Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 13 Dec 2010 19:58:24 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/13/t1292270222tvftv1w82votykk.htm/, Retrieved Mon, 06 May 2024 16:47:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109134, Retrieved Mon, 06 May 2024 16:47:54 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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 totaalm...] [2010-12-13 19:52:07] [6ca0fc48dd5333d51a15728999009c83]
-    D              [Multiple Regression] [Bonus: MR SWS tot...] [2010-12-13 19:58:24] [b4ba846736d082ffaee409a197f454c7] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

6,30000000	0,65321251	0,00000000	0,81954394	1,62324929	3	1	3
2,10000000	1,83884909	3,40602894	3,66304097	2,79518459	3	5	4
9,10000000	1,43136376	1,02325246	2,25406445	2,25527251	4	4	4
15,80000000	1,27875360	-1,63827216	-0,52287875	1,54406804	1	1	1
5,20000000	1,48287358	2,20411998	2,22788670	2,59328607	4	5	4
10,90000000	1,44715803	0,51851394	1,40823997	1,79934055	1	2	1
8,30000000	1,69897000	1,71733758	2,64345268	2,36172784	1	1	1
11,00000000	0,84509804	-0,37161107	0,80617997	2,04921802	5	4	4
3,20000000	1,47712125	2,66745295	2,62634037	2,44870632	5	5	5
6,30000000	0,54406804	-1,12493874	0,07918125	1,62324929	1	1	1
6,60000000	0,77815125	-0,10513034	0,54406804	1,62324929	2	2	2
9,50000000	1,01703334	-0,69897000	0,69897000	2,07918125	2	2	2
3,30000000	1,30103000	1,44185218	2,06069784	2,17026172	5	5	5
11,00000000	0,59106461	-0,92081875	0,00000000	1,20411998	3	1	2
4,70000000	1,61278386	1,92941893	2,51188336	2,49136169	1	3	1
10,40000000	0,95424251	-0,99567863	0,60205999	1,44715803	5	1	3
7,40000000	0,88081359	0,01703334	0,74036269	1,83250891	5	3	4
2,10000000	1,66275783	2,71683772	2,81624130	2,52633928	5	5	5
17,90000000	1,38021124	-2,00000000	-0,60205999	1,69897000	1	1	1
6,10000000	2,00000000	1,79239169	3,12057393	2,42651126	1	1	1
11,90000000	0,50514998	-1,63827216	-0,39794001	1,27875360	4	1	3
13,80000000	0,69897000	0,23044892	0,79934055	1,07918125	2	1	1
14,30000000	0,81291336	0,54406804	1,03342376	2,07918125	2	1	1
15,20000000	1,07918125	-0,31875876	1,19033170	2,14612804	2	2	2
10,00000000	1,30535137	1,00000000	2,06069784	2,23044892	4	4	4
11,90000000	1,11394335	0,20951501	1,05690485	1,23044892	2	1	2
6,50000000	1,43136376	2,28330123	2,25527251	2,06069784	4	4	4
7,50000000	1,25527251	0,39794001	1,08278537	1,49136169	5	5	5
10,60000000	0,67209786	-0,55284197	0,27875360	1,32221929	3	1	3
7,40000000	0,99122608	0,62685341	1,70243054	1,71600334	1	1	1
8,40000000	1,46239800	0,83250891	2,25285303	2,21484385	2	3	2
5,70000000	0,84509804	-0,12493874	1,08990511	2,35218252	2	2	2
4,90000000	0,77815125	0,55630250	1,32221929	2,35218252	3	2	3
3,20000000	1,30103000	1,74429298	2,24303805	2,17897695	5	5	5
11,00000000	0,65321251	-0,04575749	0,41497335	1,77815125	2	1	2
4,90000000	0,87506126	0,30103000	1,08990511	2,30103000	3	1	3
13,20000000	0,36172784	-0,98296666	0,39794001	1,66275783	3	2	2
9,70000000	1,38021124	0,62221402	1,76342799	2,32221929	4	3	4
