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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 06 Dec 2017 16:44:01 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/06/t15125750677jw85nmphtdlwg8.htm/, Retrieved Mon, 13 May 2024 22:45:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308620, Retrieved Mon, 13 May 2024 22:45:24 +0000

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No (this computation is public)

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Estimated Impact

110

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

-       [Multiple Regression] [dataset 2: groei ...] [2017-12-06 15:44:01] [4bbd12ea3a6c2ab532848261ff0d9984] [Current]

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1,998695158	2,979092901	2,996511672
2,003029471	4,56944414	4,524642737
2,002166062	3,018700499	2,990338855
2,001300933	3,161368002	3,145196406
2,001733713	3,008174184	2,989894564
2,001300933	3,433449794	3,412964272
2,003891166	3,290924559	3,229681842
2,005180513	3,271841607	3,20871002
2,004321374	3,035029282	2,957128198
2,003460532	2,92890769	2,881954971
2,000434077	2,718501689	2,710117365
2,003460532	3,06669855	3,004751156
2,000000	3,351796307	3,351409752
2,001733713	3,147676324	3,122870923
1,997823081	2,784617293	2,812244697
2,000434077	3,353146546	3,344785123
2,004321374	2,943988875	2,891537458
2,003029471	3,161966616	3,127428778
1,998259338	3,012415375	3,035829825
2,005180513	2,766412847	2,686636269
2,001733713	3,144262774	3,116939647
2,002597981	3,393399695	3,354300562
1,999565488	3,112605002	3,118264726
2,013258665	3,006893708	2,858537198
2,006466042	3,030194785	2,942999593
2,004751156	3,114944416	3,028571253
2,002597981	2,898725182	2,855519156
2,001733713	3,147676324	3,117933835
2,000434077	3,10002573	3,08849047
2,003029471	3,04060234	3,001300933
2,006893708	2,909556029	2,799340549
2,003460532	2,996511672	2,938519725
2,001733713	3,071513805	3,036229544
2,000434077	3,079904468	3,075911761
2,003029471	3,37051309	3,308137379
2,004321374	3,419129308	3,352761192
2,006037955	3,771587481	3,677606953
2,000867722	3,07114529	3,053462605
2,003891166	3,070037867	3,009025742
2,004751156	3,062957834	2,986771734
2,004321374	2,777426822	2,709269961
2,001300933	3,148602655	3,125806458
2,005609445	3,248708736	3,155032229
2,003891166	2,942999593	2,881954971
2,005180513	2,320146286	2,247973266
2,003460532	3,10754913	3,036229544
2,001733713	3,060320029	3,029789471
2,004751156	2,745855195	2,653212514
2,003891166	3,379305518	3,314288661
2,001300933	2,847572659	2,823474229
2,001300933	3,231724383	3,212986185
2,001733713	2,711807229	2,677606953
1,997386384	2,892651034	2,935507266
2,002597981	3,111934276	3,070037867
2,003029471	2,815577748	2,764176132
2,003891166	3,004321374	2,923761961
2,000000	2,937016107	2,934498451
2,003891166	2,829303773	2,764176132
1,999130541	2,738780558	2,753583059
2,001300933	3,325925956	3,29907126
2,004751156	2,894869657	2,810232518
2,004751156	2,968949681	2,898176483
2,002166062	2,985426474	2,951823035
2,003460532	2,822821645	2,759667845
2,002166062	2,892651034	2,860936621
2,005180513	3,552546548	3,487703863
2,000000	2,596597096	2,595496222
2,002597981	2,838849091	2,792391689
1,999565488	3,111598525	3,115943177
2,000434077	2,923244019	2,912222057

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308620&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
a[t] = + 1.99944 + 0.0672105b[t] -0.06708c[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  1.99944 +  0.0672105b[t] -0.06708c[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  1.99944 +  0.0672105b[t] -0.06708c[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&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
a[t] = + 1.99944 + 0.0672105b[t] -0.06708c[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)	+1.999	0.000821	+2.4350e+03	1.819e-167	9.093e-168
b	+0.06721	0.00222	+3.0270e+01	8.318e-41	4.159e-41
c	-0.06708	0.002221	-3.0200e+01	9.721e-41	4.861e-41

\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) & +1.999 &  0.000821 & +2.4350e+03 &  1.819e-167 &  9.093e-168 \tabularnewline
b & +0.06721 &  0.00222 & +3.0270e+01 &  8.318e-41 &  4.159e-41 \tabularnewline
c & -0.06708 &  0.002221 & -3.0200e+01 &  9.721e-41 &  4.861e-41 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&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]+1.999[/C][C] 0.000821[/C][C]+2.4350e+03[/C][C] 1.819e-167[/C][C] 9.093e-168[/C][/ROW]
[ROW][C]b[/C][C]+0.06721[/C][C] 0.00222[/C][C]+3.0270e+01[/C][C] 8.318e-41[/C][C] 4.159e-41[/C][/ROW]
[ROW][C]c[/C][C]-0.06708[/C][C] 0.002221[/C][C]-3.0200e+01[/C][C] 9.721e-41[/C][C] 4.861e-41[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&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)	+1.999	0.000821	+2.4350e+03	1.819e-167	9.093e-168
b	+0.06721	0.00222	+3.0270e+01	8.318e-41	4.159e-41
c	-0.06708	0.002221	-3.0200e+01	9.721e-41	4.861e-41

