168700228[/C][/ROW]
[ROW][C]18[/C][C]98.32[/C][C]99.1285908609317[/C][C]-0.808590860931683[/C][/ROW]
[ROW][C]19[/C][C]98.27[/C][C]99.650813356574[/C][C]-1.38081335657405[/C][/ROW]
[ROW][C]20[/C][C]97.22[/C][C]98.888593321713[/C][C]-1.66859332171298[/C][/ROW]
[ROW][C]21[/C][C]99.28[/C][C]99.9049616870023[/C][C]-0.624961687002287[/C][/ROW]
[ROW][C]22[/C][C]100.38[/C][C]99.8539975392187[/C][C]0.526002460781295[/C][/ROW]
[ROW][C]23[/C][C]99.02[/C][C]98.6191100174305[/C][C]0.40088998256946[/C][/ROW]
[ROW][C]24[/C][C]100.32[/C][C]98.7689616870023[/C][C]1.55103831299771[/C][/ROW]
[ROW][C]25[/C][C]99.81[/C][C]99.9997057999248[/C][C]-0.189705799924808[/C][/ROW]
[ROW][C]26[/C][C]100.6[/C][C]100.127040773779[/C][C]0.472959226220986[/C][/ROW]
[ROW][C]27[/C][C]101.19[/C][C]100.783782425920[/C][C]0.406217574079767[/C][/ROW]
[ROW][C]28[/C][C]100.47[/C][C]99.7920024607813[/C][C]0.677997539218702[/C][/ROW]
[ROW][C]29[/C][C]101.77[/C][C]99.7075574694966[/C][C]2.06244253050343[/C][/ROW]
[ROW][C]30[/C][C]102.32[/C][C]100.122669947708[/C][C]2.1973300522916[/C][/ROW]
[ROW][C]31[/C][C]102.39[/C][C]100.554521617280[/C][C]1.83547838271985[/C][/ROW]
[ROW][C]32[/C][C]101.16[/C][C]99.2500766259954[/C][C]1.90992337400458[/C][/ROW]
[ROW][C]33[/C][C]100.63[/C][C]100.627928295567[/C][C]0.00207170443282080[/C][/ROW]
[ROW][C]34[/C][C]101.48[/C][C]101.209559930278[/C][C]0.270440069722142[/C][/ROW]
[ROW][C]35[/C][C]101.44[/C][C]99.7035599302779[/C][C]1.73644006972213[/C][/ROW]
[ROW][C]36[/C][C]100.09[/C][C]99.763040773779[/C][C]0.326959226220998[/C][/ROW]
[ROW][C]37[/C][C]100.7[/C][C]100.993784886702[/C][C]-0.293784886701526[/C][/ROW]
[ROW][C]38[/C][C]100.78[/C][C]100.578894904132[/C][C]0.201105095867939[/C][/ROW]
[ROW][C]39[/C][C]99.81[/C][C]100.331928295567[/C][C]-0.521928295567174[/C][/ROW]
[ROW][C]40[/C][C]98.45[/C][C]98.9786650261458[/C][C]-0.528665026145798[/C][/ROW]
[ROW][C]41[/C][C]98.49[/C][C]98.8038492087905[/C][C]-0.31384920879046[/C][/ROW]
[ROW][C]42[/C][C]97.48[/C][C]98.4959950784374[/C][C]-1.01599507843740[/C][/ROW]
[ROW][C]43[/C][C]97.91[/C][C]98.9278467480092[/C][C]-1.01784674800916[/C][/ROW]
[ROW][C]44[/C][C]96.94[/C][C]96.9004351481595[/C][C]0.0395648518404578[/C][/ROW]
[ROW][C]45[/C][C]98.53[/C][C]98.5493992959431[/C][C]-0.0193992959431265[/C][/ROW]
[ROW][C]46[/C][C]96.82[/C][C]97.7754685395947[/C][C]-0.95546853959466[/C][/ROW]
[ROW][C]47[/C][C]95.76[/C][C]95.6368727571004[/C][C]0.123127242899625[/C][/ROW]
[ROW][C]48[/C][C]95.27[/C][C]95.605982774531[/C][C]-0.335982774530914[/C][/ROW]
[ROW][C]49[/C][C]97.32[/C][C]97.1982101917359[/C][C]0.121789808264117[/C][/ROW]
[ROW][C]50[/C][C]96.68[/C][C]96.873691035237[/C][C]-0.193691035237014[/C][/ROW]
[ROW][C]51[/C][C]97.87[/C][C]97.9822868177313[/C][C]-0.112286817731292[/C][/ROW]
[ROW][C]52[/C][C]97.42[/C][C]98.707552547934[/C][C]-1.28755254793397[/C][/ROW]
[ROW][C]53[/C][C]97.94[/C][C]99.1653325130729[/C][C]-1.2253325130729[/C][/ROW]
[ROW][C]54[/C][C]99.52[/C][C]99.761186643426[/C][C]-0.241186643425955[/C][/ROW]
[ROW][C]55[/C][C]100.99[/C][C]100.464150791210[/C][C]0.525849208790454[/C][/ROW]
[ROW][C]56[/C][C]99.92[/C][C]100.334526538843[/C][C]-0.414526538842747[/C][/ROW]
