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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 13:41:09 -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/19/t1258663487fwbv92mcd9banm7.htm/, Retrieved Thu, 25 Apr 2024 16:40:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57946, Retrieved Thu, 25 Apr 2024 16:40:40 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

167

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]
F R PD    [Multiple Regression] [Model 1] [2009-11-19 20:32:12] [c0117c881d5fcd069841276db0c34efe]
F    D      [Multiple Regression] [Model 2] [2009-11-19 20:38:54] [c0117c881d5fcd069841276db0c34efe]
-               [Multiple Regression] [Model 3] [2009-11-19 20:41:09] [d5837f25ec8937f9733a894c487f865c] [Current]
-    D            [Multiple Regression] [Model 4] [2009-11-19 20:47:35] [c0117c881d5fcd069841276db0c34efe] 
-   PD              [Multiple Regression] [Model 5] [2009-11-20 15:42:18] [c0117c881d5fcd069841276db0c34efe] 

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57946&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57946&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57946&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

Multiple Linear Regression - Estimated Regression Equation
Tprod[t] = + 102.226427377846 -0.601785698427419Rente[t] + 0.0235445632537601t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tprod[t] =  +  102.226427377846 -0.601785698427419Rente[t] +  0.0235445632537601t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57946&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tprod[t] =  +  102.226427377846 -0.601785698427419Rente[t] +  0.0235445632537601t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57946&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57946&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
Tprod[t] = + 102.226427377846 -0.601785698427419Rente[t] + 0.0235445632537601t + 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)	102.226427377846	0.828026	123.458	0	0
Rente	-0.601785698427419	0.173837	-3.4618	0.001024	0.000512
t	0.0235445632537601	0.009938	2.3692	0.021236	0.010618

\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) & 102.226427377846 & 0.828026 & 123.458 & 0 & 0 \tabularnewline
Rente & -0.601785698427419 & 0.173837 & -3.4618 & 0.001024 & 0.000512 \tabularnewline
t & 0.0235445632537601 & 0.009938 & 2.3692 & 0.021236 & 0.010618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57946&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]102.226427377846[/C][C]0.828026[/C][C]123.458[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Rente[/C][C]-0.601785698427419[/C][C]0.173837[/C][C]-3.4618[/C][C]0.001024[/C][C]0.000512[/C][/ROW]
[ROW][C]t[/C][C]0.0235445632537601[/C][C]0.009938[/C][C]2.3692[/C][C]0.021236[/C][C]0.010618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57946&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57946&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)	102.226427377846	0.828026	123.458	0	0
Rente	-0.601785698427419	0.173837	-3.4618	0.001024	0.000512
t	0.0235445632537601	0.009938	2.3692	0.021236	0.010618

Multiple Linear Regression - Regression Statistics
Multiple R	0.821645845867635
R-squared	0.675101896031541
Adjusted R-squared	0.663701962558964
F-TEST (value)	59.2198101555151
F-TEST (DF numerator)	2
F-TEST (DF denominator)	57
p-value	1.22124532708767e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.689015006741784
Sum Squared Residuals	27.0602757323767

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.821645845867635 \tabularnewline
R-squared & 0.675101896031541 \tabularnewline
Adjusted R-squared & 0.663701962558964 \tabularnewline
F-TEST (value) & 59.2198101555151 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 1.22124532708767e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.689015006741784 \tabularnewline
Sum Squared Residuals & 27.0602757323767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57946&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.821645845867635[/C][/ROW]
[ROW][C]R-squared[/C][C]0.675101896031541[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.663701962558964[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]59.2198101555151[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]1.22124532708767e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.689015006741784[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]27.0602757323767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57946&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57946&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	0.821645845867635