Multiple Linear Regression - Regression Statistics
Multiple R	0.9654
R-squared	0.932
Adjusted R-squared	0.9299
F-TEST (value)	458.9
F-TEST (DF numerator)	2
F-TEST (DF denominator)	67
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0006539
Sum Squared Residuals	2.865e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9654 \tabularnewline
R-squared &  0.932 \tabularnewline
Adjusted R-squared &  0.9299 \tabularnewline
F-TEST (value) &  458.9 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.0006539 \tabularnewline
Sum Squared Residuals &  2.865e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9654[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.932[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9299[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 458.9[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.0006539[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.865e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&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	0.9654
R-squared	0.932
Adjusted R-squared	0.9299
F-TEST (value)	458.9
F-TEST (DF numerator)	2
F-TEST (DF denominator)	67
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0006539
Sum Squared Residuals	2.865e-05

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.999	1.999	3.253e-05
2	2.003	2.003	-1.432e-05
3	2.002	2.002	0.0004273
4	2.001	2.001	0.0003613
5	2.002	2.001	0.0006726
6	2.001	2.001	3.642e-05
7	2.004	2.004	-8.875e-05
8	2.005	2.004	0.001076
9	2.004	2.005	-0.0007426
10	2.003	2.003	0.0004864
11	2	2	7.456e-05
12	2.003	2.004	-0.0005374
13	2	2	9.438e-05
14	2.002	2.002	0.0002167
15	1.998	1.998	-0.0001294
16	2	2	-6.678e-06
17	2.004	2.003	0.0009764
18	2.003	2.002	0.0008577
19	1.998	1.998	-5.45e-06
20	2.005	2.005	2.575e-05
21	2.002	2.002	4.825e-05
22	2.003	2.003	9.009e-05
23	2	1.999	9.665e-05
24	2.013	2.01	0.003472
25	2.006	2.006	0.0007792
26	2.005	2.006	-0.0008916
27	2.003	2.003	-0.0001209
28	2.002	2.002	-0.0001145
29	2	2.001	-0.0001866
30	2.003	2.002	0.000554
31	2.007	2.007	-0.0003216
32	2.003	2.004	-0.0002629
33	2.002	2.002	-0.0004763
34	2	2	0.000322
35	2.003	2.004	-0.001037
36	2.004	2.004	-1.908e-05
37	2.006	2.006	-0.0002007
38	2.001	2.001	-0.0001615
39	2.004	2.004	-4.447e-05
40	2.005	2.005	-0.0002014
41	2.004	2.004	-5.537e-05
42	2.001	2.001	-8.143e-05
43	2.006	2.006	-0.0005406
44	2.004	2.004	-3.009e-05
45	2.005	2.005	0.000594
46	2.003	2.005	-0.001171
47	2.002	2.002	-0.000156
48	2.005	2.006	-0.001264
49	2.004	2.004	-0.0003534
50	2.001	2.001	-0.0001295
51	2.001	2.001	0.0001799
52	2.002	2.002	-0.0003567
53	1.997	1.997	0.0004414
54	2.003	2.003	-6.084e-05
55	2.003	2.003	-0.0002283
56	2.004	2.005	-0.001347
57	2	2	5.52e-06
58	2.004	2.004	-0.0002891
59	1.999	1.999	0.0003238
60	2.001	2.002	-0.0003768
61	2.005	2.006	-0.0007464
62	2.005	2.005	0.0001739
63	2.002	2.002	8.003e-05
64	2.003	2.004	-0.0005865
65	2.002	2.002	0.0002188
66	2.005	2.004	0.000925
67	2	2	0.000145
68	2.003	2.003	-0.0003312
69	2	2	8.561e-06
70	2	2.001	-0.0001291

