Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

Patrick.Wessarwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 14 Dec 2010 10:11:46 +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/14/t1292321474s5rx0vaxsuxadqk.htm/, Retrieved Thu, 02 May 2024 16:29:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109347, Retrieved Thu, 02 May 2024 16:29:04 +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

125

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

-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
-   PD  [Kendall tau Correlation Matrix] [kendall correlati...] [2010-12-11 13:52:59] [95e8426e0df851c9330605aa1e892ab5]
- RMPD      [Multiple Regression] [multiple regressi...] [2010-12-14 10:11:46] [dc77c696707133dea0955379c56a2acd] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

6,3	2	4,5	1	6,6	42	3	1	3
2,1	1,8	69	2547	4603	624	3	5	4
9,1	0,7	27	10,55	179,5	180	4	4	4
15,8	3,9	19	0,023	0,3	35	1	1	1
5,2	1	30,4	160	169	392	4	5	4
10,9	3,6	28	3,3	25,6	63	1	2	1
8,3	1,4	50	52,16	440	230	1	1	1
11	1,5	7	0,425	6,4	112	5	4	4
3,2	0,7	30	465	423	281	5	5	5
6,3	2,1	3,5	0,075	1,2	42	1	1	1
8,6	0	50	3	25	28	2	2	2
6,6	4,1	6	0,785	3,5	42	2	2	2
9,5	1,2	10,4	0,2	5	120	2	2	2
3,3	0,5	20	27,66	115	148	5	5	5
11	3,4	3,9	0,12	1	16	3	1	2
4,7	1,5	41	85	325	310	1	3	1
10,4	3,4	9	0,101	4	28	5	1	3
7,4	0,8	7,6	1,04	5,5	68	5	3	4
2,1	0,8	46	521	655	336	5	5	5
7,7	1,4	2,6	0,005	0,14	21,5	5	2	4
17,9	2	24	0,01	0,25	50	1	1	1
6,1	1,9	100	62	1320	267	1	1	1
11,9	1,3	3,2	0,023	0,4	19	4	1	3
10,8	2	2	0,048	0,33	30	4	1	3
13,8	5,6	5	1,7	6,3	12	2	1	1
14,3	3,1	6,5	3,5	10,8	120	2	1	1
15,2	1,8	12	0,48	15,5	140	2	2	2
10	0,9	20,2	10	115	170	4	4	4
11,9	1,8	13	1,62	11,4	17	2	1	2
6,5	1,9	27	192	180	115	4	4	4
7,5	0,9	18	2,5	12,1	31	5	5	5
10,6	2,6	4,7	0,28	1,9	21	3	1	3
7,4	2,4	9,8	4,235	50,4	52	1	1	1
8,4	1,2	29	6,8	179	164	2	3	2
5,7	0,9	7	0,75	12,3	225	2	2	2
4,9	0,5	6	3,6	21	225	3	2	3
3,2	0,6	20	55,5	175	151	5	5	5
11	2,3	4,5	0,9	2,6	60	2	1	2
4,9	0,5	7,5	2	12,3	200	3	1	3
13,2	2,6	2,3	0,104	2,5	46	3	2	2
9,7	0,6	24	4,19	58	210	4	3	4
12,8	6,6	3	3,5	3,9	14	2	1	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109347&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]7 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=109347&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109347&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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 3.62388684905666 + 0.0115328862754582SWS[t] -0.0133813431715984LifeSpan[t] + 0.00133188408343262BodyW[t] + 0.000311047350003712BrainW[t] -0.00484781985414119GT[t] + 0.8840450491086PI[t] + 0.357433139265944SEI[t] -1.70617590983705ODI[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  3.62388684905666 +  0.0115328862754582SWS[t] -0.0133813431715984LifeSpan[t] +  0.00133188408343262BodyW[t] +  0.000311047350003712BrainW[t] -0.00484781985414119GT[t] +  0.8840450491086PI[t] +  0.357433139265944SEI[t] -1.70617590983705ODI[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109347&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  3.62388684905666 +  0.0115328862754582SWS[t] -0.0133813431715984LifeSpan[t] +  0.00133188408343262BodyW[t] +  0.000311047350003712BrainW[t] -0.00484781985414119GT[t] +  0.8840450491086PI[t] +  0.357433139265944SEI[t] -1.70617590983705ODI[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109347&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109347&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
PS[t] = + 3.62388684905666 + 0.0115328862754582SWS[t] -0.0133813431715984LifeSpan[t] + 0.00133188408343262BodyW[t] + 0.000311047350003712BrainW[t] -0.00484781985414119GT[t] + 0.8840450491086PI[t] + 0.357433139265944SEI[t] -1.70617590983705ODI[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)	3.62388684905666	0.870444	4.1633	0.000211	0.000106
SWS	0.0115328862754582	0.057221	0.2016	0.841505	0.420753
LifeSpan	-0.0133813431715984	0.014517	-0.9218	0.363343	0.181672
BodyW	0.00133188408343262	0.001867	0.7132	0.480724	0.240362
BrainW	0.000311047350003712	0.001117	0.2784	0.782462	0.391231
GT	-0.00484781985414119	0.002328	-2.0827	0.045111	0.022555
PI	0.8840450491086	0.352207	2.51	0.017156	0.008578
SEI	0.357433139265944	0.215137	1.6614	0.106101	0.05305
ODI	-1.70617590983705	0.454756	-3.7519	0.000677	0.000338