R-squared	0.675101896031541
Adjusted R-squared	0.663701962558964
F-TEST (value)	59.2198101555151
F-TEST (DF numerator)	2
F-TEST (DF denominator)	57
p-value	1.22124532708767e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.689015006741784
Sum Squared Residuals	27.0602757323767

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.2	100.444614845818	0.755385154181666
2	101.1	100.341784412402	0.758215587598025
3	100.7	100.269043263907	0.430956736092665
4	100.1	100.208337829381	-0.108337829381265
5	99.9	100.087453825012	-0.187453825012433
6	99.7	99.8943555368323	-0.194355536832326
7	99.5	99.8336501023063	-0.33365010230625
8	99.2	99.85719466556	-0.657194665560007
9	99	99.730292804207	-0.730292804206915
10	99	99.6334802277752	-0.633480227775192
11	99.3	99.6269355061076	-0.326935506107584
12	99.5	99.6504800693613	-0.150480069361341
13	99.7	99.6740246326151	0.0259753673849017
14	100	99.6975691958689	0.302430804131139
15	100.4	99.7211137591226	0.678886240877384
16	100.6	99.7446583223764	0.855341677623613
17	100.7	99.8705064543628	0.8294935456372
18	100.7	99.9421938734908	0.757806126509247
19	100.6	99.9657384367445	0.634261563255478
20	100.5	99.9953008569826	0.50469914301745
21	100.6	100.295666841513	0.304333158487071
22	100.5	100.487711400326	0.0122885996736398
23	100.4	100.727898815014	-0.327898815013985
24	100.3	100.835693376048	-0.535693376047592
25	100.4	100.859237939301	-0.459237939301344
26	100.4	100.882782502555	-0.482782502555104
27	100.4	100.906327065809	-0.506327065808864
28	100.4	100.929871629063	-0.529871629062624
29	100.4	100.953416192316	-0.553416192316384
30	100.5	100.97696075557	-0.47696075557015
31	100.6	101.000505318824	-0.400505318823916
32	100.6	101.024049882078	-0.424049882077676
33	100.5	101.047594445331	-0.54759444533143
34	100.5	101.071139008585	-0.57113900858519
35	100.7	101.094683571839	-0.394683571838948
36	101.1	101.358942414464	-0.258942414463683
37	101.5	101.442665547560	0.0573344524398202
38	101.9	101.466210110814	0.433789889186066
39	102.1	101.610111813753	0.489888186246811
40	102.1	101.663745661928	0.43625433807168
41	102.1	101.687290225182	0.41270977481792
42	102.4	101.951549067807	0.448450932193204
43	102.8	102.035272200903	0.764727799096693
44	103.1	102.058816764157	1.04118323584293
45	103.1	102.082361327411	1.01763867258917
46	102.9	102.105905890665	0.794094109335421
47	102.4	102.129450453918	0.270549546081661
48	101.9	102.152995017172	-0.252995017172099
49	101.3	102.176539580426	-0.876539580425867
50	100.7	102.200084143680	-1.50008414367962
51	100.6	102.223628706933	-1.62362870693339
52	101	102.247173270187	-1.24717327018714
53	101.5	102.270717833441	-0.770717833440905
54	101.9	102.294262396695	-0.394262396694659
55	102.1	102.317806959948	-0.217806959948431
56	102.3	102.341351523202	-0.0413515232021879
57	102.5	102.364896086456	0.135103913544055
58	102.9	102.388440649710	0.5115593502903
59	103.6	102.411985212963	1.18801478703653
60	104.3	102.435529776217	1.86447022378277

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.2 & 100.444614845818 & 0.755385154181666 \tabularnewline
2 & 101.1 & 100.341784412402 & 0.758215587598025 \tabularnewline
3 & 100.7 & 100.269043263907 & 0.430956736092665 \tabularnewline
4 & 100.1 & 100.208337829381 & -0.108337829381265 \tabularnewline
5 & 99.9 & 100.087453825012 & -0.187453825012433 \tabularnewline
6 & 99.7 & 99.8943555368323 & -0.194355536832326 \tabularnewline
7 & 99.5 & 99.8336501023063 & -0.33365010230625 \tabularnewline
8 & 99.2 & 99.85719466556 & -0.657194665560007 \tabularnewline
9 & 99 & 99.730292804207 & -0.730292804206915 \tabularnewline
10 & 99 & 99.6334802277752 & -0.633480227775192 \tabularnewline
11 & 99.3 & 99.6269355061076 & -0.326935506107584 \tabularnewline
12 & 99.5 & 99.6504800693613 & -0.150480069361341 \tabularnewline
13 & 99.7 & 99.6740246326151 & 0.0259753673849017 \tabularnewline
14 & 100 & 99.6975691958689 & 0.302430804131139 \tabularnewline
15 & 100.4 & 99.7211137591226 & 0.678886240877384 \tabularnewline
16 & 100.6 & 99.7446583223764 & 0.855341677623613 \tabularnewline
17 & 100.7 & 99.8705064543628 & 0.8294935456372 \tabularnewline
18 & 100.7 & 99.9421938734908 & 0.757806126509247 \tabularnewline
