Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 23 Nov 2008 07:31:37 -0700

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/2008/Nov/23/t12274509143drz8mswac03sk2.htm/, Retrieved Mon, 20 May 2024 04:56:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25267, Retrieved Mon, 20 May 2024 04:56:58 +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

128

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

F       [Multiple Regression] [] [2008-11-23 14:31:37] [1768685c15539a739b6b33586be71b78] [Current]

Feedback Forum

2008-11-30 10:03:43 [Tinneke De Bock] [reply] 
Hierbij moet je wel opletten met de standaarddeviatie. Het is waar dat we een dalende waarde van 0,83 waarnemen voor de investeringen, maar hierbij hebben we wel een standaarddeviatie van 1,31. Dit betekent dat we er 1,31 zouden kunnen naast zitten en dat de investeringen evengoed zouden kunnen toenemen. 
 
We moeten dus rekening houden met de 2-tailed p-value, omdat we er niet zeker van zijn of de investeringen wel zeker zullen dalen bij een intrest hoger dan 4,5%. Deze p-waarden zijn zeer hoog. De kans dat we ons vergissen bij het verwerpen van de hypothese is dus groot. (nulhypothese= een verhoogde intrestvoet heeft geen effect op het aantal investeringen) De kans dat dit verband op toeval is berust is dus reëel.  
 
Wat we wel duidelijk waarnemen is een dalende trend op lange termijn van 0.7 per maand. Hiervoor is de p-waarde gelijk aan 0 en de absolute waarde van de t-verdeling groter dan 2, waaruit we kunnen afleiden dat deze dalende trend significant is en dat de kans dat we ons vergissen zeer klein is.  
Bij de andere variabelen stellen we vast dat de absolute waarde van de t-verdeling nooit groter is dan 2, hieruit kunnen we afleiden dat het geen goed model is. 
 
Bij deze oefening is het ook zeker nuttig om de volgende figuren te bespreken: 
-	actuals and interpolations 
-	residuals 
-	residual histogram 
-	residual density plot 
-	residual normal QQ-plot 
-	residual lag-plot 
-	residual autocorrelation 
Aan de hand van al deze gegevens kan je dan een uitspraak doen over je model, rekening gehouden met de volgende assumpties: Is er een patroon of autocorrelatie? Is het gemiddelde constant en gelijk aan 0? 
2008-11-30 13:09:09 [c00776cbed2786c9c4960950021bd861] [reply] 
De conclusie bij de tabel is niet helemaal correct : we kunnen 80% van de schommelingen in de investeringen verklaren. 
ook hier is de residual standard deviation handig om te vermelden. 
 
Ook bij de tweede tabel had veel meer informatie moeten staat. 
 
De maand december is hier de standaardmaand met een intercept van 131 dat ongeveerd 2 à 3 eenheden kan afwijken naar boven of naar beneden (standaard deviatie). 
Als het interestniveau stijgt boven 4.5% is er inderdaad een gemiddelde daling van 0.83%. 
 
Ook wordt hier weer de toevoeging van de lineaire trend niet vermeld: deze bedraagt -0.70 wat betekent dat we op lange termijn een dalende trend zullen hebben. 
 
Je zou ook de grafieken moeten toevoegen en hier wat informatie over geven om je conclusie te ondersteunen. 
2008-12-01 22:34:47 [Li Tang Hu] [reply] 
heel beknopte bespreking, hier kan duidelijk meer over worden gezegd...analyseer al de grafieken en concludeer dan of het een betrowbaar model is of niet.