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3.62388684905666 & 0.870444 & 4.1633 & 0.000211 & 0.000106 \tabularnewline
SWS & 0.0115328862754582 & 0.057221 & 0.2016 & 0.841505 & 0.420753 \tabularnewline
LifeSpan & -0.0133813431715984 & 0.014517 & -0.9218 & 0.363343 & 0.181672 \tabularnewline
BodyW & 0.00133188408343262 & 0.001867 & 0.7132 & 0.480724 & 0.240362 \tabularnewline
BrainW & 0.000311047350003712 & 0.001117 & 0.2784 & 0.782462 & 0.391231 \tabularnewline
GT & -0.00484781985414119 & 0.002328 & -2.0827 & 0.045111 & 0.022555 \tabularnewline
PI & 0.8840450491086 & 0.352207 & 2.51 & 0.017156 & 0.008578 \tabularnewline
SEI & 0.357433139265944 & 0.215137 & 1.6614 & 0.106101 & 0.05305 \tabularnewline
ODI & -1.70617590983705 & 0.454756 & -3.7519 & 0.000677 & 0.000338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109347&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]3.62388684905666[/C][C]0.870444[/C][C]4.1633[/C][C]0.000211[/C][C]0.000106[/C][/ROW]
[ROW][C]SWS[/C][C]0.0115328862754582[/C][C]0.057221[/C][C]0.2016[/C][C]0.841505[/C][C]0.420753[/C][/ROW]
[ROW][C]LifeSpan[/C][C]-0.0133813431715984[/C][C]0.014517[/C][C]-0.9218[/C][C]0.363343[/C][C]0.181672[/C][/ROW]
[ROW][C]BodyW[/C][C]0.00133188408343262[/C][C]0.001867[/C][C]0.7132[/C][C]0.480724[/C][C]0.240362[/C][/ROW]
[ROW][C]BrainW[/C][C]0.000311047350003712[/C][C]0.001117[/C][C]0.2784[/C][C]0.782462[/C][C]0.391231[/C][/ROW]
[ROW][C]GT[/C][C]-0.00484781985414119[/C][C]0.002328[/C][C]-2.0827[/C][C]0.045111[/C][C]0.022555[/C][/ROW]
[ROW][C]PI[/C][C]0.8840450491086[/C][C]0.352207[/C][C]2.51[/C][C]0.017156[/C][C]0.008578[/C][/ROW]
[ROW][C]SEI[/C][C]0.357433139265944[/C][C]0.215137[/C][C]1.6614[/C][C]0.106101[/C][C]0.05305[/C][/ROW]
[ROW][C]ODI[/C][C]-1.70617590983705[/C][C]0.454756[/C][C]-3.7519[/C][C]0.000677[/C][C]0.000338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109347&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109347&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)	3.62388684905666	0.870444	4.1633	0.000211	0.000106
SWS	0.0115328862754582	0.057221	0.2016	0.841505	0.420753
LifeSpan	-0.0133813431715984	0.014517	-0.9218	0.363343	0.181672
BodyW	0.00133188408343262	0.001867	0.7132	0.480724	0.240362
BrainW	0.000311047350003712	0.001117	0.2784	0.782462	0.391231
GT	-0.00484781985414119	0.002328	-2.0827	0.045111	0.022555
PI	0.8840450491086	0.352207	2.51	0.017156	0.008578
SEI	0.357433139265944	0.215137	1.6614	0.106101	0.05305
ODI	-1.70617590983705	0.454756	-3.7519	0.000677	0.000338