19 & 100.6 & 99.9657384367445 & 0.634261563255478 \tabularnewline
20 & 100.5 & 99.9953008569826 & 0.50469914301745 \tabularnewline
21 & 100.6 & 100.295666841513 & 0.304333158487071 \tabularnewline
22 & 100.5 & 100.487711400326 & 0.0122885996736398 \tabularnewline
23 & 100.4 & 100.727898815014 & -0.327898815013985 \tabularnewline
24 & 100.3 & 100.835693376048 & -0.535693376047592 \tabularnewline
25 & 100.4 & 100.859237939301 & -0.459237939301344 \tabularnewline
26 & 100.4 & 100.882782502555 & -0.482782502555104 \tabularnewline
27 & 100.4 & 100.906327065809 & -0.506327065808864 \tabularnewline
28 & 100.4 & 100.929871629063 & -0.529871629062624 \tabularnewline
29 & 100.4 & 100.953416192316 & -0.553416192316384 \tabularnewline
30 & 100.5 & 100.97696075557 & -0.47696075557015 \tabularnewline
31 & 100.6 & 101.000505318824 & -0.400505318823916 \tabularnewline
32 & 100.6 & 101.024049882078 & -0.424049882077676 \tabularnewline
33 & 100.5 & 101.047594445331 & -0.54759444533143 \tabularnewline
34 & 100.5 & 101.071139008585 & -0.57113900858519 \tabularnewline
35 & 100.7 & 101.094683571839 & -0.394683571838948 \tabularnewline
36 & 101.1 & 101.358942414464 & -0.258942414463683 \tabularnewline
37 & 101.5 & 101.442665547560 & 0.0573344524398202 \tabularnewline
38 & 101.9 & 101.466210110814 & 0.433789889186066 \tabularnewline
39 & 102.1 & 101.610111813753 & 0.489888186246811 \tabularnewline
40 & 102.1 & 101.663745661928 & 0.43625433807168 \tabularnewline
41 & 102.1 & 101.687290225182 & 0.41270977481792 \tabularnewline
42 & 102.4 & 101.951549067807 & 0.448450932193204 \tabularnewline
43 & 102.8 & 102.035272200903 & 0.764727799096693 \tabularnewline
44 & 103.1 & 102.058816764157 & 1.04118323584293 \tabularnewline
45 & 103.1 & 102.082361327411 & 1.01763867258917 \tabularnewline
46 & 102.9 & 102.105905890665 & 0.794094109335421 \tabularnewline
47 & 102.4 & 102.129450453918 & 0.270549546081661 \tabularnewline
48 & 101.9 & 102.152995017172 & -0.252995017172099 \tabularnewline
49 & 101.3 & 102.176539580426 & -0.876539580425867 \tabularnewline
50 & 100.7 & 102.200084143680 & -1.50008414367962 \tabularnewline
51 & 100.6 & 102.223628706933 & -1.62362870693339 \tabularnewline
52 & 101 & 102.247173270187 & -1.24717327018714 \tabularnewline
53 & 101.5 & 102.270717833441 & -0.770717833440905 \tabularnewline
54 & 101.9 & 102.294262396695 & -0.394262396694659 \tabularnewline
55 & 102.1 & 102.317806959948 & -0.217806959948431 \tabularnewline
56 & 102.3 & 102.341351523202 & -0.0413515232021879 \tabularnewline
57 & 102.5 & 102.364896086456 & 0.135103913544055 \tabularnewline
58 & 102.9 & 102.388440649710 & 0.5115593502903 \tabularnewline
59 & 103.6 & 102.411985212963 & 1.18801478703653 \tabularnewline
60 & 104.3 & 102.435529776217 & 1.86447022378277 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57946&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]101.2[/C][C]100.444614845818[/C][C]0.755385154181666[/C][/ROW]
[ROW][C]2[/C][C]101.1[/C][C]100.341784412402[/C][C]0.758215587598025[/C][/ROW]
[ROW][C]3[/C][C]100.7[/C][C]100.269043263907[/C][C]0.430956736092665[/C][/ROW]
[ROW][C]4[/C][C]100.1[/C][C]100.208337829381[/C][C]-0.108337829381265[/C][/ROW]
[ROW][C]5[/C][C]99.9[/C][C]100.087453825012[/C][C]-0.187453825012433[/C][/ROW]
[ROW][C]6[/C][C]99.7[/C][C]99.8943555368323[/C][C]-0.194355536832326[/C][/ROW]
[ROW][C]7[/C][C]99.5[/C][C]99.8336501023063[/C][C]-0.33365010230625[/C][/ROW]
[ROW][C]8[/C][C]99.2[/C][C]99.85719466556[/C][C]-0.657194665560007[/C][/ROW]
[ROW][C]9[/C][C]99[/C][C]99.730292804207[/C][C]-0.730292804206915[/C][/ROW]
[ROW][C]10[/C][C]99[/C][C]99.6334802277752[/C][C]-0.633480227775192[/C][/ROW]
[ROW][C]11[/C][C]99.3[/C][C]99.6269355061076[/C][C]-0.326935506107584[/C][/ROW]
[ROW][C]12[/C][C]99.5[/C][C]99.6504800693613[/C][C]-0.150480069361341[/C][/ROW]
[ROW][C]13[/C][C]99.7[/C][C]99.6740246326151[/C][C]0.0259753673849017[/C][/ROW]
[ROW][C]14[/C][C]100[/C][C]99.6975691958689[/C][C]0.302430804131139[/C][/ROW]
[ROW][C]15[/C][C]100.4[/C][C]99.7211137591226[/C][C]0.678886240877384[/C][/ROW]
[ROW][C]16[/C][C]100.6[/C][C]99.7446583223764[/C][C]0.855341677623613[/C][/ROW]
[ROW][C]17[/C][C]100.7[/C][C]99.8705064543628[/C][C]0.8294935456372[/C][/ROW]
[ROW][C]18[/C][C]100.7[/C][C]99.9421938734908[/C][C]0.757806126509247[/C][/ROW]
[ROW][C]19[/C][C]100.6[/C][C]99.9657384367445[/C][C]0.634261563255478[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]99.9953008569826[/C][C]0.50469914301745[/C][/ROW]