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25267&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25267&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25267&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	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Multiple Linear Regression - Estimated Regression Equation
Investeringsgoederen[t] = + 131.620492662474 -0.834905660377361Intres[t] -0.978850017470295M1[t] -1.93630328441650M2[t] -0.676303721174005M3[t] -0.458756988120192M4[t] -4.18721392382950M5[t] -0.352607442348018M6[t] + 0.215332372466799M7[t] -1.25006114605172M8[t] -2.08212133123690M9[t] -0.302546296296298M10[t] -1.16793981481482M11[t] -0.701273148148148t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Investeringsgoederen[t] =  +  131.620492662474 -0.834905660377361Intres[t] -0.978850017470295M1[t] -1.93630328441650M2[t] -0.676303721174005M3[t] -0.458756988120192M4[t] -4.18721392382950M5[t] -0.352607442348018M6[t] +  0.215332372466799M7[t] -1.25006114605172M8[t] -2.08212133123690M9[t] -0.302546296296298M10[t] -1.16793981481482M11[t] -0.701273148148148t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25267&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Investeringsgoederen[t] =  +  131.620492662474 -0.834905660377361Intres[t] -0.978850017470295M1[t] -1.93630328441650M2[t] -0.676303721174005M3[t] -0.458756988120192M4[t] -4.18721392382950M5[t] -0.352607442348018M6[t] +  0.215332372466799M7[t] -1.25006114605172M8[t] -2.08212133123690M9[t] -0.302546296296298M10[t] -1.16793981481482M11[t] -0.701273148148148t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25267&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25267&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
Investeringsgoederen[t] = + 131.620492662474 -0.834905660377361Intres[t] -0.978850017470295M1[t] -1.93630328441650M2[t] -0.676303721174005M3[t] -0.458756988120192M4[t] -4.18721392382950M5[t] -0.352607442348018M6[t] + 0.215332372466799M7[t] -1.25006114605172M8[t] -2.08212133123690M9[t] -0.302546296296298M10[t] -1.16793981481482M11[t] -0.701273148148148t + 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)	131.620492662474	2.73873	48.059	0	0
Intres	-0.834905660377361	1.310057	-0.6373	0.529499	0.264749
M1	-0.978850017470295	2.994032	-0.3269	0.746336	0.373168
M2	-1.93630328441650	3.031204	-0.6388	0.528547	0.264274
M3	-0.676303721174005	2.986208	-0.2265	0.822603	0.411301
M4	-0.458756988120192	3.02541	-0.1516	0.880646	0.440323
M5	-4.18721392382950	3.23122	-1.2959	0.20641	0.103205
M6	-0.352607442348018	3.225332	-0.1093	0.913785	0.456892
M7	0.215332372466799	3.220342	0.0669	0.9472	0.4736
M8	-1.25006114605172	3.216252	-0.3887	0.700685	0.350342
M9	-2.08212133123690	3.213068	-0.648	0.522656	0.261328
M10	-0.302546296296298	3.180958	-0.0951	0.924955	0.462478
M11	-1.16793981481482	3.179578	-0.3673	0.716349	0.358175
t	-0.701273148148148	0.054078	-12.9678	0	0