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  1.999 &  1.999 &  3.253e-05 \tabularnewline
2 &  2.003 &  2.003 & -1.432e-05 \tabularnewline
3 &  2.002 &  2.002 &  0.0004273 \tabularnewline
4 &  2.001 &  2.001 &  0.0003613 \tabularnewline
5 &  2.002 &  2.001 &  0.0006726 \tabularnewline
6 &  2.001 &  2.001 &  3.642e-05 \tabularnewline
7 &  2.004 &  2.004 & -8.875e-05 \tabularnewline
8 &  2.005 &  2.004 &  0.001076 \tabularnewline
9 &  2.004 &  2.005 & -0.0007426 \tabularnewline
10 &  2.003 &  2.003 &  0.0004864 \tabularnewline
11 &  2 &  2 &  7.456e-05 \tabularnewline
12 &  2.003 &  2.004 & -0.0005374 \tabularnewline
13 &  2 &  2 &  9.438e-05 \tabularnewline
14 &  2.002 &  2.002 &  0.0002167 \tabularnewline
15 &  1.998 &  1.998 & -0.0001294 \tabularnewline
16 &  2 &  2 & -6.678e-06 \tabularnewline
17 &  2.004 &  2.003 &  0.0009764 \tabularnewline
18 &  2.003 &  2.002 &  0.0008577 \tabularnewline
19 &  1.998 &  1.998 & -5.45e-06 \tabularnewline
20 &  2.005 &  2.005 &  2.575e-05 \tabularnewline
21 &  2.002 &  2.002 &  4.825e-05 \tabularnewline
22 &  2.003 &  2.003 &  9.009e-05 \tabularnewline
23 &  2 &  1.999 &  9.665e-05 \tabularnewline
24 &  2.013 &  2.01 &  0.003472 \tabularnewline
25 &  2.006 &  2.006 &  0.0007792 \tabularnewline
26 &  2.005 &  2.006 & -0.0008916 \tabularnewline
27 &  2.003 &  2.003 & -0.0001209 \tabularnewline
28 &  2.002 &  2.002 & -0.0001145 \tabularnewline
29 &  2 &  2.001 & -0.0001866 \tabularnewline
30 &  2.003 &  2.002 &  0.000554 \tabularnewline
31 &  2.007 &  2.007 & -0.0003216 \tabularnewline
32 &  2.003 &  2.004 & -0.0002629 \tabularnewline
33 &  2.002 &  2.002 & -0.0004763 \tabularnewline
34 &  2 &  2 &  0.000322 \tabularnewline
35 &  2.003 &  2.004 & -0.001037 \tabularnewline
36 &  2.004 &  2.004 & -1.908e-05 \tabularnewline
37 &  2.006 &  2.006 & -0.0002007 \tabularnewline
38 &  2.001 &  2.001 & -0.0001615 \tabularnewline
39 &  2.004 &  2.004 & -4.447e-05 \tabularnewline
40 &  2.005 &  2.005 & -0.0002014 \tabularnewline
41 &  2.004 &  2.004 & -5.537e-05 \tabularnewline
42 &  2.001 &  2.001 & -8.143e-05 \tabularnewline
43 &  2.006 &  2.006 & -0.0005406 \tabularnewline
44 &  2.004 &  2.004 & -3.009e-05 \tabularnewline
45 &  2.005 &  2.005 &  0.000594 \tabularnewline
46 &  2.003 &  2.005 & -0.001171 \tabularnewline
47 &  2.002 &  2.002 & -0.000156 \tabularnewline
48 &  2.005 &  2.006 & -0.001264 \tabularnewline
49 &  2.004 &  2.004 & -0.0003534 \tabularnewline
50 &  2.001 &  2.001 & -0.0001295 \tabularnewline
51 &  2.001 &  2.001 &  0.0001799 \tabularnewline
52 &  2.002 &  2.002 & -0.0003567 \tabularnewline
53 &  1.997 &  1.997 &  0.0004414 \tabularnewline
54 &  2.003 &  2.003 & -6.084e-05 \tabularnewline
55 &  2.003 &  2.003 & -0.0002283 \tabularnewline
56 &  2.004 &  2.005 & -0.001347 \tabularnewline
57 &  2 &  2 &  5.52e-06 \tabularnewline
58 &  2.004 &  2.004 & -0.0002891 \tabularnewline
59 &  1.999 &  1.999 &  0.0003238 \tabularnewline
60 &  2.001 &  2.002 & -0.0003768 \tabularnewline
61 &  2.005 &  2.006 & -0.0007464 \tabularnewline
62 &  2.005 &  2.005 &  0.0001739 \tabularnewline
63 &  2.002 &  2.002 &  8.003e-05 \tabularnewline
64 &  2.003 &  2.004 & -0.0005865 \tabularnewline
65 &  2.002 &  2.002 &  0.0002188 \tabularnewline
66 &  2.005 &  2.004 &  0.000925 \tabularnewline
67 &  2 &  2 &  0.000145 \tabularnewline
68 &  2.003 &  2.003 & -0.0003312 \tabularnewline
69 &  2 &  2 &  8.561e-06 \tabularnewline
70 &  2 &  2.001 & -0.0001291 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=5

