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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 13 Dec 2010 20:09:08 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/13/t1292270864jfru6yyx6741jhd.htm/, Retrieved Mon, 06 May 2024 12:51:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109145, Retrieved Mon, 06 May 2024 12:51:35 +0000

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Estimated Impact

144

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

-     [Kendall tau Correlation Matrix] [Pearsons Corr Mam...] [2010-12-09 13:00:40] [6ca0fc48dd5333d51a15728999009c83]
- RMPD  [Multiple Regression] [Bonus: MR SWS] [2010-12-11 15:01:53] [6ca0fc48dd5333d51a15728999009c83]
-    D    [Multiple Regression] [Bonus: MR SWS 2 var] [2010-12-11 15:29:26] [6ca0fc48dd5333d51a15728999009c83]
-    D      [Multiple Regression] [Bonus: MR SWS cor...] [2010-12-13 19:20:15] [6ca0fc48dd5333d51a15728999009c83]
-    D        [Multiple Regression] [Bonus: MR PS correct] [2010-12-13 19:32:39] [6ca0fc48dd5333d51a15728999009c83]
-    D            [Multiple Regression] [Bonus: MR  PS tot...] [2010-12-13 20:09:08] [b4ba846736d082ffaee409a197f454c7] [Current]

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0,30103000	0,65321251	0,00000000	0,81954394	1,62324929	3	1	3
0,25527251	1,83884909	3,40602894	3,66304097	2,79518459	3	5	4
-0,15490196	1,43136376	1,02325246	2,25406445	2,25527251	4	4	4
0,59106461	1,27875360	-1,63827216	-0,52287875	1,54406804	1	1	1
0,00000000	1,48287358	2,20411998	2,22788670	2,59328607	4	5	4
0,55630250	1,44715803	0,51851394	1,40823997	1,79934055	1	2	1
0,14612804	1,69897000	1,71733758	2,64345268	2,36172784	1	1	1
0,17609126	0,84509804	-0,37161107	0,80617997	2,04921802	5	4	4
-0,15490196	1,47712125	2,66745295	2,62634037	2,44870632	5	5	5
0,32221929	0,54406804	-1,12493874	0,07918125	1,62324929	1	1	1
0,61278386	0,77815125	-0,10513034	0,54406804	1,62324929	2	2	2
0,07918125	1,01703334	-0,69897000	0,69897000	2,07918125	2	2	2
-0,30103000	1,30103000	1,44185218	2,06069784	2,17026172	5	5	5
0,53147892	0,59106461	-0,92081875	0,00000000	1,20411998	3	1	2
0,17609126	1,61278386	1,92941893	2,51188336	2,49136169	1	3	1
0,53147892	0,95424251	-0,99567863	0,60205999	1,44715803	5	1	3
-0,09691001	0,88081359	0,01703334	0,74036269	1,83250891	5	3	4
-0,09691001	1,66275783	2,71683772	2,81624130	2,52633928	5	5	5
0,30103000	1,38021124	-2,00000000	-0,60205999	1,69897000	1	1	1
0,27875360	2,00000000	1,79239169	3,12057393	2,42651126	1	1	1
0,11394335	0,50514998	-1,63827216	-0,39794001	1,27875360	4	1	3
0,74818803	0,69897000	0,23044892	0,79934055	1,07918125	2	1	1
0,49136169	0,81291336	0,54406804	1,03342376	2,07918125	2	1	1
0,25527251	1,07918125	-0,31875876	1,19033170	2,14612804	2	2	2
-0,04575749	1,30535137	1,00000000	2,06069784	2,23044892	4	4	4
0,25527251	1,11394335	0,20951501	1,05690485	1,23044892	2	1	2
0,27875360	1,43136376	2,28330123	2,25527251	2,06069784	4	4	4
-0,04575749	1,25527251	0,39794001	1,08278537	1,49136169	5	5	5
0,41497335	0,67209786	-0,55284197	0,27875360	1,32221929	3	1	3
0,38021124	0,99122608	0,62685341	1,70243054	1,71600334	1	1	1
0,07918125	1,46239800	0,83250891	2,25285303	2,21484385	2	3	2
-0,04575749	0,84509804	-0,12493874	1,08990511	2,35218252	2	2	2
-0,30103000	0,77815125	0,55630250	1,32221929	2,35218252	3	2	3
-0,22184875	1,30103000	1,74429298	2,24303805	2,17897695	5	5	5
