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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sun, 12 Dec 2010 17:12:54 +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/12/t1292173937c2r8mkgu90ve73b.htm/, Retrieved Tue, 07 May 2024 13:05:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108566, Retrieved Tue, 07 May 2024 13:05:53 +0000

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113

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-       [Multiple Regression] [onderzoek link 1] [2010-12-12 17:12:54] [b881b0959d750616b68c30017e4e0761] [Current]

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1000.00	6600.00	6.3	2.00	8.30	4.50	42.00	3.00	1.00	3.00
2547000.00	4603000.00	2.1	1.80	3.90	69.00	624.00	3.00	5.00	4.00
10550.00	179500.00	9.1	0.70	9.80	27.00	180.00	4.00	4.00	4.00
0.02	.300	15.8	3.90	19.70	19.00	35.00	1.00	1.00	1.00
160000.00	169000.00	5.2	1.00	6.20	30.40	392.00	4.00	5.00	4.00
3300.00	25600.00	10.9	3.60	14.50	28.00	63.00	1.00	2.00	1.00
52160.00	440000.00	8.3	1.40	9.70	50.00	230.00	1.00	1.00	1.00
0.43	6400.00	11.0	1.40	12.50	7.00	112.00	5.00	4.00	4.00
465000.00	423000.00	3.2	0.70	3.90	30.00	281.00	5.00	5.00	5.00
0.75	1200.00	6.3	2.10	8.40	3.50	42.00	1.00	1.00	1.00
0.79	3500.00	6.6	4.10	10.70	6.00	42.00	2.00	2.00	2.00
0.20	5000.00	9.5	1.20	10.70	10.40	120.00	2.00	2.00	2.00
27660.00	115000.00	3.3	0.50	3.80	20.00	148.00	5.00	5.00	5.00
0.12	1000.00	11.0	3.40	14.40	3.90	16.00	3.00	1.00	2.00
85000.00	325000.00	4.7	1.50	6.20	41.00	310.00	1.00	3.00	1.00
0.10	4000.00	10.4	3.40	13.80	9.00	28.00	5.00	1.00	3.00
1040.00	5500.00	7.4	0.80	8.20	7.60	68.00	5.00	3.00	4.00
521000.00	655000.00	2.1	0.80	2.90	46.00	336.00	5.00	5.00	5.00
0.10	0.25	17.9	2.00	19.90	24.00	50.00	1.00	1.00	1.00
62000.00	1320000.00	6.1	1.90	8.00	100.00	267.00	1.00	1.00	1.00
0.23	0.40	11.9	1.30	13.20	3.20	19.00	4.00	1.00	3.00
1700.00	6300.00	13.8	5.60	19.40	5.00	12.00	2.00	1.00	1.00
3500.00	10800.00	14.3	3.10	17.40	6.50	120.00	2.00	1.00	1.00
0.48	15500.00	15.2	1.80	17.00	12.00	140.00	2.00	2.00	2.00
10000.00	115000.00	10.0	0.90	10.90	20.20	170.00	4.00	4.00	4.00
1620.00	11400.00	11.9	1.80	13.70	13.00	17.00	2.00	1.00	2.00
192000.00	180000.00	6.5	1.90	8.40	27.00	115.00	4.00	4.00	4.00
2500.00	12100.00	7.5	0.90	8.40	18.00	31.00	5.00	5.00	5.00
0.28	1900.00	10.6	2.60	13.20	4.70	21.00	3.00	1.00	3.00
4235.00	50400.00	7.4	2.40	9.80	9.80	52.00	1.00	1.00	1.00
6800.00	179000.00	8.4	1.20	9.60	29.00	164.00	2.00	3.00	2.00
0.75	12300.00	5.7	0.90	6.60	7.00	225.00	2.00	2.00	2.00
3600.00	21000.00	4.9	0.50	5.40	6.00	225.00	3.00	2.00	3.00
55500.00	175000.00	3.2	0.60	3.80	20.00	151.00	5.00	5.00	5.00
0.90	2600.00	11.0	2.30	13.30	4.50	60.00	2.00	1.00	2.00
2000.00	12300.00	4.9	0.50	5.40	7.50	200.00	3.00	1.00	3.00
0.10	2500.00	13.2	2.60	15.80	2.30	46.00	3.00	2.00	2.00
4190.00	58000.00	9.7	0.60	10.30	24.00	210.00	4.00	3.00	4.00
3500.00	3900.00	12.8	6.60	19.40	3.00	14.00	2.00	1.00	1.00

