Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 14 Dec 2012 14:03:56 -0500

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/2012/Dec/14/t1355511856eui1nm9b3qfozr2.htm/, Retrieved Fri, 26 Apr 2024 00:02:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199720, Retrieved Fri, 26 Apr 2024 00:02:18 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Multiple Regression] [multiple regression] [2012-12-14 19:03:56] [91c3d91830a25c0bc67fd9a0665302b1] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

25      2      10      1.5        0        6    5.70     11.40
 24      2      10      1.5        0       10   17.56     35.12
 30      2      10      1.5        2        6   11.28     22.56
  2      2      10      1.5        2       10    8.39     16.78
 40      2      10      2.5        0        6   16.67     33.34
 37      2      10      2.5        0       10   12.04     24.08
 16      2      10      2.5        2        6    9.22     18.44
 22      2      10      2.5        2       10    3.94      7.88
 33      2      30      1.5        0        6   27.02     18.01
 17      2      30      1.5        0       10   19.46     12.97
 28      2      30      1.5        2        6   18.54     12.36
 27      2      30      1.5        2       10   25.70     17.13
 14      2      30      2.5        0        6   19.02     12.68
 13      2      30      2.5        0       10   22.39     14.93
  4      2      30      2.5        2        6   23.85     15.90
 21      2      30      2.5        2       10   30.12     20.08
 23      6      10      1.5        0        6   13.42     26.84
 35      6      10      1.5        0       10   34.26     68.52
 19      6      10      1.5        2        6   39.74     79.48
 34      6      10      1.5        2       10   10.60     21.20
 31      6      10      2.5        0        6   28.89     57.78
  9      6      10      2.5        0       10   35.61     71.22
 38      6      10      2.5        2        6   17.20     34.40
 15      6      10      2.5        2       10    6.00     12.00
 39      6      30      1.5        0        6  129.45     86.30
  8      6      30      1.5        0       10  107.38     71.59
 26      6      30      1.5        2        6  111.66     74.44
 11      6      30      1.5        2       10  109.10     72.73
  6      6      30      2.5        0        6  100.43     66.95
 20      6      30      2.5        0       10  109.28     72.85
 10      6      30      2.5        2        6  106.46     70.97
 32      6      30      2.5        2       10  134.01     89.34
  1      4      20      2.0        1        8   10.78     10.78
  3      4      20      2.0        1        8    9.39      9.39
  5      4      20      2.0        1        8    9.84      9.84
  7      4      20      2.0        1        8   13.94     13.94
 12      4      20      2.0        1        8   12.33     12.33
 18      4      20      2.0        1        8    7.32      7.32
 29      4      20      2.0        1        8    7.91      7.91
 36      4      20      2.0        1        8   15.58     15.58

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'George Udny Yule' @ yule.wessa.net

\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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199720&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199720&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199720&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	8 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
RUN[t] = + 45.136512850162 -1.61042016498947SPEED1[t] -0.468106132737128TOTAL[t] -3.12177834831424SPEED2[t] -0.872499786308419NUMBER2[t] -0.844953325355799SENS[t] + 0.0495519672711723TIME[t] + 0.0956233074188925T20BOLT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
RUN[t] =  +  45.136512850162 -1.61042016498947SPEED1[t] -0.468106132737128TOTAL[t] -3.12177834831424SPEED2[t] -0.872499786308419NUMBER2[t] -0.844953325355799SENS[t] +  0.0495519672711723TIME[t] +  0.0956233074188925T20BOLT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199720&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]RUN[t] =  +  45.136512850162 -1.61042016498947SPEED1[t] -0.468106132737128TOTAL[t] -3.12177834831424SPEED2[t] -0.872499786308419NUMBER2[t] -0.844953325355799SENS[t] +  0.0495519672711723TIME[t] +  0.0956233074188925T20BOLT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199720&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199720&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
RUN[t] = + 45.136512850162 -1.61042016498947SPEED1[t] -0.468106132737128TOTAL[t] -3.12177834831424SPEED2[t] -0.872499786308419NUMBER2[t] -0.844953325355799SENS[t] + 0.0495519672711723TIME[t] + 0.0956233074188925T20BOLT[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)	45.136512850162	16.173554	2.7908	0.008791	0.004396
SPEED1	-1.61042016498947	1.534615	-1.0494	0.301857	0.150928
TOTAL	-0.468106132737128	0.394753	-1.1858	0.244424	0.122212
SPEED2	-3.12177834831424	4.211655	-0.7412	0.463963	0.231982
NUMBER2	-0.872499786308419	2.165438	-0.4029	0.689686	0.344843
SENS	-0.844953325355799	1.053468	-0.8021	0.42843	0.214215
TIME	0.0495519672711723	0.170535	0.2906	0.773257	0.386629
T20BOLT	0.0956233074188925	0.214047	0.4467	0.658072	0.329036

