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Description of Statistical Computation

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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 09 Dec 2017 13:59:45 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/09/t15128245324qi5xzeb50v9yvn.htm/, Retrieved Tue, 14 May 2024 12:04:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308849, Retrieved Tue, 14 May 2024 12:04:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2017-12-09 12:59:45] [467f8ec0164a59d5b0902e2bf4d37c85] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
1	0,455	0,365	0,095	0,514	0,2245	0,101	0,15	15
1	0,35	0,265	0,09	0,2255	0,0995	0,0485	0,07	7
-1	0,53	0,42	0,135	0,677	0,2565	0,1415	0,21	9
1	0,44	0,365	0,125	0,516	0,2155	0,114	0,155	10
0	0,33	0,255	0,08	0,205	0,0895	0,0395	0,055	7
0	0,425	0,3	0,095	0,3515	0,141	0,0775	0,12	8
-1	0,53	0,415	0,15	0,7775	0,237	0,1415	0,33	20
-1	0,545	0,425	0,125	0,768	0,294	0,1495	0,26	16
1	0,475	0,37	0,125	0,5095	0,2165	0,1125	0,165	9
-1	0,55	0,44	0,15	0,8945	0,3145	0,151	0,32	19
-1	0,525	0,38	0,14	0,6065	0,194	0,1475	0,21	14
1	0,43	0,35	0,11	0,406	0,1675	0,081	0,135	10
1	0,49	0,38	0,135	0,5415	0,2175	0,095	0,19	11
-1	0,535	0,405	0,145	0,6845	0,2725	0,171	0,205	10
-1	0,47	0,355	0,1	0,4755	0,1675	0,0805	0,185	10
1	0,5	0,4	0,13	0,6645	0,258	0,133	0,24	12
0	0,355	0,28	0,085	0,2905	0,095	0,0395	0,115	7
-1	0,44	0,34	0,1	0,451	0,188	0,087	0,13	10
1	0,365	0,295	0,08	0,2555	0,097	0,043	0,1	7
1	0,45	0,32	0,1	0,381	0,1705	0,075	0,115	9
1	0,355	0,28	0,095	0,2455	0,0955	0,062	0,075	11
0	0,38	0,275	0,1	0,2255	0,08	0,049	0,085	10
-1	0,565	0,44	0,155	0,9395	0,4275	0,214	0,27	12
-1	0,55	0,415	0,135	0,7635	0,318	0,21	0,2	9
-1	0,615	0,48	0,165	1,1615	0,513	0,301	0,305	10
-1	0,56	0,44	0,14	0,9285	0,3825	0,188	0,3	11
-1	0,58	0,45	0,185	0,9955	0,3945	0,272	0,285	11
1	0,59	0,445	0,14	0,931	0,356	0,234	0,28	12
1	0,605	0,475	0,18	0,9365	0,394	0,219	0,295	15
1	0,575	0,425	0,14	0,8635	0,393	0,227	0,2	11
1	0,58	0,47	0,165	0,9975	0,3935	0,242	0,33	10
-1	0,68	0,56	0,165	1,639	0,6055	0,2805	0,46	15
1	0,665	0,525	0,165	1,338	0,5515	0,3575	0,35	18
-1	0,68	0,55	0,175	1,798	0,815	0,3925	0,455	19
-1	0,705	0,55	0,2	1,7095	0,633	0,4115	0,49	13
1	0,465	0,355	0,105	0,4795	0,227	0,124	0,125	8
-1	0,54	0,475	0,155	1,217	0,5305	0,3075	0,34	16
-1	0,45	0,355	0,105	0,5225	0,237	0,1165	0,145	8
-1	0,575	0,445	0,135	0,883	0,381	0,2035	0,26	11
1	0,355	0,29	0,09	0,3275	0,134	0,086	0,09	9
-1	0,45	0,335	0,105	0,425	0,1865	0,091	0,115	9
-1	0,55	0,425	0,135	0,8515	0,362	0,196	0,27	14
0	0,24	0,175	0,045	0,07	0,0315	0,0235	0,02	5
0	0,205	0,15	0,055	0,042	0,0255	0,015	0,012	5
0	0,21	0,15	0,05	0,042	0,0175	0,0125	0,015	4
0	0,39	0,295	0,095	0,203	0,0875	0,045	0,075	7
1	0,47	0,37	0,12	0,5795	0,293	0,227	0,14	9
-1	0,46	0,375	0,12	0,4605	0,1775	0,11	0,15	7
0	0,325	0,245	0,07	0,161	0,0755	0,0255	0,045	6
-1	0,525	0,425	0,16	0,8355	0,3545	0,2135	0,245	9
0	0,52	0,41	0,12	0,595	0,2385	0,111	0,19	8
1	0,4	0,32	0,095	0,303	0,1335	0,06	0,1	7
1	0,485	0,36	0,13	0,5415	0,2595	0,096	0,16	10
-1	0,47	0,36	0,12	0,4775	0,2105	0,1055	0,15	10
1	0,405	0,31	0,1	0,385	0,173	0,0915	0,11	7
-1	0,5	0,4	0,14	0,6615	0,2565	0,1755	0,22	8
1	0,445	0,35	0,12	0,4425	0,192	0,0955	0,135	8
1	0,47	0,385	0,135	0,5895	0,2765	0,12	0,17	8
0	0,245	0,19	0,06	0,086	0,042	0,014	0,025	4
-1	0,505	0,4	0,125	0,583	0,246	0,13	0,175	7
1	0,45	0,345	0,105	0,4115	0,18	0,1125	0,135	7
1	0,505	0,405	0,11	0,625	0,305	0,16	0,175	9
-1	0,53	0,41	0,13	0,6965	0,302	0,1935	0,2	10
1	0,425	0,325	0,095	0,3785	0,1705	0,08	0,1	7
1	0,52	0,4	0,12	0,58	0,234	0,1315	0,185	8
1	0,475	0,355	0,12	0,48	0,234	0,1015	0,135	8
-1	0,565	0,44	0,16	0,915	0,354	0,1935	0,32	12
-1	0,595	0,495	0,185	1,285	0,416	0,224	0,485	13
-1	0,475	0,39	0,12	0,5305	0,2135	0,1155	0,17	10
0	0,31	0,235	0,07	0,151	0,063	0,0405	0,045	6
1	0,555	0,425	0,13	0,7665	0,264	0,168	0,275	13
-1	0,4	0,32	0,11	0,353	0,1405	0,0985	0,1	8
-1	0,595	0,475	0,17	1,247	0,48	0,225	0,425	20
1	0,57	0,48	0,175	1,185	0,474	0,261	0,38	11
-1	0,605	0,45	0,195	1,098	0,481	0,2895	0,315	13
-1	0,6	0,475	0,15	1,0075	0,4425	0,221	0,28	15
1	0,595	0,475	0,14	0,944	0,3625	0,189	0,315	9
-1	0,6	0,47	0,15	0,922	0,363	0,194	0,305	10
-1	0,555	0,425	0,14	0,788	0,282	0,1595	0,285	11
-1	0,615	0,475	0,17	1,1025	0,4695	0,2355	0,345	14
-1	0,575	0,445	0,14	0,941	0,3845	0,252	0,285	9
1	0,62	0,51	0,175	1,615	0,5105	0,192	0,675	12
-1	0,52	0,425	0,165	0,9885	0,396	0,225	0,32	16
1	0,595	0,475	0,16	1,3175	0,408	0,234	0,58	21
1	0,58	0,45	0,14	1,013	0,38	0,216	0,36	14
-1	0,57	0,465	0,18	1,295	0,339	0,2225	0,44	12
1	0,625	0,465	0,14	1,195	0,4825	0,205	0,4	13
1	0,56	0,44	0,16	0,8645	0,3305	0,2075	0,26	10
-1	0,46	0,355	0,13	0,517	0,2205	0,114	0,165	9
-1	0,575	0,45	0,16	0,9775	0,3135	0,231	0,33	12
1	0,565	0,425	0,135	0,8115	0,341	0,1675	0,255	15
1	0,555	0,44	0,15	0,755	0,307	0,1525	0,26	12
1	0,595	0,465	0,175	1,115	0,4015	0,254	0,39	13
-1	0,625	0,495	0,165	1,262	0,507	0,318	0,39	10
1	0,695	0,56	0,19	1,494	0,588	0,3425	0,485	15
1	0,665	0,535	0,195	1,606	0,5755	0,388	0,48	14
1	0,535	0,435	0,15	0,725	0,269	0,1385	0,25	9
1	0,47	0,375	0,13	0,523	0,214	0,132	0,145	8
1	0,47	0,37	0,13	0,5225	0,201	0,133	0,165	7