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	'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108566&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108566&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108566&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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
total_sleep[t] = -0.00900208154294444 -3.72441341963713e-08body[t] + 2.46161008525863e-08brain[t] + 1.00092848893275slowwave[t] + 0.997987624777618paradoxical[t] -0.000325842902289568lifespan[t] + 5.94573591251674e-06gestation[t] + 0.0095473178732136predation[t] + 0.00590955874921987sleepexp.[t] -0.0115209272387338danger[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
total_sleep[t] =  -0.00900208154294444 -3.72441341963713e-08body[t] +  2.46161008525863e-08brain[t] +  1.00092848893275slowwave[t] +  0.997987624777618paradoxical[t] -0.000325842902289568lifespan[t] +  5.94573591251674e-06gestation[t] +  0.0095473178732136predation[t] +  0.00590955874921987sleepexp.[t] -0.0115209272387338danger[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108566&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]total_sleep[t] =  -0.00900208154294444 -3.72441341963713e-08body[t] +  2.46161008525863e-08brain[t] +  1.00092848893275slowwave[t] +  0.997987624777618paradoxical[t] -0.000325842902289568lifespan[t] +  5.94573591251674e-06gestation[t] +  0.0095473178732136predation[t] +  0.00590955874921987sleepexp.[t] -0.0115209272387338danger[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108566&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108566&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
total_sleep[t] = -0.00900208154294444 -3.72441341963713e-08body[t] + 2.46161008525863e-08brain[t] + 1.00092848893275slowwave[t] + 0.997987624777618paradoxical[t] -0.000325842902289568lifespan[t] + 5.94573591251674e-06gestation[t] + 0.0095473178732136predation[t] + 0.00590955874921987sleepexp.[t] -0.0115209272387338danger[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)	-0.00900208154294444	0.020359	-0.4422	0.661644	0.330822
body	-3.72441341963713e-08	0	-0.9898	0.330437	0.165219
brain	2.46161008525863e-08	0	1.1454	0.261403	0.130702
slowwave	1.00092848893275	0.001001	1000.1432	0	0
paradoxical	0.997987624777618	0.003398	293.6964	0	0
lifespan	-0.000325842902289568	0.000302	-1.0793	0.289364	0.144682
gestation	5.94573591251674e-06	5e-05	0.1179	0.906929	0.453465
predation	0.0095473178732136	0.00691	1.3817	0.177612	0.088806
sleepexp.	0.00590955874921987	0.003919	1.508	0.142367	0.071183
danger	-0.0115209272387338	0.00973	-1.184	0.246025	0.123012