0,36172784	0,65321251	-0,04575749	0,41497335	1,77815125	2	1	2
-0,30103000	0,87506126	0,30103000	1,08990511	2,30103000	3	1	3
0,41497335	0,36172784	-0,98296666	0,39794001	1,66275783	3	2	2
-0,22184875	1,38021124	0,62221402	1,76342799	2,32221929	4	3	4
0,81954394	0,47712125	0,54406804	0,59106461	1,14612804	2	1	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109145&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109145&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109145&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	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
LogPS[t] = + 1.27884910482951 + 0.0665627478119498LogL[t] + 0.140956520982315LogWb[t] -0.114322631263947LogWbr[t] -0.397394506097767LogTg[t] + 0.0934828417449626P[t] + 0.0527035657009435S[t] -0.263996392119805D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LogPS[t] =  +  1.27884910482951 +  0.0665627478119498LogL[t] +  0.140956520982315LogWb[t] -0.114322631263947LogWbr[t] -0.397394506097767LogTg[t] +  0.0934828417449626P[t] +  0.0527035657009435S[t] -0.263996392119805D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109145&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LogPS[t] =  +  1.27884910482951 +  0.0665627478119498LogL[t] +  0.140956520982315LogWb[t] -0.114322631263947LogWbr[t] -0.397394506097767LogTg[t] +  0.0934828417449626P[t] +  0.0527035657009435S[t] -0.263996392119805D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109145&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109145&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
LogPS[t] = + 1.27884910482951 + 0.0665627478119498LogL[t] + 0.140956520982315LogWb[t] -0.114322631263947LogWbr[t] -0.397394506097767LogTg[t] + 0.0934828417449626P[t] + 0.0527035657009435S[t] -0.263996392119805D[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)	1.27884910482951	0.185936	6.8779	0	0
LogL	0.0665627478119498	0.115977	0.5739	0.570156	0.285078
LogWb	0.140956520982315	0.071364	1.9752	0.057209	0.028604
LogWbr	-0.114322631263947	0.104	-1.0993	0.280122	0.140061
LogTg	-0.397394506097767	0.102682	-3.8701	0.000523	0.000262
P	0.0934828417449626	0.062811	1.4883	0.146772	0.073386
S	0.0527035657009435	0.039853	1.3225	0.19569	0.097845
D	-0.263996392119805	0.07413	-3.5613	0.001216	0.000608

\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) & 1.27884910482951 & 0.185936 & 6.8779 & 0 & 0 \tabularnewline
LogL & 0.0665627478119498 & 0.115977 & 0.5739 & 0.570156 & 0.285078 \tabularnewline
LogWb & 0.140956520982315 & 0.071364 & 1.9752 & 0.057209 & 0.028604 \tabularnewline
LogWbr & -0.114322631263947 & 0.104 & -1.0993 & 0.280122 & 0.140061 \tabularnewline
LogTg & -0.397394506097767 & 0.102682 & -3.8701 & 0.000523 & 0.000262 \tabularnewline
P & 0.0934828417449626 & 0.062811 & 1.4883 & 0.146772 & 0.073386 \tabularnewline
S & 0.0527035657009435 & 0.039853 & 1.3225 & 0.19569 & 0.097845 \tabularnewline
D & -0.263996392119805 & 0.07413 & -3.5613 & 0.001216 & 0.000608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109145&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]1.27884910482951[/C][C]0.185936[/C][C]6.8779[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]LogL[/C][C]0.0665627478119498[/C][C]0.115977[/C][C]0.5739[/C][C]0.570156[/C][C]0.285078[/C][/ROW]