[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] 1.999[/C][C] 1.999[/C][C] 3.253e-05[/C][/ROW]
[ROW][C]2[/C][C] 2.003[/C][C] 2.003[/C][C]-1.432e-05[/C][/ROW]
[ROW][C]3[/C][C] 2.002[/C][C] 2.002[/C][C] 0.0004273[/C][/ROW]
[ROW][C]4[/C][C] 2.001[/C][C] 2.001[/C][C] 0.0003613[/C][/ROW]
[ROW][C]5[/C][C] 2.002[/C][C] 2.001[/C][C] 0.0006726[/C][/ROW]
[ROW][C]6[/C][C] 2.001[/C][C] 2.001[/C][C] 3.642e-05[/C][/ROW]
[ROW][C]7[/C][C] 2.004[/C][C] 2.004[/C][C]-8.875e-05[/C][/ROW]
[ROW][C]8[/C][C] 2.005[/C][C] 2.004[/C][C] 0.001076[/C][/ROW]
[ROW][C]9[/C][C] 2.004[/C][C] 2.005[/C][C]-0.0007426[/C][/ROW]
[ROW][C]10[/C][C] 2.003[/C][C] 2.003[/C][C] 0.0004864[/C][/ROW]
[ROW][C]11[/C][C] 2[/C][C] 2[/C][C] 7.456e-05[/C][/ROW]
[ROW][C]12[/C][C] 2.003[/C][C] 2.004[/C][C]-0.0005374[/C][/ROW]
[ROW][C]13[/C][C] 2[/C][C] 2[/C][C] 9.438e-05[/C][/ROW]
[ROW][C]14[/C][C] 2.002[/C][C] 2.002[/C][C] 0.0002167[/C][/ROW]
[ROW][C]15[/C][C] 1.998[/C][C] 1.998[/C][C]-0.0001294[/C][/ROW]
[ROW][C]16[/C][C] 2[/C][C] 2[/C][C]-6.678e-06[/C][/ROW]
[ROW][C]17[/C][C] 2.004[/C][C] 2.003[/C][C] 0.0009764[/C][/ROW]
[ROW][C]18[/C][C] 2.003[/C][C] 2.002[/C][C] 0.0008577[/C][/ROW]
[ROW][C]19[/C][C] 1.998[/C][C] 1.998[/C][C]-5.45e-06[/C][/ROW]
[ROW][C]20[/C][C] 2.005[/C][C] 2.005[/C][C] 2.575e-05[/C][/ROW]
[ROW][C]21[/C][C] 2.002[/C][C] 2.002[/C][C] 4.825e-05[/C][/ROW]
[ROW][C]22[/C][C] 2.003[/C][C] 2.003[/C][C] 9.009e-05[/C][/ROW]
[ROW][C]23[/C][C] 2[/C][C] 1.999[/C][C] 9.665e-05[/C][/ROW]
[ROW][C]24[/C][C] 2.013[/C][C] 2.01[/C][C] 0.003472[/C][/ROW]
[ROW][C]25[/C][C] 2.006[/C][C] 2.006[/C][C] 0.0007792[/C][/ROW]
[ROW][C]26[/C][C] 2.005[/C][C] 2.006[/C][C]-0.0008916[/C][/ROW]
[ROW][C]27[/C][C] 2.003[/C][C] 2.003[/C][C]-0.0001209[/C][/ROW]
[ROW][C]28[/C][C] 2.002[/C][C] 2.002[/C][C]-0.0001145[/C][/ROW]
[ROW][C]29[/C][C] 2[/C][C] 2.001[/C][C]-0.0001866[/C][/ROW]
[ROW][C]30[/C][C] 2.003[/C][C] 2.002[/C][C] 0.000554[/C][/ROW]
[ROW][C]31[/C][C] 2.007[/C][C] 2.007[/C][C]-0.0003216[/C][/ROW]
[ROW][C]32[/C][C] 2.003[/C][C] 2.004[/C][C]-0.0002629[/C][/ROW]
[ROW][C]33[/C][C] 2.002[/C][C] 2.002[/C][C]-0.0004763[/C][/ROW]
[ROW][C]34[/C][C] 2[/C][C] 2[/C][C] 0.000322[/C][/ROW]
[ROW][C]35[/C][C] 2.003[/C][C] 2.004[/C][C]-0.001037[/C][/ROW]
[ROW][C]36[/C][C] 2.004[/C][C] 2.004[/C][C]-1.908e-05[/C][/ROW]
[ROW][C]37[/C][C] 2.006[/C][C] 2.006[/C][C]-0.0002007[/C][/ROW]
[ROW][C]38[/C][C] 2.001[/C][C] 2.001[/C][C]-0.0001615[/C][/ROW]
[ROW][C]39[/C][C] 2.004[/C][C] 2.004[/C][C]-4.447e-05[/C][/ROW]
[ROW][C]40[/C][C] 2.005[/C][C] 2.005[/C][C]-0.0002014[/C][/ROW]
[ROW][C]41[/C][C] 2.004[/C][C] 2.004[/C][C]-5.537e-05[/C][/ROW]
[ROW][C]42[/C][C] 2.001[/C][C] 2.001[/C][C]-8.143e-05[/C][/ROW]
[ROW][C]43[/C][C] 2.006[/C][C] 2.006[/C][C]-0.0005406[/C][/ROW]
[ROW][C]44[/C][C] 2.004[/C][C] 2.004[/C][C]-3.009e-05[/C][/ROW]
[ROW][C]45[/C][C] 2.005[/C][C] 2.005[/C][C] 0.000594[/C][/ROW]
[ROW][C]46[/C][C] 2.003[/C][C] 2.005[/C][C]-0.001171[/C][/ROW]
[ROW][C]47[/C][C] 2.002[/C][C] 2.002[/C][C]-0.000156[/C][/ROW]
[ROW][C]48[/C][C] 2.005[/C][C] 2.006[/C][C]-0.001264[/C][/ROW]
[ROW][C]49[/C][C] 2.004[/C][C] 2.004[/C][C]-0.0003534[/C][/ROW]
[ROW][C]50[/C][C] 2.001[/C][C] 2.001[/C][C]-0.0001295[/C][/ROW]
[ROW][C]51[/C][C] 2.001[/C][C] 2.001[/C][C] 0.0001799[/C][/ROW]
[ROW][C]52[/C][C] 2.002[/C][C] 2.002[/C][C]-0.0003567[/C][/ROW]
[ROW][C]53[/C][C] 1.997[/C][C] 1.997[/C][C] 0.0004414[/C][/ROW]
[ROW][C]54[/C][C] 2.003[/C][C] 2.003[/C][C]-6.084e-05[/C][/ROW]
[ROW][C]55[/C][C] 2.003[/C][C] 2.003[/C][C]-0.0002283[/C][/ROW]
[ROW][C]56[/C][C] 2.004[/C][C] 2.005[/C][C]-0.001347[/C][/ROW]
[ROW][C]57[/C][C] 2[/C][C] 2[/C][C] 5.52e-06[/C][/ROW]
[ROW][C]58[/C][C] 2.004[/C][C] 2.004[/C][C]-0.0002891[/C][/ROW]
[ROW][C]59[/C][C] 1.999[/C][C] 1.999[/C][C] 0.0003238[/C][/ROW]
[ROW][C]60[/C][C] 2.001[/C][C] 2.002[/C][C]-0.0003768[/C][/ROW]
[ROW][C]61[/C][C] 2.005[/C][C] 2.006[/C][C]-0.0007464[/C][/ROW]
[ROW][C]62[/C][C] 2.005[/C][C] 2.005[/C][C] 0.0001739[/C][/ROW]
[ROW][C]63[/C][C] 2.002[/C][C] 2.002[/C][C] 8.003e-05[/C][/ROW]
[ROW][C]64[/C][C] 2.003[/C][C] 2.004[/C][C]-0.0005865[/C][/ROW]
[ROW][C]65[/C][C] 2.002[/C][C] 2.002[/C][C] 0.0002188[/C][/ROW]
[ROW][C]66[/C][C] 2.005[/C][C] 2.004[/C][C] 0.000925[/C][/ROW]
[ROW][C]67[/C][C] 2[/C][C] 2[/C][C] 0.000145[/C][/ROW]
[ROW][C]68[/C][C] 2.003[/C][C] 2.003[/C][C]-0.0003312[/C][/ROW]
[ROW][C]69[/C][C] 2[/C][C] 2[/C][C] 8.561e-06[/C][/ROW]
[ROW][C]70[/C][C] 2[/C][C] 2.001[/C][C]-0.0001291[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&T=5