\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) & -0.00900208154294444 & 0.020359 & -0.4422 & 0.661644 & 0.330822 \tabularnewline
body & -3.72441341963713e-08 & 0 & -0.9898 & 0.330437 & 0.165219 \tabularnewline
brain & 2.46161008525863e-08 & 0 & 1.1454 & 0.261403 & 0.130702 \tabularnewline
slowwave & 1.00092848893275 & 0.001001 & 1000.1432 & 0 & 0 \tabularnewline
paradoxical & 0.997987624777618 & 0.003398 & 293.6964 & 0 & 0 \tabularnewline
lifespan & -0.000325842902289568 & 0.000302 & -1.0793 & 0.289364 & 0.144682 \tabularnewline
gestation & 5.94573591251674e-06 & 5e-05 & 0.1179 & 0.906929 & 0.453465 \tabularnewline
predation & 0.0095473178732136 & 0.00691 & 1.3817 & 0.177612 & 0.088806 \tabularnewline
sleepexp. & 0.00590955874921987 & 0.003919 & 1.508 & 0.142367 & 0.071183 \tabularnewline
danger & -0.0115209272387338 & 0.00973 & -1.184 & 0.246025 & 0.123012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108566&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]-0.00900208154294444[/C][C]0.020359[/C][C]-0.4422[/C][C]0.661644[/C][C]0.330822[/C][/ROW]
[ROW][C]body[/C][C]-3.72441341963713e-08[/C][C]0[/C][C]-0.9898[/C][C]0.330437[/C][C]0.165219[/C][/ROW]
[ROW][C]brain[/C][C]2.46161008525863e-08[/C][C]0[/C][C]1.1454[/C][C]0.261403[/C][C]0.130702[/C][/ROW]
[ROW][C]slowwave[/C][C]1.00092848893275[/C][C]0.001001[/C][C]1000.1432[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]paradoxical[/C][C]0.997987624777618[/C][C]0.003398[/C][C]293.6964[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]lifespan[/C][C]-0.000325842902289568[/C][C]0.000302[/C][C]-1.0793[/C][C]0.289364[/C][C]0.144682[/C][/ROW]
[ROW][C]gestation[/C][C]5.94573591251674e-06[/C][C]5e-05[/C][C]0.1179[/C][C]0.906929[/C][C]0.453465[/C][/ROW]
[ROW][C]predation[/C][C]0.0095473178732136[/C][C]0.00691[/C][C]1.3817[/C][C]0.177612[/C][C]0.088806[/C][/ROW]
[ROW][C]sleepexp.[/C][C]0.00590955874921987[/C][C]0.003919[/C][C]1.508[/C][C]0.142367[/C][C]0.071183[/C][/ROW]
[ROW][C]danger[/C][C]-0.0115209272387338[/C][C]0.00973[/C][C]-1.184[/C][C]0.246025[/C][C]0.123012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108566&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108566&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)	-0.00900208154294444	0.020359	-0.4422	0.661644	0.330822
body	-3.72441341963713e-08	0	-0.9898	0.330437	0.165219
brain	2.46161008525863e-08	0	1.1454	0.261403	0.130702
slowwave	1.00092848893275	0.001001	1000.1432	0	0
paradoxical	0.997987624777618	0.003398	293.6964	0	0
lifespan	-0.000325842902289568	0.000302	-1.0793	0.289364	0.144682
gestation	5.94573591251674e-06	5e-05	0.1179	0.906929	0.453465
predation	0.0095473178732136	0.00691	1.3817	0.177612	0.088806
sleepexp.	0.00590955874921987	0.003919	1.508	0.142367	0.071183
danger	-0.0115209272387338	0.00973	-1.184	0.246025	0.123012

Multiple Linear Regression - Regression Statistics
Multiple R	0.999995669987442
R-squared	0.999991339993633
Adjusted R-squared	0.99998865240545
F-TEST (value)	372077.592243382
F-TEST (DF numerator)	9
F-TEST (DF denominator)	29
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0163762195175650
Sum Squared Residuals	0.00777723640493687

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999995669987442 \tabularnewline
R-squared & 0.999991339993633 \tabularnewline
Adjusted R-squared & 0.99998865240545 \tabularnewline
F-TEST (value) & 372077.592243382 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 29 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0163762195175650 \tabularnewline
Sum Squared Residuals & 0.00777723640493687 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108566&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999995669987442[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999991339993633[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99998865240545[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]372077.592243382[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]29[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0163762195175650[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.00777723640493687[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108566&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108566&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	0.999995669987442
R-squared	0.999991339993633
Adjusted R-squared	0.99998865240545
F-TEST (value)	372077.592243382
F-TEST (DF numerator)	9
F-TEST (DF denominator)	29
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0163762195175650
Sum Squared Residuals	0.00777723640493687