[ROW][C]57[/C][C]101.97[/C][C]101.622007382344[/C][C]0.347992617656106[/C][/ROW]
[ROW][C]58[/C][C]101.58[/C][C]102.565122321337[/C][C]-0.985122321337025[/C][/ROW]
[ROW][C]59[/C][C]99.54[/C][C]101.782088929902[/C][C]-2.24208892990191[/C][/ROW]
[ROW][C]60[/C][C]100.83[/C][C]102.474165555897[/C][C]-1.64416555589733[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57977&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57977&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	100.03	100.270818278137	-0.24081827813664
2	100.25	100.217411599850	0.0325884001503753
3	99.6	99.880074165214	-0.280074165214128
4	100.16	99.5208899825695	0.639110017430531
5	100.49	99.8882991216378	0.60170087836221
6	99.72	99.8515574694966	-0.131557469496564
7	100.14	100.102667486927	0.0373325130729031
8	98.48	98.3463683652893	0.133631634710691
9	100.38	100.085703339144	0.294296660856486
10	101.45	100.305851669572	1.14414833042825
11	98.42	98.4383683652893	-0.0183683652893126
12	98.6	98.4978492087905	0.102150791209539
13	100.06	99.4574808435011	0.602519156498856
14	98.62	99.1329616870023	-0.512961687002286
15	100.84	100.331928295567	0.508071704432827
16	100.02	99.5208899825695	0.499110017430531
17	97.95	99.0749616870023	-1.12496168700228
18	98.32	99.1285908609317	-0.808590860931683
19	98.27	99.650813356574	-1.38081335657405
20	97.22	98.888593321713	-1.66859332171298
21	99.28	99.9049616870023	-0.624961687002287
22	100.38	99.8539975392187	0.526002460781295
23	99.02	98.6191100174305	0.40088998256946
24	100.32	98.7689616870023	1.55103831299771
25	99.81	99.9997057999248	-0.189705799924808
26	100.6	100.127040773779	0.472959226220986
27	101.19	100.783782425920	0.406217574079767
28	100.47	99.7920024607813	0.677997539218702
29	101.77	99.7075574694966	2.06244253050343
30	102.32	100.122669947708	2.1973300522916
31	102.39	100.554521617280	1.83547838271985
32	101.16	99.2500766259954	1.90992337400458
33	100.63	100.627928295567	0.00207170443282080
34	101.48	101.209559930278	0.270440069722142
35	101.44	99.7035599302779	1.73644006972213
36	100.09	99.763040773779	0.326959226220998
37	100.7	100.993784886702	-0.293784886701526
38	100.78	100.578894904132	0.201105095867939
39	99.81	100.331928295567	-0.521928295567174
40	98.45	98.9786650261458	-0.528665026145798
41	98.49	98.8038492087905	-0.31384920879046
42	97.48	98.4959950784374	-1.01599507843740
43	97.91	98.9278467480092	-1.01784674800916
44	96.94	96.9004351481595	0.0395648518404578
45	98.53	98.5493992959431	-0.0193992959431265
46	96.82	97.7754685395947	-0.95546853959466
47	95.76	95.6368727571004	0.123127242899625
48	95.27	95.605982774531	-0.335982774530914
49	97.32	97.1982101917359	0.121789808264117
50	96.68	96.873691035237	-0.193691035237014
51	97.87	97.9822868177313	-0.112286817731292
52	97.42	98.707552547934	-1.28755254793397
53	97.94	99.1653325130729	-1.2253325130729
54	99.52	99.761186643426	-0.241186643425955
55	100.99	100.464150791210	0.525849208790454
56	99.92	100.334526538843	-0.414526538842747
57	101.97	101.622007382344	0.347992617656106
58	101.58	102.565122321337	-0.985122321337025
59	99.54	101.782088929902	-2.24208892990191