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&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]8 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308849&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=0

As an alternative you can also use a QR Code:  
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 Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Ringen [t] = + 3.62641 -0.239059`Geslacht-kwan`[t] + 13.4646Lengte[t] -8.50218Diameter[t] -2.57744Hoogte[t] -7.56024TotaleGewicht[t] + 14.806Afval[t] -4.64381GewichtIngewanden[t] + 25.5664GewichtSchaal[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ringen
[t] =  +  3.62641 -0.239059`Geslacht-kwan`[t] +  13.4646Lengte[t] -8.50218Diameter[t] -2.57744Hoogte[t] -7.56024TotaleGewicht[t] +  14.806Afval[t] -4.64381GewichtIngewanden[t] +  25.5664GewichtSchaal[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ringen
[t] =  +  3.62641 -0.239059`Geslacht-kwan`[t] +  13.4646Lengte[t] -8.50218Diameter[t] -2.57744Hoogte[t] -7.56024TotaleGewicht[t] +  14.806Afval[t] -4.64381GewichtIngewanden[t] +  25.5664GewichtSchaal[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308849&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Ringen [t] = + 3.62641 -0.239059`Geslacht-kwan`[t] + 13.4646Lengte[t] -8.50218Diameter[t] -2.57744Hoogte[t] -7.56024TotaleGewicht[t] + 14.806Afval[t] -4.64381GewichtIngewanden[t] + 25.5664GewichtSchaal[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+3.626 2.191+1.6550e+00 0.1013 0.05066
`Geslacht-kwan`-0.2391 0.273-8.7560e-01 0.3836 0.1918
Lengte+13.46 15.01+8.9710e-01 0.3721 0.186
Diameter-8.502 20.98-4.0510e-01 0.6863 0.3432
Hoogte-2.577 22.87-1.1270e-01 0.9105 0.4553
TotaleGewicht-7.56 7.457-1.0140e+00 0.3134 0.1567
Afval+14.81 10.32+1.4340e+00 0.155 0.07749
GewichtIngewanden-4.644 11.45-4.0540e-01 0.6861 0.3431
GewichtSchaal+25.57 11.89+2.1510e+00 0.0342 0.0171

\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) & +3.626 &  2.191 & +1.6550e+00 &  0.1013 &  0.05066 \tabularnewline
`Geslacht-kwan` & -0.2391 &  0.273 & -8.7560e-01 &  0.3836 &  0.1918 \tabularnewline
Lengte & +13.46 &  15.01 & +8.9710e-01 &  0.3721 &  0.186 \tabularnewline
Diameter & -8.502 &  20.98 & -4.0510e-01 &  0.6863 &  0.3432 \tabularnewline
Hoogte & -2.577 &  22.87 & -1.1270e-01 &  0.9105 &  0.4553 \tabularnewline
TotaleGewicht & -7.56 &  7.457 & -1.0140e+00 &  0.3134 &  0.1567 \tabularnewline
Afval & +14.81 &  10.32 & +1.4340e+00 &  0.155 &  0.07749 \tabularnewline
GewichtIngewanden & -4.644 &  11.45 & -4.0540e-01 &  0.6861 &  0.3431 \tabularnewline
GewichtSchaal & +25.57 &  11.89 & +2.1510e+00 &  0.0342 &  0.0171 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&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]+3.626[/C][C] 2.191[/C][C]+1.6550e+00[/C][C] 0.1013[/C][C] 0.05066[/C][/ROW]
[ROW][C]`Geslacht-kwan`[/C][C]-0.2391[/C][C] 0.273[/C][C]-8.7560e-01[/C][C] 0.3836[/C][C] 0.1918[/C][/ROW]
[ROW][C]Lengte[/C][C]+13.46[/C][C] 15.01[/C][C]+8.9710e-01[/C][C] 0.3721[/C][C] 0.186[/C][/ROW]
[ROW][C]Diameter[/C][C]-8.502[/C][C] 20.98[/C][C]-4.0510e-01[/C][C] 0.6863[/C][C] 0.3432[/C][/ROW]
[ROW][C]Hoogte[/C][C]-2.577[/C][C] 22.87[/C][C]-1.1270e-01[/C][C] 0.9105[/C][C] 0.4553[/C][/ROW]
[ROW][C]TotaleGewicht[/C][C]-7.56[/C][C] 7.457[/C][C]-1.0140e+00[/C][C] 0.3134[/C][C] 0.1567[/C][/ROW]
[ROW][C]Afval[/C][C]+14.81[/C][C] 10.32[/C][C]+1.4340e+00[/C][C] 0.155[/C][C] 0.07749[/C][/ROW]
[ROW][C]GewichtIngewanden[/C][C]-4.644[/C][C] 11.45[/C][C]-4.0540e-01[/C][C] 0.6861[/C][C] 0.3431[/C][/ROW]
[ROW][C]GewichtSchaal[/C][C]+25.57[/C][C] 11.89[/C][C]+2.1510e+00[/C][C] 0.0342[/C][C] 0.0171[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308849&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+3.626 2.191+1.6550e+00 0.1013 0.05066
`Geslacht-kwan`-0.2391 0.273-8.7560e-01 0.3836 0.1918
Lengte+13.46 15.01+8.9710e-01 0.3721 0.186
Diameter-8.502 20.98-4.0510e-01 0.6863 0.3432
Hoogte-2.577 22.87-1.1270e-01 0.9105 0.4553
TotaleGewicht-7.56 7.457-1.0140e+00 0.3134 0.1567
Afval+14.81 10.32+1.4340e+00 0.155 0.07749
GewichtIngewanden-4.644 11.45-4.0540e-01 0.6861 0.3431
GewichtSchaal+25.57 11.89+2.1510e+00 0.0342 0.0171






Multiple Linear Regression - Regression Statistics
Multiple R 0.7573
R-squared 0.5736
Adjusted R-squared 0.5356
F-TEST (value) 15.13
F-TEST (DF numerator)8
F-TEST (DF denominator)90
p-value 7.572e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.438
Sum Squared Residuals 534.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.7573 \tabularnewline
R-squared &  0.5736 \tabularnewline
Adjusted R-squared &  0.5356 \tabularnewline
F-TEST (value) &  15.13 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 90 \tabularnewline
p-value &  7.572e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.438 \tabularnewline
Sum Squared Residuals &  534.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.7573[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.5736[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.5356[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 15.13[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]90[/C][/ROW]
[ROW][C]p-value[/C][C] 7.572e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.438[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 534.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308849&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R 0.7573
R-squared 0.5736
Adjusted R-squared 0.5356
F-TEST (value) 15.13
F-TEST (DF numerator)8
F-TEST (DF denominator)90
p-value 7.572e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.438
Sum Squared Residuals 534.8






Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\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=308849&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=308849&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 15 8.969 6.031
2 7 6.948 0.05232
3 9 10.47-1.474
4 10 8.609 1.391
5 7 6.693 0.3065
6 8 8.692-0.6916
7 20 12.5 7.503
8 16 11.77 4.232
9 9 9.365-0.3646
10 19 12.52 6.483
11 14 10.31 3.686
12 10 8.404 1.596
13 11 9.949 1.051
14 10 10.56-0.5586
15 10 10.16-0.1589
16 12 10.7 1.302
17 7 7.774-0.7737
18 10 8.935 1.065
19 7 7.449-0.4491
20 9 8.704 0.2962
21 11 6.729 4.271
22 10 7.572 2.428
23 12 12.47-0.4683
24 9 10.47-1.469
25 10 12.85-2.854
26 11 12.74-1.744
27 11 11.71-0.7101
28 12 11.49 0.5086
29 15 12.31 2.691
30 11 10.5 0.4952
31 10 12.37-2.373
32 15 14.87 0.1334
33 18 12.79 5.21
34 19 16.18 2.822
35 13 15.23-2.231
36 8 8.715-0.7153
37 16 12.62 3.383
38 8 9.361-1.361
39 11 12.14-1.144
40 9 6.879 2.121
41 9 8.871 0.1286
42 14 12.22 1.775
43 5 5.593-0.5934
44 5 5.267-0.2667
45 4 5.317-1.317
46 7 7.594-0.5939
47 9 8.743 0.2573
48 7 9.032-2.032
49 6 6.672-0.6717
50 9 11.11-2.113
51 8 10.21-2.208
52 7 7.771-0.7715
53 10 9.915 0.08509
54 10 9.675 0.3246
55 7 7.985-0.9852
56 8 10.44-2.442
57 8 8.599-0.5993
58 8 9.521-1.521
59 4 5.701-1.701
60 7 10.05-3.047
61 7 8.726-1.726
62 9 9.982-0.9818
63 10 10.6-0.6011
64 7 7.95-0.9497
65 8 9.878-1.878
66 8 9.271-1.271
67 12 12.93-0.926
68 13 15-1.995
69 10 9.596 0.4037
70 6 6.376-0.3756
71 13 11.28 1.724
72 8 7.758 0.2422
73 20 14.9 5.1
74 11 13.09-2.092
75 13 13.21-0.2125
76 15 12.59 2.414
77 9 12.41-3.405
78 10 12.86-2.862
79 11 12.13-1.128
80 14 14.01-0.0125
81 9 12.16-3.158
82 12 18.66-6.663
83 16 12.35 3.645
84 21 16.77 4.23
85 14 13.18 0.8214
86 12 12.57-0.5675
87 13 14.87-1.872
88 10 10.82-0.8154
89 9 9.751-0.751
90 12 11.98 0.01506
91 15 11.69 3.311
92 12 11.51 0.4909
93 13 13.3-0.3005
94 10 14.11-4.107
95 15 15.71-0.7144
96 14 14.14-0.1392
97 9 10.76-1.756
98 8 8.501-0.5009
99 7 8.861-1.861