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	1.999	1.999	3.253e-05
2	2.003	2.003	-1.432e-05
3	2.002	2.002	0.0004273
4	2.001	2.001	0.0003613
5	2.002	2.001	0.0006726
6	2.001	2.001	3.642e-05
7	2.004	2.004	-8.875e-05
8	2.005	2.004	0.001076
9	2.004	2.005	-0.0007426
10	2.003	2.003	0.0004864
11	2	2	7.456e-05
12	2.003	2.004	-0.0005374
13	2	2	9.438e-05
14	2.002	2.002	0.0002167
15	1.998	1.998	-0.0001294
16	2	2	-6.678e-06
17	2.004	2.003	0.0009764
18	2.003	2.002	0.0008577
19	1.998	1.998	-5.45e-06
20	2.005	2.005	2.575e-05
21	2.002	2.002	4.825e-05
22	2.003	2.003	9.009e-05
23	2	1.999	9.665e-05
24	2.013	2.01	0.003472
25	2.006	2.006	0.0007792
26	2.005	2.006	-0.0008916
27	2.003	2.003	-0.0001209
28	2.002	2.002	-0.0001145
29	2	2.001	-0.0001866
30	2.003	2.002	0.000554
31	2.007	2.007	-0.0003216
32	2.003	2.004	-0.0002629
33	2.002	2.002	-0.0004763
34	2	2	0.000322
35	2.003	2.004	-0.001037
36	2.004	2.004	-1.908e-05
37	2.006	2.006	-0.0002007
38	2.001	2.001	-0.0001615
39	2.004	2.004	-4.447e-05
40	2.005	2.005	-0.0002014
41	2.004	2.004	-5.537e-05
42	2.001	2.001	-8.143e-05
43	2.006	2.006	-0.0005406
44	2.004	2.004	-3.009e-05
45	2.005	2.005	0.000594
46	2.003	2.005	-0.001171
47	2.002	2.002	-0.000156
48	2.005	2.006	-0.001264
49	2.004	2.004	-0.0003534
50	2.001	2.001	-0.0001295
51	2.001	2.001	0.0001799
52	2.002	2.002	-0.0003567
53	1.997	1.997	0.0004414
54	2.003	2.003	-6.084e-05
55	2.003	2.003	-0.0002283
56	2.004	2.005	-0.001347
57	2	2	5.52e-06
58	2.004	2.004	-0.0002891
59	1.999	1.999	0.0003238
60	2.001	2.002	-0.0003768
61	2.005	2.006	-0.0007464
62	2.005	2.005	0.0001739
63	2.002	2.002	8.003e-05
64	2.003	2.004	-0.0005865
65	2.002	2.002	0.0002188
66	2.005	2.004	0.000925
67	2	2	0.000145
68	2.003	2.003	-0.0003312
69	2	2	8.561e-06
70	2	2.001	-0.0001291

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.0318	0.06361	0.9682
7	0.06283	0.1257	0.9372
8	0.1248	0.2496	0.8752
9	0.3899	0.7799	0.6101
10	0.2988	0.5976	0.7012
11	0.2182	0.4363	0.7818
12	0.2214	0.4429	0.7786
13	0.1553	0.3106	0.8447
14	0.1017	0.2034	0.8983
15	0.08029	0.1606	0.9197
16	0.05129	0.1026	0.9487
17	0.08588	0.1718	0.9141
18	0.101	0.202	0.899
19	0.06949	0.139	0.9305
20	0.04786	0.09572	0.9521
21	0.03083	0.06166	0.9692
22	0.01909	0.03818	0.9809
23	0.01151	0.02302	0.9885
24	0.9887	0.02252	0.01126
25	0.9954	0.009136	0.004568
26	0.9994	0.001298	0.0006488
27	0.999	0.001995	0.0009973
28	0.9983	0.003375	0.001688
29	0.9973	0.005368	0.002684
30	0.9975	0.005046	0.002523
31	0.9983	0.003323	0.001661
32	0.9977	0.004669	0.002334
33	0.9973	0.005421	0.002711
34	0.9958	0.008326	0.004163
35	0.9987	0.002635	0.001317
36	0.9979	0.004259	0.00213
37	0.9969	0.006289	0.003144
38	0.9949	0.0102	0.005099
39	0.9923	0.01532	0.007659
40	0.9892	0.02151	0.01076
41	0.9855	0.02901	0.0145
42	0.9771	0.04582	0.02291
43	0.9713	0.05741	0.02871
44	0.9607	0.07862	0.03931
45	0.9939	0.01215	0.006074
46	0.9981	0.003701	0.00185
47	0.9966	0.006758	0.003379
48	0.998	0.00407	0.002035
49	0.9969	0.006257	0.003128
50	0.994	0.01197	0.005987
51	0.989	0.02193	0.01097
52	0.9811	0.03776	0.01888
53	0.9688	0.06249	0.03125
54	0.9476	0.1048	0.05241
55	0.9182	0.1636	0.08182
56	0.9815	0.03697	0.01848
57	0.9654	0.06922	0.03461
58	0.9376	0.1248	0.06242
59	0.9104	0.1792	0.08962
60	0.9566	0.0867	0.04335
61	0.9577	0.08464	0.04232
62	0.922	0.1559	0.07797
63	0.8388	0.3224	0.1612
64	0.8091	0.3817	0.1909