60	100.83	102.474165555897	-1.64416555589733

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.093377256290533	0.186754512581066	0.906622743709467
17	0.128343192461834	0.256686384923668	0.871656807538166
18	0.0609806336679589	0.121961267335918	0.93901936633204
19	0.05667594053722	0.11335188107444	0.94332405946278
20	0.167555224161334	0.335110448322668	0.832444775838666
21	0.119938374781386	0.239876749562772	0.880061625218614
22	0.0757757315049425	0.151551463009885	0.924224268495057
23	0.0432512796556601	0.0865025593113203	0.95674872034434
24	0.0527618225758892	0.105523645151778	0.94723817742411
25	0.0312898219827941	0.0625796439655881	0.968710178017206
26	0.0178183108396721	0.0356366216793442	0.982181689160328
27	0.00945218629562	0.01890437259124	0.99054781370438
28	0.00591539844874431	0.0118307968974886	0.994084601551256
29	0.0471966389344575	0.094393277868915	0.952803361065543
30	0.181854678661941	0.363709357323881	0.81814532133806
31	0.301158267732361	0.602316535464722	0.698841732267639
32	0.473421265777375	0.94684253155475	0.526578734222624
33	0.39652107770793	0.79304215541586	0.60347892229207
34	0.493648509761575	0.98729701952315	0.506351490238425
35	0.831659113601575	0.33668177279685	0.168340886398425
36	0.899017117404902	0.201965765190196	0.100982882595098
37	0.861632025541726	0.276735948916549	0.138367974458274
38	0.830357879625222	0.339284240749556	0.169642120374778
39	0.751528170692735	0.49694365861453	0.248471829307265
40	0.691369985331837	0.617260029336326	0.308630014668163
41	0.620353882613647	0.759292234772705	0.379646117386353
42	0.559848203396805	0.88030359320639	0.440151796603195
43	0.732549255768553	0.534901488462894	0.267450744231447
44	0.591623034770257	0.816753930459486	0.408376965229743

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.093377256290533 & 0.186754512581066 & 0.906622743709467 \tabularnewline
17 & 0.128343192461834 & 0.256686384923668 & 0.871656807538166 \tabularnewline
18 & 0.0609806336679589 & 0.121961267335918 & 0.93901936633204 \tabularnewline
19 & 0.05667594053722 & 0.11335188107444 & 0.94332405946278 \tabularnewline
20 & 0.167555224161334 & 0.335110448322668 & 0.832444775838666 \tabularnewline
21 & 0.119938374781386 & 0.239876749562772 & 0.880061625218614 \tabularnewline
22 & 0.0757757315049425 & 0.151551463009885 & 0.924224268495057 \tabularnewline
23 & 0.0432512796556601 & 0.0865025593113203 & 0.95674872034434 \tabularnewline
24 & 0.0527618225758892 & 0.105523645151778 & 0.94723817742411 \tabularnewline
25 & 0.0312898219827941 & 0.0625796439655881 & 0.968710178017206 \tabularnewline
26 & 0.0178183108396721 & 0.0356366216793442 & 0.982181689160328 \tabularnewline
27 & 0.00945218629562 & 0.01890437259124 & 0.99054781370438 \tabularnewline
28 & 0.00591539844874431 & 0.0118307968974886 & 0.994084601551256 \tabularnewline
29 & 0.0471966389344575 & 0.094393277868915 & 0.952803361065543 \tabularnewline
30 & 0.181854678661941 & 0.363709357323881 & 0.81814532133806 \tabularnewline
31 & 0.301158267732361 & 0.602316535464722 & 0.698841732267639 \tabularnewline
32 & 0.473421265777375 & 0.94684253155475 & 0.526578734222624 \tabularnewline