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  15 &  8.969 &  6.031 \tabularnewline
2 &  7 &  6.948 &  0.05232 \tabularnewline
3 &  9 &  10.47 & -1.474 \tabularnewline
4 &  10 &  8.609 &  1.391 \tabularnewline
5 &  7 &  6.693 &  0.3065 \tabularnewline
6 &  8 &  8.692 & -0.6916 \tabularnewline
7 &  20 &  12.5 &  7.503 \tabularnewline
8 &  16 &  11.77 &  4.232 \tabularnewline
9 &  9 &  9.365 & -0.3646 \tabularnewline
10 &  19 &  12.52 &  6.483 \tabularnewline
11 &  14 &  10.31 &  3.686 \tabularnewline
12 &  10 &  8.404 &  1.596 \tabularnewline
13 &  11 &  9.949 &  1.051 \tabularnewline
14 &  10 &  10.56 & -0.5586 \tabularnewline
15 &  10 &  10.16 & -0.1589 \tabularnewline
16 &  12 &  10.7 &  1.302 \tabularnewline
17 &  7 &  7.774 & -0.7737 \tabularnewline
18 &  10 &  8.935 &  1.065 \tabularnewline
19 &  7 &  7.449 & -0.4491 \tabularnewline
20 &  9 &  8.704 &  0.2962 \tabularnewline
21 &  11 &  6.729 &  4.271 \tabularnewline
22 &  10 &  7.572 &  2.428 \tabularnewline
23 &  12 &  12.47 & -0.4683 \tabularnewline
24 &  9 &  10.47 & -1.469 \tabularnewline
25 &  10 &  12.85 & -2.854 \tabularnewline
26 &  11 &  12.74 & -1.744 \tabularnewline
27 &  11 &  11.71 & -0.7101 \tabularnewline
28 &  12 &  11.49 &  0.5086 \tabularnewline
29 &  15 &  12.31 &  2.691 \tabularnewline
30 &  11 &  10.5 &  0.4952 \tabularnewline
31 &  10 &  12.37 & -2.373 \tabularnewline
32 &  15 &  14.87 &  0.1334 \tabularnewline
33 &  18 &  12.79 &  5.21 \tabularnewline
34 &  19 &  16.18 &  2.822 \tabularnewline
35 &  13 &  15.23 & -2.231 \tabularnewline
36 &  8 &  8.715 & -0.7153 \tabularnewline
37 &  16 &  12.62 &  3.383 \tabularnewline
38 &  8 &  9.361 & -1.361 \tabularnewline
39 &  11 &  12.14 & -1.144 \tabularnewline
40 &  9 &  6.879 &  2.121 \tabularnewline
41 &  9 &  8.871 &  0.1286 \tabularnewline
42 &  14 &  12.22 &  1.775 \tabularnewline
43 &  5 &  5.593 & -0.5934 \tabularnewline
44 &  5 &  5.267 & -0.2667 \tabularnewline
45 &  4 &  5.317 & -1.317 \tabularnewline
46 &  7 &  7.594 & -0.5939 \tabularnewline
47 &  9 &  8.743 &  0.2573 \tabularnewline
48 &  7 &  9.032 & -2.032 \tabularnewline
49 &  6 &  6.672 & -0.6717 \tabularnewline
50 &  9 &  11.11 & -2.113 \tabularnewline
51 &  8 &  10.21 & -2.208 \tabularnewline
52 &  7 &  7.771 & -0.7715 \tabularnewline
53 &  10 &  9.915 &  0.08509 \tabularnewline
54 &  10 &  9.675 &  0.3246 \tabularnewline
55 &  7 &  7.985 & -0.9852 \tabularnewline
56 &  8 &  10.44 & -2.442 \tabularnewline
57 &  8 &  8.599 & -0.5993 \tabularnewline
58 &  8 &  9.521 & -1.521 \tabularnewline
59 &  4 &  5.701 & -1.701 \tabularnewline
60 &  7 &  10.05 & -3.047 \tabularnewline
61 &  7 &  8.726 & -1.726 \tabularnewline
62 &  9 &  9.982 & -0.9818 \tabularnewline
63 &  10 &  10.6 & -0.6011 \tabularnewline
64 &  7 &  7.95 & -0.9497 \tabularnewline
65 &  8 &  9.878 & -1.878 \tabularnewline
66 &  8 &  9.271 & -1.271 \tabularnewline
67 &  12 &  12.93 & -0.926 \tabularnewline
68 &  13 &  15 & -1.995 \tabularnewline
69 &  10 &  9.596 &  0.4037 \tabularnewline
70 &  6 &  6.376 & -0.3756 \tabularnewline
71 &  13 &  11.28 &  1.724 \tabularnewline
72 &  8 &  7.758 &  0.2422 \tabularnewline
73 &  20 &  14.9 &  5.1 \tabularnewline
74 &  11 &  13.09 & -2.092 \tabularnewline
75 &  13 &  13.21 & -0.2125 \tabularnewline
76 &  15 &  12.59 &  2.414 \tabularnewline
77 &  9 &  12.41 & -3.405 \tabularnewline
78 &  10 &  12.86 & -2.862 \tabularnewline
79 &  11 &  12.13 & -1.128 \tabularnewline
80 &  14 &  14.01 & -0.0125 \tabularnewline
81 &  9 &  12.16 & -3.158 \tabularnewline
82 &  12 &  18.66 & -6.663 \tabularnewline
83 &  16 &  12.35 &  3.645 \tabularnewline
84 &  21 &  16.77 &  4.23 \tabularnewline
85 &  14 &  13.18 &  0.8214 \tabularnewline
86 &  12 &  12.57 & -0.5675 \tabularnewline
87 &  13 &  14.87 & -1.872 \tabularnewline
88 &  10 &  10.82 & -0.8154 \tabularnewline
89 &  9 &  9.751 & -0.751 \tabularnewline
90 &  12 &  11.98 &  0.01506 \tabularnewline
91 &  15 &  11.69 &  3.311 \tabularnewline
92 &  12 &  11.51 &  0.4909 \tabularnewline
93 &  13 &  13.3 & -0.3005 \tabularnewline
94 &  10 &  14.11 & -4.107 \tabularnewline
95 &  15 &  15.71 & -0.7144 \tabularnewline
96 &  14 &  14.14 & -0.1392 \tabularnewline
97 &  9 &  10.76 & -1.756 \tabularnewline
98 &  8 &  8.501 & -0.5009 \tabularnewline
99 &  7 &  8.861 & -1.861 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&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] 15[/C][C] 8.969[/C][C] 6.031[/C][/ROW]
[ROW][C]2[/C][C] 7[/C][C] 6.948[/C][C] 0.05232[/C][/ROW]
[ROW][C]3[/C][C] 9[/C][C] 10.47[/C][C]-1.474[/C][/ROW]
[ROW][C]4[/C][C] 10[/C][C] 8.609[/C][C] 1.391[/C][/ROW]
[ROW][C]5[/C][C] 7[/C][C] 6.693[/C][C] 0.3065[/C][/ROW]
[ROW][C]6[/C][C] 8[/C][C] 8.692[/C][C]-0.6916[/C][/ROW]
[ROW][C]7[/C][C] 20[/C][C] 12.5[/C][C] 7.503[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 11.77[/C][C] 4.232[/C][/ROW]
[ROW][C]9[/C][C] 9[/C][C] 9.365[/C][C]-0.3646[/C][/ROW]
[ROW][C]10[/C][C] 19[/C][C] 12.52[/C][C] 6.483[/C][/ROW]
[ROW][C]11[/C][C] 14[/C][C] 10.31[/C][C] 3.686[/C][/ROW]
[ROW][C]12[/C][C] 10[/C][C] 8.404[/C][C] 1.596[/C][/ROW]
[ROW][C]13[/C][C] 11[/C][C] 9.949[/C][C] 1.051[/C][/ROW]
[ROW][C]14[/C][C] 10[/C][C] 10.56[/C][C]-0.5586[/C][/ROW]
[ROW][C]15[/C][C] 10[/C][C] 10.16[/C][C]-0.1589[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 10.7[/C][C] 1.302[/C][/ROW]
[ROW][C]17[/C][C] 7[/C][C] 7.774[/C][C]-0.7737[/C][/ROW]
[ROW][C]18[/C][C] 10[/C][C] 8.935[/C][C] 1.065[/C][/ROW]
[ROW][C]19[/C][C] 7[/C][C] 7.449[/C][C]-0.4491[/C][/ROW]
[ROW][C]20[/C][C] 9[/C][C] 8.704[/C][C] 0.2962[/C][/ROW]
[ROW][C]21[/C][C] 11[/C][C] 6.729[/C][C] 4.271[/C][/ROW]
[ROW][C]22[/C][C] 10[/C][C] 7.572[/C][C] 2.428[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 12.47[/C][C]-0.4683[/C][/ROW]
[ROW][C]24[/C][C] 9[/C][C] 10.47[/C][C]-1.469[/C][/ROW]
[ROW][C]25[/C][C] 10[/C][C] 12.85[/C][C]-2.854[/C][/ROW]
[ROW][C]26[/C][C] 11[/C][C] 12.74[/C][C]-1.744[/C][/ROW]
[ROW][C]27[/C][C] 11[/C][C] 11.71[/C][C]-0.7101[/C][/ROW]
[ROW][C]28[/C][C] 12[/C][C] 11.49[/C][C] 0.5086[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 12.31[/C][C] 2.691[/C][/ROW]
[ROW][C]30[/C][C] 11[/C][C] 10.5[/C][C] 0.4952[/C][/ROW]
[ROW][C]31[/C][C] 10[/C][C] 12.37[/C][C]-2.373[/C][/ROW]
[ROW][C]32[/C][C] 15[/C][C] 14.87[/C][C] 0.1334[/C][/ROW]
[ROW][C]33[/C][C] 18[/C][C] 12.79[/C][C] 5.21[/C][/ROW]
[ROW][C]34[/C][C] 19[/C][C] 16.18[/C][C] 2.822[/C][/ROW]
[ROW][C]35[/C][C] 13[/C][C] 15.23[/C][C]-2.231[/C][/ROW]
[ROW][C]36[/C][C] 8[/C][C] 8.715[/C][C]-0.7153[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 12.62[/C][C] 3.383[/C][/ROW]
[ROW][C]38[/C][C] 8[/C][C] 9.361[/C][C]-1.361[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 12.14[/C][C]-1.144[/C][/ROW]
[ROW][C]40[/C][C] 9[/C][C] 6.879[/C][C] 2.121[/C][/ROW]
[ROW][C]41[/C][C] 9[/C][C] 8.871[/C][C] 0.1286[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 12.22[/C][C] 1.775[/C][/ROW]
[ROW][C]43[/C][C] 5[/C][C] 5.593[/C][C]-0.5934[/C][/ROW]
[ROW][C]44[/C][C] 5[/C][C] 5.267[/C][C]-0.2667[/C][/ROW]
[ROW][C]45[/C][C] 4[/C][C] 5.317[/C][C]-1.317[/C][/ROW]
[ROW][C]46[/C][C] 7[/C][C] 7.594[/C][C]-0.5939[/C][/ROW]
[ROW][C]47[/C][C] 9[/C][C] 8.743[/C][C] 0.2573[/C][/ROW]
[ROW][C]48[/C][C] 7[/C][C] 9.032[/C][C]-2.032[/C][/ROW]
[ROW][C]49[/C][C] 6[/C][C] 6.672[/C][C]-0.6717[/C][/ROW]
[ROW][C]50[/C][C] 9[/C][C] 11.11[/C][C]-2.113[/C][/ROW]
[ROW][C]51[/C][C] 8[/C][C] 10.21[/C][C]-2.208[/C][/ROW]
[ROW][C]52[/C][C] 7[/C][C] 7.771[/C][C]-0.7715[/C][/ROW]
[ROW][C]53[/C][C] 10[/C][C] 9.915[/C][C] 0.08509[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 9.675[/C][C] 0.3246[/C][/ROW]
[ROW][C]55[/C][C] 7[/C][C] 7.985[/C][C]-0.9852[/C][/ROW]
[ROW][C]56[/C][C] 8[/C][C] 10.44[/C][C]-2.442[/C][/ROW]
[ROW][C]57[/C][C] 8[/C][C] 8.599[/C][C]-0.5993[/C][/ROW]
[ROW][C]58[/C][C] 8[/C][C] 9.521[/C][C]-1.521[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C] 5.701[/C][C]-1.701[/C][/ROW]
[ROW][C]60[/C][C] 7[/C][C] 10.05[/C][C]-3.047[/C][/ROW]
[ROW][C]61[/C][C] 7[/C][C] 8.726[/C][C]-1.726[/C][/ROW]
[ROW][C]62[/C][C] 9[/C][C] 9.982[/C][C]-0.9818[/C][/ROW]
[ROW][C]63[/C][C] 10[/C][C] 10.6[/C][C]-0.6011[/C][/ROW]
[ROW][C]64[/C][C] 7[/C][C] 7.95[/C][C]-0.9497[/C][/ROW]
[ROW][C]65[/C][C] 8[/C][C] 9.878[/C][C]-1.878[/C][/ROW]
[ROW][C]66[/C][C] 8[/C][C] 9.271[/C][C]-1.271[/C][/ROW]
[ROW][C]67[/C][C] 12[/C][C] 12.93[/C][C]-0.926[/C][/ROW]
[ROW][C]68[/C][C] 13[/C][C] 15[/C][C]-1.995[/C][/ROW]
[ROW][C]69[/C][C] 10[/C][C] 9.596[/C][C] 0.4037[/C][/ROW]
[ROW][C]70[/C][C] 6[/C][C] 6.376[/C][C]-0.3756[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 11.28[/C][C] 1.724[/C][/ROW]
[ROW][C]72[/C][C] 8[/C][C] 7.758[/C][C] 0.2422[/C][/ROW]
[ROW][C]73[/C][C] 20[/C][C] 14.9[/C][C] 5.1[/C][/ROW]
[ROW][C]74[/C][C] 11[/C][C] 13.09[/C][C]-2.092[/C][/ROW]
[ROW][C]75[/C][C] 13[/C][C] 13.21[/C][C]-0.2125[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 12.59[/C][C] 2.414[/C][/ROW]
[ROW][C]77[/C][C] 9[/C][C] 12.41[/C][C]-3.405[/C][/ROW]
[ROW][C]78[/C][C] 10[/C][C] 12.86[/C][C]-2.862[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 12.13[/C][C]-1.128[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 14.01[/C][C]-0.0125[/C][/ROW]
[ROW][C]81[/C][C] 9[/C][C] 12.16[/C][C]-3.158[/C][/ROW]
[ROW][C]82[/C][C] 12[/C][C] 18.66[/C][C]-6.663[/C][/ROW]
[ROW][C]83[/C][C] 16[/C][C] 12.35[/C][C] 3.645[/C][/ROW]
[ROW][C]84[/C][C] 21[/C][C] 16.77[/C][C] 4.23[/C][/ROW]
[ROW][C]85[/C][C] 14[/C][C] 13.18[/C][C] 0.8214[/C][/ROW]
[ROW][C]86[/C][C] 12[/C][C] 12.57[/C][C]-0.5675[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 14.87[/C][C]-1.872[/C][/ROW]
[ROW][C]88[/C][C] 10[/C][C] 10.82[/C][C]-0.8154[/C][/ROW]
[ROW][C]89[/C][C] 9[/C][C] 9.751[/C][C]-0.751[/C][/ROW]
[ROW][C]90[/C][C] 12[/C][C] 11.98[/C][C] 0.01506[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 11.69[/C][C] 3.311[/C][/ROW]
[ROW][C]92[/C][C] 12[/C][C] 11.51[/C][C] 0.4909[/C][/ROW]
[ROW][C]93[/C][C] 13[/C][C] 13.3[/C][C]-0.3005[/C][/ROW]
[ROW][C]94[/C][C] 10[/C][C] 14.11[/C][C]-4.107[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 15.71[/C][C]-0.7144[/C][/ROW]
[ROW][C]96[/C][C] 14[/C][C] 14.14[/C][C]-0.1392[/C][/ROW]
[ROW][C]97[/C][C] 9[/C][C] 10.76[/C][C]-1.756[/C][/ROW]
[ROW][C]98[/C][C] 8[/C][C] 8.501[/C][C]-0.5009[/C][/ROW]
[ROW][C]99[/C][C] 7[/C][C] 8.861[/C][C]-1.861[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308849&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 15 8.969 6.031
2 7 6.948 0.05232
3 9 10.47-1.474
4 10 8.609 1.391
5 7 6.693 0.3065
6 8 8.692-0.6916
7 20 12.5 7.503
8 16 11.77 4.232
9 9 9.365-0.3646
10 19 12.52 6.483
11 14 10.31 3.686
12 10 8.404 1.596
13 11 9.949 1.051
14 10 10.56-0.5586
15 10 10.16-0.1589
16 12 10.7 1.302
17 7 7.774-0.7737
18 10 8.935 1.065
19 7 7.449-0.4491
20 9 8.704 0.2962
21 11 6.729 4.271
22 10 7.572 2.428
23 12 12.47-0.4683
24 9 10.47-1.469
25 10 12.85-2.854
26 11 12.74-1.744
27 11 11.71-0.7101
28 12 11.49 0.5086
29 15 12.31 2.691
30 11 10.5 0.4952
31 10 12.37-2.373
32 15 14.87 0.1334
33 18 12.79 5.21
34 19 16.18 2.822
35 13 15.23-2.231
36 8 8.715-0.7153
37 16 12.62 3.383
38 8 9.361-1.361
39 11 12.14-1.144
40 9 6.879 2.121
41 9 8.871 0.1286
42 14 12.22 1.775
43 5 5.593-0.5934
44 5 5.267-0.2667
45 4 5.317-1.317
46 7 7.594-0.5939
47 9 8.743 0.2573
48 7 9.032-2.032
49 6 6.672-0.6717
50 9 11.11-2.113
51 8 10.21-2.208
52 7 7.771-0.7715
53 10 9.915 0.08509
54 10 9.675 0.3246
55 7 7.985-0.9852
56 8 10.44-2.442
57 8 8.599-0.5993
58 8 9.521-1.521
59 4 5.701-1.701
60 7 10.05-3.047
61 7 8.726-1.726
62 9 9.982-0.9818
63 10 10.6-0.6011
64 7 7.95-0.9497
65 8 9.878-1.878
66 8 9.271-1.271
67 12 12.93-0.926
68 13 15-1.995
69 10 9.596 0.4037
70 6 6.376-0.3756
71 13 11.28 1.724
72 8 7.758 0.2422
73 20 14.9 5.1
74 11 13.09-2.092
75 13 13.21-0.2125
76 15 12.59 2.414
77 9 12.41-3.405
78 10 12.86-2.862
79 11 12.13-1.128
80 14 14.01-0.0125
81 9 12.16-3.158
82 12 18.66-6.663
83 16 12.35 3.645
84 21 16.77 4.23
85 14 13.18 0.8214
86 12 12.57-0.5675
87 13 14.87-1.872
88 10 10.82-0.8154
89 9 9.751-0.751
90 12 11.98 0.01506
91 15 11.69 3.311
92 12 11.51 0.4909
93 13 13.3-0.3005
94 10 14.11-4.107
95 15 15.71-0.7144
96 14 14.14-0.1392
97 9 10.76-1.756
98 8 8.501-0.5009
99 7 8.861-1.861