Multiple Linear Regression - Regression Statistics
Multiple R	0.787947573648145
R-squared	0.620861378817999
Adjusted R-squared	0.52894898580418
F-TEST (value)	6.75492562493347
F-TEST (DF numerator)	8
F-TEST (DF denominator)	33
p-value	3.14250718375098e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.953180992750902
Sum Squared Residuals	29.9822821630727

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.787947573648145 \tabularnewline
R-squared & 0.620861378817999 \tabularnewline
Adjusted R-squared & 0.52894898580418 \tabularnewline
F-TEST (value) & 6.75492562493347 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 33 \tabularnewline
p-value & 3.14250718375098e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.953180992750902 \tabularnewline
Sum Squared Residuals & 29.9822821630727 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109347&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.787947573648145[/C][/ROW]
[ROW][C]R-squared[/C][C]0.620861378817999[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.52894898580418[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.75492562493347[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]33[/C][/ROW]
[ROW][C]p-value[/C][C]3.14250718375098e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.953180992750902[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]29.9822821630727[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109347&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109347&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	0.787947573648145
R-squared	0.620861378817999
Adjusted R-squared	0.52894898580418
F-TEST (value)	6.75492562493347
F-TEST (DF numerator)	8
F-TEST (DF denominator)	33
p-value	3.14250718375098e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.953180992750902
Sum Squared Residuals	29.9822821630727

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2	1.32714490812000	0.672855091880002
2	1.8	2.13841055928803	-0.338410559288035
3	0.7	0.706025765340633	-0.00602576534063304
4	3.9	2.91761346313001	0.982386536869987
5	1	0.141030351364891	0.85896964863511
6	3.6	2.97459849728238	0.625401502717623
7	1.4	1.67717826644156	-0.277178266441559
8	1.5	2.14193428925577	-0.641934289255774
9	0.7	0.324524931511732	0.375475068488268
10	2.1	2.98187632428129	-0.88187632428129
11	0	2.00063994960512	-2.00063994960512
12	4.1	2.48884615737667	1.61115384262333
13	1.2	2.08497108783365	-0.884971087833654
14	0.5	0.425962923606475	0.0740370763935246
15	3.4	3.21868358240888	0.18131641759112
16	1.5	2.09110128264438	-0.591101282644382
17	3.4	3.14816718685134	0.251832813148663
18	0.8	1.94879719317374	-1.14879719317374
19	0.8	-0.0221443322415236	0.822144332241524
20	1.4	1.88410854504362	-0.484108545043622
21	2	2.80217564377778	-0.80217564377778
22	1.9	1.09019683083664	0.809803169163356
23	1.3	2.40143997881994	-1.10143997881994
24	2	2.35149692111489	-0.351496921114892
25	5.6	4.08153125444326	1.51846874555674
26	3.1	3.54745824300154	-0.447458243001537
27	1.8	2.03598091816478	-0.235980918164783
28	0.9	0.8350816047757	0.0649183952242993
29	1.8	2.22363280679769	-0.423632806797694
30	1.9	1.23297444215747	0.667025557842527
31	0.9	1.00284167938657	-0.102841679386569
32	2.6	1.47344338832202	1.12655661167798
33	2.4	2.88262600606907	-0.482626006069074
34	1.2	2.03043366947395	-0.830433669473952
35	0.9	1.58062478398644	-0.680624783986437
36	0.5	0.769150938992037	-0.269150938992037
37	0.6	0.466008669299492	0.133991330700508
38	2.3	2.11484219916019	0.185157800839807
39	0.5	0.508004154843701	-0.0080041548437012
40	2.6	3.47790988581082	-0.877909885810824
41	0.6	0.203958755934443	0.396041244065557
42	6.6	4.08871629251288	2.51128370748712