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	8.3	8.29172002892073	0.00827997107927139
2	3.9	3.90110558960408	-0.00110558960407568
3	9.8	9.8100804412139	-0.0100804412139085
4	19.7	19.6957728218643	0.00422717813570631
5	6.2	6.20609320578976	-0.00609320578976078
6	14.5	14.4954776517937	0.00452234820627043
7	9.7	9.6937868051521	0.00621319484790134
8	12.5	12.4222276359506	0.0777723640493684
9	3.9	3.89722972028675	0.00277027971325233
10	8.4	8.39569614228831	0.00430385771169088
11	10.7	10.6951278961935	0.00487210380645961
12	10.7	10.7027234069998	-0.0027234069998447
13	3.8	3.80889928095787	-0.00889928095787298
14	14.4	14.4047278349398	-0.00472783493982172
15	6.2	6.20141642099895	-0.00141642099895089
16	13.8	13.8102278491646	-0.0102278491645794
17	8.2	8.21366495168006	-0.0136649516800653
18	2.9	2.89474593785984	0.0052540621401587
19	19.9	19.9000061288625	-6.12886249673201e-06
20	8	7.99596147547292	0.00403852452707572
21	13.2	13.2080371704689	-0.00803717046885378
22	19.4	19.4045589324673	-0.00455893246731552
23	17.4	17.4102512231270	-0.010251223127047
24	17	17.0066643934883	-0.0066643934882688
25	10.9	10.9111026263542	-0.0111026263541683
26	13.7	13.6964724092077	0.00352759079226147
27	8.4	8.39811940678808	0.00188059321191678
28	8.4	8.41035408472793	-0.0103540847279327
29	13.2	13.1942366151953	0.00576338480466837
30	9.8	9.79517382580905	0.00482617419095389
31	9.6	9.60584251120525	-0.00584251120524597
32	6.6	6.60171070681266	-0.00171070681265964
33	5.4	5.40020520841959	-0.000205208419591209
34	3.8	3.80906312110523	-0.00906312110522631
35	13.3	13.2974995931609	0.00250040683905529
36	5.4	5.39350367245642	0.00649632754357749
37	15.8	15.8150266151362	-0.0150266151361960
38	10.3	10.3033311305707	-0.0033311305707203
39	19.4	19.402155527505	-0.00215552750498613