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
12 0.1905 0.381 0.8095
13 0.1904 0.3807 0.8096
14 0.2324 0.4648 0.7676
15 0.1473 0.2946 0.8527
16 0.4615 0.9229 0.5385
17 0.5998 0.8004 0.4002
18 0.6007 0.7986 0.3993
19 0.544 0.9121 0.456
20 0.4545 0.9089 0.5455
21 0.6023 0.7954 0.3977
22 0.7379 0.5242 0.2621
23 0.6654 0.6692 0.3346
24 0.6764 0.6472 0.3236
25 0.7368 0.5265 0.2632
26 0.7551 0.4898 0.2449
27 0.7066 0.5869 0.2934
28 0.7023 0.5955 0.2977
29 0.6979 0.6042 0.3021
30 0.6486 0.7028 0.3514
31 0.7028 0.5944 0.2972
32 0.7874 0.4252 0.2126
33 0.9394 0.1212 0.06059
34 0.9621 0.07583 0.03791
35 0.972 0.05594 0.02797
36 0.962 0.07603 0.03802
37 0.9714 0.05717 0.02858
38 0.9627 0.07453 0.03727
39 0.9518 0.09638 0.04819
40 0.9534 0.09329 0.04664
41 0.9371 0.1258 0.06292
42 0.9282 0.1436 0.07178
43 0.9073 0.1854 0.09269
44 0.8784 0.2433 0.1216
45 0.8528 0.2943 0.1472
46 0.8175 0.365 0.1825
47 0.7737 0.4527 0.2263
48 0.7573 0.4855 0.2427
49 0.707 0.586 0.293
50 0.6882 0.6237 0.3118
51 0.6859 0.6282 0.3141
52 0.644 0.7119 0.356
53 0.5844 0.8312 0.4156
54 0.5286 0.9429 0.4714
55 0.4763 0.9526 0.5237
56 0.4816 0.9631 0.5184
57 0.424 0.848 0.576
58 0.3789 0.7577 0.6211
59 0.3407 0.6814 0.6593
60 0.3354 0.6709 0.6646
61 0.3161 0.6321 0.6839
62 0.2669 0.5339 0.7331
63 0.2168 0.4336 0.7832
64 0.1735 0.347 0.8265
65 0.1563 0.3125 0.8437
66 0.1272 0.2544 0.8728
67 0.1097 0.2193 0.8903
68 0.1361 0.2721 0.8639
69 0.1069 0.2139 0.8931
70 0.08075 0.1615 0.9192
71 0.07034 0.1407 0.9297
72 0.05034 0.1007 0.9497
73 0.1535 0.307 0.8465
74 0.1395 0.2791 0.8605
75 0.1306 0.2611 0.8694
76 0.3189 0.6378 0.6811
77 0.3025 0.6051 0.6975
78 0.2537 0.5075 0.7463
79 0.1948 0.3895 0.8052
80 0.1396 0.2793 0.8604
81 0.1339 0.2679 0.8661
82 0.5134 0.9733 0.4866
83 0.736 0.5281 0.264
84 0.7481 0.5038 0.2519
85 0.7458 0.5084 0.2542
86 0.6224 0.7552 0.3776
87 0.8232 0.3536 0.1768