33 & 0.39652107770793 & 0.79304215541586 & 0.60347892229207 \tabularnewline
34 & 0.493648509761575 & 0.98729701952315 & 0.506351490238425 \tabularnewline
35 & 0.831659113601575 & 0.33668177279685 & 0.168340886398425 \tabularnewline
36 & 0.899017117404902 & 0.201965765190196 & 0.100982882595098 \tabularnewline
37 & 0.861632025541726 & 0.276735948916549 & 0.138367974458274 \tabularnewline
38 & 0.830357879625222 & 0.339284240749556 & 0.169642120374778 \tabularnewline
39 & 0.751528170692735 & 0.49694365861453 & 0.248471829307265 \tabularnewline
40 & 0.691369985331837 & 0.617260029336326 & 0.308630014668163 \tabularnewline
41 & 0.620353882613647 & 0.759292234772705 & 0.379646117386353 \tabularnewline
42 & 0.559848203396805 & 0.88030359320639 & 0.440151796603195 \tabularnewline
43 & 0.732549255768553 & 0.534901488462894 & 0.267450744231447 \tabularnewline
44 & 0.591623034770257 & 0.816753930459486 & 0.408376965229743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57977&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.093377256290533[/C][C]0.186754512581066[/C][C]0.906622743709467[/C][/ROW]
[ROW][C]17[/C][C]0.128343192461834[/C][C]0.256686384923668[/C][C]0.871656807538166[/C][/ROW]
[ROW][C]18[/C][C]0.0609806336679589[/C][C]0.121961267335918[/C][C]0.93901936633204[/C][/ROW]
[ROW][C]19[/C][C]0.05667594053722[/C][C]0.11335188107444[/C][C]0.94332405946278[/C][/ROW]
[ROW][C]20[/C][C]0.167555224161334[/C][C]0.335110448322668[/C][C]0.832444775838666[/C][/ROW]
[ROW][C]21[/C][C]0.119938374781386[/C][C]0.239876749562772[/C][C]0.880061625218614[/C][/ROW]
[ROW][C]22[/C][C]0.0757757315049425[/C][C]0.151551463009885[/C][C]0.924224268495057[/C][/ROW]
[ROW][C]23[/C][C]0.0432512796556601[/C][C]0.0865025593113203[/C][C]0.95674872034434[/C][/ROW]
[ROW][C]24[/C][C]0.0527618225758892[/C][C]0.105523645151778[/C][C]0.94723817742411[/C][/ROW]
[ROW][C]25[/C][C]0.0312898219827941[/C][C]0.0625796439655881[/C][C]0.968710178017206[/C][/ROW]
[ROW][C]26[/C][C]0.0178183108396721[/C][C]0.0356366216793442[/C][C]0.982181689160328[/C][/ROW]
[ROW][C]27[/C][C]0.00945218629562[/C][C]0.01890437259124[/C][C]0.99054781370438[/C][/ROW]
[ROW][C]28[/C][C]0.00591539844874431[/C][C]0.0118307968974886[/C][C]0.994084601551256[/C][/ROW]
[ROW][C]29[/C][C]0.0471966389344575[/C][C]0.094393277868915[/C][C]0.952803361065543[/C][/ROW]
[ROW][C]30[/C][C]0.181854678661941[/C][C]0.363709357323881[/C][C]0.81814532133806[/C][/ROW]
[ROW][C]31[/C][C]0.301158267732361[/C][C]0.602316535464722[/C][C]0.698841732267639[/C][/ROW]
[ROW][C]32[/C][C]0.473421265777375[/C][C]0.94684253155475[/C][C]0.526578734222624[/C][/ROW]
[ROW][C]33[/C][C]0.39652107770793[/C][C]0.79304215541586[/C][C]0.60347892229207[/C][/ROW]
[ROW][C]34[/C][C]0.493648509761575[/C][C]0.98729701952315[/C][C]0.506351490238425[/C][/ROW]
[ROW][C]35[/C][C]0.831659113601575[/C][C]0.33668177279685[/C][C]0.168340886398425[/C][/ROW]
[ROW][C]36[/C][C]0.899017117404902[/C][C]0.201965765190196[/C][C]0.100982882595098[/C][/ROW]
[ROW][C]37[/C][C]0.861632025541726[/C][C]0.276735948916549[/C][C]0.138367974458274[/C][/ROW]
[ROW][C]38[/C][C]0.830357879625222[/C][C]0.339284240749556[/C][C]0.169642120374778[/C][/ROW]