[ROW][C]LogWb[/C][C]0.140956520982315[/C][C]0.071364[/C][C]1.9752[/C][C]0.057209[/C][C]0.028604[/C][/ROW]
[ROW][C]LogWbr[/C][C]-0.114322631263947[/C][C]0.104[/C][C]-1.0993[/C][C]0.280122[/C][C]0.140061[/C][/ROW]
[ROW][C]LogTg[/C][C]-0.397394506097767[/C][C]0.102682[/C][C]-3.8701[/C][C]0.000523[/C][C]0.000262[/C][/ROW]
[ROW][C]P[/C][C]0.0934828417449626[/C][C]0.062811[/C][C]1.4883[/C][C]0.146772[/C][C]0.073386[/C][/ROW]
[ROW][C]S[/C][C]0.0527035657009435[/C][C]0.039853[/C][C]1.3225[/C][C]0.19569[/C][C]0.097845[/C][/ROW]
[ROW][C]D[/C][C]-0.263996392119805[/C][C]0.07413[/C][C]-3.5613[/C][C]0.001216[/C][C]0.000608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109145&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109145&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)	1.27884910482951	0.185936	6.8779	0	0
LogL	0.0665627478119498	0.115977	0.5739	0.570156	0.285078
LogWb	0.140956520982315	0.071364	1.9752	0.057209	0.028604
LogWbr	-0.114322631263947	0.104	-1.0993	0.280122	0.140061
LogTg	-0.397394506097767	0.102682	-3.8701	0.000523	0.000262
P	0.0934828417449626	0.062811	1.4883	0.146772	0.073386
S	0.0527035657009435	0.039853	1.3225	0.19569	0.097845
D	-0.263996392119805	0.07413	-3.5613	0.001216	0.000608

Multiple Linear Regression - Regression Statistics
Multiple R	0.87048379182848
R-squared	0.757742031836087
Adjusted R-squared	0.703038619670043
F-TEST (value)	13.8518238960316
F-TEST (DF numerator)	7
F-TEST (DF denominator)	31
p-value	5.56941184282067e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.164049391721402
Sum Squared Residuals	0.834278290649018

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.87048379182848 \tabularnewline
R-squared & 0.757742031836087 \tabularnewline
Adjusted R-squared & 0.703038619670043 \tabularnewline
F-TEST (value) & 13.8518238960316 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 31 \tabularnewline
p-value & 5.56941184282067e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.164049391721402 \tabularnewline
Sum Squared Residuals & 0.834278290649018 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109145&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.87048379182848[/C][/ROW]
[ROW][C]R-squared[/C][C]0.757742031836087[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.703038619670043[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.8518238960316[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]31[/C][/ROW]
[ROW][C]p-value[/C][C]5.56941184282067e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.164049391721402[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.834278290649018[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109145&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109145&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	0.87048379182848
R-squared	0.757742031836087
Adjusted R-squared	0.703038619670043
F-TEST (value)	13.8518238960316
F-TEST (DF numerator)	7
F-TEST (DF denominator)	31
p-value	5.56941184282067e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.164049391721402
Sum Squared Residuals	0.834278290649018

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.30103	0.124728869446341	0.176301130553659
2	0.25527251	-0.160228753633906	0.415501263633906
3	-0.15490196	-0.106804706223694	-0.0480972537763059
4	0.59106461	0.461404047845105	0.129660562154895