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.3 & 8.29172002892073 & 0.00827997107927139 \tabularnewline
2 & 3.9 & 3.90110558960408 & -0.00110558960407568 \tabularnewline
3 & 9.8 & 9.8100804412139 & -0.0100804412139085 \tabularnewline
4 & 19.7 & 19.6957728218643 & 0.00422717813570631 \tabularnewline
5 & 6.2 & 6.20609320578976 & -0.00609320578976078 \tabularnewline
6 & 14.5 & 14.4954776517937 & 0.00452234820627043 \tabularnewline
7 & 9.7 & 9.6937868051521 & 0.00621319484790134 \tabularnewline
8 & 12.5 & 12.4222276359506 & 0.0777723640493684 \tabularnewline
9 & 3.9 & 3.89722972028675 & 0.00277027971325233 \tabularnewline
10 & 8.4 & 8.39569614228831 & 0.00430385771169088 \tabularnewline
11 & 10.7 & 10.6951278961935 & 0.00487210380645961 \tabularnewline
12 & 10.7 & 10.7027234069998 & -0.0027234069998447 \tabularnewline
13 & 3.8 & 3.80889928095787 & -0.00889928095787298 \tabularnewline
14 & 14.4 & 14.4047278349398 & -0.00472783493982172 \tabularnewline
15 & 6.2 & 6.20141642099895 & -0.00141642099895089 \tabularnewline
16 & 13.8 & 13.8102278491646 & -0.0102278491645794 \tabularnewline
17 & 8.2 & 8.21366495168006 & -0.0136649516800653 \tabularnewline
18 & 2.9 & 2.89474593785984 & 0.0052540621401587 \tabularnewline
19 & 19.9 & 19.9000061288625 & -6.12886249673201e-06 \tabularnewline
20 & 8 & 7.99596147547292 & 0.00403852452707572 \tabularnewline
21 & 13.2 & 13.2080371704689 & -0.00803717046885378 \tabularnewline
22 & 19.4 & 19.4045589324673 & -0.00455893246731552 \tabularnewline
23 & 17.4 & 17.4102512231270 & -0.010251223127047 \tabularnewline
24 & 17 & 17.0066643934883 & -0.0066643934882688 \tabularnewline
25 & 10.9 & 10.9111026263542 & -0.0111026263541683 \tabularnewline
26 & 13.7 & 13.6964724092077 & 0.00352759079226147 \tabularnewline
27 & 8.4 & 8.39811940678808 & 0.00188059321191678 \tabularnewline
28 & 8.4 & 8.41035408472793 & -0.0103540847279327 \tabularnewline
29 & 13.2 & 13.1942366151953 & 0.00576338480466837 \tabularnewline
30 & 9.8 & 9.79517382580905 & 0.00482617419095389 \tabularnewline
31 & 9.6 & 9.60584251120525 & -0.00584251120524597 \tabularnewline
32 & 6.6 & 6.60171070681266 & -0.00171070681265964 \tabularnewline
33 & 5.4 & 5.40020520841959 & -0.000205208419591209 \tabularnewline
34 & 3.8 & 3.80906312110523 & -0.00906312110522631 \tabularnewline
35 & 13.3 & 13.2974995931609 & 0.00250040683905529 \tabularnewline
36 & 5.4 & 5.39350367245642 & 0.00649632754357749 \tabularnewline
37 & 15.8 & 15.8150266151362 & -0.0150266151361960 \tabularnewline
38 & 10.3 & 10.3033311305707 & -0.0033311305707203 \tabularnewline
39 & 19.4 & 19.402155527505 & -0.00215552750498613 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108566&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]8.3[/C][C]8.29172002892073[/C][C]0.00827997107927139[/C][/ROW]
[ROW][C]2[/C][C]3.9[/C][C]3.90110558960408[/C][C]-0.00110558960407568[/C][/ROW]
[ROW][C]3[/C][C]9.8[/C][C]9.8100804412139[/C][C]-0.0100804412139085[/C][/ROW]
[ROW][C]4[/C][C]19.7[/C][C]19.6957728218643[/C][C]0.00422717813570631[/C][/ROW]
[ROW][C]5[/C][C]6.2[/C][C]6.20609320578976[/C][C]-0.00609320578976078[/C][/ROW]
[ROW][C]6[/C][C]14.5[/C][C]14.4954776517937[/C][C]0.00452234820627043[/C][/ROW]
[ROW][C]7[/C][C]9.7[/C][C]9.6937868051521[/C][C]0.00621319484790134[/C][/ROW]
[ROW][C]8[/C][C]12.5[/C][C]12.4222276359506[/C][C]0.0777723640493684[/C][/ROW]
[ROW][C]9[/C][C]3.9[/C][C]3.89722972028675[/C][C]0.00277027971325233[/C][/ROW]
[ROW][C]10[/C][C]8.4[/C][C]8.39569614228831[/C][C]0.00430385771169088[/C][/ROW]
[ROW][C]11[/C][C]10.7[/C][C]10.6951278961935[/C][C]0.00487210380645961[/C][/ROW]
[ROW][C]12[/C][C]10.7[/C][C]10.7027234069998[/C][C]-0.0027234069998447[/C][/ROW]
[ROW][C]13[/C][C]3.8[/C][C]3.80889928095787[/C][C]-0.00889928095787298[/C][/ROW]
[ROW][C]14[/C][C]14.4[/C][C]14.4047278349398[/C][C]-0.00472783493982172[/C][/ROW]
[ROW][C]15[/C][C]6.2[/C][C]6.20141642099895[/C][C]-0.00141642099895089[/C][/ROW]
