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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 21 Dec 2011 14:09:04 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/21/t1324494667k10s71q0b6jk2ku.htm/, Retrieved Tue, 07 May 2024 16:04:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158959, Retrieved Tue, 07 May 2024 16:04:18 +0000

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

-     [Cronbach Alpha] [Intrinsic Motivat...] [2010-10-12 11:46:14] [b98453cac15ba1066b407e146608df68]
- RM D  [Random Number Generator - Log-Normal Distribution] [] [2011-10-18 15:45:21] [493236dcc414c5f9e1823f06b33a5ad6]
- RMPD      [Multiple Regression] [] [2011-12-21 19:09:04] [75a32e1bc492240bc1028714aca23077] [Current]

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1.0622	2.1	8.93	2.5974	5.8
1.0773	2.4	8.96	2.9809	5.9
1.0807	2.5	8.99	3.0201	5.9
1.0848	2.1	8.98	2.2247	6
1.1582	1.8	9	2.0578	6.1
1.1663	1.9	9.03	2.1123	6.3
1.1372	1.9	9.02	2.1099	6.2
1.1139	2.1	9	2.1583	6.1
1.1222	2.2	9.03	2.3204	6.1
1.1692	2	9.03	2.0408	6
1.1702	2.2	9.03	1.765	5.8
1.2286	2	9.07	1.8795	5.7
1.2613	1.9	9.15	1.9263	5.7
1.2646	1.6	9.1	1.6931	5.6
1.2262	1.7	9.15	1.7372	5.8
1.1985	2	9.15	2.2851	5.6
1.2007	2.5	9.22	3.0518	5.6
1.2138	2.4	9.22	3.2662	5.6
1.2266	2.3	9.24	2.9908	5.5
1.2176	2.3	9.26	2.6544	5.4
1.2218	2.1	9.3	2.5378	5.4
1.249	2.4	9.27	3.1892	5.5
1.2991	2.2	9.32	3.523	5.4
1.3408	2.4	9.33	3.2556	5.4
1.3119	1.9	9.32	2.9698	5.3
1.3014	2.1	9.34	3.0075	5.4
1.3201	2.1	9.32	3.1483	5.2
1.2938	2.1	9.32	3.5106	5.2
1.2694	2	9.24	2.8027	5.1
1.2165	2.1	9.24	2.5303	5
1.2037	2.2	9.15	3.1679	5
1.2292	2.2	9.17	3.6412	4.9
1.2256	2.6	9.14	4.6867	5
1.2015	2.5	9.11	4.3478	5
1.1786	2.3	9.04	3.4555	5
1.1856	2.2	8.96	3.4157	4.9
1.2103	2.4	8.86	3.9853	4.7
1.1938	2.3	8.85	3.5975	4.8
1.202	2.2	8.75	3.3626	4.7
1.2271	2.5	8.65	3.5457	4.7
1.277	2.5	8.61	4.1667	4.6
1.265	2.5	8.46	4.3188	4.6
1.2684	2.4	8.38	4.1453	4.7
1.2811	2.3	8.33	3.8187	4.7
1.2727	1.7	8.27	2.0624	4.5
1.2611	1.6	8.21	1.3052	4.4
1.2881	1.9	8.18	1.9737	4.5
1.3213	1.9	8.04	2.5407	4.4
1.2999	1.8	7.97	2.0756	4.6
1.3074	1.8	7.86	2.4152	4.5
1.3242	1.9	7.75	2.7788	4.4
1.3516	1.9	7.65	2.5737	4.5
1.3511	1.9	7.62	2.6909	4.4
1.3419	1.9	7.55	2.687	4.6
1.3716	1.8	7.6	2.3582	4.7
1.3622	1.7	7.54	1.9701	4.6
1.3896	2.1	7.48	2.7551	4.7
1.4227	2.6	7.44	3.5362	4.7
1.4684	3.1	7.41	4.3062	4.7
1.457	3.1	7.45	4.0813	5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158959&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158959&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158959&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	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Dollar/Euro[t] = + 2.01861062029043 + 0.0147026940524336`Inf-Eu`[t] -0.0586567406048217`Werkl-Eu`[t] + 0.00436724045623842`Inf-USA`[t] -0.0587101388833743`Werkl-USA`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Dollar/Euro[t] =  +  2.01861062029043 +  0.0147026940524336`Inf-Eu`[t] -0.0586567406048217`Werkl-Eu`[t] +  0.00436724045623842`Inf-USA`[t] -0.0587101388833743`Werkl-USA`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158959&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Dollar/Euro[t] =  +  2.01861062029043 +  0.0147026940524336`Inf-Eu`[t] -0.0586567406048217`Werkl-Eu`[t] +  0.00436724045623842`Inf-USA`[t] -0.0587101388833743`Werkl-USA`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158959&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158959&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
Dollar/Euro[t] = + 2.01861062029043 + 0.0147026940524336`Inf-Eu`[t] -0.0586567406048217`Werkl-Eu`[t] + 0.00436724045623842`Inf-USA`[t] -0.0587101388833743`Werkl-USA`[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)	2.01861062029043	0.129038	15.6435	0	0
`Inf-Eu`	0.0147026940524336	0.054838	0.2681	0.789617	0.394808
`Werkl-Eu`	-0.0586567406048217	0.020246	-2.8972	0.005395	0.002697
`Inf-USA`	0.00436724045623842	0.02429	0.1798	0.857974	0.428987
`Werkl-USA`	-0.0587101388833743	0.0271	-2.1665	0.034626	0.017313

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 2.01861062029043 & 0.129038 & 15.6435 & 0 & 0 \tabularnewline
`Inf-Eu` & 0.0147026940524336 & 0.054838 & 0.2681 & 0.789617 & 0.394808 \tabularnewline
`Werkl-Eu` & -0.0586567406048217 & 0.020246 & -2.8972 & 0.005395 & 0.002697 \tabularnewline
`Inf-USA` & 0.00436724045623842 & 0.02429 & 0.1798 & 0.857974 & 0.428987 \tabularnewline
`Werkl-USA` & -0.0587101388833743 & 0.0271 & -2.1665 & 0.034626 & 0.017313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158959&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]2.01861062029043[/C][C]0.129038[/C][C]15.6435[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Inf-Eu`[/C][C]0.0147026940524336[/C][C]0.054838[/C][C]0.2681[/C][C]0.789617[/C][C]0.394808[/C][/ROW]