5	0	-0.0155535504740161	0.0155535504740161
6	0.5563025	0.507115673924316	0.0491868260756842
7	0.14612804	0.275453027921185	-0.129324987921185
8	0.17609126	-0.00154954638118404	0.177640806381184
9	-0.15490196	-0.109237261880564	-0.0456646981194357
10	0.32221929	0.384563774066172	-0.0623444840661717
11	0.61278386	0.372936574126405	0.239847285873595
12	0.07918125	0.106237994231416	-0.0270567442314159
13	-0.30103	-0.118396703321665	-0.182633296678335
14	0.53147892	0.51504522391189	0.0164336960881105
15	0.17609126	0.368543193344305	-0.192451933344305
16	0.53147892	0.286225577926369	0.245253342073631
17	-0.09691001	0.0965495913658846	-0.193459601365885
18	-0.09691001	-0.142480561445818	0.0455705514458182
19	0.30103	0.36466446909694	-0.0636344690969395
20	0.2787536	0.225779446199872	0.0529741538001275
21	0.11394335	0.253517781516714	-0.139574431516714
22	0.74818803	0.81328718898033	-0.0650991589803296
23	0.49136169	0.440922717585913	0.0504389724140875
24	0.25527251	0.0811899345273506	0.174082575472649
25	-0.04575749	-0.0864990850849126	0.0407415950849126
26	0.25527251	0.484403422677231	-0.229130912677231
27	0.2787536	0.147992180949412	0.130761419050588
28	-0.04575749	0.112999993544494	-0.158757483544494
29	0.41497335	0.229511492293056	0.185461857706944
30	0.38021124	0.438820288944455	-0.0586090489444552
31	0.07918125	0.172902626002748	-0.093721376002748
32	-0.04575749	0.0225250222778648	-0.0682825122778648
33	-0.30103	-0.0829780635940158	-0.218051936405984
34	-0.22184875	-0.100074697464356	-0.121774052535644
35	0.36172784	0.273486989714931	0.0882408502850691
36	-0.30103	-0.118326857482751	-0.182703142517249
37	0.41497335	0.3159696399405	0.0990037100594996
38	-0.22184875	-0.189955538967626	-0.0318932110323744
39	0.81954394	0.83993335359331	-0.0203894135933097

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.30103 & 0.124728869446341 & 0.176301130553659 \tabularnewline
2 & 0.25527251 & -0.160228753633906 & 0.415501263633906 \tabularnewline
3 & -0.15490196 & -0.106804706223694 & -0.0480972537763059 \tabularnewline
4 & 0.59106461 & 0.461404047845105 & 0.129660562154895 \tabularnewline
5 & 0 & -0.0155535504740161 & 0.0155535504740161 \tabularnewline
6 & 0.5563025 & 0.507115673924316 & 0.0491868260756842 \tabularnewline
7 & 0.14612804 & 0.275453027921185 & -0.129324987921185 \tabularnewline
8 & 0.17609126 & -0.00154954638118404 & 0.177640806381184 \tabularnewline
9 & -0.15490196 & -0.109237261880564 & -0.0456646981194357 \tabularnewline
10 & 0.32221929 & 0.384563774066172 & -0.0623444840661717 \tabularnewline
11 & 0.61278386 & 0.372936574126405 & 0.239847285873595 \tabularnewline
12 & 0.07918125 & 0.106237994231416 & -0.0270567442314159 \tabularnewline
13 & -0.30103 & -0.118396703321665 & -0.182633296678335 \tabularnewline
14 & 0.53147892 & 0.51504522391189 & 0.0164336960881105 \tabularnewline
15 & 0.17609126 & 0.368543193344305 & -0.192451933344305 \tabularnewline
16 & 0.53147892 & 0.286225577926369 & 0.245253342073631 \tabularnewline
17 & -0.09691001 & 0.0965495913658846 & -0.193459601365885 \tabularnewline
18 & -0.09691001 & -0.142480561445818 & 0.0455705514458182 \tabularnewline
19 & 0.30103 & 0.36466446909694 & -0.0636344690969395 \tabularnewline
20 & 0.2787536 & 0.225779446199872 & 0.0529741538001275 \tabularnewline
21 & 0.11394335 & 0.253517781516714 & -0.139574431516714 \tabularnewline