\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) & 131.620492662474 & 2.73873 & 48.059 & 0 & 0 \tabularnewline
Intres & -0.834905660377361 & 1.310057 & -0.6373 & 0.529499 & 0.264749 \tabularnewline
M1 & -0.978850017470295 & 2.994032 & -0.3269 & 0.746336 & 0.373168 \tabularnewline
M2 & -1.93630328441650 & 3.031204 & -0.6388 & 0.528547 & 0.264274 \tabularnewline
M3 & -0.676303721174005 & 2.986208 & -0.2265 & 0.822603 & 0.411301 \tabularnewline
M4 & -0.458756988120192 & 3.02541 & -0.1516 & 0.880646 & 0.440323 \tabularnewline
M5 & -4.18721392382950 & 3.23122 & -1.2959 & 0.20641 & 0.103205 \tabularnewline
M6 & -0.352607442348018 & 3.225332 & -0.1093 & 0.913785 & 0.456892 \tabularnewline
M7 & 0.215332372466799 & 3.220342 & 0.0669 & 0.9472 & 0.4736 \tabularnewline
M8 & -1.25006114605172 & 3.216252 & -0.3887 & 0.700685 & 0.350342 \tabularnewline
M9 & -2.08212133123690 & 3.213068 & -0.648 & 0.522656 & 0.261328 \tabularnewline
M10 & -0.302546296296298 & 3.180958 & -0.0951 & 0.924955 & 0.462478 \tabularnewline
M11 & -1.16793981481482 & 3.179578 & -0.3673 & 0.716349 & 0.358175 \tabularnewline
t & -0.701273148148148 & 0.054078 & -12.9678 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25267&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]131.620492662474[/C][C]2.73873[/C][C]48.059[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Intres[/C][C]-0.834905660377361[/C][C]1.310057[/C][C]-0.6373[/C][C]0.529499[/C][C]0.264749[/C][/ROW]
[ROW][C]M1[/C][C]-0.978850017470295[/C][C]2.994032[/C][C]-0.3269[/C][C]0.746336[/C][C]0.373168[/C][/ROW]
[ROW][C]M2[/C][C]-1.93630328441650[/C][C]3.031204[/C][C]-0.6388[/C][C]0.528547[/C][C]0.264274[/C][/ROW]
[ROW][C]M3[/C][C]-0.676303721174005[/C][C]2.986208[/C][C]-0.2265[/C][C]0.822603[/C][C]0.411301[/C][/ROW]
[ROW][C]M4[/C][C]-0.458756988120192[/C][C]3.02541[/C][C]-0.1516[/C][C]0.880646[/C][C]0.440323[/C][/ROW]
[ROW][C]M5[/C][C]-4.18721392382950[/C][C]3.23122[/C][C]-1.2959[/C][C]0.20641[/C][C]0.103205[/C][/ROW]
[ROW][C]M6[/C][C]-0.352607442348018[/C][C]3.225332[/C][C]-0.1093[/C][C]0.913785[/C][C]0.456892[/C][/ROW]
[ROW][C]M7[/C][C]0.215332372466799[/C][C]3.220342[/C][C]0.0669[/C][C]0.9472[/C][C]0.4736[/C][/ROW]
[ROW][C]M8[/C][C]-1.25006114605172[/C][C]3.216252[/C][C]-0.3887[/C][C]0.700685[/C][C]0.350342[/C][/ROW]
[ROW][C]M9[/C][C]-2.08212133123690[/C][C]3.213068[/C][C]-0.648[/C][C]0.522656[/C][C]0.261328[/C][/ROW]
[ROW][C]M10[/C][C]-0.302546296296298[/C][C]3.180958[/C][C]-0.0951[/C][C]0.924955[/C][C]0.462478[/C][/ROW]
[ROW][C]M11[/C][C]-1.16793981481482[/C][C]3.179578[/C][C]-0.3673[/C][C]0.716349[/C][C]0.358175[/C][/ROW]
[ROW][C]t[/C][C]-0.701273148148148[/C][C]0.054078[/C][C]-12.9678[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25267&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25267&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)	131.620492662474	2.73873	48.059	0	0
Intres	-0.834905660377361	1.310057	-0.6373	0.529499	0.264749
M1	-0.978850017470295	2.994032	-0.3269	0.746336	0.373168
M2	-1.93630328441650	3.031204	-0.6388	0.528547	0.264274
M3	-0.676303721174005	2.986208	-0.2265	0.822603	0.411301
M4	-0.458756988120192	3.02541	-0.1516	0.880646	0.440323
M5	-4.18721392382950	3.23122	-1.2959	0.20641	0.103205
M6	-0.352607442348018	3.225332	-0.1093	0.913785	0.456892
M7	0.215332372466799	3.220342	0.0669	0.9472	0.4736
M8	-1.25006114605172	3.216252	-0.3887	0.700685	0.350342
M9	-2.08212133123690	3.213068	-0.648	0.522656	0.261328
M10	-0.302546296296298	3.180958	-0.0951	0.924955	0.462478
M11	-1.16793981481482	3.179578	-0.3673	0.716349	0.358175
t	-0.701273148148148	0.054078	-12.9678	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.93244384713827
R-squared	0.869451528066018
Adjusted R-squared	0.804177292099026
F-TEST (value)	13.3199801604065
F-TEST (DF numerator)	13
F-TEST (DF denominator)	26
p-value	2.43365920749028e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.89360916863614
Sum Squared Residuals	394.165001310273