12,80000000	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	5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109134&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]5 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=109134&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109134&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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 11.5008154131736 + 3.66140924697595LogL[t] -1.17640728797974LogWb[t] -1.26534145695381LogWbr[t] -1.65360291718712LogTg[t] + 1.65207205038963P[t] + 0.492421512940471S[t] -2.77256131954632D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  11.5008154131736 +  3.66140924697595LogL[t] -1.17640728797974LogWb[t] -1.26534145695381LogWbr[t] -1.65360291718712LogTg[t] +  1.65207205038963P[t] +  0.492421512940471S[t] -2.77256131954632D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109134&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  11.5008154131736 +  3.66140924697595LogL[t] -1.17640728797974LogWb[t] -1.26534145695381LogWbr[t] -1.65360291718712LogTg[t] +  1.65207205038963P[t] +  0.492421512940471S[t] -2.77256131954632D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109134&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109134&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
SWS[t] = + 11.5008154131736 + 3.66140924697595LogL[t] -1.17640728797974LogWb[t] -1.26534145695381LogWbr[t] -1.65360291718712LogTg[t] + 1.65207205038963P[t] + 0.492421512940471S[t] -2.77256131954632D[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)	11.5008154131736	2.864922	4.0144	0.000351	0.000176
LogL	3.66140924697595	1.786981	2.0489	0.049014	0.024507
LogWb	-1.17640728797974	1.099575	-1.0699	0.292936	0.146468
LogWbr	-1.26534145695381	1.602445	-0.7896	0.435741	0.217871
LogTg	-1.65360291718712	1.582137	-1.0452	0.304026	0.152013
P	1.65207205038963	0.967803	1.707	0.097815	0.048908
S	0.492421512940471	0.614054	0.8019	0.428704	0.214352
D	-2.77256131954632	1.142203	-2.4274	0.021211	0.010606

\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) & 11.5008154131736 & 2.864922 & 4.0144 & 0.000351 & 0.000176 \tabularnewline
LogL & 3.66140924697595 & 1.786981 & 2.0489 & 0.049014 & 0.024507 \tabularnewline
LogWb & -1.17640728797974 & 1.099575 & -1.0699 & 0.292936 & 0.146468 \tabularnewline
LogWbr & -1.26534145695381 & 1.602445 & -0.7896 & 0.435741 & 0.217871 \tabularnewline
LogTg & -1.65360291718712 & 1.582137 & -1.0452 & 0.304026 & 0.152013 \tabularnewline
P & 1.65207205038963 & 0.967803 & 1.707 & 0.097815 & 0.048908 \tabularnewline
S & 0.492421512940471 & 0.614054 & 0.8019 & 0.428704 & 0.214352 \tabularnewline
D & -2.77256131954632 & 1.142203 & -2.4274 & 0.021211 & 0.010606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109134&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]11.5008154131736[/C][C]2.864922[/C][C]4.0144[/C][C]0.000351[/C][C]0.000176[/C][/ROW]
[ROW][C]LogL[/C][C]3.66140924697595[/C][C]1.786981[/C][C]2.0489[/C][C]0.049014[/C][C]0.024507[/C][/ROW]
[ROW][C]LogWb[/C][C]-1.17640728797974[/C][C]1.099575[/C][C]-1.0699[/C][C]0.292936[/C][C]0.146468[/C][/ROW]
[ROW][C]LogWbr[/C][C]-1.26534145695381[/C][C]1.602445[/C][C]-0.7896[/C][C]0.435741[/C][C]0.217871[/C][/ROW]
[ROW][C]LogTg[/C][C]-1.65360291718712[/C][C]1.582137[/C][C]-1.0452[/C][C]0.304026[/C][C]0.152013[/C][/ROW]
[ROW][C]P[/C][C]1.65207205038963[/C][C]0.967803[/C][C]1.707[/C][C]0.097815[/C][C]0.048908[/C][/ROW]
[ROW][C]S[/C][C]0.492421512940471[/C][C]0.614054[/C][C]0.8019[/C][C]0.428704[/C][C]0.214352[/C][/ROW]
[ROW][C]D[/C][C]-2.77256131954632[/C][C]1.142203[/C][C]-2.4274[/C][C]0.021211[/C][C]0.010606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109134&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109134&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)	11.5008154131736	2.864922	4.0144	0.000351	0.000176
LogL	3.66140924697595	1.786981	2.0489	0.049014	0.024507