22 & 0.74818803 & 0.81328718898033 & -0.0650991589803296 \tabularnewline
23 & 0.49136169 & 0.440922717585913 & 0.0504389724140875 \tabularnewline
24 & 0.25527251 & 0.0811899345273506 & 0.174082575472649 \tabularnewline
25 & -0.04575749 & -0.0864990850849126 & 0.0407415950849126 \tabularnewline
26 & 0.25527251 & 0.484403422677231 & -0.229130912677231 \tabularnewline
27 & 0.2787536 & 0.147992180949412 & 0.130761419050588 \tabularnewline
28 & -0.04575749 & 0.112999993544494 & -0.158757483544494 \tabularnewline
29 & 0.41497335 & 0.229511492293056 & 0.185461857706944 \tabularnewline
30 & 0.38021124 & 0.438820288944455 & -0.0586090489444552 \tabularnewline
31 & 0.07918125 & 0.172902626002748 & -0.093721376002748 \tabularnewline
32 & -0.04575749 & 0.0225250222778648 & -0.0682825122778648 \tabularnewline
33 & -0.30103 & -0.0829780635940158 & -0.218051936405984 \tabularnewline
34 & -0.22184875 & -0.100074697464356 & -0.121774052535644 \tabularnewline
35 & 0.36172784 & 0.273486989714931 & 0.0882408502850691 \tabularnewline
36 & -0.30103 & -0.118326857482751 & -0.182703142517249 \tabularnewline
37 & 0.41497335 & 0.3159696399405 & 0.0990037100594996 \tabularnewline
38 & -0.22184875 & -0.189955538967626 & -0.0318932110323744 \tabularnewline
39 & 0.81954394 & 0.83993335359331 & -0.0203894135933097 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109145&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]0.30103[/C][C]0.124728869446341[/C][C]0.176301130553659[/C][/ROW]
[ROW][C]2[/C][C]0.25527251[/C][C]-0.160228753633906[/C][C]0.415501263633906[/C][/ROW]
[ROW][C]3[/C][C]-0.15490196[/C][C]-0.106804706223694[/C][C]-0.0480972537763059[/C][/ROW]
[ROW][C]4[/C][C]0.59106461[/C][C]0.461404047845105[/C][C]0.129660562154895[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0155535504740161[/C][C]0.0155535504740161[/C][/ROW]
[ROW][C]6[/C][C]0.5563025[/C][C]0.507115673924316[/C][C]0.0491868260756842[/C][/ROW]
[ROW][C]7[/C][C]0.14612804[/C][C]0.275453027921185[/C][C]-0.129324987921185[/C][/ROW]
[ROW][C]8[/C][C]0.17609126[/C][C]-0.00154954638118404[/C][C]0.177640806381184[/C][/ROW]
[ROW][C]9[/C][C]-0.15490196[/C][C]-0.109237261880564[/C][C]-0.0456646981194357[/C][/ROW]
[ROW][C]10[/C][C]0.32221929[/C][C]0.384563774066172[/C][C]-0.0623444840661717[/C][/ROW]
[ROW][C]11[/C][C]0.61278386[/C][C]0.372936574126405[/C][C]0.239847285873595[/C][/ROW]
[ROW][C]12[/C][C]0.07918125[/C][C]0.106237994231416[/C][C]-0.0270567442314159[/C][/ROW]
[ROW][C]13[/C][C]-0.30103[/C][C]-0.118396703321665[/C][C]-0.182633296678335[/C][/ROW]
[ROW][C]14[/C][C]0.53147892[/C][C]0.51504522391189[/C][C]0.0164336960881105[/C][/ROW]
[ROW][C]15[/C][C]0.17609126[/C][C]0.368543193344305[/C][C]-0.192451933344305[/C][/ROW]
[ROW][C]16[/C][C]0.53147892[/C][C]0.286225577926369[/C][C]0.245253342073631[/C][/ROW]
[ROW][C]17[/C][C]-0.09691001[/C][C]0.0965495913658846[/C][C]-0.193459601365885[/C][/ROW]
[ROW][C]18[/C][C]-0.09691001[/C][C]-0.142480561445818[/C][C]0.0455705514458182[/C][/ROW]
[ROW][C]19[/C][C]0.30103[/C][C]0.36466446909694[/C][C]-0.0636344690969395[/C][/ROW]
[ROW][C]20[/C][C]0.2787536[/C][C]0.225779446199872[/C][C]0.0529741538001275[/C][/ROW]
[ROW][C]21[/C][C]0.11394335[/C][C]0.253517781516714[/C][C]-0.139574431516714[/C][/ROW]
[ROW][C]22[/C][C]0.74818803[/C][C]0.81328718898033[/C][C]-0.0650991589803296[/C][/ROW]