[ROW][C]`Werkl-Eu`[/C][C]-0.0586567406048217[/C][C]0.020246[/C][C]-2.8972[/C][C]0.005395[/C][C]0.002697[/C][/ROW]
[ROW][C]`Inf-USA`[/C][C]0.00436724045623842[/C][C]0.02429[/C][C]0.1798[/C][C]0.857974[/C][C]0.428987[/C][/ROW]
[ROW][C]`Werkl-USA`[/C][C]-0.0587101388833743[/C][C]0.0271[/C][C]-2.1665[/C][C]0.034626[/C][C]0.017313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158959&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158959&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	2.01861062029043	0.129038	15.6435	0	0
`Inf-Eu`	0.0147026940524336	0.054838	0.2681	0.789617	0.394808
`Werkl-Eu`	-0.0586567406048217	0.020246	-2.8972	0.005395	0.002697
`Inf-USA`	0.00436724045623842	0.02429	0.1798	0.857974	0.428987
`Werkl-USA`	-0.0587101388833743	0.0271	-2.1665	0.034626	0.017313

Multiple Linear Regression - Regression Statistics
Multiple R	0.72754748106061
R-squared	0.52932533719764
Adjusted R-squared	0.495094452630195
F-TEST (value)	15.4633847148975
F-TEST (DF numerator)	4
F-TEST (DF denominator)	55
p-value	1.55500919918339e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0635414612194941
Sum Squared Residuals	0.222063451164966

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.72754748106061 \tabularnewline
R-squared & 0.52932533719764 \tabularnewline
Adjusted R-squared & 0.495094452630195 \tabularnewline
F-TEST (value) & 15.4633847148975 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 1.55500919918339e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0635414612194941 \tabularnewline
Sum Squared Residuals & 0.222063451164966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158959&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.72754748106061[/C][/ROW]
[ROW][C]R-squared[/C][C]0.52932533719764[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.495094452630195[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.4633847148975[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]1.55500919918339e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0635414612194941[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.222063451164966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158959&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158959&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	0.72754748106061
R-squared	0.52932533719764
Adjusted R-squared	0.495094452630195
F-TEST (value)	15.4633847148975
F-TEST (DF numerator)	4
F-TEST (DF denominator)	55
p-value	1.55500919918339e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0635414612194941
Sum Squared Residuals	0.222063451164966

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.0622	1.19650624903695	-0.134306249036947
2	1.0773	1.19496117786116	-0.11766117786116
3	1.0807	1.19484294087414	-0.114142940874143
4	1.0848	1.18020371371199	-0.0954037137119885
5	1.1582	1.16801986436368	-0.00981986436367845
6	1.1663	1.15622641837897	0.0100735816210326
7	1.1372	1.16267351829626	-0.0254735182962579
8	1.1139	1.17286958024526	-0.0589695802452605
9	1.1222	1.17328807711032	-0.0510880771103153
10	1.1692	1.1749974717566	-0.0057974717566018
11	1.1702	1.18847555342593	-0.018275553425933
12	1.2286	1.18955980791183	0.03904019208817
13	1.2613	1.18360138611155	0.0776986138884473
14	1.2646	1.18697598834001	0.0776240116599935
15	1.2262	1.17396398824245	0.052236011757546
16	1.1985	1.19250963528083	0.00599036471916796
17	1.2007	1.19910337372251	0.00159662627749088
18	1.2138	1.19856944067108	0.0152305593289166
19	1.2266	1.20059431232043	0.026005687679567
20	1.2176	1.2038230517072	0.0137769482928047
21	1.2218	1.19802702303532	0.0237729769646817
22	1.249	1.20117134001405	0.0478286599859507
23	1.2991	1.20262676292595	0.0964732370740486
24	1.3408	1.20381293423239	0.136987065767608
25	1.3119	1.20167101117817	0.110228988821832
26	1.3014	1.19773204625342	0.103667953746579
27	1.3201	1.21126211629843	0.10883788370157
28	1.2938	1.21284436751573	0.0809556324842746
29	1.2694	1.21884608172823	0.050553918271766
30	1.2165	1.22499772872154	-0.00849772872153563
31	1.2037	1.23453165729611	-0.0308316572961105
32	1.2292	1.24129655128029	-0.012096551280289
33	1.2256	1.24763226712807	-0.022032267128067
34	1.2015	1.24644164215035	-0.0449416421503492
35	1.1786	1.2437101865231	-0.0651101865230984
36	1.1856	1.25262965408442	-0.0670296540844199
37	1.2103	1.27566547489594	-0.0653654748959372
38	1.1938	1.26721714315948	-0.0734171431594753
39	1.202	1.27645769691988	-0.0744576969198811
40	1.2271	1.28753382092363	-0.0604338209236305