LogWb	-1.17640728797974	1.099575	-1.0699	0.292936	0.146468
LogWbr	-1.26534145695381	1.602445	-0.7896	0.435741	0.217871
LogTg	-1.65360291718712	1.582137	-1.0452	0.304026	0.152013
P	1.65207205038963	0.967803	1.707	0.097815	0.048908
S	0.492421512940471	0.614054	0.8019	0.428704	0.214352
D	-2.77256131954632	1.142203	-2.4274	0.021211	0.010606

Multiple Linear Regression - Regression Statistics
Multiple R	0.817931033361346
R-squared	0.66901117533556
Adjusted R-squared	0.594271763314557
F-TEST (value)	8.95125018039426
F-TEST (DF numerator)	7
F-TEST (DF denominator)	31
p-value	5.28420544410046e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.52768478054786
Sum Squared Residuals	198.064900844213

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.817931033361346 \tabularnewline
R-squared & 0.66901117533556 \tabularnewline
Adjusted R-squared & 0.594271763314557 \tabularnewline
F-TEST (value) & 8.95125018039426 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 31 \tabularnewline
p-value & 5.28420544410046e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.52768478054786 \tabularnewline
Sum Squared Residuals & 198.064900844213 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109134&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.817931033361346[/C][/ROW]
[ROW][C]R-squared[/C][C]0.66901117533556[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.594271763314557[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.95125018039426[/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]5.28420544410046e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.52768478054786[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]198.064900844213[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109134&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109134&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	0.817931033361346
R-squared	0.66901117533556
Adjusted R-squared	0.594271763314557
F-TEST (value)	8.95125018039426
F-TEST (DF numerator)	7
F-TEST (DF denominator)	31
p-value	5.28420544410046e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.52768478054786
Sum Squared Residuals	198.064900844213

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.3	7.30223475865519	-1.00223475865519
2	2.1	1.29767265473116	0.802327345268838
3	9.1	6.44410484675307	2.65589515324693
4	15.8	15.5904079653753	0.209592034624731
5	5.2	5.21012731774501	-0.0101273177450104
6	10.9	11.2965241869668	-0.39652418696679
7	8.3	7.82285336893292	0.477146631067078
8	11	9.76334635436635	1.23665364563365
9	3.2	3.25840568070516	-0.0584056807051563
10	6.3	11.4037884623599	-5.10378846235987
11	6.6	9.84484457351932	-3.24484457351932
12	9.5	10.4681526798514	-0.968152679851427
13	3.3	5.23163697348543	-1.93163697348543
14	11	12.6605814436417	-1.66058144364165
15	4.7	8.19475682160514	-3.49475682160514
16	10.4	13.4462729612535	-3.04627296125351
17	7.4	9.38612211954509	-1.98612211954509
18	2.1	3.51133695938199	-1.41133695938199
19	17.9	16.2314701465398	1.6685298534602
20	6.1	8.12590584263728	-2.02590584263728
21	11.9	12.4496565943437	-0.54965659434374
22	13.8	12.0169571403978	1.78304285960217
23	14.3	10.1154084867386	4.1845915132614
24	15.2	9.51597460188823	5.68402539811177
25	10	6.29579942904366	3.70420057095634
26	11.9	10.2123664390706	1.68763356092938
27	6.5	5.28199492403739	1.21800507596261
28	7.5	8.65219022092929	-1.15219022092929
29	10.6	9.20381759926962	1.39618240073038
30	7.4	8.77285300332105	-1.37285300332105
31	8.4	8.59906883785173	-0.199068837851732