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.32714490812000 & 0.672855091880002 \tabularnewline
2 & 1.8 & 2.13841055928803 & -0.338410559288035 \tabularnewline
3 & 0.7 & 0.706025765340633 & -0.00602576534063304 \tabularnewline
4 & 3.9 & 2.91761346313001 & 0.982386536869987 \tabularnewline
5 & 1 & 0.141030351364891 & 0.85896964863511 \tabularnewline
6 & 3.6 & 2.97459849728238 & 0.625401502717623 \tabularnewline
7 & 1.4 & 1.67717826644156 & -0.277178266441559 \tabularnewline
8 & 1.5 & 2.14193428925577 & -0.641934289255774 \tabularnewline
9 & 0.7 & 0.324524931511732 & 0.375475068488268 \tabularnewline
10 & 2.1 & 2.98187632428129 & -0.88187632428129 \tabularnewline
11 & 0 & 2.00063994960512 & -2.00063994960512 \tabularnewline
12 & 4.1 & 2.48884615737667 & 1.61115384262333 \tabularnewline
13 & 1.2 & 2.08497108783365 & -0.884971087833654 \tabularnewline
14 & 0.5 & 0.425962923606475 & 0.0740370763935246 \tabularnewline
15 & 3.4 & 3.21868358240888 & 0.18131641759112 \tabularnewline
16 & 1.5 & 2.09110128264438 & -0.591101282644382 \tabularnewline
17 & 3.4 & 3.14816718685134 & 0.251832813148663 \tabularnewline
18 & 0.8 & 1.94879719317374 & -1.14879719317374 \tabularnewline
19 & 0.8 & -0.0221443322415236 & 0.822144332241524 \tabularnewline
20 & 1.4 & 1.88410854504362 & -0.484108545043622 \tabularnewline
21 & 2 & 2.80217564377778 & -0.80217564377778 \tabularnewline
22 & 1.9 & 1.09019683083664 & 0.809803169163356 \tabularnewline
23 & 1.3 & 2.40143997881994 & -1.10143997881994 \tabularnewline
24 & 2 & 2.35149692111489 & -0.351496921114892 \tabularnewline
25 & 5.6 & 4.08153125444326 & 1.51846874555674 \tabularnewline
26 & 3.1 & 3.54745824300154 & -0.447458243001537 \tabularnewline
27 & 1.8 & 2.03598091816478 & -0.235980918164783 \tabularnewline
28 & 0.9 & 0.8350816047757 & 0.0649183952242993 \tabularnewline
29 & 1.8 & 2.22363280679769 & -0.423632806797694 \tabularnewline
30 & 1.9 & 1.23297444215747 & 0.667025557842527 \tabularnewline
31 & 0.9 & 1.00284167938657 & -0.102841679386569 \tabularnewline
32 & 2.6 & 1.47344338832202 & 1.12655661167798 \tabularnewline
33 & 2.4 & 2.88262600606907 & -0.482626006069074 \tabularnewline
34 & 1.2 & 2.03043366947395 & -0.830433669473952 \tabularnewline
35 & 0.9 & 1.58062478398644 & -0.680624783986437 \tabularnewline
36 & 0.5 & 0.769150938992037 & -0.269150938992037 \tabularnewline
37 & 0.6 & 0.466008669299492 & 0.133991330700508 \tabularnewline
38 & 2.3 & 2.11484219916019 & 0.185157800839807 \tabularnewline
39 & 0.5 & 0.508004154843701 & -0.0080041548437012 \tabularnewline
40 & 2.6 & 3.47790988581082 & -0.877909885810824 \tabularnewline
41 & 0.6 & 0.203958755934443 & 0.396041244065557 \tabularnewline
42 & 6.6 & 4.08871629251288 & 2.51128370748712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109347&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]2[/C][C]1.32714490812000[/C][C]0.672855091880002[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]2.13841055928803[/C][C]-0.338410559288035[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.706025765340633[/C][C]-0.00602576534063304[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]2.91761346313001[/C][C]0.982386536869987[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.141030351364891[/C][C]0.85896964863511[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]2.97459849728238[/C][C]0.625401502717623[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]1.67717826644156[/C][C]-0.277178266441559[/C][/ROW]
[ROW][C]8[/C][C]1.5[/C][C]2.14193428925577[/C][C]-0.641934289255774[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.324524931511732[/C][C]0.375475068488268[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.98187632428129[/C][C]-0.88187632428129[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]2.00063994960512[/C][C]-2.00063994960512[/C][/ROW]
[ROW][C]12[/C][C]4.1[/C][C]2.48884615737667[/C][C]1.61115384262333[/C][/ROW]
[ROW][C]13[/C][C]1.2[/C][C]2.08497108783365[/C][C]-0.884971087833654[/C][/ROW]
[ROW][C]14[/C][C]0.5[/C][C]0.425962923606475[/C][C]0.0740370763935246[/C][/ROW]
[ROW][C]15[/C][C]3.4[/C][C]3.21868358240888[/C][C]0.18131641759112[/C][/ROW]