41	1.277	1.29846316075948	-0.0214631607594851
42	1.265	1.3079259291236	-0.0429259291236021
43	1.2684	1.30451946885925	-0.0361194688592496
44	1.2811	1.30455569575124	-0.02345569575124
45	1.2727	1.30332532711945	-0.0306253271194524
46	1.2611	1.30793860156537	-0.0468386015653718
47	1.2881	1.3111575983559	-0.0230575983559046
48	1.3213	1.3277167812676	-0.0064167812676044
49	1.2999	1.31657925239183	-0.0166792523918271
50	1.3074	1.33038562260563	-0.0229856226056336
51	1.3242	1.34576707599563	-0.0215670759956329
52	1.3516	1.3448660151502	0.00673398484979675
53	1.3511	1.35300857183816	-0.00190857183815641
54	1.3419	1.34535548366604	-0.00345548366603967
55	1.3716	1.33364541468021	0.0379545853197933
56	1.3622	1.33987063757852	0.0223293624214762
57	1.3896	1.3468283895056	0.0427716104944036
58	1.4227	1.35993725767637	0.0627627423236262
59	1.4684	1.37241108207204	0.095988917927961
60	1.457	1.35146957840423	0.105530421595774

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.0622 & 1.19650624903695 & -0.134306249036947 \tabularnewline
2 & 1.0773 & 1.19496117786116 & -0.11766117786116 \tabularnewline
3 & 1.0807 & 1.19484294087414 & -0.114142940874143 \tabularnewline
4 & 1.0848 & 1.18020371371199 & -0.0954037137119885 \tabularnewline
5 & 1.1582 & 1.16801986436368 & -0.00981986436367845 \tabularnewline
6 & 1.1663 & 1.15622641837897 & 0.0100735816210326 \tabularnewline
7 & 1.1372 & 1.16267351829626 & -0.0254735182962579 \tabularnewline
8 & 1.1139 & 1.17286958024526 & -0.0589695802452605 \tabularnewline
9 & 1.1222 & 1.17328807711032 & -0.0510880771103153 \tabularnewline
10 & 1.1692 & 1.1749974717566 & -0.0057974717566018 \tabularnewline
11 & 1.1702 & 1.18847555342593 & -0.018275553425933 \tabularnewline
12 & 1.2286 & 1.18955980791183 & 0.03904019208817 \tabularnewline
13 & 1.2613 & 1.18360138611155 & 0.0776986138884473 \tabularnewline
14 & 1.2646 & 1.18697598834001 & 0.0776240116599935 \tabularnewline
15 & 1.2262 & 1.17396398824245 & 0.052236011757546 \tabularnewline
16 & 1.1985 & 1.19250963528083 & 0.00599036471916796 \tabularnewline
17 & 1.2007 & 1.19910337372251 & 0.00159662627749088 \tabularnewline
18 & 1.2138 & 1.19856944067108 & 0.0152305593289166 \tabularnewline
19 & 1.2266 & 1.20059431232043 & 0.026005687679567 \tabularnewline
20 & 1.2176 & 1.2038230517072 & 0.0137769482928047 \tabularnewline
21 & 1.2218 & 1.19802702303532 & 0.0237729769646817 \tabularnewline
22 & 1.249 & 1.20117134001405 & 0.0478286599859507 \tabularnewline
23 & 1.2991 & 1.20262676292595 & 0.0964732370740486 \tabularnewline
24 & 1.3408 & 1.20381293423239 & 0.136987065767608 \tabularnewline
25 & 1.3119 & 1.20167101117817 & 0.110228988821832 \tabularnewline
26 & 1.3014 & 1.19773204625342 & 0.103667953746579 \tabularnewline
27 & 1.3201 & 1.21126211629843 & 0.10883788370157 \tabularnewline
28 & 1.2938 & 1.21284436751573 & 0.0809556324842746 \tabularnewline
29 & 1.2694 & 1.21884608172823 & 0.050553918271766 \tabularnewline
30 & 1.2165 & 1.22499772872154 & -0.00849772872153563 \tabularnewline
31 & 1.2037 & 1.23453165729611 & -0.0308316572961105 \tabularnewline
32 & 1.2292 & 1.24129655128029 & -0.012096551280289 \tabularnewline
33 & 1.2256 & 1.24763226712807 & -0.022032267128067 \tabularnewline
34 & 1.2015 & 1.24644164215035 & -0.0449416421503492 \tabularnewline
35 & 1.1786 & 1.2437101865231 & -0.0651101865230984 \tabularnewline
36 & 1.1856 & 1.25262965408442 & -0.0670296540844199 \tabularnewline
37 & 1.2103 & 1.27566547489594 & -0.0653654748959372 \tabularnewline
38 & 1.1938 & 1.26721714315948 & -0.0734171431594753 \tabularnewline
39 & 1.202 & 1.27645769691988 & -0.0744576969198811 \tabularnewline
40 & 1.2271 & 1.28753382092363 & -0.0604338209236305 \tabularnewline
41 & 1.277 & 1.29846316075948 & -0.0214631607594851 \tabularnewline
42 & 1.265 & 1.3079259291236 & -0.0429259291236021 \tabularnewline
43 & 1.2684 & 1.30451946885925 & -0.0361194688592496 \tabularnewline
44 & 1.2811 & 1.30455569575124 & -0.02345569575124 \tabularnewline
45 & 1.2727 & 1.30332532711945 & -0.0306253271194524 \tabularnewline
46 & 1.2611 & 1.30793860156537 & -0.0468386015653718 \tabularnewline
47 & 1.2881 & 1.3111575983559 & -0.0230575983559046 \tabularnewline
48 & 1.3213 & 1.3277167812676 & -0.0064167812676044 \tabularnewline
49 & 1.2999 & 1.31657925239183 & -0.0166792523918271 \tabularnewline
50 & 1.3074 & 1.33038562260563 & -0.0229856226056336 \tabularnewline
51 & 1.3242 & 1.34576707599563 & -0.0215670759956329 \tabularnewline
52 & 1.3516 & 1.3448660151502 & 0.00673398484979675 \tabularnewline
53 & 1.3511 & 1.35300857183816 & -0.00190857183815641 \tabularnewline
54 & 1.3419 & 1.34535548366604 & -0.00345548366603967 \tabularnewline