32	5.7	8.21723052662806	-2.51723052662806
33	4.9	5.75624773890943	-0.856247738909427
34	3.2	4.63070925544838	-1.43070925544838
35	11	8.7323270793848	2.26767292061521
36	4.9	6.297500581414	-1.397500581414
37	13.2	11.1244835617632	2.07551643823681
38	9.7	6.74629683008128	2.95370316991872
39	12.8	10.9885710314277	1.81142896857231

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 7.30223475865519 & -1.00223475865519 \tabularnewline
2 & 2.1 & 1.29767265473116 & 0.802327345268838 \tabularnewline
3 & 9.1 & 6.44410484675307 & 2.65589515324693 \tabularnewline
4 & 15.8 & 15.5904079653753 & 0.209592034624731 \tabularnewline
5 & 5.2 & 5.21012731774501 & -0.0101273177450104 \tabularnewline
6 & 10.9 & 11.2965241869668 & -0.39652418696679 \tabularnewline
7 & 8.3 & 7.82285336893292 & 0.477146631067078 \tabularnewline
8 & 11 & 9.76334635436635 & 1.23665364563365 \tabularnewline
9 & 3.2 & 3.25840568070516 & -0.0584056807051563 \tabularnewline
10 & 6.3 & 11.4037884623599 & -5.10378846235987 \tabularnewline
11 & 6.6 & 9.84484457351932 & -3.24484457351932 \tabularnewline
12 & 9.5 & 10.4681526798514 & -0.968152679851427 \tabularnewline
13 & 3.3 & 5.23163697348543 & -1.93163697348543 \tabularnewline
14 & 11 & 12.6605814436417 & -1.66058144364165 \tabularnewline
15 & 4.7 & 8.19475682160514 & -3.49475682160514 \tabularnewline
16 & 10.4 & 13.4462729612535 & -3.04627296125351 \tabularnewline
17 & 7.4 & 9.38612211954509 & -1.98612211954509 \tabularnewline
18 & 2.1 & 3.51133695938199 & -1.41133695938199 \tabularnewline
19 & 17.9 & 16.2314701465398 & 1.6685298534602 \tabularnewline
20 & 6.1 & 8.12590584263728 & -2.02590584263728 \tabularnewline
21 & 11.9 & 12.4496565943437 & -0.54965659434374 \tabularnewline
22 & 13.8 & 12.0169571403978 & 1.78304285960217 \tabularnewline
23 & 14.3 & 10.1154084867386 & 4.1845915132614 \tabularnewline
24 & 15.2 & 9.51597460188823 & 5.68402539811177 \tabularnewline
25 & 10 & 6.29579942904366 & 3.70420057095634 \tabularnewline
26 & 11.9 & 10.2123664390706 & 1.68763356092938 \tabularnewline
27 & 6.5 & 5.28199492403739 & 1.21800507596261 \tabularnewline
28 & 7.5 & 8.65219022092929 & -1.15219022092929 \tabularnewline
29 & 10.6 & 9.20381759926962 & 1.39618240073038 \tabularnewline
30 & 7.4 & 8.77285300332105 & -1.37285300332105 \tabularnewline
31 & 8.4 & 8.59906883785173 & -0.199068837851732 \tabularnewline
32 & 5.7 & 8.21723052662806 & -2.51723052662806 \tabularnewline
33 & 4.9 & 5.75624773890943 & -0.856247738909427 \tabularnewline
34 & 3.2 & 4.63070925544838 & -1.43070925544838 \tabularnewline
35 & 11 & 8.7323270793848 & 2.26767292061521 \tabularnewline
36 & 4.9 & 6.297500581414 & -1.397500581414 \tabularnewline
37 & 13.2 & 11.1244835617632 & 2.07551643823681 \tabularnewline
38 & 9.7 & 6.74629683008128 & 2.95370316991872 \tabularnewline
39 & 12.8 & 10.9885710314277 & 1.81142896857231 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109134&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]6.3[/C][C]7.30223475865519[/C][C]-1.00223475865519[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]1.29767265473116[/C][C]0.802327345268838[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]6.44410484675307[/C][C]2.65589515324693[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]15.5904079653753[/C][C]0.209592034624731[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]5.21012731774501[/C][C]-0.0101273177450104[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.2965241869668[/C][C]-0.39652418