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.0318 &  0.06361 &  0.9682 \tabularnewline
7 &  0.06283 &  0.1257 &  0.9372 \tabularnewline
8 &  0.1248 &  0.2496 &  0.8752 \tabularnewline
9 &  0.3899 &  0.7799 &  0.6101 \tabularnewline
10 &  0.2988 &  0.5976 &  0.7012 \tabularnewline
11 &  0.2182 &  0.4363 &  0.7818 \tabularnewline
12 &  0.2214 &  0.4429 &  0.7786 \tabularnewline
13 &  0.1553 &  0.3106 &  0.8447 \tabularnewline
14 &  0.1017 &  0.2034 &  0.8983 \tabularnewline
15 &  0.08029 &  0.1606 &  0.9197 \tabularnewline
16 &  0.05129 &  0.1026 &  0.9487 \tabularnewline
17 &  0.08588 &  0.1718 &  0.9141 \tabularnewline
18 &  0.101 &  0.202 &  0.899 \tabularnewline
19 &  0.06949 &  0.139 &  0.9305 \tabularnewline
20 &  0.04786 &  0.09572 &  0.9521 \tabularnewline
21 &  0.03083 &  0.06166 &  0.9692 \tabularnewline
22 &  0.01909 &  0.03818 &  0.9809 \tabularnewline
23 &  0.01151 &  0.02302 &  0.9885 \tabularnewline
24 &  0.9887 &  0.02252 &  0.01126 \tabularnewline
25 &  0.9954 &  0.009136 &  0.004568 \tabularnewline
26 &  0.9994 &  0.001298 &  0.0006488 \tabularnewline
27 &  0.999 &  0.001995 &  0.0009973 \tabularnewline
28 &  0.9983 &  0.003375 &  0.001688 \tabularnewline
29 &  0.9973 &  0.005368 &  0.002684 \tabularnewline
30 &  0.9975 &  0.005046 &  0.002523 \tabularnewline
31 &  0.9983 &  0.003323 &  0.001661 \tabularnewline
32 &  0.9977 &  0.004669 &  0.002334 \tabularnewline
33 &  0.9973 &  0.005421 &  0.002711 \tabularnewline
34 &  0.9958 &  0.008326 &  0.004163 \tabularnewline
35 &  0.9987 &  0.002635 &  0.001317 \tabularnewline
36 &  0.9979 &  0.004259 &  0.00213 \tabularnewline
37 &  0.9969 &  0.006289 &  0.003144 \tabularnewline
38 &  0.9949 &  0.0102 &  0.005099 \tabularnewline
39 &  0.9923 &  0.01532 &  0.007659 \tabularnewline
40 &  0.9892 &  0.02151 &  0.01076 \tabularnewline
41 &  0.9855 &  0.02901 &  0.0145 \tabularnewline
42 &  0.9771 &  0.04582 &  0.02291 \tabularnewline
43 &  0.9713 &  0.05741 &  0.02871 \tabularnewline
44 &  0.9607 &  0.07862 &  0.03931 \tabularnewline
45 &  0.9939 &  0.01215 &  0.006074 \tabularnewline
46 &  0.9981 &  0.003701 &  0.00185 \tabularnewline
47 &  0.9966 &  0.006758 &  0.003379 \tabularnewline
48 &  0.998 &  0.00407 &  0.002035 \tabularnewline
49 &  0.9969 &  0.006257 &  0.003128 \tabularnewline
50 &  0.994 &  0.01197 &  0.005987 \tabularnewline
51 &  0.989 &  0.02193 &  0.01097 \tabularnewline
52 &  0.9811 &  0.03776 &  0.01888 \tabularnewline
53 &  0.9688 &  0.06249 &  0.03125 \tabularnewline
54 &  0.9476 &  0.1048 &  0.05241 \tabularnewline
55 &  0.9182 &  0.1636 &  0.08182 \tabularnewline
56 &  0.9815 &  0.03697 &  0.01848 \tabularnewline
57 &  0.9654 &  0.06922 &  0.03461 \tabularnewline
58 &  0.9376 &  0.1248 &  0.06242 \tabularnewline
59 &  0.9104 &  0.1792 &  0.08962 \tabularnewline
60 &  0.9566 &  0.0867 &  0.04335 \tabularnewline
61 &  0.9577 &  0.08464 &  0.04232 \tabularnewline
62 &  0.922 &  0.1559 &  0.07797 \tabularnewline
63 &  0.8388 &  0.3224 &  0.1612 \tabularnewline
64 &  0.8091 &  0.3817 &  0.1909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=6