55 & 1.3716 & 1.33364541468021 & 0.0379545853197933 \tabularnewline
56 & 1.3622 & 1.33987063757852 & 0.0223293624214762 \tabularnewline
57 & 1.3896 & 1.3468283895056 & 0.0427716104944036 \tabularnewline
58 & 1.4227 & 1.35993725767637 & 0.0627627423236262 \tabularnewline
59 & 1.4684 & 1.37241108207204 & 0.095988917927961 \tabularnewline
60 & 1.457 & 1.35146957840423 & 0.105530421595774 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158959&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.0622[/C][C]1.19650624903695[/C][C]-0.134306249036947[/C][/ROW]
[ROW][C]2[/C][C]1.0773[/C][C]1.19496117786116[/C][C]-0.11766117786116[/C][/ROW]
[ROW][C]3[/C][C]1.0807[/C][C]1.19484294087414[/C][C]-0.114142940874143[/C][/ROW]
[ROW][C]4[/C][C]1.0848[/C][C]1.18020371371199[/C][C]-0.0954037137119885[/C][/ROW]
[ROW][C]5[/C][C]1.1582[/C][C]1.16801986436368[/C][C]-0.00981986436367845[/C][/ROW]
[ROW][C]6[/C][C]1.1663[/C][C]1.15622641837897[/C][C]0.0100735816210326[/C][/ROW]
[ROW][C]7[/C][C]1.1372[/C][C]1.16267351829626[/C][C]-0.0254735182962579[/C][/ROW]
[ROW][C]8[/C][C]1.1139[/C][C]1.17286958024526[/C][C]-0.0589695802452605[/C][/ROW]
[ROW][C]9[/C][C]1.1222[/C][C]1.17328807711032[/C][C]-0.0510880771103153[/C][/ROW]
[ROW][C]10[/C][C]1.1692[/C][C]1.1749974717566[/C][C]-0.0057974717566018[/C][/ROW]
[ROW][C]11[/C][C]1.1702[/C][C]1.18847555342593[/C][C]-0.018275553425933[/C][/ROW]
[ROW][C]12[/C][C]1.2286[/C][C]1.18955980791183[/C][C]0.03904019208817[/C][/ROW]
[ROW][C]13[/C][C]1.2613[/C][C]1.18360138611155[/C][C]0.0776986138884473[/C][/ROW]
[ROW][C]14[/C][C]1.2646[/C][C]1.18697598834001[/C][C]0.0776240116599935[/C][/ROW]
[ROW][C]15[/C][C]1.2262[/C][C]1.17396398824245[/C][C]0.052236011757546[/C][/ROW]
[ROW][C]16[/C][C]1.1985[/C][C]1.19250963528083[/C][C]0.00599036471916796[/C][/ROW]
[ROW][C]17[/C][C]1.2007[/C][C]1.19910337372251[/C][C]0.00159662627749088[/C][/ROW]
[ROW][C]18[/C][C]1.2138[/C][C]1.19856944067108[/C][C]0.0152305593289166[/C][/ROW]
[ROW][C]19[/C][C]1.2266[/C][C]1.20059431232043[/C][C]0.026005687679567[/C][/ROW]
[ROW][C]20[/C][C]1.2176[/C][C]1.2038230517072[/C][C]0.0137769482928047[/C][/ROW]
[ROW][C]21[/C][C]1.2218[/C][C]1.19802702303532[/C][C]0.0237729769646817[/C][/ROW]
[ROW][C]22[/C][C]1.249[/C][C]1.20117134001405[/C][C]0.0478286599859507[/C][/ROW]
[ROW][C]23[/C][C]1.2991[/C][C]1.20262676292595[/C][C]0.0964732370740486[/C][/ROW]
[ROW][C]24[/C][C]1.3408[/C][C]1.20381293423239[/C][C]0.136987065767608[/C][/ROW]
[ROW][C]25[/C][C]1.3119[/C][C]1.20167101117817[/C][C]0.110228988821832[/C][/ROW]
[ROW][C]26[/C][C]1.3014[/C][C]1.19773204625342[/C][C]0.103667953746579[/C][/ROW]
[ROW][C]27[/C][C]1.3201[/C][C]1.21126211629843[/C][C]0.10883788370157[/C][/ROW]
[ROW][C]28[/C][C]1.2938[/C][C]1.21284436751573[/C][C]0.0809556324842746[/C][/ROW]
[ROW][C]29[/C][C]1.2694[/C][C]1.21884608172823[/C][C]0.050553918271766[/C][/ROW]
[ROW][C]30[/C][C]1.2165[/C][C]1.22499772872154[/C][C]-0.00849772872153563[/C][/ROW]
[ROW][C]31[/C][C]1.2037[/C][C]1.23453165729611[/C][C]-0.0308316572961105[/C][/ROW]
[ROW][C]32[/C][C]1.2292[/C][C]1.24129655128029[/C][C]-0.012096551280289[/C][/ROW]
[ROW][C]33[/C][C]1.2256[/C][C]1.24763226712807[/C][C]-0.022032267128067[/C][/ROW]
[ROW][C]34[/C][C]1.2015[/C][C]1.24644164215035[/C][C]-0.0449416421503492[/C][/ROW]
[ROW][C]35[/C][C]1.1786[/C][C]1.2437101865231[/C][C]-0.0651101865230984[/C][/ROW]
[ROW][C]36[/C][C]1.1856[/C][C]1.25262965408442[/C][C]-0.0670296540844199[/C][/ROW]
[ROW][C]37[/C][C]1.2103[/C][C]1.27566547489594[/C][C]-0.0653654748959372[/C][/ROW]
[ROW][C]38[/C][C]1.1938[/C][C]1.26721714315948[/C][C]-0.0734171431594753[/C][/ROW]
[ROW][C]39[/C][C]1.202[/C][C]1.27645769691988[/C][C]-0.0744576969198811[/C][/ROW]
[ROW][C]40[/C][C]1.2271[/C][C]1.28753382092363[/C][C]-0.0604338209236305[/C][/ROW]
[ROW][C]41[/C][C]1.277[/C][C]1.29846316075948[/C][C]-0.0214631607594851[/C][/ROW]
[ROW][C]42[/C][C]1.265[/C][C]1.3079259291236[/C][C]-0.0429259291236021[/C][/ROW]
[ROW][C]43[/C][C]1.2684[/C][C]1.30451946885925[/C][C]-0.0361194688592496[/C][/ROW]
[ROW][C]44[/C][C]1.2811[/C][C]1.30455569575124[/C][C]-0.02345569575124[/C][/ROW]
[ROW][C]45[/C][C]1.2727[/C][C]1.30332532711945[/C][C]-0.0306253271194524[/C][/ROW]
[ROW][C]46[/C][C]1.2611[/C][C]1.30793860156537[/C][C]-0.0468386015653718[/C][/ROW]
[ROW][C]47[/C][C]1.2881[/C][C]1.3111575983559[/C][C]-0.0230575983559046[/C][/ROW]
[ROW][C]48[/C][C]1.3213[/C][C]1.3277167812676[/C][C]-0.0064167812676044[/C][/ROW]
[ROW][C]49[/C][C]1.2999[/C][C]1.31657925239183[/C][C]-0.0166792523918271[/C][/ROW]
[ROW][C]50[/C][C]1.3074[/C][C]1.33038562260563[/C][C]-0.0229856226056336[/C][/ROW]