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.93244384713827 \tabularnewline
R-squared & 0.869451528066018 \tabularnewline
Adjusted R-squared & 0.804177292099026 \tabularnewline
F-TEST (value) & 13.3199801604065 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 26 \tabularnewline
p-value & 2.43365920749028e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.89360916863614 \tabularnewline
Sum Squared Residuals & 394.165001310273 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25267&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.93244384713827[/C][/ROW]
[ROW][C]R-squared[/C][C]0.869451528066018[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.804177292099026[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.3199801604065[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]26[/C][/ROW]
[ROW][C]p-value[/C][C]2.43365920749028e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.89360916863614[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]394.165001310273[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25267&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25267&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	0.93244384713827
R-squared	0.869451528066018
Adjusted R-squared	0.804177292099026
F-TEST (value)	13.3199801604065
F-TEST (DF numerator)	13
F-TEST (DF denominator)	26
p-value	2.43365920749028e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.89360916863614
Sum Squared Residuals	394.165001310273

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	132.7	129.940369496855	2.75963050314468
2	128.6	128.281643081761	0.318356918238984
3	127.8	128.840369496855	-1.04036949685536
4	128.9	128.356643081761	0.543356918238993
5	124.6	123.926912997904	0.673087002096431
6	129.2	127.060246331237	2.13975366876310
7	130.5	126.926912997904	3.57308700209644
8	124.3	124.760246331237	-0.4602463312369
9	125.8	123.226912997904	2.57308700209643
10	123.5	123.470309224319	0.0296907756813441
11	120.7	121.903642557652	-1.20364255765199
12	123.1	122.370309224319	0.729690775681334
13	122	120.690186058700	1.30981394129978
14	121	119.866365303983	1.13363469601678
15	121.2	119.590186058700	1.60981394129979
16	117.4	119.106459643606	-1.70645964360587
17	113	114.676729559748	-1.67672955974842
18	113.1	117.810062893082	-4.71006289308176
19	116.1	117.676729559748	-1.57672955974843
20	121.3	115.510062893082	5.78993710691824
21	108.6	113.976729559748	-5.37672955974843
22	114.3	115.055031446541	-0.755031446540882
23	113.5	113.488364779874	0.0116352201257889
24	111.2	113.955031446541	-2.75503144654088
25	109.3	113.109813941300	-3.8098139412998
26	108.2	111.451087526205	-3.25108752620545
27	102.7	112.009813941300	-9.30981394129979
28	110.4	111.526087526205	-1.12608752620545
29	108.1	107.096357442348	1.00364255765199
30	112.8	110.229690775681	2.57030922431866
31	108.1	110.096357442348	-1.99635744234801
32	102.6	107.929690775681	-5.32969077568134
33	109.2	106.396357442348	2.803642557652
34	108.2	107.474659329140	0.725340670859537
35	107.1	105.907992662474	1.19200733752620
36	108.4	106.374659329140	2.02534067085954
37	103.6	103.859630503145	-0.259630503144666
38	104	102.200904088050	1.79909591194969
39	111.5	102.759630503145	8.74036949685535
40	105.4	103.110809748428	2.28919025157233