[ROW][C]21[/C][C]100.6[/C][C]100.295666841513[/C][C]0.304333158487071[/C][/ROW]
[ROW][C]22[/C][C]100.5[/C][C]100.487711400326[/C][C]0.0122885996736398[/C][/ROW]
[ROW][C]23[/C][C]100.4[/C][C]100.727898815014[/C][C]-0.327898815013985[/C][/ROW]
[ROW][C]24[/C][C]100.3[/C][C]100.835693376048[/C][C]-0.535693376047592[/C][/ROW]
[ROW][C]25[/C][C]100.4[/C][C]100.859237939301[/C][C]-0.459237939301344[/C][/ROW]
[ROW][C]26[/C][C]100.4[/C][C]100.882782502555[/C][C]-0.482782502555104[/C][/ROW]
[ROW][C]27[/C][C]100.4[/C][C]100.906327065809[/C][C]-0.506327065808864[/C][/ROW]
[ROW][C]28[/C][C]100.4[/C][C]100.929871629063[/C][C]-0.529871629062624[/C][/ROW]
[ROW][C]29[/C][C]100.4[/C][C]100.953416192316[/C][C]-0.553416192316384[/C][/ROW]
[ROW][C]30[/C][C]100.5[/C][C]100.97696075557[/C][C]-0.47696075557015[/C][/ROW]
[ROW][C]31[/C][C]100.6[/C][C]101.000505318824[/C][C]-0.400505318823916[/C][/ROW]
[ROW][C]32[/C][C]100.6[/C][C]101.024049882078[/C][C]-0.424049882077676[/C][/ROW]
[ROW][C]33[/C][C]100.5[/C][C]101.047594445331[/C][C]-0.54759444533143[/C][/ROW]
[ROW][C]34[/C][C]100.5[/C][C]101.071139008585[/C][C]-0.57113900858519[/C][/ROW]
[ROW][C]35[/C][C]100.7[/C][C]101.094683571839[/C][C]-0.394683571838948[/C][/ROW]
[ROW][C]36[/C][C]101.1[/C][C]101.358942414464[/C][C]-0.258942414463683[/C][/ROW]
[ROW][C]37[/C][C]101.5[/C][C]101.442665547560[/C][C]0.0573344524398202[/C][/ROW]
[ROW][C]38[/C][C]101.9[/C][C]101.466210110814[/C][C]0.433789889186066[/C][/ROW]
[ROW][C]39[/C][C]102.1[/C][C]101.610111813753[/C][C]0.489888186246811[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]101.663745661928[/C][C]0.43625433807168[/C][/ROW]
[ROW][C]41[/C][C]102.1[/C][C]101.687290225182[/C][C]0.41270977481792[/C][/ROW]
[ROW][C]42[/C][C]102.4[/C][C]101.951549067807[/C][C]0.448450932193204[/C][/ROW]
[ROW][C]43[/C][C]102.8[/C][C]102.035272200903[/C][C]0.764727799096693[/C][/ROW]
[ROW][C]44[/C][C]103.1[/C][C]102.058816764157[/C][C]1.04118323584293[/C][/ROW]
[ROW][C]45[/C][C]103.1[/C][C]102.082361327411[/C][C]1.01763867258917[/C][/ROW]
[ROW][C]46[/C][C]102.9[/C][C]102.105905890665[/C][C]0.794094109335421[/C][/ROW]
[ROW][C]47[/C][C]102.4[/C][C]102.129450453918[/C][C]0.270549546081661[/C][/ROW]
[ROW][C]48[/C][C]101.9[/C][C]102.152995017172[/C][C]-0.252995017172099[/C][/ROW]
[ROW][C]49[/C][C]101.3[/C][C]102.176539580426[/C][C]-0.876539580425867[/C][/ROW]
[ROW][C]50[/C][C]100.7[/C][C]102.200084143680[/C][C]-1.50008414367962[/C][/ROW]
[ROW][C]51[/C][C]100.6[/C][C]102.223628706933[/C][C]-1.62362870693339[/C][/ROW]
[ROW][C]52[/C][C]101[/C][C]102.247173270187[/C][C]-1.24717327018714[/C][/ROW]
[ROW][C]53[/C][C]101.5[/C][C]102.270717833441[/C][C]-0.770717833440905[/C][/ROW]
[ROW][C]54[/C][C]101.9[/C][C]102.294262396695[/C][C]-0.394262396694659[/C][/ROW]
[ROW][C]55[/C][C]102.1[/C][C]102.317806959948[/C][C]-0.217806959948431[/C][/ROW]
[ROW][C]56[/C][C]102.3[/C][C]102.341351523202[/C][C]-0.0413515232021879[/C][/ROW]
[ROW][C]57[/C][C]102.5[/C][C]102.364896086456[/C][C]0.135103913544055[/C][/ROW]
[ROW][C]58[/C][C]102.9[/C][C]102.388440649710[/C][C]0.5115593502903[/C][/ROW]
[ROW][C]59[/C][C]103.6[/C][C]102.411985212963[/C][C]1.18801478703653[/C][/ROW]
[ROW][C]60[/C][C]104.3[/C][C]102.435529776217[/C][C]1.86447022378277[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57946&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57946&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.2	100.444614845818	0.755385154181666
2	101.1	100.341784412402	0.758215587598025
3	100.7	100.269043263907	0.430956736092665
4	100.1	100.208337829381	-0.108337829381265
5	99.9	100.087453825012	-0.187453825012433
6	99.7	99.8943555368323	-0.194355536832326
7	99.5	99.8336501023063	-0.33365010230625
8	99.2	99.85719466556	-0.657194665560007
9	99	99.730292804207	-0.730292804206915
10	99	99.6334802277752	-0.633480227775192
11	99.3	99.6269355061076	-0.326935506107584
12	99.5	99.6504800693613	-0.150480069361341
13	99.7	99.6740246326151	0.0259753673849017
14	100	99.6975691958689	0.302430804131139
15	100.4	99.7211137591226	0.678886240877384
16	100.6	99.7446583223764	0.855341677623613
17	100.7	99.8705064543628	0.8294935456372
18	100.7	99.9421938734908	0.757806126509247
19	100.6	99.9657384367445	0.634261563255478
20	100.5	99.9953008569826	0.50469914301745
21	100.6	100.295666841513	0.304333158487071
22	100.5	100.487711400326	0.0122885996736398
23	100.4	100.727898815014	-0.327898815013985
24	100.3	100.835693376048	-0.535693376047592