[ROW][C]51[/C][C]1.3242[/C][C]1.34576707599563[/C][C]-0.0215670759956329[/C][/ROW]
[ROW][C]52[/C][C]1.3516[/C][C]1.3448660151502[/C][C]0.00673398484979675[/C][/ROW]
[ROW][C]53[/C][C]1.3511[/C][C]1.35300857183816[/C][C]-0.00190857183815641[/C][/ROW]
[ROW][C]54[/C][C]1.3419[/C][C]1.34535548366604[/C][C]-0.00345548366603967[/C][/ROW]
[ROW][C]55[/C][C]1.3716[/C][C]1.33364541468021[/C][C]0.0379545853197933[/C][/ROW]
[ROW][C]56[/C][C]1.3622[/C][C]1.33987063757852[/C][C]0.0223293624214762[/C][/ROW]
[ROW][C]57[/C][C]1.3896[/C][C]1.3468283895056[/C][C]0.0427716104944036[/C][/ROW]
[ROW][C]58[/C][C]1.4227[/C][C]1.35993725767637[/C][C]0.0627627423236262[/C][/ROW]
[ROW][C]59[/C][C]1.4684[/C][C]1.37241108207204[/C][C]0.095988917927961[/C][/ROW]
[ROW][C]60[/C][C]1.457[/C][C]1.35146957840423[/C][C]0.105530421595774[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158959&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158959&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.0622	1.19650624903695	-0.134306249036947
2	1.0773	1.19496117786116	-0.11766117786116
3	1.0807	1.19484294087414	-0.114142940874143
4	1.0848	1.18020371371199	-0.0954037137119885
5	1.1582	1.16801986436368	-0.00981986436367845
6	1.1663	1.15622641837897	0.0100735816210326
7	1.1372	1.16267351829626	-0.0254735182962579
8	1.1139	1.17286958024526	-0.0589695802452605
9	1.1222	1.17328807711032	-0.0510880771103153
10	1.1692	1.1749974717566	-0.0057974717566018
11	1.1702	1.18847555342593	-0.018275553425933
12	1.2286	1.18955980791183	0.03904019208817
13	1.2613	1.18360138611155	0.0776986138884473
14	1.2646	1.18697598834001	0.0776240116599935
15	1.2262	1.17396398824245	0.052236011757546
16	1.1985	1.19250963528083	0.00599036471916796
17	1.2007	1.19910337372251	0.00159662627749088
18	1.2138	1.19856944067108	0.0152305593289166
19	1.2266	1.20059431232043	0.026005687679567
20	1.2176	1.2038230517072	0.0137769482928047
21	1.2218	1.19802702303532	0.0237729769646817
22	1.249	1.20117134001405	0.0478286599859507
23	1.2991	1.20262676292595	0.0964732370740486
24	1.3408	1.20381293423239	0.136987065767608
25	1.3119	1.20167101117817	0.110228988821832
26	1.3014	1.19773204625342	0.103667953746579
27	1.3201	1.21126211629843	0.10883788370157
28	1.2938	1.21284436751573	0.0809556324842746
29	1.2694	1.21884608172823	0.050553918271766
30	1.2165	1.22499772872154	-0.00849772872153563
31	1.2037	1.23453165729611	-0.0308316572961105
32	1.2292	1.24129655128029	-0.012096551280289
33	1.2256	1.24763226712807	-0.022032267128067
34	1.2015	1.24644164215035	-0.0449416421503492
35	1.1786	1.2437101865231	-0.0651101865230984
36	1.1856	1.25262965408442	-0.0670296540844199
37	1.2103	1.27566547489594	-0.0653654748959372
38	1.1938	1.26721714315948	-0.0734171431594753
39	1.202	1.27645769691988	-0.0744576969198811
40	1.2271	1.28753382092363	-0.0604338209236305
41	1.277	1.29846316075948	-0.0214631607594851
42	1.265	1.3079259291236	-0.0429259291236021
43	1.2684	1.30451946885925	-0.0361194688592496
44	1.2811	1.30455569575124	-0.02345569575124
45	1.2727	1.30332532711945	-0.0306253271194524
46	1.2611	1.30793860156537	-0.0468386015653718
47	1.2881	1.3111575983559	-0.0230575983559046
48	1.3213	1.3277167812676	-0.0064167812676044
49	1.2999	1.31657925239183	-0.0166792523918271
50	1.3074	1.33038562260563	-0.0229856226056336
51	1.3242	1.34576707599563	-0.0215670759956329
52	1.3516	1.3448660151502	0.00673398484979675
53	1.3511	1.35300857183816	-0.00190857183815641
54	1.3419	1.34535548366604	-0.00345548366603967
55	1.3716	1.33364541468021	0.0379545853197933
56	1.3622	1.33987063757852	0.0223293624214762
57	1.3896	1.3468283895056	0.0427716104944036
58	1.4227	1.35993725767637	0.0627627423236262
59	1.4684	1.37241108207204	0.095988917927961
60	1.457	1.35146957840423	0.105530421595774

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0243754136309008	0.0487508272618016	0.9756245863691
9	0.00722053155145978	0.0144410631029196	0.99277946844854
10	0.00641146093832648	0.012822921876653	0.993588539061674
11	0.00619174075194116	0.0123834815038823	0.993808259248059
12	0.00263038288015946	0.00526076576031893	0.99736961711984
13	0.00537229257267128	0.0107445851453426	0.994627707427329
14	0.00193894768389811	0.00387789536779623	0.998061052316102
15	0.012814202344701	0.0256284046894021	0.987185797655299
16	0.0190029031831665	0.0380058063663331	0.980997096816833
17	0.0145572823916878	0.0291145647833756	0.985442717608312
18	0.0157725736691419	0.0315451473382839	0.984227426330858
19	0.0125762222652225	0.0251524445304451	0.987423777734778
20	0.026839469698553	0.0536789393971059	0.973160530301447
21	0.104253292243231	0.208506584486462	0.895746707756769