\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) & 45.136512850162 & 16.173554 & 2.7908 & 0.008791 & 0.004396 \tabularnewline
SPEED1 & -1.61042016498947 & 1.534615 & -1.0494 & 0.301857 & 0.150928 \tabularnewline
TOTAL & -0.468106132737128 & 0.394753 & -1.1858 & 0.244424 & 0.122212 \tabularnewline
SPEED2 & -3.12177834831424 & 4.211655 & -0.7412 & 0.463963 & 0.231982 \tabularnewline
NUMBER2 & -0.872499786308419 & 2.165438 & -0.4029 & 0.689686 & 0.344843 \tabularnewline
SENS & -0.844953325355799 & 1.053468 & -0.8021 & 0.42843 & 0.214215 \tabularnewline
TIME & 0.0495519672711723 & 0.170535 & 0.2906 & 0.773257 & 0.386629 \tabularnewline
T20BOLT & 0.0956233074188925 & 0.214047 & 0.4467 & 0.658072 & 0.329036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199720&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]45.136512850162[/C][C]16.173554[/C][C]2.7908[/C][C]0.008791[/C][C]0.004396[/C][/ROW]
[ROW][C]SPEED1[/C][C]-1.61042016498947[/C][C]1.534615[/C][C]-1.0494[/C][C]0.301857[/C][C]0.150928[/C][/ROW]
[ROW][C]TOTAL[/C][C]-0.468106132737128[/C][C]0.394753[/C][C]-1.1858[/C][C]0.244424[/C][C]0.122212[/C][/ROW]
[ROW][C]SPEED2[/C][C]-3.12177834831424[/C][C]4.211655[/C][C]-0.7412[/C][C]0.463963[/C][C]0.231982[/C][/ROW]
[ROW][C]NUMBER2[/C][C]-0.872499786308419[/C][C]2.165438[/C][C]-0.4029[/C][C]0.689686[/C][C]0.344843[/C][/ROW]
[ROW][C]SENS[/C][C]-0.844953325355799[/C][C]1.053468[/C][C]-0.8021[/C][C]0.42843[/C][C]0.214215[/C][/ROW]
[ROW][C]TIME[/C][C]0.0495519672711723[/C][C]0.170535[/C][C]0.2906[/C][C]0.773257[/C][C]0.386629[/C][/ROW]
[ROW][C]T20BOLT[/C][C]0.0956233074188925[/C][C]0.214047[/C][C]0.4467[/C][C]0.658072[/C][C]0.329036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199720&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199720&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)	45.136512850162	16.173554	2.7908	0.008791	0.004396
SPEED1	-1.61042016498947	1.534615	-1.0494	0.301857	0.150928
TOTAL	-0.468106132737128	0.394753	-1.1858	0.244424	0.122212
SPEED2	-3.12177834831424	4.211655	-0.7412	0.463963	0.231982
NUMBER2	-0.872499786308419	2.165438	-0.4029	0.689686	0.344843
SENS	-0.844953325355799	1.053468	-0.8021	0.42843	0.214215
TIME	0.0495519672711723	0.170535	0.2906	0.773257	0.386629
T20BOLT	0.0956233074188925	0.214047	0.4467	0.658072	0.329036

Multiple Linear Regression - Regression Statistics
Multiple R	0.387503800557626
R-squared	0.150159195446605
Adjusted R-squared	-0.0357434805494505
F-TEST (value)	0.807730145045309
F-TEST (DF numerator)	7
F-TEST (DF denominator)	32
p-value	0.587271581998802
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	11.8975463440335
Sum Squared Residuals	4529.6514882696

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.387503800557626 \tabularnewline
R-squared & 0.150159195446605 \tabularnewline
Adjusted R-squared & -0.0357434805494505 \tabularnewline
F-TEST (value) & 0.807730145045309 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 32 \tabularnewline
p-value & 0.587271581998802 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.8975463440335 \tabularnewline
Sum Squared Residuals & 4529.6514882696 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199720&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.387503800557626[/C][/ROW]
[ROW][C]R-squared[/C][C]0.150159195446605[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0357434805494505[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.807730145045309[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]32[/C][/ROW]
[ROW][C]p-value[/C][C]0.587271581998802[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]11.8975463440335[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4529.6514882696[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199720&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199720&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	0.387503800557626
R-squared	0.150159195446605
Adjusted R-squared	-0.0357434805494505
F-TEST (value)	0.807730145045309
F-TEST (DF numerator)	7
F-TEST (DF denominator)	32
p-value	0.587271581998802
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	11.8975463440335
Sum Squared Residuals	4529.6514882696