[ROW][C]23[/C][C]0.49136169[/C][C]0.440922717585913[/C][C]0.0504389724140875[/C][/ROW]
[ROW][C]24[/C][C]0.25527251[/C][C]0.0811899345273506[/C][C]0.174082575472649[/C][/ROW]
[ROW][C]25[/C][C]-0.04575749[/C][C]-0.0864990850849126[/C][C]0.0407415950849126[/C][/ROW]
[ROW][C]26[/C][C]0.25527251[/C][C]0.484403422677231[/C][C]-0.229130912677231[/C][/ROW]
[ROW][C]27[/C][C]0.2787536[/C][C]0.147992180949412[/C][C]0.130761419050588[/C][/ROW]
[ROW][C]28[/C][C]-0.04575749[/C][C]0.112999993544494[/C][C]-0.158757483544494[/C][/ROW]
[ROW][C]29[/C][C]0.41497335[/C][C]0.229511492293056[/C][C]0.185461857706944[/C][/ROW]
[ROW][C]30[/C][C]0.38021124[/C][C]0.438820288944455[/C][C]-0.0586090489444552[/C][/ROW]
[ROW][C]31[/C][C]0.07918125[/C][C]0.172902626002748[/C][C]-0.093721376002748[/C][/ROW]
[ROW][C]32[/C][C]-0.04575749[/C][C]0.0225250222778648[/C][C]-0.0682825122778648[/C][/ROW]
[ROW][C]33[/C][C]-0.30103[/C][C]-0.0829780635940158[/C][C]-0.218051936405984[/C][/ROW]
[ROW][C]34[/C][C]-0.22184875[/C][C]-0.100074697464356[/C][C]-0.121774052535644[/C][/ROW]
[ROW][C]35[/C][C]0.36172784[/C][C]0.273486989714931[/C][C]0.0882408502850691[/C][/ROW]
[ROW][C]36[/C][C]-0.30103[/C][C]-0.118326857482751[/C][C]-0.182703142517249[/C][/ROW]
[ROW][C]37[/C][C]0.41497335[/C][C]0.3159696399405[/C][C]0.0990037100594996[/C][/ROW]
[ROW][C]38[/C][C]-0.22184875[/C][C]-0.189955538967626[/C][C]-0.0318932110323744[/C][/ROW]
[ROW][C]39[/C][C]0.81954394[/C][C]0.83993335359331[/C][C]-0.0203894135933097[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109145&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109145&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.30103	0.124728869446341	0.176301130553659
2	0.25527251	-0.160228753633906	0.415501263633906
3	-0.15490196	-0.106804706223694	-0.0480972537763059
4	0.59106461	0.461404047845105	0.129660562154895
5	0	-0.0155535504740161	0.0155535504740161
6	0.5563025	0.507115673924316	0.0491868260756842
7	0.14612804	0.275453027921185	-0.129324987921185
8	0.17609126	-0.00154954638118404	0.177640806381184
9	-0.15490196	-0.109237261880564	-0.0456646981194357
10	0.32221929	0.384563774066172	-0.0623444840661717
11	0.61278386	0.372936574126405	0.239847285873595
12	0.07918125	0.106237994231416	-0.0270567442314159
13	-0.30103	-0.118396703321665	-0.182633296678335
14	0.53147892	0.51504522391189	0.0164336960881105
15	0.17609126	0.368543193344305	-0.192451933344305
16	0.53147892	0.286225577926369	0.245253342073631
17	-0.09691001	0.0965495913658846	-0.193459601365885
18	-0.09691001	-0.142480561445818	0.0455705514458182
19	0.30103	0.36466446909694	-0.0636344690969395
20	0.2787536	0.225779446199872	0.0529741538001275
21	0.11394335	0.253517781516714	-0.139574431516714
22	0.74818803	0.81328718898033	-0.0650991589803296
23	0.49136169	0.440922717585913	0.0504389724140875
24	0.25527251	0.0811899345273506	0.174082575472649
25	-0.04575749	-0.0864990850849126	0.0407415950849126
26	0.25527251	0.484403422677231	-0.229130912677231
27	0.2787536	0.147992180949412	0.130761419050588
28	-0.04575749	0.112999993544494	-0.158757483544494
29	0.41497335	0.229511492293056	0.185461857706944
30	0.38021124	0.438820288944455	-0.0586090489444552
31	0.07918125	0.172902626002748	-0.093721376002748
32	-0.04575749	0.0225250222778648	-0.0682825122778648