696679[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]7.82285336893292[/C][C]0.477146631067078[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]9.76334635436635[/C][C]1.23665364563365[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]3.25840568070516[/C][C]-0.0584056807051563[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.4037884623599[/C][C]-5.10378846235987[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]9.84484457351932[/C][C]-3.24484457351932[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]10.4681526798514[/C][C]-0.968152679851427[/C][/ROW]
[ROW][C]13[/C][C]3.3[/C][C]5.23163697348543[/C][C]-1.93163697348543[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]12.6605814436417[/C][C]-1.66058144364165[/C][/ROW]
[ROW][C]15[/C][C]4.7[/C][C]8.19475682160514[/C][C]-3.49475682160514[/C][/ROW]
[ROW][C]16[/C][C]10.4[/C][C]13.4462729612535[/C][C]-3.04627296125351[/C][/ROW]
[ROW][C]17[/C][C]7.4[/C][C]9.38612211954509[/C][C]-1.98612211954509[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]3.51133695938199[/C][C]-1.41133695938199[/C][/ROW]
[ROW][C]19[/C][C]17.9[/C][C]16.2314701465398[/C][C]1.6685298534602[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]8.12590584263728[/C][C]-2.02590584263728[/C][/ROW]
[ROW][C]21[/C][C]11.9[/C][C]12.4496565943437[/C][C]-0.54965659434374[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]12.0169571403978[/C][C]1.78304285960217[/C][/ROW]
[ROW][C]23[/C][C]14.3[/C][C]10.1154084867386[/C][C]4.1845915132614[/C][/ROW]
[ROW][C]24[/C][C]15.2[/C][C]9.51597460188823[/C][C]5.68402539811177[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]6.29579942904366[/C][C]3.70420057095634[/C][/ROW]
[ROW][C]26[/C][C]11.9[/C][C]10.2123664390706[/C][C]1.68763356092938[/C][/ROW]
[ROW][C]27[/C][C]6.5[/C][C]5.28199492403739[/C][C]1.21800507596261[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]8.65219022092929[/C][C]-1.15219022092929[/C][/ROW]
[ROW][C]29[/C][C]10.6[/C][C]9.20381759926962[/C][C]1.39618240073038[/C][/ROW]
[ROW][C]30[/C][C]7.4[/C][C]8.77285300332105[/C][C]-1.37285300332105[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.59906883785173[/C][C]-0.199068837851732[/C][/ROW]
[ROW][C]32[/C][C]5.7[/C][C]8.21723052662806[/C][C]-2.51723052662806[/C][/ROW]
[ROW][C]33[/C][C]4.9[/C][C]5.75624773890943[/C][C]-0.856247738909427[/C][/ROW]
[ROW][C]34[/C][C]3.2[/C][C]4.63070925544838[/C][C]-1.43070925544838[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]8.7323270793848[/C][C]2.26767292061521[/C][/ROW]
[ROW][C]36[/C][C]4.9[/C][C]6.297500581414[/C][C]-1.397500581414[/C][/ROW]
[ROW][C]37[/C][C]13.2[/C][C]11.1244835617632[/C][C]2.07551643823681[/C][/ROW]
[ROW][C]38[/C][C]9.7[/C][C]6.74629683008128[/C][C]2.95370316991872[/C][/ROW]
[ROW][C]39[/C][C]12.8[/C][C]10.9885710314277[/C][C]1.81142896857231[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109134&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109134&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	6.3	7.30223475865519	-1.00223475865519
2	2.1	1.29767265473116	0.802327345268838
3	9.1	6.44410484675307	2.65589515324693
4	15.8	15.5904079653753	0.209592034624731
5	5.2	5.21012731774501	-0.0101273177450104
6	10.9	11.2965241869668	-0.39652418696679
7	8.3	7.82285336893292	0.477146631067078
8	11	9.76334635436635	1.23665364563365
9	3.2	3.25840568070516	-0.0584056807051563
10	6.3	11.4037884623599	-5.10378846235987
11	6.6	9.84484457351932	-3.24484457351932
12	9.5	10.4681526798514	-0.968152679851427