25	100.4	100.859237939301	-0.459237939301344
26	100.4	100.882782502555	-0.482782502555104
27	100.4	100.906327065809	-0.506327065808864
28	100.4	100.929871629063	-0.529871629062624
29	100.4	100.953416192316	-0.553416192316384
30	100.5	100.97696075557	-0.47696075557015
31	100.6	101.000505318824	-0.400505318823916
32	100.6	101.024049882078	-0.424049882077676
33	100.5	101.047594445331	-0.54759444533143
34	100.5	101.071139008585	-0.57113900858519
35	100.7	101.094683571839	-0.394683571838948
36	101.1	101.358942414464	-0.258942414463683
37	101.5	101.442665547560	0.0573344524398202
38	101.9	101.466210110814	0.433789889186066
39	102.1	101.610111813753	0.489888186246811
40	102.1	101.663745661928	0.43625433807168
41	102.1	101.687290225182	0.41270977481792
42	102.4	101.951549067807	0.448450932193204
43	102.8	102.035272200903	0.764727799096693
44	103.1	102.058816764157	1.04118323584293
45	103.1	102.082361327411	1.01763867258917
46	102.9	102.105905890665	0.794094109335421
47	102.4	102.129450453918	0.270549546081661
48	101.9	102.152995017172	-0.252995017172099
49	101.3	102.176539580426	-0.876539580425867
50	100.7	102.200084143680	-1.50008414367962
51	100.6	102.223628706933	-1.62362870693339
52	101	102.247173270187	-1.24717327018714
53	101.5	102.270717833441	-0.770717833440905
54	101.9	102.294262396695	-0.394262396694659
55	102.1	102.317806959948	-0.217806959948431
56	102.3	102.341351523202	-0.0413515232021879
57	102.5	102.364896086456	0.135103913544055
58	102.9	102.388440649710	0.5115593502903
59	103.6	102.411985212963	1.18801478703653
60	104.3	102.435529776217	1.86447022378277

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.00808250383865616	0.0161650076773123	0.991917496161344
7	0.00226721635627179	0.00453443271254359	0.997732783643728
8	0.00131569383772138	0.00263138767544276	0.998684306162279
9	0.000434651757135615	0.00086930351427123	0.999565348242864
10	0.00061029236718969	0.00122058473437938	0.99938970763281
11	0.00396572816705542	0.00793145633411084	0.996034271832945
12	0.00308745799923273	0.00617491599846547	0.996912542000767
13	0.00124667684849381	0.00249335369698761	0.998753323151506
14	0.000451043533484907	0.000902087066969814	0.999548956466515
15	0.000172273225088986	0.000344546450177973	0.99982772677491
16	5.94591794488002e-05	0.000118918358897600	0.999940540820551
17	0.000112262175233024	0.000224524350466049	0.999887737824767
18	0.000322503011659071	0.000645006023318142	0.99967749698834
19	0.000695812827501956	0.00139162565500391	0.999304187172498
20	0.00149893655288699	0.00299787310577397	0.998501063447113
21	0.0108022481718477	0.0216044963436955	0.989197751828152
22	0.0329522599627337	0.0659045199254674	0.967047740037266
23	0.0618725545397259	0.123745109079452	0.938127445460274
24	0.0748471765074719	0.149694353014944	0.925152823492528
25	0.0628209662961335	0.125641932592267	0.937179033703866
26	0.0480901840427665	0.096180368085533	0.951909815957233
27	0.0347449902525305	0.0694899805050609	0.96525500974747
28	0.0240519115555437	0.0481038231110874	0.975948088444456
29	0.0160985312168022	0.0321970624336044	0.983901468783198
30	0.00996304871308032	0.0199260974261606	0.99003695128692
31	0.00582479603999258	0.0116495920799852	0.994175203960007
32	0.00331583488326872	0.00663166976653745	0.996684165116731
33	0.00194730565383747	0.00389461130767494	0.998052694346163
34	0.00118064045659334	0.00236128091318668	0.998819359543407
35	0.000710970426036025	0.00142194085207205	0.999289029573964
36	0.000469341395018991	0.000938682790037982	0.99953065860498
37	0.000369508954605003	0.000739017909210005	0.999630491045395
38	0.000423239054801883	0.000846478109603767	0.999576760945198
39	0.000432152889863948	0.000864305779727895	0.999567847110136
40	0.000340362585049752	0.000680725170099504	0.99965963741495
41	0.000249563419334772	0.000499126838669544	0.999750436580665
42	0.000160499889555568	0.000320999779111136	0.999839500110444
43	0.000219969511172204	0.000439939022344408	0.999780030488828
44	0.000796456942773482	0.00159291388554696	0.999203543057227
45	0.00475524190305482	0.00951048380610965	0.995244758096945
46	0.0401257256510328	0.0802514513020656	0.959874274348967
47	0.240874337226897	0.481748674453793	0.759125662773103
48	0.7707594591131	0.4584810817738	0.2292405408869
49	0.9858237181948	0.0283525636103984	0.0141762818051992
50	0.991085683126021	0.0178286337479575	0.00891431687397873
51	0.98349506915711	0.0330098616857813	0.0165049308428907