33	-0.30103	-0.0829780635940158	-0.218051936405984
34	-0.22184875	-0.100074697464356	-0.121774052535644
35	0.36172784	0.273486989714931	0.0882408502850691
36	-0.30103	-0.118326857482751	-0.182703142517249
37	0.41497335	0.3159696399405	0.0990037100594996
38	-0.22184875	-0.189955538967626	-0.0318932110323744
39	0.81954394	0.83993335359331	-0.0203894135933097

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.933474603415896	0.133050793168208	0.0665253965841039
12	0.875001168062336	0.249997663875329	0.124998831937664
13	0.929450886043647	0.141098227912706	0.0705491139563529
14	0.873734866667351	0.252530266665297	0.126265133332649
15	0.851449968590607	0.297100062818786	0.148550031409393
16	0.916512603488514	0.166974793022971	0.0834873965114856
17	0.921177537174506	0.157644925650989	0.0788224628254944
18	0.870862305884298	0.258275388231404	0.129137694115702
19	0.819571442363008	0.360857115273983	0.180428557636992
20	0.750812731452747	0.498374537094507	0.249187268547253
21	0.721289883713821	0.557420232572358	0.278710116286179
22	0.62574450195348	0.748510996093039	0.374255498046519
23	0.519298273848515	0.96140345230297	0.480701726151485
24	0.515043239136808	0.969913521726383	0.484956760863192
25	0.424495087914194	0.848990175828389	0.575504912085806
26	0.511383985224765	0.97723202955047	0.488616014775235
27	0.700708384742012	0.598583230515977	0.299291615257988
28	0.978938746485072	0.0421225070298569	0.0210612535149284

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.933474603415896 & 0.133050793168208 & 0.0665253965841039 \tabularnewline
12 & 0.875001168062336 & 0.249997663875329 & 0.124998831937664 \tabularnewline
13 & 0.929450886043647 & 0.141098227912706 & 0.0705491139563529 \tabularnewline
14 & 0.873734866667351 & 0.252530266665297 & 0.126265133332649 \tabularnewline
15 & 0.851449968590607 & 0.297100062818786 & 0.148550031409393 \tabularnewline
16 & 0.916512603488514 & 0.166974793022971 & 0.0834873965114856 \tabularnewline
17 & 0.921177537174506 & 0.157644925650989 & 0.0788224628254944 \tabularnewline
18 & 0.870862305884298 & 0.258275388231404 & 0.129137694115702 \tabularnewline
19 & 0.819571442363008 & 0.360857115273983 & 0.180428557636992 \tabularnewline
20 & 0.750812731452747 & 0.498374537094507 & 0.249187268547253 \tabularnewline
21 & 0.721289883713821 & 0.557420232572358 & 0.278710116286179 \tabularnewline
22 & 0.62574450195348 & 0.748510996093039 & 0.374255498046519 \tabularnewline
23 & 0.519298273848515 & 0.96140345230297 & 0.480701726151485 \tabularnewline
24 & 0.515043239136808 & 0.969913521726383 & 0.484956760863192 \tabularnewline
25 & 0.424495087914194 & 0.848990175828389 & 0.575504912085806 \tabularnewline
26 & 0.511383985224765 & 0.97723202955047 & 0.488616014775235 \tabularnewline
27 & 0.700708384742012 & 0.598583230515977 & 0.299291615257988 \tabularnewline
28 & 0.978938746485072 & 0.0421225070298569 & 0.0210612535149284 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109145&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.933474603415896[/C][C]0.133050793168208[/C][C]0.0665253965841039[/C][/ROW]
[ROW][C]12[/C][C]0.875001168062336[/C][C]0.249997663875329[/C][C]0.124998831937664[/C][/ROW]
[ROW][C]13[/C][C]0.929450886043647[/C][C]0.141098227912706[/C][C]0.0705491139563529[/C][/ROW]