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.1905 &  0.381 &  0.8095 \tabularnewline
13 &  0.1904 &  0.3807 &  0.8096 \tabularnewline
14 &  0.2324 &  0.4648 &  0.7676 \tabularnewline
15 &  0.1473 &  0.2946 &  0.8527 \tabularnewline
16 &  0.4615 &  0.9229 &  0.5385 \tabularnewline
17 &  0.5998 &  0.8004 &  0.4002 \tabularnewline
18 &  0.6007 &  0.7986 &  0.3993 \tabularnewline
19 &  0.544 &  0.9121 &  0.456 \tabularnewline
20 &  0.4545 &  0.9089 &  0.5455 \tabularnewline
21 &  0.6023 &  0.7954 &  0.3977 \tabularnewline
22 &  0.7379 &  0.5242 &  0.2621 \tabularnewline
23 &  0.6654 &  0.6692 &  0.3346 \tabularnewline
24 &  0.6764 &  0.6472 &  0.3236 \tabularnewline
25 &  0.7368 &  0.5265 &  0.2632 \tabularnewline
26 &  0.7551 &  0.4898 &  0.2449 \tabularnewline
27 &  0.7066 &  0.5869 &  0.2934 \tabularnewline
28 &  0.7023 &  0.5955 &  0.2977 \tabularnewline
29 &  0.6979 &  0.6042 &  0.3021 \tabularnewline
30 &  0.6486 &  0.7028 &  0.3514 \tabularnewline
31 &  0.7028 &  0.5944 &  0.2972 \tabularnewline
32 &  0.7874 &  0.4252 &  0.2126 \tabularnewline
33 &  0.9394 &  0.1212 &  0.06059 \tabularnewline
34 &  0.9621 &  0.07583 &  0.03791 \tabularnewline
35 &  0.972 &  0.05594 &  0.02797 \tabularnewline
36 &  0.962 &  0.07603 &  0.03802 \tabularnewline
37 &  0.9714 &  0.05717 &  0.02858 \tabularnewline
38 &  0.9627 &  0.07453 &  0.03727 \tabularnewline
39 &  0.9518 &  0.09638 &  0.04819 \tabularnewline
40 &  0.9534 &  0.09329 &  0.04664 \tabularnewline
41 &  0.9371 &  0.1258 &  0.06292 \tabularnewline
42 &  0.9282 &  0.1436 &  0.07178 \tabularnewline
43 &  0.9073 &  0.1854 &  0.09269 \tabularnewline
44 &  0.8784 &  0.2433 &  0.1216 \tabularnewline
45 &  0.8528 &  0.2943 &  0.1472 \tabularnewline
46 &  0.8175 &  0.365 &  0.1825 \tabularnewline
47 &  0.7737 &  0.4527 &  0.2263 \tabularnewline
48 &  0.7573 &  0.4855 &  0.2427 \tabularnewline
49 &  0.707 &  0.586 &  0.293 \tabularnewline
50 &  0.6882 &  0.6237 &  0.3118 \tabularnewline
51 &  0.6859 &  0.6282 &  0.3141 \tabularnewline
52 &  0.644 &  0.7119 &  0.356 \tabularnewline
53 &  0.5844 &  0.8312 &  0.4156 \tabularnewline
54 &  0.5286 &  0.9429 &  0.4714 \tabularnewline
55 &  0.4763 &  0.9526 &  0.5237 \tabularnewline
56 &  0.4816 &  0.9631 &  0.5184 \tabularnewline
57 &  0.424 &  0.848 &  0.576 \tabularnewline
58 &  0.3789 &  0.7577 &  0.6211 \tabularnewline
59 &  0.3407 &  0.6814 &  0.6593 \tabularnewline
60 &  0.3354 &  0.6709 &  0.6646 \tabularnewline
61 &  0.3161 &  0.6321 &  0.6839 \tabularnewline
62 &  0.2669 &  0.5339 &  0.7331 \tabularnewline
63 &  0.2168 &  0.4336 &  0.7832 \tabularnewline
64 &  0.1735 &  0.347 &  0.8265 \tabularnewline
65 &  0.1563 &  0.3125 &  0.8437 \tabularnewline
66 &  0.1272 &  0.2544 &  0.8728 \tabularnewline
67 &  0.1097 &  0.2193 &  0.8903 \tabularnewline
68 &  0.1361 &  0.2721 &  0.8639 \tabularnewline
69 &  0.1069 &  0.2139 &  0.8931 \tabularnewline
70 &  0.08075 &  0.1615 &  0.9192 \tabularnewline
71 &  0.07034 &  0.1407 &  0.9297 \tabularnewline
72 &  0.05034 &  0.1007 &  0.9497 \tabularnewline
73 &  0.1535 &  0.307 &  0.8465 \tabularnewline
74 &  0.1395 &  0.2791 &  0.8605 \tabularnewline
75 &  0.1306 &  0.2611 &  0.8694 \tabularnewline
76 &  0.3189 &  0.6378 &  0.6811 \tabularnewline
77 &  0.3025 &  0.6051 &  0.6975 \tabularnewline
78 &  0.2537 &  0.5075 &  0.7463 \tabularnewline
79 &  0.1948 &  0.3895 &  0.8052 \tabularnewline
80 &  0.1396 &  0.2793 &  0.8604 \tabularnewline
81 &  0.1339 &  0.2679 &  0.8661 \tabularnewline
82 &  0.5134 &  0.9733 &  0.4866 \tabularnewline
83 &  0.736 &  0.5281 &  0.264 \tabularnewline
84 &  0.7481 &  0.5038 &  0.2519 \tabularnewline
85 &  0.7458 &  0.5084 &  0.2542 \tabularnewline
86 &  0.6224 &  0.7552 &  0.3776 \tabularnewline
87 &  0.8232 &  0.3536 &  0.1768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&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]12[/C][C] 0.1905[/C][C] 0.381[/C][C] 0.8095[/C][/ROW]
[ROW][C]13[/C][C] 0.1904[/C][C] 0.3807[/C][C] 0.8096[/C][/ROW]
[ROW][C]14[/C][C] 0.2324[/C][C] 0.4648[/C][C] 0.7676[/C][/ROW]
[ROW][C]15[/C][C] 0.1473[/C][C] 0.2946[/C][C] 0.8527[/C][/ROW]
[ROW][C]16[/C][C] 0.4615[/C][C] 0.9229[/C][C] 0.5385[/C][/ROW]
[ROW][C]17[/C][C] 0.5998[/C][C] 0.8004[/C][C] 0.4002[/C][/ROW]
[ROW][C]18[/C][C] 0.6007[/C][C] 0.7986[/C][C] 0.3993[/C][/ROW]
[ROW][C]19[/C][C] 0.544[/C][C] 0.9121[/C][C] 0.456[/C][/ROW]
[ROW][C]20[/C][C] 0.4545[/C][C] 0.9089[/C][C] 0.5455[/C][/ROW]
[ROW][C]21[/C][C] 0.6023[/C][C] 0.7954[/C][C] 0.3977[/C][/ROW]
[ROW][C]22[/C][C] 0.7379[/C][C] 0.5242[/C][C] 0.2621[/C][/ROW]
[ROW][C]23[/C][C] 0.6654[/C][C] 0.6692[/C][C] 0.3346[/C][/ROW]
[ROW][C]24[/C][C] 0.6764[/C][C] 0.6472[/C][C] 0.3236[/C][/ROW]
[ROW][C]25[/C][C] 0.7368[/C][C] 0.5265[/C][C] 0.2632[/C][/ROW]
[ROW][C]26[/C][C] 0.7551[/C][C] 0.4898[/C][C] 0.2449[/C][/ROW]
[ROW][C]27[/C][C] 0.7066[/C][C] 0.5869[/C][C] 0.2934[/C][/ROW]
[ROW][C]28[/C][C] 0.7023[/C][C] 0.5955[/C][C] 0.2977[/C][/ROW]
[ROW][C]29[/C][C] 0.6979[/C][C] 0.6042[/C][C] 0.3021[/C][/ROW]
[ROW][C]30[/C][C] 0.6486[/C][C] 0.7028[/C][C] 0.3514[/C][/ROW]
[ROW][C]31[/C][C] 0.7028[/C][C] 0.5944[/C][C] 0.2972[/C][/ROW]
[ROW][C]32[/C][C] 0.7874[/C][C] 0.4252[/C][C] 0.2126[/C][/ROW]
[ROW][C]33[/C][C] 0.9394[/C][C] 0.1212[/C][C] 0.06059[/C][/ROW]
[ROW][C]34[/C][C] 0.9621[/C][C] 0.07583[/C][C] 0.03791[/C][/ROW]
[ROW][C]35[/C][C] 0.972[/C][C] 0.05594[/C][C] 0.02797[/C][/ROW]
[ROW][C]36[/C][C] 0.962[/C][C] 0.07603[/C][C] 0.03802[/C][/ROW]
[ROW][C]37[/C][C] 0.9714[/C][C] 0.05717[/C][C] 0.02858[/C][/ROW]
[ROW][C]38[/C][C] 0.9627[/C][C] 0.07453[/C][C] 0.03727[/C][/ROW]
[ROW][C]39[/C][C] 0.9518[/C][C] 0.09638[/C][C] 0.04819[/C][/ROW]
[ROW][C]40[/C][C] 0.9534[/C][C] 0.09329[/C][C] 0.04664[/C][/ROW]
[ROW][C]41[/C][C] 0.9371[/C][C] 0.1258[/C][C] 0.06292[/C][/ROW]
[ROW][C]42[/C][C] 0.9282[/C][C] 0.1436[/C][C] 0.07178[/C][/ROW]
[ROW][C]43[/C][C] 0.9073[/C][C] 0.1854[/C][C] 0.09269[/C][/ROW]
[ROW][C]44[/C][C] 0.8784[/C][C] 0.2433[/C][C] 0.1216[/C][/ROW]
[ROW][C]45[/C][C] 0.8528[/C][C] 0.2943[/C][C] 0.1472[/C][/ROW]
[ROW][C]46[/C][C] 0.8175[/C][C] 0.365[/C][C] 0.1825[/C][/ROW]
[ROW][C]47[/C][C] 0.7737[/C][C] 0.4527[/C][C] 0.2263[/C][/ROW]
[ROW][C]48[/C][C] 0.7573[/C][C] 0.4855[/C][C] 0.2427[/C][/ROW]
[ROW][C]49[/C][C] 0.707[/C][C] 0.586[/C][C] 0.293[/C][/ROW]
[ROW][C]50[/C][C] 0.6882[/C][C] 0.6237[/C][C] 0.3118[/C][/ROW]
[ROW][C]51[/C][C] 0.6859[/C][C] 0.6282[/C][C] 0.3141[/C][/ROW]
[ROW][C]52[/C][C] 0.644[/C][C] 0.7119[/C][C] 0.356[/C][/ROW]
[ROW][C]53[/C][C] 0.5844[/C][C] 0.8312[/C][C] 0.4156[/C][/ROW]
[ROW][C]54[/C][C] 0.5286[/C][C] 0.9429[/C][C] 0.4714[/C][/ROW]
[ROW][C]55[/C][C] 0.4763[/C][C] 0.9526[/C][C] 0.5237[/C][/ROW]
[ROW][C]56[/C][C] 0.4816[/C][C] 0.9631[/C][C] 0.5184[/C][/ROW]
[ROW][C]57[/C][C] 0.424[/C][C] 0.848[/C][C] 0.576[/C][/ROW]
[ROW][C]58[/C][C] 0.3789[/C][C] 0.7577[/C][C] 0.6211[/C][/ROW]
[ROW][C]59[/C][C] 0.3407[/C][C] 0.6814[/C][C] 0.6593[/C][/ROW]
[ROW][C]60[/C][C] 0.3354[/C][C] 0.6709[/C][C] 0.6646[/C][/ROW]
[ROW][C]61[/C][C] 0.3161[/C][C] 0.6321[/C][C] 0.6839[/C][/ROW]
[ROW][C]62[/C][C] 0.2669[/C][C] 0.5339[/C][C] 0.7331[/C][/ROW]
[ROW][C]63[/C][C] 0.2168[/C][C] 0.4336[/C][C] 0.7832[/C][/ROW]
[ROW][C]64[/C][C] 0.1735[/C][C] 0.347[/C][C] 0.8265[/C][/ROW]
[ROW][C]65[/C][C] 0.1563[/C][C] 0.3125[/C][C] 0.8437[/C][/ROW]
[ROW][C]66[/C][C] 0.1272[/C][C] 0.2544[/C][C] 0.8728[/C][/ROW]
[ROW][C]67[/C][C] 0.1097[/C][C] 0.2193[/C][C] 0.8903[/C][/ROW]
[ROW][C]68[/C][C] 0.1361[/C][C] 0.2721[/C][C] 0.8639[/C][/ROW]
[ROW][C]69[/C][C] 0.1069[/C][C] 0.2139[/C][C] 0.8931[/C][/ROW]
[ROW][C]70[/C][C] 0.08075[/C][C] 0.1615[/C][C] 0.9192[/C][/ROW]
[ROW][C]71[/C][C] 0.07034[/C][C] 0.1407[/C][C] 0.9297[/C][/ROW]
[ROW][C]72[/C][C] 0.05034[/C][C] 0.1007[/C][C] 0.9497[/C][/ROW]
[ROW][C]73[/C][C] 0.1535[/C][C] 0.307[/C][C] 0.8465[/C][/ROW]
[ROW][C]74[/C][C] 0.1395[/C][C] 0.2791[/C][C] 0.8605[/C][/ROW]
[ROW][C]75[/C][C] 0.1306[/C][C] 0.2611[/C][C] 0.8694[/C][/ROW]
[ROW][C]76[/C][C] 0.3189[/C][C] 0.6378[/C][C] 0.6811[/C][/ROW]
[ROW][C]77[/C][C] 0.3025[/C][C] 0.6051[/C][C] 0.6975[/C][/ROW]
[ROW][C]78[/C][C] 0.2537[/C][C] 0.5075[/C][C] 0.7463[/C][/ROW]
[ROW][C]79[/C][C] 0.1948[/C][C] 0.3895[/C][C] 0.8052[/C][/ROW]
[ROW][C]80[/C][C] 0.1396[/C][C] 0.2793[/C][C] 0.8604[/C][/ROW]
[ROW][C]81[/C][C] 0.1339[/C][C] 0.2679[/C][C] 0.8661[/C][/ROW]
[ROW][C]82[/C][C] 0.5134[/C][C] 0.9733[/C][C] 0.4866[/C][/ROW]
[ROW][C]83[/C][C] 0.736[/C][C] 0.5281[/C][C] 0.264[/C][/ROW]
[ROW][C]84[/C][C] 0.7481[/C][C] 0.5038[/C][C] 0.2519[/C][/ROW]
[ROW][C]85[/C][C] 0.7458[/C][C] 0.5084[/C][C] 0.2542[/C][/ROW]
[ROW][C]86[/C][C] 0.6224[/C][C] 0.7552[/C][C] 0.3776[/C][/ROW]
[ROW][C]87[/C][C] 0.8232[/C][C] 0.3536[/C][C] 0.1768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308849&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=6