[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] 0.0318[/C][C] 0.06361[/C][C] 0.9682[/C][/ROW]
[ROW][C]7[/C][C] 0.06283[/C][C] 0.1257[/C][C] 0.9372[/C][/ROW]
[ROW][C]8[/C][C] 0.1248[/C][C] 0.2496[/C][C] 0.8752[/C][/ROW]
[ROW][C]9[/C][C] 0.3899[/C][C] 0.7799[/C][C] 0.6101[/C][/ROW]
[ROW][C]10[/C][C] 0.2988[/C][C] 0.5976[/C][C] 0.7012[/C][/ROW]
[ROW][C]11[/C][C] 0.2182[/C][C] 0.4363[/C][C] 0.7818[/C][/ROW]
[ROW][C]12[/C][C] 0.2214[/C][C] 0.4429[/C][C] 0.7786[/C][/ROW]
[ROW][C]13[/C][C] 0.1553[/C][C] 0.3106[/C][C] 0.8447[/C][/ROW]
[ROW][C]14[/C][C] 0.1017[/C][C] 0.2034[/C][C] 0.8983[/C][/ROW]
[ROW][C]15[/C][C] 0.08029[/C][C] 0.1606[/C][C] 0.9197[/C][/ROW]
[ROW][C]16[/C][C] 0.05129[/C][C] 0.1026[/C][C] 0.9487[/C][/ROW]
[ROW][C]17[/C][C] 0.08588[/C][C] 0.1718[/C][C] 0.9141[/C][/ROW]
[ROW][C]18[/C][C] 0.101[/C][C] 0.202[/C][C] 0.899[/C][/ROW]
[ROW][C]19[/C][C] 0.06949[/C][C] 0.139[/C][C] 0.9305[/C][/ROW]
[ROW][C]20[/C][C] 0.04786[/C][C] 0.09572[/C][C] 0.9521[/C][/ROW]
[ROW][C]21[/C][C] 0.03083[/C][C] 0.06166[/C][C] 0.9692[/C][/ROW]
[ROW][C]22[/C][C] 0.01909[/C][C] 0.03818[/C][C] 0.9809[/C][/ROW]
[ROW][C]23[/C][C] 0.01151[/C][C] 0.02302[/C][C] 0.9885[/C][/ROW]
[ROW][C]24[/C][C] 0.9887[/C][C] 0.02252[/C][C] 0.01126[/C][/ROW]
[ROW][C]25[/C][C] 0.9954[/C][C] 0.009136[/C][C] 0.004568[/C][/ROW]
[ROW][C]26[/C][C] 0.9994[/C][C] 0.001298[/C][C] 0.0006488[/C][/ROW]
[ROW][C]27[/C][C] 0.999[/C][C] 0.001995[/C][C] 0.0009973[/C][/ROW]
[ROW][C]28[/C][C] 0.9983[/C][C] 0.003375[/C][C] 0.001688[/C][/ROW]
[ROW][C]29[/C][C] 0.9973[/C][C] 0.005368[/C][C] 0.002684[/C][/ROW]
[ROW][C]30[/C][C] 0.9975[/C][C] 0.005046[/C][C] 0.002523[/C][/ROW]
[ROW][C]31[/C][C] 0.9983[/C][C] 0.003323[/C][C] 0.001661[/C][/ROW]
[ROW][C]32[/C][C] 0.9977[/C][C] 0.004669[/C][C] 0.002334[/C][/ROW]
[ROW][C]33[/C][C] 0.9973[/C][C] 0.005421[/C][C] 0.002711[/C][/ROW]
[ROW][C]34[/C][C] 0.9958[/C][C] 0.008326[/C][C] 0.004163[/C][/ROW]
[ROW][C]35[/C][C] 0.9987[/C][C] 0.002635[/C][C] 0.001317[/C][/ROW]
[ROW][C]36[/C][C] 0.9979[/C][C] 0.004259[/C][C] 0.00213[/C][/ROW]
[ROW][C]37[/C][C] 0.9969[/C][C] 0.006289[/C][C] 0.003144[/C][/ROW]
[ROW][C]38[/C][C] 0.9949[/C][C] 0.0102[/C][C] 0.005099[/C][/ROW]
[ROW][C]39[/C][C] 0.9923[/C][C] 0.01532[/C][C] 0.007659[/C][/ROW]
[ROW][C]40[/C][C] 0.9892[/C][C] 0.02151[/C][C] 0.01076[/C][/ROW]
[ROW][C]41[/C][C] 0.9855[/C][C] 0.02901[/C][C] 0.0145[/C][/ROW]
[ROW][C]42[/C][C] 0.9771[/C][C] 0.04582[/C][C] 0.02291[/C][/ROW]
[ROW][C]43[/C][C] 0.9713[/C][C] 0.05741[/C][C] 0.02871[/C][/ROW]
[ROW][C]44[/C][C] 0.9607[/C][C] 0.07862[/C][C] 0.03931[/C][/ROW]
[ROW][C]45[/C][C] 0.9939[/C][C] 0.01215[/C][C] 0.006074[/C][/ROW]
[ROW][C]46[/C][C] 0.9981[/C][C] 0.003701[/C][C] 0.00185[/C][/ROW]
[ROW][C]47[/C][C] 0.9966[/C][C] 0.006758[/C][C] 0.003379[/C][/ROW]
[ROW][C]48[/C][C] 0.998[/C][C] 0.00407[/C][C] 0.002035[/C][/ROW]
[ROW][C]49[/C][C] 0.9969[/C][C] 0.006257[/C][C] 0.003128[/C][/ROW]
[ROW][C]50[/C][C] 0.994[/C][C] 0.01197[/C][C] 0.005987[/C][/ROW]
[ROW][C]51[/C][C] 0.989[/C][C] 0.02193[/C][C] 0.01097[/C][/ROW]
[ROW][C]52[/C][C] 0.9811[/C][C] 0.03776[/C][C] 0.01888[/C][/ROW]
[ROW][C]53[/C][C] 0.9688[/C][C] 0.06249[/C][C] 0.03125[/C][/ROW]
[ROW][C]54[/C][C] 0.9476[/C][C] 0.1048[/C][C] 0.05241[/C][/ROW]
[ROW][C]55[/C][C] 0.9182[/C][C] 0.1636[/C][C] 0.08182[/C][/ROW]
[ROW][C]56[/C][C] 0.9815[/C][C] 0.03697[/C][C] 0.01848[/C][/ROW]
[ROW][C]57[/C][C] 0.9654[/C][C] 0.06922[/C][C] 0.03461[/C][/ROW]
[ROW][C]58[/C][C] 0.9376[/C][C] 0.1248[/C][C] 0.06242[/C][/ROW]
[ROW][C]59[/C][C] 0.9104[/C][C] 0.1792[/C][C] 0.08962[/C][/ROW]
[ROW][C]60[/C][C] 0.9566[/C][C] 0.0867[/C][C] 0.04335[/C][/ROW]
[ROW][C]61[/C][C] 0.9577[/C][C] 0.08464[/C][C] 0.04232[/C][/ROW]
[ROW][C]62[/C][C] 0.922[/C][C] 0.1559[/C][C] 0.07797[/C][/ROW]
[ROW][C]63[/C][C] 0.8388[/C][C] 0.3224[/C][C] 0.1612[/C][/ROW]
[ROW][C]64[/C][C] 0.8091[/C][C] 0.3817[/C][C] 0.1909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&T=6