22	0.163371306516907	0.326742613033813	0.836628693483093
23	0.182749554842043	0.365499109684086	0.817250445157957
24	0.398719906804462	0.797439813608925	0.601280093195538
25	0.371179071668955	0.74235814333791	0.628820928331045
26	0.311954527548473	0.623909055096946	0.688045472451527
27	0.504518428741937	0.990963142516125	0.495481571258063
28	0.750947517835532	0.498104964328936	0.249052482164468
29	0.940773754657361	0.118452490685277	0.0592262453426386
30	0.97360523406135	0.0527895318772997	0.0263947659386499
31	0.968117183348206	0.0637656333035885	0.0318828166517943
32	0.997679477008172	0.00464104598365586	0.00232052299182793
33	0.99980057008015	0.000398859839698567	0.000199429919849283
34	0.999871672329208	0.000256655341584858	0.000128327670792429
35	0.999725432418824	0.000549135162352745	0.000274567581176373
36	0.99960620233031	0.000787595339382212	0.000393797669691106
37	0.999821336985778	0.000357326028444936	0.000178663014222468
38	0.999746836384176	0.00050632723164863	0.000253163615824315
39	0.999735674440593	0.000528651118814235	0.000264325559407118
40	0.999985063619295	2.98727614106847e-05	1.49363807053423e-05
41	0.999997419529864	5.16094027225501e-06	2.58047013612751e-06
42	0.999996146341156	7.70731768862185e-06	3.85365884431092e-06
43	0.99999343162	1.31367599984261e-05	6.56837999921303e-06
44	0.999984476951395	3.10460972093965e-05	1.55230486046983e-05
45	0.999951857807816	9.62843843682066e-05	4.81421921841033e-05
46	0.999847768632448	0.000304462735104801	0.000152231367552401
47	0.999598846282584	0.000802307434831252	0.000401153717415626
48	0.999800181804203	0.000399636391593161	0.000199818195796581
49	0.999224529959151	0.00155094008169755	0.000775470040848774
50	0.99718994629934	0.00562010740131808	0.00281005370065904
51	0.99007485966975	0.0198502806605004	0.00992514033025018
52	0.9655281148544	0.0689437702911981	0.0344718851455991

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0243754136309008 & 0.0487508272618016 & 0.9756245863691 \tabularnewline
9 & 0.00722053155145978 & 0.0144410631029196 & 0.99277946844854 \tabularnewline
10 & 0.00641146093832648 & 0.012822921876653 & 0.993588539061674 \tabularnewline
11 & 0.00619174075194116 & 0.0123834815038823 & 0.993808259248059 \tabularnewline
12 & 0.00263038288015946 & 0.00526076576031893 & 0.99736961711984 \tabularnewline
13 & 0.00537229257267128 & 0.0107445851453426 & 0.994627707427329 \tabularnewline
14 & 0.00193894768389811 & 0.00387789536779623 & 0.998061052316102 \tabularnewline
15 & 0.012814202344701 & 0.0256284046894021 & 0.987185797655299 \tabularnewline
16 & 0.0190029031831665 & 0.0380058063663331 & 0.980997096816833 \tabularnewline
17 & 0.0145572823916878 & 0.0291145647833756 & 0.985442717608312 \tabularnewline
18 & 0.0157725736691419 & 0.0315451473382839 & 0.984227426330858 \tabularnewline
19 & 0.0125762222652225 & 0.0251524445304451 & 0.987423777734778 \tabularnewline
20 & 0.026839469698553 & 0.0536789393971059 & 0.973160530301447 \tabularnewline
21 & 0.104253292243231 & 0.208506584486462 & 0.895746707756769 \tabularnewline
22 & 0.163371306516907 & 0.326742613033813 & 0.836628693483093 \tabularnewline
23 & 0.182749554842043 & 0.365499109684086 & 0.817250445157957 \tabularnewline
24 & 0.398719906804462 & 0.797439813608925 & 0.601280093195538 \tabularnewline
25 & 0.371179071668955 & 0.74235814333791 & 0.628820928331045 \tabularnewline
26 & 0.311954527548473 & 0.623909055096946 & 0.688045472451527 \tabularnewline
27 & 0.504518428741937 & 0.990963142516125 & 0.495481571258063 \tabularnewline
28 & 0.750947517835532 & 0.498104964328936 & 0.249052482164468 \tabularnewline
29 & 0.940773754657361 & 0.118452490685277 & 0.0592262453426386 \tabularnewline
30 & 0.97360523406135 & 0.0527895318772997 & 0.0263947659386499 \tabularnewline
31 & 0.968117183348206 & 0.0637656333035885 & 0.0318828166517943 \tabularnewline
32 & 0.997679477008172 & 0.00464104598365586 & 0.00232052299182793 \tabularnewline
33 & 0.99980057008015 & 0.000398859839698567 & 0.000199429919849283 \tabularnewline
34 & 0.999871672329208 & 0.000256655341584858 & 0.000128327670792429 \tabularnewline
35 & 0.999725432418824 & 0.000549135162352745 & 0.000274567581176373 \tabularnewline
36 & 0.99960620233031 & 0.000787595339382212 & 0.000393797669691106 \tabularnewline
37 & 0.999821336985778 & 0.000357326028444936 & 0.000178663014222468 \tabularnewline
38 & 0.999746836384176 & 0.00050632723164863 & 0.000253163615824315 \tabularnewline
39 & 0.999735674440593 & 0.000528651118814235 & 0.000264325559407118 \tabularnewline
40 & 0.999985063619295 & 2.98727614106847e-05 & 1.49363807053423e-05 \tabularnewline