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 132.7 & 129.940369496855 & 2.75963050314468 \tabularnewline
2 & 128.6 & 128.281643081761 & 0.318356918238984 \tabularnewline
3 & 127.8 & 128.840369496855 & -1.04036949685536 \tabularnewline
4 & 128.9 & 128.356643081761 & 0.543356918238993 \tabularnewline
5 & 124.6 & 123.926912997904 & 0.673087002096431 \tabularnewline
6 & 129.2 & 127.060246331237 & 2.13975366876310 \tabularnewline
7 & 130.5 & 126.926912997904 & 3.57308700209644 \tabularnewline
8 & 124.3 & 124.760246331237 & -0.4602463312369 \tabularnewline
9 & 125.8 & 123.226912997904 & 2.57308700209643 \tabularnewline
10 & 123.5 & 123.470309224319 & 0.0296907756813441 \tabularnewline
11 & 120.7 & 121.903642557652 & -1.20364255765199 \tabularnewline
12 & 123.1 & 122.370309224319 & 0.729690775681334 \tabularnewline
13 & 122 & 120.690186058700 & 1.30981394129978 \tabularnewline
14 & 121 & 119.866365303983 & 1.13363469601678 \tabularnewline
15 & 121.2 & 119.590186058700 & 1.60981394129979 \tabularnewline
16 & 117.4 & 119.106459643606 & -1.70645964360587 \tabularnewline
17 & 113 & 114.676729559748 & -1.67672955974842 \tabularnewline
18 & 113.1 & 117.810062893082 & -4.71006289308176 \tabularnewline
19 & 116.1 & 117.676729559748 & -1.57672955974843 \tabularnewline
20 & 121.3 & 115.510062893082 & 5.78993710691824 \tabularnewline
21 & 108.6 & 113.976729559748 & -5.37672955974843 \tabularnewline
22 & 114.3 & 115.055031446541 & -0.755031446540882 \tabularnewline
23 & 113.5 & 113.488364779874 & 0.0116352201257889 \tabularnewline
24 & 111.2 & 113.955031446541 & -2.75503144654088 \tabularnewline
25 & 109.3 & 113.109813941300 & -3.8098139412998 \tabularnewline
26 & 108.2 & 111.451087526205 & -3.25108752620545 \tabularnewline
27 & 102.7 & 112.009813941300 & -9.30981394129979 \tabularnewline
28 & 110.4 & 111.526087526205 & -1.12608752620545 \tabularnewline
29 & 108.1 & 107.096357442348 & 1.00364255765199 \tabularnewline
30 & 112.8 & 110.229690775681 & 2.57030922431866 \tabularnewline
31 & 108.1 & 110.096357442348 & -1.99635744234801 \tabularnewline
32 & 102.6 & 107.929690775681 & -5.32969077568134 \tabularnewline
33 & 109.2 & 106.396357442348 & 2.803642557652 \tabularnewline
34 & 108.2 & 107.474659329140 & 0.725340670859537 \tabularnewline
35 & 107.1 & 105.907992662474 & 1.19200733752620 \tabularnewline
36 & 108.4 & 106.374659329140 & 2.02534067085954 \tabularnewline
37 & 103.6 & 103.859630503145 & -0.259630503144666 \tabularnewline
38 & 104 & 102.200904088050 & 1.79909591194969 \tabularnewline
39 & 111.5 & 102.759630503145 & 8.74036949685535 \tabularnewline
40 & 105.4 & 103.110809748428 & 2.28919025157233 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25267&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]132.7[/C][C]129.940369496855[/C][C]2.75963050314468[/C][/ROW]
[ROW][C]2[/C][C]128.6[/C][C]128.281643081761[/C][C]0.318356918238984[/C][/ROW]
[ROW][C]3[/C][C]127.8[/C][C]128.840369496855[/C][C]-1.04036949685536[/C][/ROW]
[ROW][C]4[/C][C]128.9[/C][C]128.356643081761[/C][C]0.543356918238993[/C][/ROW]
[ROW][C]5[/C][C]124.6[/C][C]123.926912997904[/C][C]0.673087002096431[/C][/ROW]
[ROW][C]6[/C][C]129.2[/C][C]127.060246331237[/C][C]2.13975366876310[/C][/ROW]
[ROW][C]7[/C][C]130.5[/C][C]126.926912997904[/C][C]3.57308700209644[/C][/ROW]
[ROW][C]8[/C][C]124.3[/C][C]124.760246331237[/C][C]-0.4602463312369[/C][/ROW]
[ROW][C]9[/C][C]125.8[/C][C]123.226912997904[/C][C]2.57308700209643[/C][/ROW]
[ROW][C]10[/C][C]123.5[/C][C]123.470309224319[/C][C]0.0296907756813441[/C][/ROW]
[ROW][C]11[/C][C]120.7[/C][C]121.903642557652[/C][C]-1.20364255765199[/C][/ROW]
[ROW][C]12[/C][C]123.1[/C][C]122.370309224319[/C][C]0.729690775681334[/C][/ROW]
[ROW][C]13[/C][C]122[/C][C]120.690186058700[/C][C]1.30981394129978[/C][/ROW]
[ROW][C]14[/C][C]121[/C][C]119.866365303983[/C][C]1.13363469601678[/C][/ROW]
[ROW][C]15[/C][C]121.2[/C][C]119.590186058700[/C][C]1.60981394129979[/C][/ROW]