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	0.0318	0.06361	0.9682
7	0.06283	0.1257	0.9372
8	0.1248	0.2496	0.8752
9	0.3899	0.7799	0.6101
10	0.2988	0.5976	0.7012
11	0.2182	0.4363	0.7818
12	0.2214	0.4429	0.7786
13	0.1553	0.3106	0.8447
14	0.1017	0.2034	0.8983
15	0.08029	0.1606	0.9197
16	0.05129	0.1026	0.9487
17	0.08588	0.1718	0.9141
18	0.101	0.202	0.899
19	0.06949	0.139	0.9305
20	0.04786	0.09572	0.9521
21	0.03083	0.06166	0.9692
22	0.01909	0.03818	0.9809
23	0.01151	0.02302	0.9885
24	0.9887	0.02252	0.01126
25	0.9954	0.009136	0.004568
26	0.9994	0.001298	0.0006488
27	0.999	0.001995	0.0009973
28	0.9983	0.003375	0.001688
29	0.9973	0.005368	0.002684
30	0.9975	0.005046	0.002523
31	0.9983	0.003323	0.001661
32	0.9977	0.004669	0.002334
33	0.9973	0.005421	0.002711
34	0.9958	0.008326	0.004163
35	0.9987	0.002635	0.001317
36	0.9979	0.004259	0.00213
37	0.9969	0.006289	0.003144
38	0.9949	0.0102	0.005099
39	0.9923	0.01532	0.007659
40	0.9892	0.02151	0.01076
41	0.9855	0.02901	0.0145
42	0.9771	0.04582	0.02291
43	0.9713	0.05741	0.02871
44	0.9607	0.07862	0.03931
45	0.9939	0.01215	0.006074
46	0.9981	0.003701	0.00185
47	0.9966	0.006758	0.003379
48	0.998	0.00407	0.002035
49	0.9969	0.006257	0.003128
50	0.994	0.01197	0.005987
51	0.989	0.02193	0.01097
52	0.9811	0.03776	0.01888
53	0.9688	0.06249	0.03125
54	0.9476	0.1048	0.05241
55	0.9182	0.1636	0.08182
56	0.9815	0.03697	0.01848
57	0.9654	0.06922	0.03461
58	0.9376	0.1248	0.06242
59	0.9104	0.1792	0.08962
60	0.9566	0.0867	0.04335
61	0.9577	0.08464	0.04232
62	0.922	0.1559	0.07797
63	0.8388	0.3224	0.1612
64	0.8091	0.3817	0.1909

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	17	0.2881	NOK
5% type I error level	30	0.508475	NOK
10% type I error level	39	0.661017	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 & 17 &  0.2881 & NOK \tabularnewline
5% type I error level & 30 & 0.508475 & NOK \tabularnewline
10% type I error level & 39 & 0.661017 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=7

[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]17[/C][C] 0.2881[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.508475[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]0.661017[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&T=7

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	17	0.2881	NOK
5% type I error level	30	0.508475	NOK
10% type I error level	39	0.661017	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 21.33, df1 = 2, df2 = 65, p-value = 7.55e-08
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.1756, df1 = 4, df2 = 63, p-value = 0.9501
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.1451, df1 = 2, df2 = 65, p-value = 0.8652

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 21.33, df1 = 2, df2 = 65, p-value = 7.55e-08
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.1756, df1 = 4, df2 = 63, p-value = 0.9501
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.1451, df1 = 2, df2 = 65, p-value = 0.8652
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 21.33, df1 = 2, df2 = 65, p-value = 7.55e-08
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.1756, df1 = 4, df2 = 63, p-value = 0.9501
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.1451, df1 = 2, df2 = 65, p-value = 0.8652
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 21.33, df1 = 2, df2 = 65, p-value = 7.55e-08
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.1756, df1 = 4, df2 = 63, p-value = 0.9501
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.1451, df1 = 2, df2 = 65, p-value = 0.8652

Variance Inflation Factors (Multicollinearity)
> vif b c 69.5284 69.5284

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      b       c 
69.5284 69.5284 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308620&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      b       c 
69.5284 69.5284 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308620&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308620&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif b c 69.5284 69.5284

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code