41 & 0.999997419529864 & 5.16094027225501e-06 & 2.58047013612751e-06 \tabularnewline
42 & 0.999996146341156 & 7.70731768862185e-06 & 3.85365884431092e-06 \tabularnewline
43 & 0.99999343162 & 1.31367599984261e-05 & 6.56837999921303e-06 \tabularnewline
44 & 0.999984476951395 & 3.10460972093965e-05 & 1.55230486046983e-05 \tabularnewline
45 & 0.999951857807816 & 9.62843843682066e-05 & 4.81421921841033e-05 \tabularnewline
46 & 0.999847768632448 & 0.000304462735104801 & 0.000152231367552401 \tabularnewline
47 & 0.999598846282584 & 0.000802307434831252 & 0.000401153717415626 \tabularnewline
48 & 0.999800181804203 & 0.000399636391593161 & 0.000199818195796581 \tabularnewline
49 & 0.999224529959151 & 0.00155094008169755 & 0.000775470040848774 \tabularnewline
50 & 0.99718994629934 & 0.00562010740131808 & 0.00281005370065904 \tabularnewline
51 & 0.99007485966975 & 0.0198502806605004 & 0.00992514033025018 \tabularnewline
52 & 0.9655281148544 & 0.0689437702911981 & 0.0344718851455991 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158959&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.0243754136309008[/C][C]0.0487508272618016[/C][C]0.9756245863691[/C][/ROW]
[ROW][C]9[/C][C]0.00722053155145978[/C][C]0.0144410631029196[/C][C]0.99277946844854[/C][/ROW]
[ROW][C]10[/C][C]0.00641146093832648[/C][C]0.012822921876653[/C][C]0.993588539061674[/C][/ROW]
[ROW][C]11[/C][C]0.00619174075194116[/C][C]0.0123834815038823[/C][C]0.993808259248059[/C][/ROW]
[ROW][C]12[/C][C]0.00263038288015946[/C][C]0.00526076576031893[/C][C]0.99736961711984[/C][/ROW]
[ROW][C]13[/C][C]0.00537229257267128[/C][C]0.0107445851453426[/C][C]0.994627707427329[/C][/ROW]
[ROW][C]14[/C][C]0.00193894768389811[/C][C]0.00387789536779623[/C][C]0.998061052316102[/C][/ROW]
[ROW][C]15[/C][C]0.012814202344701[/C][C]0.0256284046894021[/C][C]0.987185797655299[/C][/ROW]
[ROW][C]16[/C][C]0.0190029031831665[/C][C]0.0380058063663331[/C][C]0.980997096816833[/C][/ROW]
[ROW][C]17[/C][C]0.0145572823916878[/C][C]0.0291145647833756[/C][C]0.985442717608312[/C][/ROW]
[ROW][C]18[/C][C]0.0157725736691419[/C][C]0.0315451473382839[/C][C]0.984227426330858[/C][/ROW]
[ROW][C]19[/C][C]0.0125762222652225[/C][C]0.0251524445304451[/C][C]0.987423777734778[/C][/ROW]
[ROW][C]20[/C][C]0.026839469698553[/C][C]0.0536789393971059[/C][C]0.973160530301447[/C][/ROW]
[ROW][C]21[/C][C]0.104253292243231[/C][C]0.208506584486462[/C][C]0.895746707756769[/C][/ROW]
[ROW][C]22[/C][C]0.163371306516907[/C][C]0.326742613033813[/C][C]0.836628693483093[/C][/ROW]
[ROW][C]23[/C][C]0.182749554842043[/C][C]0.365499109684086[/C][C]0.817250445157957[/C][/ROW]
[ROW][C]24[/C][C]0.398719906804462[/C][C]0.797439813608925[/C][C]0.601280093195538[/C][/ROW]
[ROW][C]25[/C][C]0.371179071668955[/C][C]0.74235814333791[/C][C]0.628820928331045[/C][/ROW]
[ROW][C]26[/C][C]0.311954527548473[/C][C]0.623909055096946[/C][C]0.688045472451527[/C][/ROW]
[ROW][C]27[/C][C]0.504518428741937[/C][C]0.990963142516125[/C][C]0.495481571258063[/C][/ROW]
[ROW][C]28[/C][C]0.750947517835532[/C][C]0.498104964328936[/C][C]0.249052482164468[/C][/ROW]
[ROW][C]29[/C][C]0.940773754657361[/C][C]0.118452490685277[/C][C]0.0592262453426386[/C][/ROW]
[ROW][C]30[/C][C]0.97360523406135[/C][C]0.0527895318772997[/C][C]0.0263947659386499[/C][/ROW]
[ROW][C]31[/C][C]0.968117183348206[/C][C]0.0637656333035885[/C][C]0.0318828166517943[/C][/ROW]
[ROW][C]32[/C][C]0.997679477008172[/C][C]0.00464104598365586[/C][C]0.00232052299182793[/C][/ROW]
[ROW][C]33[/C][C]0.99980057008015[/C][C]0.000398859839698567[/C][C]0.000199429919849283[/C][/ROW]
[ROW][C]34[/C][C]0.999871672329208[/C][C]0.000256655341584858[/C][C]0.000128327670792429[/C][/ROW]
[ROW][C]35[/C][C]0.999725432418824[/C][C]0.000549135162352745[/C][C]0.000274567581176373[/C][/ROW]
[ROW][C]36[/C][C]0.99960620233031[/C][C]0.000787595339382212[/C][C]0.000393797669691106[/C][/ROW]
[ROW][C]37[/C][C]0.999821336985778[/C][C]0.000357326028444936[/C][C]0.000178663014222468[/C][/ROW]
[ROW][C]38[/C][C]0.999746836384176[/C][C]0.00050632723164863[/C][C]0.000253163615824315[/C][/ROW]
[ROW][C]39[/C][C]0.999735674440593[/C][C]0.000528651118814235[/C][C]0.000264325559407118[/C][/ROW]
[ROW][C]40[/C][C]0.999985063619295[/C][C]2.98727614106847e-05[/C][C]1.49363807053423e-05[/C][/ROW]
[ROW][C]41[/C][C]0.999997419529864[/C][C]5.16094027225501e-06[/C][C]2.58047013612751e-06[/C][/ROW]
[ROW][C]42[/C][C]0.999996146341156[/C][C]7.70731768862185e-06[/C][C]3.85365884431092e-06[/C][/ROW]
[ROW][C]43[/C][C]0.99999343162[/C][C]1.31367599984261e-05[/C][C]6.56837999921303e-06[/C][/ROW]
[ROW][C]44[/C][C]0.999984476951395[/C][C]3.10460972093965e-05[/C][C]1.55230486046983e-05[/C][/ROW]