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
12 0.1905 0.381 0.8095
13 0.1904 0.3807 0.8096
14 0.2324 0.4648 0.7676
15 0.1473 0.2946 0.8527
16 0.4615 0.9229 0.5385
17 0.5998 0.8004 0.4002
18 0.6007 0.7986 0.3993
19 0.544 0.9121 0.456
20 0.4545 0.9089 0.5455
21 0.6023 0.7954 0.3977
22 0.7379 0.5242 0.2621
23 0.6654 0.6692 0.3346
24 0.6764 0.6472 0.3236
25 0.7368 0.5265 0.2632
26 0.7551 0.4898 0.2449
27 0.7066 0.5869 0.2934
28 0.7023 0.5955 0.2977
29 0.6979 0.6042 0.3021
30 0.6486 0.7028 0.3514
31 0.7028 0.5944 0.2972
32 0.7874 0.4252 0.2126
33 0.9394 0.1212 0.06059
34 0.9621 0.07583 0.03791
35 0.972 0.05594 0.02797
36 0.962 0.07603 0.03802
37 0.9714 0.05717 0.02858
38 0.9627 0.07453 0.03727
39 0.9518 0.09638 0.04819
40 0.9534 0.09329 0.04664
41 0.9371 0.1258 0.06292
42 0.9282 0.1436 0.07178
43 0.9073 0.1854 0.09269
44 0.8784 0.2433 0.1216
45 0.8528 0.2943 0.1472
46 0.8175 0.365 0.1825
47 0.7737 0.4527 0.2263
48 0.7573 0.4855 0.2427
49 0.707 0.586 0.293
50 0.6882 0.6237 0.3118
51 0.6859 0.6282 0.3141
52 0.644 0.7119 0.356
53 0.5844 0.8312 0.4156
54 0.5286 0.9429 0.4714
55 0.4763 0.9526 0.5237
56 0.4816 0.9631 0.5184
57 0.424 0.848 0.576
58 0.3789 0.7577 0.6211
59 0.3407 0.6814 0.6593
60 0.3354 0.6709 0.6646
61 0.3161 0.6321 0.6839
62 0.2669 0.5339 0.7331
63 0.2168 0.4336 0.7832
64 0.1735 0.347 0.8265
65 0.1563 0.3125 0.8437
66 0.1272 0.2544 0.8728
67 0.1097 0.2193 0.8903
68 0.1361 0.2721 0.8639
69 0.1069 0.2139 0.8931
70 0.08075 0.1615 0.9192
71 0.07034 0.1407 0.9297
72 0.05034 0.1007 0.9497
73 0.1535 0.307 0.8465
74 0.1395 0.2791 0.8605
75 0.1306 0.2611 0.8694
76 0.3189 0.6378 0.6811
77 0.3025 0.6051 0.6975
78 0.2537 0.5075 0.7463
79 0.1948 0.3895 0.8052
80 0.1396 0.2793 0.8604
81 0.1339 0.2679 0.8661
82 0.5134 0.9733 0.4866
83 0.736 0.5281 0.264
84 0.7481 0.5038 0.2519
85 0.7458 0.5084 0.2542
86 0.6224 0.7552 0.3776
87 0.8232 0.3536 0.1768