[ROW][C]16[/C][C]13.8[/C][C]13.8102278491646[/C][C]-0.0102278491645794[/C][/ROW]
[ROW][C]17[/C][C]8.2[/C][C]8.21366495168006[/C][C]-0.0136649516800653[/C][/ROW]
[ROW][C]18[/C][C]2.9[/C][C]2.89474593785984[/C][C]0.0052540621401587[/C][/ROW]
[ROW][C]19[/C][C]19.9[/C][C]19.9000061288625[/C][C]-6.12886249673201e-06[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]7.99596147547292[/C][C]0.00403852452707572[/C][/ROW]
[ROW][C]21[/C][C]13.2[/C][C]13.2080371704689[/C][C]-0.00803717046885378[/C][/ROW]
[ROW][C]22[/C][C]19.4[/C][C]19.4045589324673[/C][C]-0.00455893246731552[/C][/ROW]
[ROW][C]23[/C][C]17.4[/C][C]17.4102512231270[/C][C]-0.010251223127047[/C][/ROW]
[ROW][C]24[/C][C]17[/C][C]17.0066643934883[/C][C]-0.0066643934882688[/C][/ROW]
[ROW][C]25[/C][C]10.9[/C][C]10.9111026263542[/C][C]-0.0111026263541683[/C][/ROW]
[ROW][C]26[/C][C]13.7[/C][C]13.6964724092077[/C][C]0.00352759079226147[/C][/ROW]
[ROW][C]27[/C][C]8.4[/C][C]8.39811940678808[/C][C]0.00188059321191678[/C][/ROW]
[ROW][C]28[/C][C]8.4[/C][C]8.41035408472793[/C][C]-0.0103540847279327[/C][/ROW]
[ROW][C]29[/C][C]13.2[/C][C]13.1942366151953[/C][C]0.00576338480466837[/C][/ROW]
[ROW][C]30[/C][C]9.8[/C][C]9.79517382580905[/C][C]0.00482617419095389[/C][/ROW]
[ROW][C]31[/C][C]9.6[/C][C]9.60584251120525[/C][C]-0.00584251120524597[/C][/ROW]
[ROW][C]32[/C][C]6.6[/C][C]6.60171070681266[/C][C]-0.00171070681265964[/C][/ROW]
[ROW][C]33[/C][C]5.4[/C][C]5.40020520841959[/C][C]-0.000205208419591209[/C][/ROW]
[ROW][C]34[/C][C]3.8[/C][C]3.80906312110523[/C][C]-0.00906312110522631[/C][/ROW]
[ROW][C]35[/C][C]13.3[/C][C]13.2974995931609[/C][C]0.00250040683905529[/C][/ROW]
[ROW][C]36[/C][C]5.4[/C][C]5.39350367245642[/C][C]0.00649632754357749[/C][/ROW]
[ROW][C]37[/C][C]15.8[/C][C]15.8150266151362[/C][C]-0.0150266151361960[/C][/ROW]
[ROW][C]38[/C][C]10.3[/C][C]10.3033311305707[/C][C]-0.0033311305707203[/C][/ROW]
[ROW][C]39[/C][C]19.4[/C][C]19.402155527505[/C][C]-0.00215552750498613[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108566&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108566&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	8.3	8.29172002892073	0.00827997107927139
2	3.9	3.90110558960408	-0.00110558960407568
3	9.8	9.8100804412139	-0.0100804412139085
4	19.7	19.6957728218643	0.00422717813570631
5	6.2	6.20609320578976	-0.00609320578976078
6	14.5	14.4954776517937	0.00452234820627043
7	9.7	9.6937868051521	0.00621319484790134
8	12.5	12.4222276359506	0.0777723640493684
9	3.9	3.89722972028675	0.00277027971325233
10	8.4	8.39569614228831	0.00430385771169088
11	10.7	10.6951278961935	0.00487210380645961
12	10.7	10.7027234069998	-0.0027234069998447
13	3.8	3.80889928095787	-0.00889928095787298
14	14.4	14.4047278349398	-0.00472783493982172
15	6.2	6.20141642099895	-0.00141642099895089
16	13.8	13.8102278491646	-0.0102278491645794
17	8.2	8.21366495168006	-0.0136649516800653
18	2.9	2.89474593785984	0.0052540621401587
19	19.9	19.9000061288625	-6.12886249673201e-06
20	8	7.99596147547292	0.00403852452707572
21	13.2	13.2080371704689	-0.00803717046885378
22	19.4	19.4045589324673	-0.00455893246731552
23	17.4	17.4102512231270	-0.010251223127047
24	17	17.0066643934883	-0.0066643934882688
25	10.9	10.9111026263542	-0.0111026263541683
26	13.7	13.6964724092077	0.00352759079226147
27	8.4	8.39811940678808	0.00188059321191678
28	8.4	8.41035408472793	-0.0103540847279327
29	13.2	13.1942366151953	0.00576338480466837
30	9.8	9.79517382580905	0.00482617419095389
31	9.6	9.60584251120525	-0.00584251120524597
32	6.6	6.60171070681266	-0.00171070681265964
33	5.4	5.40020520841959	-0.000205208419591209
34	3.8	3.80906312110523	-0.00906312110522631
35	13.3	13.2974995931609	0.00250040683905529
36	5.4	5.39350367245642	0.00649632754357749
37	15.8	15.8150266151362	-0.0150266151361960
38	10.3	10.3033311305707	-0.0033311305707203
39	19.4	19.402155527505	-0.00215552750498613