52	0.96212658574448	0.0757468285110397	0.0378734142555198
53	0.921843134745118	0.156313730509765	0.0781568652548823
54	0.891041274078858	0.217917451842283	0.108958725921142

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.00808250383865616 & 0.0161650076773123 & 0.991917496161344 \tabularnewline
7 & 0.00226721635627179 & 0.00453443271254359 & 0.997732783643728 \tabularnewline
8 & 0.00131569383772138 & 0.00263138767544276 & 0.998684306162279 \tabularnewline
9 & 0.000434651757135615 & 0.00086930351427123 & 0.999565348242864 \tabularnewline
10 & 0.00061029236718969 & 0.00122058473437938 & 0.99938970763281 \tabularnewline
11 & 0.00396572816705542 & 0.00793145633411084 & 0.996034271832945 \tabularnewline
12 & 0.00308745799923273 & 0.00617491599846547 & 0.996912542000767 \tabularnewline
13 & 0.00124667684849381 & 0.00249335369698761 & 0.998753323151506 \tabularnewline
14 & 0.000451043533484907 & 0.000902087066969814 & 0.999548956466515 \tabularnewline
15 & 0.000172273225088986 & 0.000344546450177973 & 0.99982772677491 \tabularnewline
16 & 5.94591794488002e-05 & 0.000118918358897600 & 0.999940540820551 \tabularnewline
17 & 0.000112262175233024 & 0.000224524350466049 & 0.999887737824767 \tabularnewline
18 & 0.000322503011659071 & 0.000645006023318142 & 0.99967749698834 \tabularnewline
19 & 0.000695812827501956 & 0.00139162565500391 & 0.999304187172498 \tabularnewline
20 & 0.00149893655288699 & 0.00299787310577397 & 0.998501063447113 \tabularnewline
21 & 0.0108022481718477 & 0.0216044963436955 & 0.989197751828152 \tabularnewline
22 & 0.0329522599627337 & 0.0659045199254674 & 0.967047740037266 \tabularnewline
23 & 0.0618725545397259 & 0.123745109079452 & 0.938127445460274 \tabularnewline
24 & 0.0748471765074719 & 0.149694353014944 & 0.925152823492528 \tabularnewline
25 & 0.0628209662961335 & 0.125641932592267 & 0.937179033703866 \tabularnewline
26 & 0.0480901840427665 & 0.096180368085533 & 0.951909815957233 \tabularnewline
27 & 0.0347449902525305 & 0.0694899805050609 & 0.96525500974747 \tabularnewline
28 & 0.0240519115555437 & 0.0481038231110874 & 0.975948088444456 \tabularnewline
29 & 0.0160985312168022 & 0.0321970624336044 & 0.983901468783198 \tabularnewline
30 & 0.00996304871308032 & 0.0199260974261606 & 0.99003695128692 \tabularnewline
31 & 0.00582479603999258 & 0.0116495920799852 & 0.994175203960007 \tabularnewline
32 & 0.00331583488326872 & 0.00663166976653745 & 0.996684165116731 \tabularnewline
33 & 0.00194730565383747 & 0.00389461130767494 & 0.998052694346163 \tabularnewline
34 & 0.00118064045659334 & 0.00236128091318668 & 0.998819359543407 \tabularnewline
35 & 0.000710970426036025 & 0.00142194085207205 & 0.999289029573964 \tabularnewline
36 & 0.000469341395018991 & 0.000938682790037982 & 0.99953065860498 \tabularnewline
37 & 0.000369508954605003 & 0.000739017909210005 & 0.999630491045395 \tabularnewline
38 & 0.000423239054801883 & 0.000846478109603767 & 0.999576760945198 \tabularnewline
39 & 0.000432152889863948 & 0.000864305779727895 & 0.999567847110136 \tabularnewline
40 & 0.000340362585049752 & 0.000680725170099504 & 0.99965963741495 \tabularnewline
41 & 0.000249563419334772 & 0.000499126838669544 & 0.999750436580665 \tabularnewline
42 & 0.000160499889555568 & 0.000320999779111136 & 0.999839500110444 \tabularnewline
43 & 0.000219969511172204 & 0.000439939022344408 & 0.999780030488828 \tabularnewline
44 & 0.000796456942773482 & 0.00159291388554696 & 0.999203543057227 \tabularnewline
45 & 0.00475524190305482 & 0.00951048380610965 & 0.995244758096945 \tabularnewline
46 & 0.0401257256510328 & 0.0802514513020656 & 0.959874274348967 \tabularnewline
47 & 0.240874337226897 & 0.481748674453793 & 0.759125662773103 \tabularnewline
48 & 0.7707594591131 & 0.4584810817738 & 0.2292405408869 \tabularnewline
49 & 0.9858237181948 & 0.0283525636103984 & 0.0141762818051992 \tabularnewline
50 & 0.991085683126021 & 0.0178286337479575 & 0.00891431687397873 \tabularnewline
51 & 0.98349506915711 & 0.0330098616857813 & 0.0165049308428907 \tabularnewline
52 & 0.96212658574448 & 0.0757468285110397 & 0.0378734142555198 \tabularnewline
53 & 0.921843134745118 & 0.156313730509765 & 0.0781568652548823 \tabularnewline
54 & 0.891041274078858 & 0.217917451842283 & 0.108958725921142 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57946&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]6[/C][C]0.00808250383865616[/C][C]0.0161650076773123[/C][C]0.991917496161344[/C][/ROW]