[ROW][C]45[/C][C]0.999951857807816[/C][C]9.62843843682066e-05[/C][C]4.81421921841033e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999847768632448[/C][C]0.000304462735104801[/C][C]0.000152231367552401[/C][/ROW]
[ROW][C]47[/C][C]0.999598846282584[/C][C]0.000802307434831252[/C][C]0.000401153717415626[/C][/ROW]
[ROW][C]48[/C][C]0.999800181804203[/C][C]0.000399636391593161[/C][C]0.000199818195796581[/C][/ROW]
[ROW][C]49[/C][C]0.999224529959151[/C][C]0.00155094008169755[/C][C]0.000775470040848774[/C][/ROW]
[ROW][C]50[/C][C]0.99718994629934[/C][C]0.00562010740131808[/C][C]0.00281005370065904[/C][/ROW]
[ROW][C]51[/C][C]0.99007485966975[/C][C]0.0198502806605004[/C][C]0.00992514033025018[/C][/ROW]
[ROW][C]52[/C][C]0.9655281148544[/C][C]0.0689437702911981[/C][C]0.0344718851455991[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158959&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158959&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0243754136309008	0.0487508272618016	0.9756245863691
9	0.00722053155145978	0.0144410631029196	0.99277946844854
10	0.00641146093832648	0.012822921876653	0.993588539061674
11	0.00619174075194116	0.0123834815038823	0.993808259248059
12	0.00263038288015946	0.00526076576031893	0.99736961711984
13	0.00537229257267128	0.0107445851453426	0.994627707427329
14	0.00193894768389811	0.00387789536779623	0.998061052316102
15	0.012814202344701	0.0256284046894021	0.987185797655299
16	0.0190029031831665	0.0380058063663331	0.980997096816833
17	0.0145572823916878	0.0291145647833756	0.985442717608312
18	0.0157725736691419	0.0315451473382839	0.984227426330858
19	0.0125762222652225	0.0251524445304451	0.987423777734778
20	0.026839469698553	0.0536789393971059	0.973160530301447
21	0.104253292243231	0.208506584486462	0.895746707756769
22	0.163371306516907	0.326742613033813	0.836628693483093
23	0.182749554842043	0.365499109684086	0.817250445157957
24	0.398719906804462	0.797439813608925	0.601280093195538
25	0.371179071668955	0.74235814333791	0.628820928331045
26	0.311954527548473	0.623909055096946	0.688045472451527
27	0.504518428741937	0.990963142516125	0.495481571258063
28	0.750947517835532	0.498104964328936	0.249052482164468
29	0.940773754657361	0.118452490685277	0.0592262453426386
30	0.97360523406135	0.0527895318772997	0.0263947659386499
31	0.968117183348206	0.0637656333035885	0.0318828166517943
32	0.997679477008172	0.00464104598365586	0.00232052299182793
33	0.99980057008015	0.000398859839698567	0.000199429919849283
34	0.999871672329208	0.000256655341584858	0.000128327670792429
35	0.999725432418824	0.000549135162352745	0.000274567581176373
36	0.99960620233031	0.000787595339382212	0.000393797669691106
37	0.999821336985778	0.000357326028444936	0.000178663014222468
38	0.999746836384176	0.00050632723164863	0.000253163615824315
39	0.999735674440593	0.000528651118814235	0.000264325559407118
40	0.999985063619295	2.98727614106847e-05	1.49363807053423e-05
41	0.999997419529864	5.16094027225501e-06	2.58047013612751e-06
42	0.999996146341156	7.70731768862185e-06	3.85365884431092e-06
43	0.99999343162	1.31367599984261e-05	6.56837999921303e-06
44	0.999984476951395	3.10460972093965e-05	1.55230486046983e-05
45	0.999951857807816	9.62843843682066e-05	4.81421921841033e-05
46	0.999847768632448	0.000304462735104801	0.000152231367552401
47	0.999598846282584	0.000802307434831252	0.000401153717415626
48	0.999800181804203	0.000399636391593161	0.000199818195796581
49	0.999224529959151	0.00155094008169755	0.000775470040848774
50	0.99718994629934	0.00562010740131808	0.00281005370065904
51	0.99007485966975	0.0198502806605004	0.00992514033025018
52	0.9655281148544	0.0689437702911981	0.0344718851455991

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	21	0.466666666666667	NOK
5% type I error level	32	0.711111111111111	NOK
10% type I error level	36	0.8	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 & 21 & 0.466666666666667 & NOK \tabularnewline
5% type I error level & 32 & 0.711111111111111 & NOK \tabularnewline
10% type I error level & 36 & 0.8 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158959&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]21[/C][C]0.466666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.711111111111111[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]36[/C][C]0.8[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158959&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158959&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	21	0.466666666666667	NOK
5% type I error level	32	0.711111111111111	NOK
10% type I error level	36	0.8	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

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