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	1	2.11594465495877e-182	1.05797232747939e-182
14	1	6.26945300391241e-191	3.13472650195621e-191
15	1	5.22481740860646e-181	2.61240870430323e-181
16	1	7.31876007331515e-163	3.65938003665757e-163
17	1	3.62965043449936e-155	1.81482521724968e-155
18	1	4.30267848084242e-139	2.15133924042121e-139
19	1	3.24300765314171e-131	1.62150382657085e-131
20	1	1.63372475288249e-115	8.16862376441247e-116
21	1	3.34446835943484e-103	1.67223417971742e-103
22	1	2.65816720596003e-90	1.32908360298001e-90
23	1	1.66526548015799e-75	8.32632740078997e-76
24	1	6.3810386192272e-63	3.1905193096136e-63
25	1	9.84038404540628e-53	4.92019202270314e-53
26	1	1.4316862483047e-40	7.1584312415235e-41

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 1 & 2.11594465495877e-182 & 1.05797232747939e-182 \tabularnewline
14 & 1 & 6.26945300391241e-191 & 3.13472650195621e-191 \tabularnewline
15 & 1 & 5.22481740860646e-181 & 2.61240870430323e-181 \tabularnewline
16 & 1 & 7.31876007331515e-163 & 3.65938003665757e-163 \tabularnewline
17 & 1 & 3.62965043449936e-155 & 1.81482521724968e-155 \tabularnewline
18 & 1 & 4.30267848084242e-139 & 2.15133924042121e-139 \tabularnewline
19 & 1 & 3.24300765314171e-131 & 1.62150382657085e-131 \tabularnewline
20 & 1 & 1.63372475288249e-115 & 8.16862376441247e-116 \tabularnewline
21 & 1 & 3.34446835943484e-103 & 1.67223417971742e-103 \tabularnewline
22 & 1 & 2.65816720596003e-90 & 1.32908360298001e-90 \tabularnewline
23 & 1 & 1.66526548015799e-75 & 8.32632740078997e-76 \tabularnewline
24 & 1 & 6.3810386192272e-63 & 3.1905193096136e-63 \tabularnewline
25 & 1 & 9.84038404540628e-53 & 4.92019202270314e-53 \tabularnewline
26 & 1 & 1.4316862483047e-40 & 7.1584312415235e-41 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108566&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]13[/C][C]1[/C][C]2.11594465495877e-182[/C][C]1.05797232747939e-182[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]6.26945300391241e-191[/C][C]3.13472650195621e-191[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]5.22481740860646e-181[/C][C]2.61240870430323e-181[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]7.31876007331515e-163[/C][C]3.65938003665757e-163[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]3.62965043449936e-155[/C][C]1.81482521724968e-155[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]4.30267848084242e-139[/C][C]2.15133924042121e-139[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]3.24300765314171e-131[/C][C]1.62150382657085e-131[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.63372475288249e-115[/C][C]8.16862376441247e-116[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]3.34446835943484e-103[/C][C]1.67223417971742e-103[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]2.65816720596003e-90[/C][C]1.32908360298001e-90[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.66526548015799e-75[/C][C]8.32632740078997e-76[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]6.3810386192272e-63[/C][C]3.1905193096136e-63[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]9.84038404540628e-53[/C][C]4.92019202270314e-53[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.4316862483047e-40[/C][C]7.1584312415235e-41[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108566&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108566&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
13	1	2.11594465495877e-182	1.05797232747939e-182
14	1	6.26945300391241e-191	3.13472650195621e-191
15	1	5.22481740860646e-181	2.61240870430323e-181
16	1	7.31876007331515e-163	3.65938003665757e-163
17	1	3.62965043449936e-155	1.81482521724968e-155
18	1	4.30267848084242e-139	2.15133924042121e-139
19	1	3.24300765314171e-131	1.62150382657085e-131
20	1	1.63372475288249e-115	8.16862376441247e-116
21	1	3.34446835943484e-103	1.67223417971742e-103
22	1	2.65816720596003e-90	1.32908360298001e-90
23	1	1.66526548015799e-75	8.32632740078997e-76
24	1	6.3810386192272e-63	3.1905193096136e-63
25	1	9.84038404540628e-53	4.92019202270314e-53
26	1	1.4316862483047e-40	7.1584312415235e-41

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	14	1	NOK
5% type I error level	14	1	NOK
10% type I error level	14	1	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 14 & 1 & NOK \tabularnewline
5% type I error level & 14 & 1 & NOK \tabularnewline
10% type I error level & 14 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108566&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]14[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108566&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108566&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	14	1	NOK
5% type I error level	14	1	NOK
10% type I error level	14	1	NOK

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 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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