[ROW][C]7[/C][C]0.00226721635627179[/C][C]0.00453443271254359[/C][C]0.997732783643728[/C][/ROW]
[ROW][C]8[/C][C]0.00131569383772138[/C][C]0.00263138767544276[/C][C]0.998684306162279[/C][/ROW]
[ROW][C]9[/C][C]0.000434651757135615[/C][C]0.00086930351427123[/C][C]0.999565348242864[/C][/ROW]
[ROW][C]10[/C][C]0.00061029236718969[/C][C]0.00122058473437938[/C][C]0.99938970763281[/C][/ROW]
[ROW][C]11[/C][C]0.00396572816705542[/C][C]0.00793145633411084[/C][C]0.996034271832945[/C][/ROW]
[ROW][C]12[/C][C]0.00308745799923273[/C][C]0.00617491599846547[/C][C]0.996912542000767[/C][/ROW]
[ROW][C]13[/C][C]0.00124667684849381[/C][C]0.00249335369698761[/C][C]0.998753323151506[/C][/ROW]
[ROW][C]14[/C][C]0.000451043533484907[/C][C]0.000902087066969814[/C][C]0.999548956466515[/C][/ROW]
[ROW][C]15[/C][C]0.000172273225088986[/C][C]0.000344546450177973[/C][C]0.99982772677491[/C][/ROW]
[ROW][C]16[/C][C]5.94591794488002e-05[/C][C]0.000118918358897600[/C][C]0.999940540820551[/C][/ROW]
[ROW][C]17[/C][C]0.000112262175233024[/C][C]0.000224524350466049[/C][C]0.999887737824767[/C][/ROW]
[ROW][C]18[/C][C]0.000322503011659071[/C][C]0.000645006023318142[/C][C]0.99967749698834[/C][/ROW]
[ROW][C]19[/C][C]0.000695812827501956[/C][C]0.00139162565500391[/C][C]0.999304187172498[/C][/ROW]
[ROW][C]20[/C][C]0.00149893655288699[/C][C]0.00299787310577397[/C][C]0.998501063447113[/C][/ROW]
[ROW][C]21[/C][C]0.0108022481718477[/C][C]0.0216044963436955[/C][C]0.989197751828152[/C][/ROW]
[ROW][C]22[/C][C]0.0329522599627337[/C][C]0.0659045199254674[/C][C]0.967047740037266[/C][/ROW]
[ROW][C]23[/C][C]0.0618725545397259[/C][C]0.123745109079452[/C][C]0.938127445460274[/C][/ROW]
[ROW][C]24[/C][C]0.0748471765074719[/C][C]0.149694353014944[/C][C]0.925152823492528[/C][/ROW]
[ROW][C]25[/C][C]0.0628209662961335[/C][C]0.125641932592267[/C][C]0.937179033703866[/C][/ROW]
[ROW][C]26[/C][C]0.0480901840427665[/C][C]0.096180368085533[/C][C]0.951909815957233[/C][/ROW]
[ROW][C]27[/C][C]0.0347449902525305[/C][C]0.0694899805050609[/C][C]0.96525500974747[/C][/ROW]
[ROW][C]28[/C][C]0.0240519115555437[/C][C]0.0481038231110874[/C][C]0.975948088444456[/C][/ROW]
[ROW][C]29[/C][C]0.0160985312168022[/C][C]0.0321970624336044[/C][C]0.983901468783198[/C][/ROW]
[ROW][C]30[/C][C]0.00996304871308032[/C][C]0.0199260974261606[/C][C]0.99003695128692[/C][/ROW]
[ROW][C]31[/C][C]0.00582479603999258[/C][C]0.0116495920799852[/C][C]0.994175203960007[/C][/ROW]
[ROW][C]32[/C][C]0.00331583488326872[/C][C]0.00663166976653745[/C][C]0.996684165116731[/C][/ROW]
[ROW][C]33[/C][C]0.00194730565383747[/C][C]0.00389461130767494[/C][C]0.998052694346163[/C][/ROW]
[ROW][C]34[/C][C]0.00118064045659334[/C][C]0.00236128091318668[/C][C]0.998819359543407[/C][/ROW]
[ROW][C]35[/C][C]0.000710970426036025[/C][C]0.00142194085207205[/C][C]0.999289029573964[/C][/ROW]
[ROW][C]36[/C][C]0.000469341395018991[/C][C]0.000938682790037982[/C][C]0.99953065860498[/C][/ROW]
[ROW][C]37[/C][C]0.000369508954605003[/C][C]0.000739017909210005[/C][C]0.999630491045395[/C][/ROW]
[ROW][C]38[/C][C]0.000423239054801883[/C][C]0.000846478109603767[/C][C]0.999576760945198[/C][/ROW]
[ROW][C]39[/C][C]0.000432152889863948[/C][C]0.000864305779727895[/C][C]0.999567847110136[/C][/ROW]
[ROW][C]40[/C][C]0.000340362585049752[/C][C]0.000680725170099504[/C][C]0.99965963741495[/C][/ROW]
[ROW][C]41[/C][C]0.000249563419334772[/C][C]0.000499126838669544[/C][C]0.999750436580665[/C][/ROW]
[ROW][C]42[/C][C]0.000160499889555568[/C][C]0.000320999779111136[/C][C]0.999839500110444[/C][/ROW]
[ROW][C]43[/C][C]0.000219969511172204[/C][C]0.000439939022344408[/C][C]0.999780030488828[/C][/ROW]
[ROW][C]44[/C][C]0.000796456942773482[/C][C]0.00159291388554696[/C][C]0.999203543057227[/C][/ROW]
[ROW][C]45[/C][C]0.00475524190305482[/C][C]0.00951048380610965[/C][C]0.995244758096945[/C][/ROW]
[ROW][C]46[/C][C]0.0401257256510328[/C][C]0.0802514513020656[/C][C]0.959874274348967[/C][/ROW]
[ROW][C]47[/C][C]0.240874337226897[/C][C]0.481748674453793[/C][C]0.759125662773103[/C][/ROW]
[ROW][C]48[/C][C]0.7707594591131[/C][C]0.4584810817738[/C][C]0.2292405408869[/C][/ROW]
[ROW][C]49[/C][C]0.9858237181948[/C][C]0.0283525636103984[/C][C]0.0141762818051992[/C][/ROW]
[ROW][C]50[/C][C]0.991085683126021[/C][C]0.0178286337479575[/C][C]0.00891431687397873[/C][/ROW]
[ROW][C]51[/C][C]0.98349506915711[/C][C]0.0330098616857813[/C][C]0.0165049308428907[/C][/ROW]
[ROW][C]52[/C][C]0.96212658574448[/C][C]0.0757468285110397[/C][C]0.0378734142555198[/C][/ROW]