[ROW][C]39[/C][C]0.751528170692735[/C][C]0.49694365861453[/C][C]0.248471829307265[/C][/ROW]
[ROW][C]40[/C][C]0.691369985331837[/C][C]0.617260029336326[/C][C]0.308630014668163[/C][/ROW]
[ROW][C]41[/C][C]0.620353882613647[/C][C]0.759292234772705[/C][C]0.379646117386353[/C][/ROW]
[ROW][C]42[/C][C]0.559848203396805[/C][C]0.88030359320639[/C][C]0.440151796603195[/C][/ROW]
[ROW][C]43[/C][C]0.732549255768553[/C][C]0.534901488462894[/C][C]0.267450744231447[/C][/ROW]
[ROW][C]44[/C][C]0.591623034770257[/C][C]0.816753930459486[/C][C]0.408376965229743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57977&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57977&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.093377256290533	0.186754512581066	0.906622743709467
17	0.128343192461834	0.256686384923668	0.871656807538166
18	0.0609806336679589	0.121961267335918	0.93901936633204
19	0.05667594053722	0.11335188107444	0.94332405946278
20	0.167555224161334	0.335110448322668	0.832444775838666
21	0.119938374781386	0.239876749562772	0.880061625218614
22	0.0757757315049425	0.151551463009885	0.924224268495057
23	0.0432512796556601	0.0865025593113203	0.95674872034434
24	0.0527618225758892	0.105523645151778	0.94723817742411
25	0.0312898219827941	0.0625796439655881	0.968710178017206
26	0.0178183108396721	0.0356366216793442	0.982181689160328
27	0.00945218629562	0.01890437259124	0.99054781370438
28	0.00591539844874431	0.0118307968974886	0.994084601551256
29	0.0471966389344575	0.094393277868915	0.952803361065543
30	0.181854678661941	0.363709357323881	0.81814532133806
31	0.301158267732361	0.602316535464722	0.698841732267639
32	0.473421265777375	0.94684253155475	0.526578734222624
33	0.39652107770793	0.79304215541586	0.60347892229207
34	0.493648509761575	0.98729701952315	0.506351490238425
35	0.831659113601575	0.33668177279685	0.168340886398425
36	0.899017117404902	0.201965765190196	0.100982882595098
37	0.861632025541726	0.276735948916549	0.138367974458274
38	0.830357879625222	0.339284240749556	0.169642120374778
39	0.751528170692735	0.49694365861453	0.248471829307265
40	0.691369985331837	0.617260029336326	0.308630014668163
41	0.620353882613647	0.759292234772705	0.379646117386353
42	0.559848203396805	0.88030359320639	0.440151796603195
43	0.732549255768553	0.534901488462894	0.267450744231447
44	0.591623034770257	0.816753930459486	0.408376965229743

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	3	0.103448275862069	NOK
10% type I error level	6	0.206896551724138	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 & 3 & 0.103448275862069 & NOK \tabularnewline
10% type I error level & 6 & 0.206896551724138 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57977&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]3[/C][C]0.103448275862069[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.206896551724138[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57977&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57977&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	3	0.103448275862069	NOK
10% type I error level	6	0.206896551724138	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code