[ROW][C]16[/C][C]117.4[/C][C]119.106459643606[/C][C]-1.70645964360587[/C][/ROW]
[ROW][C]17[/C][C]113[/C][C]114.676729559748[/C][C]-1.67672955974842[/C][/ROW]
[ROW][C]18[/C][C]113.1[/C][C]117.810062893082[/C][C]-4.71006289308176[/C][/ROW]
[ROW][C]19[/C][C]116.1[/C][C]117.676729559748[/C][C]-1.57672955974843[/C][/ROW]
[ROW][C]20[/C][C]121.3[/C][C]115.510062893082[/C][C]5.78993710691824[/C][/ROW]
[ROW][C]21[/C][C]108.6[/C][C]113.976729559748[/C][C]-5.37672955974843[/C][/ROW]
[ROW][C]22[/C][C]114.3[/C][C]115.055031446541[/C][C]-0.755031446540882[/C][/ROW]
[ROW][C]23[/C][C]113.5[/C][C]113.488364779874[/C][C]0.0116352201257889[/C][/ROW]
[ROW][C]24[/C][C]111.2[/C][C]113.955031446541[/C][C]-2.75503144654088[/C][/ROW]
[ROW][C]25[/C][C]109.3[/C][C]113.109813941300[/C][C]-3.8098139412998[/C][/ROW]
[ROW][C]26[/C][C]108.2[/C][C]111.451087526205[/C][C]-3.25108752620545[/C][/ROW]
[ROW][C]27[/C][C]102.7[/C][C]112.009813941300[/C][C]-9.30981394129979[/C][/ROW]
[ROW][C]28[/C][C]110.4[/C][C]111.526087526205[/C][C]-1.12608752620545[/C][/ROW]
[ROW][C]29[/C][C]108.1[/C][C]107.096357442348[/C][C]1.00364255765199[/C][/ROW]
[ROW][C]30[/C][C]112.8[/C][C]110.229690775681[/C][C]2.57030922431866[/C][/ROW]
[ROW][C]31[/C][C]108.1[/C][C]110.096357442348[/C][C]-1.99635744234801[/C][/ROW]
[ROW][C]32[/C][C]102.6[/C][C]107.929690775681[/C][C]-5.32969077568134[/C][/ROW]
[ROW][C]33[/C][C]109.2[/C][C]106.396357442348[/C][C]2.803642557652[/C][/ROW]
[ROW][C]34[/C][C]108.2[/C][C]107.474659329140[/C][C]0.725340670859537[/C][/ROW]
[ROW][C]35[/C][C]107.1[/C][C]105.907992662474[/C][C]1.19200733752620[/C][/ROW]
[ROW][C]36[/C][C]108.4[/C][C]106.374659329140[/C][C]2.02534067085954[/C][/ROW]
[ROW][C]37[/C][C]103.6[/C][C]103.859630503145[/C][C]-0.259630503144666[/C][/ROW]
[ROW][C]38[/C][C]104[/C][C]102.200904088050[/C][C]1.79909591194969[/C][/ROW]
[ROW][C]39[/C][C]111.5[/C][C]102.759630503145[/C][C]8.74036949685535[/C][/ROW]
[ROW][C]40[/C][C]105.4[/C][C]103.110809748428[/C][C]2.28919025157233[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25267&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25267&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	132.7	129.940369496855	2.75963050314468
2	128.6	128.281643081761	0.318356918238984
3	127.8	128.840369496855	-1.04036949685536
4	128.9	128.356643081761	0.543356918238993
5	124.6	123.926912997904	0.673087002096431
6	129.2	127.060246331237	2.13975366876310
7	130.5	126.926912997904	3.57308700209644
8	124.3	124.760246331237	-0.4602463312369
9	125.8	123.226912997904	2.57308700209643
10	123.5	123.470309224319	0.0296907756813441
11	120.7	121.903642557652	-1.20364255765199
12	123.1	122.370309224319	0.729690775681334
13	122	120.690186058700	1.30981394129978
14	121	119.866365303983	1.13363469601678
15	121.2	119.590186058700	1.60981394129979
16	117.4	119.106459643606	-1.70645964360587
17	113	114.676729559748	-1.67672955974842
18	113.1	117.810062893082	-4.71006289308176
19	116.1	117.676729559748	-1.57672955974843
20	121.3	115.510062893082	5.78993710691824
21	108.6	113.976729559748	-5.37672955974843
22	114.3	115.055031446541	-0.755031446540882
23	113.5	113.488364779874	0.0116352201257889
24	111.2	113.955031446541	-2.75503144654088
25	109.3	113.109813941300	-3.8098139412998
26	108.2	111.451087526205	-3.25108752620545
27	102.7	112.009813941300	-9.30981394129979
28	110.4	111.526087526205	-1.12608752620545
29	108.1	107.096357442348	1.00364255765199
30	112.8	110.229690775681	2.57030922431866
31	108.1	110.096357442348	-1.99635744234801
32	102.6	107.929690775681	-5.32969077568134
33	109.2	106.396357442348	2.803642557652
34	108.2	107.474659329140	0.725340670859537
35	107.1	105.907992662474	1.19200733752620
36	108.4	106.374659329140	2.02534067085954
37	103.6	103.859630503145	-0.259630503144666
38	104	102.200904088050	1.79909591194969
39	111.5	102.759630503145	8.74036949685535
40	105.4	103.110809748428	2.28919025157233