13	3.3	5.23163697348543	-1.93163697348543
14	11	12.6605814436417	-1.66058144364165
15	4.7	8.19475682160514	-3.49475682160514
16	10.4	13.4462729612535	-3.04627296125351
17	7.4	9.38612211954509	-1.98612211954509
18	2.1	3.51133695938199	-1.41133695938199
19	17.9	16.2314701465398	1.6685298534602
20	6.1	8.12590584263728	-2.02590584263728
21	11.9	12.4496565943437	-0.54965659434374
22	13.8	12.0169571403978	1.78304285960217
23	14.3	10.1154084867386	4.1845915132614
24	15.2	9.51597460188823	5.68402539811177
25	10	6.29579942904366	3.70420057095634
26	11.9	10.2123664390706	1.68763356092938
27	6.5	5.28199492403739	1.21800507596261
28	7.5	8.65219022092929	-1.15219022092929
29	10.6	9.20381759926962	1.39618240073038
30	7.4	8.77285300332105	-1.37285300332105
31	8.4	8.59906883785173	-0.199068837851732
32	5.7	8.21723052662806	-2.51723052662806
33	4.9	5.75624773890943	-0.856247738909427
34	3.2	4.63070925544838	-1.43070925544838
35	11	8.7323270793848	2.26767292061521
36	4.9	6.297500581414	-1.397500581414
37	13.2	11.1244835617632	2.07551643823681
38	9.7	6.74629683008128	2.95370316991872
39	12.8	10.9885710314277	1.81142896857231

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.0567940919339009	0.113588183867802	0.9432059080661
12	0.0169867473883601	0.0339734947767203	0.98301325261164
13	0.111329539474057	0.222659078948115	0.888670460525943
14	0.0583578313942411	0.116715662788482	0.94164216860576
15	0.0660878508767289	0.132175701753458	0.933912149123271
16	0.215434619074514	0.430869238149028	0.784565380925486
17	0.166100200501297	0.332200401002594	0.833899799498703
18	0.142371038697051	0.284742077394102	0.857628961302949
19	0.0927265408026413	0.185453081605283	0.907273459197359
20	0.121651524197779	0.243303048395559	0.87834847580222
21	0.146350593770603	0.292701187541206	0.853649406229397
22	0.262536257001723	0.525072514003447	0.737463742998277
23	0.411556858869483	0.823113717738965	0.588443141130517
24	0.737100459307125	0.52579908138575	0.262899540692875
25	0.88159365043916	0.236812699121679	0.11840634956084
26	0.8042193557413	0.391561288517401	0.195780644258701
27	0.706485141178421	0.587029717643158	0.293514858821579
28	0.70559159215936	0.588816815681279	0.29440840784064

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0567940919339009 & 0.113588183867802 & 0.9432059080661 \tabularnewline
12 & 0.0169867473883601 & 0.0339734947767203 & 0.98301325261164 \tabularnewline
13 & 0.111329539474057 & 0.222659078948115 & 0.888670460525943 \tabularnewline
14 & 0.0583578313942411 & 0.116715662788482 & 0.94164216860576 \tabularnewline
15 & 0.0660878508767289 & 0.132175701753458 & 0.933912149123271 \tabularnewline
16 & 0.215434619074514 & 0.430869238149028 & 0.784565380925486 \tabularnewline
17 & 0.166100200501297 & 0.332200401002594 & 0.833899799498703 \tabularnewline
18 & 0.142371038697051 & 0.284742077394102 & 0.857628961302949 \tabularnewline
19 & 0.0927265408026413 & 0.185453081605283 & 0.907273459197359 \tabularnewline
20 & 0.121651524197779 & 0.243303048395559 & 0.87834847580222 \tabularnewline
21 & 0.146350593770603 & 0.292701187541206 & 0.853649406229397 \tabularnewline
22 & 0.262536257001723 & 0.525072514003447 & 0.737463742998277 \tabularnewline
23 & 0.411556858869483 & 0.823113717738965 & 0.588443141130517 \tabularnewline
24 & 0.737100459307125 & 0.52579908138575 & 0.262899540692875 \tabularnewline
25 & 0.88159365043916 & 0.236812699121679 & 0.11840634956084 \tabularnewline