[ROW][C]14[/C][C]0.873734866667351[/C][C]0.252530266665297[/C][C]0.126265133332649[/C][/ROW]
[ROW][C]15[/C][C]0.851449968590607[/C][C]0.297100062818786[/C][C]0.148550031409393[/C][/ROW]
[ROW][C]16[/C][C]0.916512603488514[/C][C]0.166974793022971[/C][C]0.0834873965114856[/C][/ROW]
[ROW][C]17[/C][C]0.921177537174506[/C][C]0.157644925650989[/C][C]0.0788224628254944[/C][/ROW]
[ROW][C]18[/C][C]0.870862305884298[/C][C]0.258275388231404[/C][C]0.129137694115702[/C][/ROW]
[ROW][C]19[/C][C]0.819571442363008[/C][C]0.360857115273983[/C][C]0.180428557636992[/C][/ROW]
[ROW][C]20[/C][C]0.750812731452747[/C][C]0.498374537094507[/C][C]0.249187268547253[/C][/ROW]
[ROW][C]21[/C][C]0.721289883713821[/C][C]0.557420232572358[/C][C]0.278710116286179[/C][/ROW]
[ROW][C]22[/C][C]0.62574450195348[/C][C]0.748510996093039[/C][C]0.374255498046519[/C][/ROW]
[ROW][C]23[/C][C]0.519298273848515[/C][C]0.96140345230297[/C][C]0.480701726151485[/C][/ROW]
[ROW][C]24[/C][C]0.515043239136808[/C][C]0.969913521726383[/C][C]0.484956760863192[/C][/ROW]
[ROW][C]25[/C][C]0.424495087914194[/C][C]0.848990175828389[/C][C]0.575504912085806[/C][/ROW]
[ROW][C]26[/C][C]0.511383985224765[/C][C]0.97723202955047[/C][C]0.488616014775235[/C][/ROW]
[ROW][C]27[/C][C]0.700708384742012[/C][C]0.598583230515977[/C][C]0.299291615257988[/C][/ROW]
[ROW][C]28[/C][C]0.978938746485072[/C][C]0.0421225070298569[/C][C]0.0210612535149284[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109145&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109145&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.933474603415896	0.133050793168208	0.0665253965841039
12	0.875001168062336	0.249997663875329	0.124998831937664
13	0.929450886043647	0.141098227912706	0.0705491139563529
14	0.873734866667351	0.252530266665297	0.126265133332649
15	0.851449968590607	0.297100062818786	0.148550031409393
16	0.916512603488514	0.166974793022971	0.0834873965114856
17	0.921177537174506	0.157644925650989	0.0788224628254944
18	0.870862305884298	0.258275388231404	0.129137694115702
19	0.819571442363008	0.360857115273983	0.180428557636992
20	0.750812731452747	0.498374537094507	0.249187268547253
21	0.721289883713821	0.557420232572358	0.278710116286179
22	0.62574450195348	0.748510996093039	0.374255498046519
23	0.519298273848515	0.96140345230297	0.480701726151485
24	0.515043239136808	0.969913521726383	0.484956760863192
25	0.424495087914194	0.848990175828389	0.575504912085806
26	0.511383985224765	0.97723202955047	0.488616014775235
27	0.700708384742012	0.598583230515977	0.299291615257988
28	0.978938746485072	0.0421225070298569	0.0210612535149284

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0555555555555556	NOK
10% type I error level	1	0.0555555555555556	OK

\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 & 1 & 0.0555555555555556 & NOK \tabularnewline
10% type I error level & 1 & 0.0555555555555556 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109145&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0555555555555556[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0555555555555556[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109145&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109145&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0555555555555556	NOK
10% type I error level	1	0.0555555555555556	OK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code