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level70.0921053OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 7 & 0.0921053 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0921053[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308849&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=7

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level70.0921053OK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 3.7659, df1 = 2, df2 = 88, p-value = 0.02696
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.0596, df1 = 16, df2 = 74, p-value = 0.01957
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.88143, df1 = 2, df2 = 88, p-value = 0.4178


\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 = 3.7659, df1 = 2, df2 = 88, p-value = 0.02696

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline

> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.0596, df1 = 16, df2 = 74, p-value = 0.01957

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.88143, df1 = 2, df2 = 88, p-value = 0.4178

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&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 = 3.7659, df1 = 2, df2 = 88, p-value = 0.02696

[/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 = 2.0596, df1 = 16, df2 = 74, p-value = 0.01957

[/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 = 0.88143, df1 = 2, df2 = 88, p-value = 0.4178

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308849&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=8

As an alternative you can also use a QR Code:  
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 = 3.7659, df1 = 2, df2 = 88, p-value = 0.02696
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.0596, df1 = 16, df2 = 74, p-value = 0.01957
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.88143, df1 = 2, df2 = 88, p-value = 0.4178






Variance Inflation Factors (Multicollinearity)
> vif
  `Geslacht-kwan`            Lengte          Diameter            Hoogte 
         1.091213         40.688151         54.387043         10.038107 
    TotaleGewicht             Afval GewichtIngewanden     GewichtSchaal 
       146.266668         40.772055         17.363426         40.734137 


\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline

> vif
  `Geslacht-kwan`            Lengte          Diameter            Hoogte 
         1.091213         40.688151         54.387043         10.038107 
    TotaleGewicht             Afval GewichtIngewanden     GewichtSchaal 
       146.266668         40.772055         17.363426         40.734137 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308849&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
  `Geslacht-kwan`            Lengte          Diameter            Hoogte 
         1.091213         40.688151         54.387043         10.038107 
    TotaleGewicht             Afval GewichtIngewanden     GewichtSchaal 
       146.266668         40.772055         17.363426         40.734137 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308849&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308849&T=9

As an alternative you can also use a QR Code:  
QR Code


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

Variance Inflation Factors (Multicollinearity)
> vif
  `Geslacht-kwan`            Lengte          Diameter            Hoogte 
         1.091213         40.688151         54.387043         10.038107 
    TotaleGewicht             Afval GewichtIngewanden     GewichtSchaal 
       146.266668         40.772055         17.363426         40.734137


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
Parameters (R input):
par1 = 9 ; 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):
par6 <- '12'
par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '9'
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')