[ROW][C]16[/C][C]1.5[/C][C]2.09110128264438[/C][C]-0.591101282644382[/C][/ROW]
[ROW][C]17[/C][C]3.4[/C][C]3.14816718685134[/C][C]0.251832813148663[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]1.94879719317374[/C][C]-1.14879719317374[/C][/ROW]
[ROW][C]19[/C][C]0.8[/C][C]-0.0221443322415236[/C][C]0.822144332241524[/C][/ROW]
[ROW][C]20[/C][C]1.4[/C][C]1.88410854504362[/C][C]-0.484108545043622[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]2.80217564377778[/C][C]-0.80217564377778[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.09019683083664[/C][C]0.809803169163356[/C][/ROW]
[ROW][C]23[/C][C]1.3[/C][C]2.40143997881994[/C][C]-1.10143997881994[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]2.35149692111489[/C][C]-0.351496921114892[/C][/ROW]
[ROW][C]25[/C][C]5.6[/C][C]4.08153125444326[/C][C]1.51846874555674[/C][/ROW]
[ROW][C]26[/C][C]3.1[/C][C]3.54745824300154[/C][C]-0.447458243001537[/C][/ROW]
[ROW][C]27[/C][C]1.8[/C][C]2.03598091816478[/C][C]-0.235980918164783[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]0.8350816047757[/C][C]0.0649183952242993[/C][/ROW]
[ROW][C]29[/C][C]1.8[/C][C]2.22363280679769[/C][C]-0.423632806797694[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.23297444215747[/C][C]0.667025557842527[/C][/ROW]
[ROW][C]31[/C][C]0.9[/C][C]1.00284167938657[/C][C]-0.102841679386569[/C][/ROW]
[ROW][C]32[/C][C]2.6[/C][C]1.47344338832202[/C][C]1.12655661167798[/C][/ROW]
[ROW][C]33[/C][C]2.4[/C][C]2.88262600606907[/C][C]-0.482626006069074[/C][/ROW]
[ROW][C]34[/C][C]1.2[/C][C]2.03043366947395[/C][C]-0.830433669473952[/C][/ROW]
[ROW][C]35[/C][C]0.9[/C][C]1.58062478398644[/C][C]-0.680624783986437[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]0.769150938992037[/C][C]-0.269150938992037[/C][/ROW]
[ROW][C]37[/C][C]0.6[/C][C]0.466008669299492[/C][C]0.133991330700508[/C][/ROW]
[ROW][C]38[/C][C]2.3[/C][C]2.11484219916019[/C][C]0.185157800839807[/C][/ROW]
[ROW][C]39[/C][C]0.5[/C][C]0.508004154843701[/C][C]-0.0080041548437012[/C][/ROW]
[ROW][C]40[/C][C]2.6[/C][C]3.47790988581082[/C][C]-0.877909885810824[/C][/ROW]
[ROW][C]41[/C][C]0.6[/C][C]0.203958755934443[/C][C]0.396041244065557[/C][/ROW]
[ROW][C]42[/C][C]6.6[/C][C]4.08871629251288[/C][C]2.51128370748712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109347&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109347&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	2	1.32714490812000	0.672855091880002
2	1.8	2.13841055928803	-0.338410559288035
3	0.7	0.706025765340633	-0.00602576534063304
4	3.9	2.91761346313001	0.982386536869987
5	1	0.141030351364891	0.85896964863511
6	3.6	2.97459849728238	0.625401502717623
7	1.4	1.67717826644156	-0.277178266441559
8	1.5	2.14193428925577	-0.641934289255774
9	0.7	0.324524931511732	0.375475068488268
10	2.1	2.98187632428129	-0.88187632428129
11	0	2.00063994960512	-2.00063994960512
12	4.1	2.48884615737667	1.61115384262333
13	1.2	2.08497108783365	-0.884971087833654
14	0.5	0.425962923606475	0.0740370763935246
15	3.4	3.21868358240888	0.18131641759112
16	1.5	2.09110128264438	-0.591101282644382
17	3.4	3.14816718685134	0.251832813148663
18	0.8	1.94879719317374	-1.14879719317374
19	0.8	-0.0221443322415236	0.822144332241524
20	1.4	1.88410854504362	-0.484108545043622
21	2	2.80217564377778	-0.80217564377778
22	1.9	1.09019683083664	0.809803169163356
23	1.3	2.40143997881994	-1.10143997881994
24	2	2.35149692111489	-0.351496921114892
25	5.6	4.08153125444326	1.51846874555674
26	3.1	3.54745824300154	-0.447458243001537
27	1.8	2.03598091816478	-0.235980918164783
28	0.9	0.8350816047757	0.0649183952242993
29	1.8	2.22363280679769	-0.423632806797694
30	1.9	1.23297444215747	0.667025557842527
31	0.9	1.00284167938657	-0.102841679386569
32	2.6	1.47344338832202	1.12655661167798
33	2.4	2.88262600606907	-0.482626006069074
34	1.2	2.03043366947395	-0.830433669473952
35	0.9	1.58062478398644	-0.680624783986437
36	0.5	0.769150938992037	-0.269150938992037
37	0.6	0.466008669299492	0.133991330700508
38	2.3	2.11484219916019	0.185157800839807
39	0.5	0.508004154843701	-0.0080041548437012
40	2.6	3.47790988581082	-0.877909885810824
41	0.6	0.203958755934443	0.396041244065557
42	6.6	4.08871629251288	2.51128370748712