26 & 0.8042193557413 & 0.391561288517401 & 0.195780644258701 \tabularnewline
27 & 0.706485141178421 & 0.587029717643158 & 0.293514858821579 \tabularnewline
28 & 0.70559159215936 & 0.588816815681279 & 0.29440840784064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109134&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]0.0567940919339009[/C][C]0.113588183867802[/C][C]0.9432059080661[/C][/ROW]
[ROW][C]12[/C][C]0.0169867473883601[/C][C]0.0339734947767203[/C][C]0.98301325261164[/C][/ROW]
[ROW][C]13[/C][C]0.111329539474057[/C][C]0.222659078948115[/C][C]0.888670460525943[/C][/ROW]
[ROW][C]14[/C][C]0.0583578313942411[/C][C]0.116715662788482[/C][C]0.94164216860576[/C][/ROW]
[ROW][C]15[/C][C]0.0660878508767289[/C][C]0.132175701753458[/C][C]0.933912149123271[/C][/ROW]
[ROW][C]16[/C][C]0.215434619074514[/C][C]0.430869238149028[/C][C]0.784565380925486[/C][/ROW]
[ROW][C]17[/C][C]0.166100200501297[/C][C]0.332200401002594[/C][C]0.833899799498703[/C][/ROW]
[ROW][C]18[/C][C]0.142371038697051[/C][C]0.284742077394102[/C][C]0.857628961302949[/C][/ROW]
[ROW][C]19[/C][C]0.0927265408026413[/C][C]0.185453081605283[/C][C]0.907273459197359[/C][/ROW]
[ROW][C]20[/C][C]0.121651524197779[/C][C]0.243303048395559[/C][C]0.87834847580222[/C][/ROW]
[ROW][C]21[/C][C]0.146350593770603[/C][C]0.292701187541206[/C][C]0.853649406229397[/C][/ROW]
[ROW][C]22[/C][C]0.262536257001723[/C][C]0.525072514003447[/C][C]0.737463742998277[/C][/ROW]
[ROW][C]23[/C][C]0.411556858869483[/C][C]0.823113717738965[/C][C]0.588443141130517[/C][/ROW]
[ROW][C]24[/C][C]0.737100459307125[/C][C]0.52579908138575[/C][C]0.262899540692875[/C][/ROW]
[ROW][C]25[/C][C]0.88159365043916[/C][C]0.236812699121679[/C][C]0.11840634956084[/C][/ROW]
[ROW][C]26[/C][C]0.8042193557413[/C][C]0.391561288517401[/C][C]0.195780644258701[/C][/ROW]
[ROW][C]27[/C][C]0.706485141178421[/C][C]0.587029717643158[/C][C]0.293514858821579[/C][/ROW]
[ROW][C]28[/C][C]0.70559159215936[/C][C]0.588816815681279[/C][C]0.29440840784064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109134&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109134&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	0.0567940919339009	0.113588183867802	0.9432059080661
12	0.0169867473883601	0.0339734947767203	0.98301325261164
13	0.111329539474057	0.222659078948115	0.888670460525943
14	0.0583578313942411	0.116715662788482	0.94164216860576
15	0.0660878508767289	0.132175701753458	0.933912149123271
16	0.215434619074514	0.430869238149028	0.784565380925486
17	0.166100200501297	0.332200401002594	0.833899799498703
18	0.142371038697051	0.284742077394102	0.857628961302949
19	0.0927265408026413	0.185453081605283	0.907273459197359
20	0.121651524197779	0.243303048395559	0.87834847580222
21	0.146350593770603	0.292701187541206	0.853649406229397
22	0.262536257001723	0.525072514003447	0.737463742998277
23	0.411556858869483	0.823113717738965	0.588443141130517
24	0.737100459307125	0.52579908138575	0.262899540692875
25	0.88159365043916	0.236812699121679	0.11840634956084
26	0.8042193557413	0.391561288517401	0.195780644258701
27	0.706485141178421	0.587029717643158	0.293514858821579
28	0.70559159215936	0.588816815681279	0.29440840784064

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=109134&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=109134&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109134&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

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

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