[ROW][C]53[/C][C]0.921843134745118[/C][C]0.156313730509765[/C][C]0.0781568652548823[/C][/ROW]
[ROW][C]54[/C][C]0.891041274078858[/C][C]0.217917451842283[/C][C]0.108958725921142[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57946&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57946&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
6	0.00808250383865616	0.0161650076773123	0.991917496161344
7	0.00226721635627179	0.00453443271254359	0.997732783643728
8	0.00131569383772138	0.00263138767544276	0.998684306162279
9	0.000434651757135615	0.00086930351427123	0.999565348242864
10	0.00061029236718969	0.00122058473437938	0.99938970763281
11	0.00396572816705542	0.00793145633411084	0.996034271832945
12	0.00308745799923273	0.00617491599846547	0.996912542000767
13	0.00124667684849381	0.00249335369698761	0.998753323151506
14	0.000451043533484907	0.000902087066969814	0.999548956466515
15	0.000172273225088986	0.000344546450177973	0.99982772677491
16	5.94591794488002e-05	0.000118918358897600	0.999940540820551
17	0.000112262175233024	0.000224524350466049	0.999887737824767
18	0.000322503011659071	0.000645006023318142	0.99967749698834
19	0.000695812827501956	0.00139162565500391	0.999304187172498
20	0.00149893655288699	0.00299787310577397	0.998501063447113
21	0.0108022481718477	0.0216044963436955	0.989197751828152
22	0.0329522599627337	0.0659045199254674	0.967047740037266
23	0.0618725545397259	0.123745109079452	0.938127445460274
24	0.0748471765074719	0.149694353014944	0.925152823492528
25	0.0628209662961335	0.125641932592267	0.937179033703866
26	0.0480901840427665	0.096180368085533	0.951909815957233
27	0.0347449902525305	0.0694899805050609	0.96525500974747
28	0.0240519115555437	0.0481038231110874	0.975948088444456
29	0.0160985312168022	0.0321970624336044	0.983901468783198
30	0.00996304871308032	0.0199260974261606	0.99003695128692
31	0.00582479603999258	0.0116495920799852	0.994175203960007
32	0.00331583488326872	0.00663166976653745	0.996684165116731
33	0.00194730565383747	0.00389461130767494	0.998052694346163
34	0.00118064045659334	0.00236128091318668	0.998819359543407
35	0.000710970426036025	0.00142194085207205	0.999289029573964
36	0.000469341395018991	0.000938682790037982	0.99953065860498
37	0.000369508954605003	0.000739017909210005	0.999630491045395
38	0.000423239054801883	0.000846478109603767	0.999576760945198
39	0.000432152889863948	0.000864305779727895	0.999567847110136
40	0.000340362585049752	0.000680725170099504	0.99965963741495
41	0.000249563419334772	0.000499126838669544	0.999750436580665
42	0.000160499889555568	0.000320999779111136	0.999839500110444
43	0.000219969511172204	0.000439939022344408	0.999780030488828
44	0.000796456942773482	0.00159291388554696	0.999203543057227
45	0.00475524190305482	0.00951048380610965	0.995244758096945
46	0.0401257256510328	0.0802514513020656	0.959874274348967
47	0.240874337226897	0.481748674453793	0.759125662773103
48	0.7707594591131	0.4584810817738	0.2292405408869
49	0.9858237181948	0.0283525636103984	0.0141762818051992
50	0.991085683126021	0.0178286337479575	0.00891431687397873
51	0.98349506915711	0.0330098616857813	0.0165049308428907
52	0.96212658574448	0.0757468285110397	0.0378734142555198
53	0.921843134745118	0.156313730509765	0.0781568652548823
54	0.891041274078858	0.217917451842283	0.108958725921142

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	28	0.571428571428571	NOK
5% type I error level	37	0.755102040816326	NOK
10% type I error level	42	0.857142857142857	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 & 28 & 0.571428571428571 & NOK \tabularnewline
5% type I error level & 37 & 0.755102040816326 & NOK \tabularnewline
10% type I error level & 42 & 0.857142857142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57946&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]28[/C][C]0.571428571428571[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.755102040816326[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]42[/C][C]0.857142857142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57946&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57946&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	28	0.571428571428571	NOK
5% type I error level	37	0.755102040816326	NOK
10% type I error level	42	0.857142857142857	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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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