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.847197977928971	0.305604044142057	0.152802022071029
13	0.918518810488964	0.162962379022071	0.0814811895110357
14	0.851928665608718	0.296142668782563	0.148071334391282
15	0.792307659070176	0.415384681859648	0.207692340929824
16	0.688933866459996	0.622132267080008	0.311066133540004
17	0.637761042915091	0.724477914169817	0.362238957084909
18	0.615801659997283	0.768396680005434	0.384198340002717
19	0.552140487220982	0.895719025558035	0.447859512779018
20	0.455568319206317	0.911136638412635	0.544431680793683
21	0.426613370503553	0.853226741007106	0.573386629496447
22	0.433715603621255	0.867431207242511	0.566284396378745
23	0.494237594028262	0.988475188056524	0.505762405971738
24	0.533064867212302	0.933870265575397	0.466935132787698
25	0.597559764038082	0.804880471923836	0.402440235961918
26	0.543602264932501	0.912795470134997	0.456397735067499
27	0.415733184939844	0.831466369879689	0.584266815060156
28	0.305790160235867	0.611580320471734	0.694209839764133
29	0.244441407490259	0.488882814980519	0.755558592509741
30	0.24222925153889	0.48445850307778	0.75777074846111

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.847197977928971 & 0.305604044142057 & 0.152802022071029 \tabularnewline
13 & 0.918518810488964 & 0.162962379022071 & 0.0814811895110357 \tabularnewline
14 & 0.851928665608718 & 0.296142668782563 & 0.148071334391282 \tabularnewline
15 & 0.792307659070176 & 0.415384681859648 & 0.207692340929824 \tabularnewline
16 & 0.688933866459996 & 0.622132267080008 & 0.311066133540004 \tabularnewline
17 & 0.637761042915091 & 0.724477914169817 & 0.362238957084909 \tabularnewline
18 & 0.615801659997283 & 0.768396680005434 & 0.384198340002717 \tabularnewline
19 & 0.552140487220982 & 0.895719025558035 & 0.447859512779018 \tabularnewline
20 & 0.455568319206317 & 0.911136638412635 & 0.544431680793683 \tabularnewline
21 & 0.426613370503553 & 0.853226741007106 & 0.573386629496447 \tabularnewline
22 & 0.433715603621255 & 0.867431207242511 & 0.566284396378745 \tabularnewline
23 & 0.494237594028262 & 0.988475188056524 & 0.505762405971738 \tabularnewline
24 & 0.533064867212302 & 0.933870265575397 & 0.466935132787698 \tabularnewline
25 & 0.597559764038082 & 0.804880471923836 & 0.402440235961918 \tabularnewline
26 & 0.543602264932501 & 0.912795470134997 & 0.456397735067499 \tabularnewline
27 & 0.415733184939844 & 0.831466369879689 & 0.584266815060156 \tabularnewline
28 & 0.305790160235867 & 0.611580320471734 & 0.694209839764133 \tabularnewline
29 & 0.244441407490259 & 0.488882814980519 & 0.755558592509741 \tabularnewline
30 & 0.24222925153889 & 0.48445850307778 & 0.75777074846111 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109347&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]12[/C][C]0.847197977928971[/C][C]0.305604044142057[/C][C]0.152802022071029[/C][/ROW]
[ROW][C]13[/C][C]0.918518810488964[/C][C]0.162962379022071[/C][C]0.0814811895110357[/C][/ROW]
[ROW][C]14[/C][C]0.851928665608718[/C][C]0.296142668782563[/C][C]0.148071334391282[/C][/ROW]