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0615004294137984	0.123000858827597	0.938499570586202
18	0.108098812429918	0.216197624859836	0.891901187570082
19	0.0570778359648064	0.114155671929613	0.942922164035194
20	0.349447681121983	0.698895362243967	0.650552318878017
21	0.386841724715829	0.773683449431658	0.613158275284171
22	0.23508626813581	0.47017253627162	0.76491373186419
23	0.122665561273279	0.245331122546558	0.877334438726721

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0615004294137984 & 0.123000858827597 & 0.938499570586202 \tabularnewline
18 & 0.108098812429918 & 0.216197624859836 & 0.891901187570082 \tabularnewline
19 & 0.0570778359648064 & 0.114155671929613 & 0.942922164035194 \tabularnewline
20 & 0.349447681121983 & 0.698895362243967 & 0.650552318878017 \tabularnewline
21 & 0.386841724715829 & 0.773683449431658 & 0.613158275284171 \tabularnewline
22 & 0.23508626813581 & 0.47017253627162 & 0.76491373186419 \tabularnewline
23 & 0.122665561273279 & 0.245331122546558 & 0.877334438726721 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25267&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]17[/C][C]0.0615004294137984[/C][C]0.123000858827597[/C][C]0.938499570586202[/C][/ROW]
[ROW][C]18[/C][C]0.108098812429918[/C][C]0.216197624859836[/C][C]0.891901187570082[/C][/ROW]
[ROW][C]19[/C][C]0.0570778359648064[/C][C]0.114155671929613[/C][C]0.942922164035194[/C][/ROW]
[ROW][C]20[/C][C]0.349447681121983[/C][C]0.698895362243967[/C][C]0.650552318878017[/C][/ROW]
[ROW][C]21[/C][C]0.386841724715829[/C][C]0.773683449431658[/C][C]0.613158275284171[/C][/ROW]
[ROW][C]22[/C][C]0.23508626813581[/C][C]0.47017253627162[/C][C]0.76491373186419[/C][/ROW]
[ROW][C]23[/C][C]0.122665561273279[/C][C]0.245331122546558[/C][C]0.877334438726721[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25267&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25267&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
17	0.0615004294137984	0.123000858827597	0.938499570586202
18	0.108098812429918	0.216197624859836	0.891901187570082
19	0.0570778359648064	0.114155671929613	0.942922164035194
20	0.349447681121983	0.698895362243967	0.650552318878017
21	0.386841724715829	0.773683449431658	0.613158275284171
22	0.23508626813581	0.47017253627162	0.76491373186419
23	0.122665561273279	0.245331122546558	0.877334438726721

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	0	0	OK
10% type I error level	0	0	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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25267&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25267&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25267&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	0	0	OK
10% type I error level	0	0	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 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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