[ROW][C]15[/C][C]0.792307659070176[/C][C]0.415384681859648[/C][C]0.207692340929824[/C][/ROW]
[ROW][C]16[/C][C]0.688933866459996[/C][C]0.622132267080008[/C][C]0.311066133540004[/C][/ROW]
[ROW][C]17[/C][C]0.637761042915091[/C][C]0.724477914169817[/C][C]0.362238957084909[/C][/ROW]
[ROW][C]18[/C][C]0.615801659997283[/C][C]0.768396680005434[/C][C]0.384198340002717[/C][/ROW]
[ROW][C]19[/C][C]0.552140487220982[/C][C]0.895719025558035[/C][C]0.447859512779018[/C][/ROW]
[ROW][C]20[/C][C]0.455568319206317[/C][C]0.911136638412635[/C][C]0.544431680793683[/C][/ROW]
[ROW][C]21[/C][C]0.426613370503553[/C][C]0.853226741007106[/C][C]0.573386629496447[/C][/ROW]
[ROW][C]22[/C][C]0.433715603621255[/C][C]0.867431207242511[/C][C]0.566284396378745[/C][/ROW]
[ROW][C]23[/C][C]0.494237594028262[/C][C]0.988475188056524[/C][C]0.505762405971738[/C][/ROW]
[ROW][C]24[/C][C]0.533064867212302[/C][C]0.933870265575397[/C][C]0.466935132787698[/C][/ROW]
[ROW][C]25[/C][C]0.597559764038082[/C][C]0.804880471923836[/C][C]0.402440235961918[/C][/ROW]
[ROW][C]26[/C][C]0.543602264932501[/C][C]0.912795470134997[/C][C]0.456397735067499[/C][/ROW]
[ROW][C]27[/C][C]0.415733184939844[/C][C]0.831466369879689[/C][C]0.584266815060156[/C][/ROW]
[ROW][C]28[/C][C]0.305790160235867[/C][C]0.611580320471734[/C][C]0.694209839764133[/C][/ROW]
[ROW][C]29[/C][C]0.244441407490259[/C][C]0.488882814980519[/C][C]0.755558592509741[/C][/ROW]
[ROW][C]30[/C][C]0.24222925153889[/C][C]0.48445850307778[/C][C]0.75777074846111[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109347&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109347&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
12	0.847197977928971	0.305604044142057	0.152802022071029
13	0.918518810488964	0.162962379022071	0.0814811895110357
14	0.851928665608718	0.296142668782563	0.148071334391282
15	0.792307659070176	0.415384681859648	0.207692340929824
16	0.688933866459996	0.622132267080008	0.311066133540004
17	0.637761042915091	0.724477914169817	0.362238957084909
18	0.615801659997283	0.768396680005434	0.384198340002717
19	0.552140487220982	0.895719025558035	0.447859512779018
20	0.455568319206317	0.911136638412635	0.544431680793683
21	0.426613370503553	0.853226741007106	0.573386629496447
22	0.433715603621255	0.867431207242511	0.566284396378745
23	0.494237594028262	0.988475188056524	0.505762405971738
24	0.533064867212302	0.933870265575397	0.466935132787698
25	0.597559764038082	0.804880471923836	0.402440235961918
26	0.543602264932501	0.912795470134997	0.456397735067499
27	0.415733184939844	0.831466369879689	0.584266815060156
28	0.305790160235867	0.611580320471734	0.694209839764133
29	0.244441407490259	0.488882814980519	0.755558592509741
30	0.24222925153889	0.48445850307778	0.75777074846111

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=109347&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=109347&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109347&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):

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 0 ;

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