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	25	28.8547756362267	-3.85477563622669
2	24	28.3308335186157	-4.33083351861573
3	30	28.4534321517778	1.54656784822216
4	2	24.3777109480598	-22.3777109480598
5	40	28.3745577336477	11.6254422663523
6	37	23.87984699706	13.1201530029399
7	16	24.8356087243191	-8.83560872431915
8	22	20.1843789093607	1.81562109063934
9	33	21.1811709857444	11.8188290142556
10	17	16.9448033423599	0.055196657640077
11	28	18.4756990437513	9.52430095624872
12	27	15.9068010043778	11.0931989956222
13	14	17.1533046707181	-3.15330467071809
14	13	14.1556339406912	-1.15563394069125
15	4	15.9555481499098	-11.9555481499098
16	21	13.2861311082879	7.71386889171213
17	23	24.27206003015	-1.27206003014995
18	35	25.9104891798774	9.08951082012257
19	19	28.8648791386409	-9.86487913864087
20	34	18.4681951545627	15.5318048454373
21	31	24.8754357470613	6.12456425293871
22	9	23.1137889174103	-14.1137889174103
23	38	20.3155007495907	17.6844992504093
24	15	14.2387433285472	0.761256671452779
25	39	26.3452139970089	12.6547860029911
26	8	20.465169925779	-12.465169925779
27	26	22.5845925006498	3.4154074993502
28	11	18.9144103073261	-7.91441030732609
29	6	19.9351265599296	-13.9351265599296
30	20	17.5580256826278	2.44197431737223
31	10	18.8733310457819	-8.8733310457819
32	32	18.6152745999646	13.3847254000354
33	1	17.0220159108372	-16.0220159108372
34	3	16.820222279018	-13.820222279018
35	5	16.8855511526285	-11.8855511526285
36	7	17.4807697788578	-10.4807697788578
37	12	17.2470375866068	-5.24703758660677
38	18	16.5197094604095	1.48029053959045
39	29	16.6053628724767	12.3946371275233
40	36	17.7188572293495	18.2811427706505

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 25 & 28.8547756362267 & -3.85477563622669 \tabularnewline
2 & 24 & 28.3308335186157 & -4.33083351861573 \tabularnewline
3 & 30 & 28.4534321517778 & 1.54656784822216 \tabularnewline
4 & 2 & 24.3777109480598 & -22.3777109480598 \tabularnewline
5 & 40 & 28.3745577336477 & 11.6254422663523 \tabularnewline
6 & 37 & 23.87984699706 & 13.1201530029399 \tabularnewline
7 & 16 & 24.8356087243191 & -8.83560872431915 \tabularnewline
8 & 22 & 20.1843789093607 & 1.81562109063934 \tabularnewline
9 & 33 & 21.1811709857444 & 11.8188290142556 \tabularnewline
10 & 17 & 16.9448033423599 & 0.055196657640077 \tabularnewline
11 & 28 & 18.4756990437513 & 9.52430095624872 \tabularnewline
12 & 27 & 15.9068010043778 & 11.0931989956222 \tabularnewline
13 & 14 & 17.1533046707181 & -3.15330467071809 \tabularnewline
14 & 13 & 14.1556339406912 & -1.15563394069125 \tabularnewline
15 & 4 & 15.9555481499098 & -11.9555481499098 \tabularnewline
16 & 21 & 13.2861311082879 & 7.71386889171213 \tabularnewline
17 & 23 & 24.27206003015 & -1.27206003014995 \tabularnewline
18 & 35 & 25.9104891798774 & 9.08951082012257 \tabularnewline
19 & 19 & 28.8648791386409 & -9.86487913864087 \tabularnewline
20 & 34 & 18.4681951545627 & 15.5318048454373 \tabularnewline
21 & 31 & 24.8754357470613 & 6.12456425293871 \tabularnewline
22 & 9 & 23.1137889174103 & -14.1137889174103 \tabularnewline
23 & 38 & 20.3155007495907 & 17.6844992504093 \tabularnewline
24 & 15 & 14.2387433285472 & 0.761256671452779 \tabularnewline
25 & 39 & 26.3452139970089 & 12.6547860029911 \tabularnewline
26 & 8 & 20.465169925779 & -12.465169925779 \tabularnewline
27 & 26 & 22.5845925006498 & 3.4154074993502 \tabularnewline
28 & 11 & 18.9144103073261 & -7.91441030732609 \tabularnewline
29 & 6 & 19.9351265599296 & -13.9351265599296 \tabularnewline
30 & 20 & 17.5580256826278 & 2.44197431737223 \tabularnewline
31 & 10 & 18.8733310457819 & -8.8733310457819 \tabularnewline
32 & 32 & 18.6152745999646 & 13.3847254000354 \tabularnewline
33 & 1 & 17.0220159108372 & -16.0220159108372 \tabularnewline
34 & 3 & 16.820222279018 & -13.820222279018 \tabularnewline
35 & 5 & 16.8855511526285 & -11.8855511526285 \tabularnewline
36 & 7 & 17.4807697788578 & -10.4807697788578 \tabularnewline
37 & 12 & 17.2470375866068 & -5.24703758660677 \tabularnewline
38 & 18 & 16.5197094604095 & 1.48029053959045 \tabularnewline
39 & 29 & 16.6053628724767 & 12.3946371275233 \tabularnewline
40 & 36 & 17.7188572293495 & 18.2811427706505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199720&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]25[/C][C]28.8547756362267[/C][C]-3.85477563622669[/C][/ROW]
[ROW][C]2[/C][C]24[/C][C]28.3308335186157[/C][C]-4.33083351861573[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]28.4534321517778[/C][C]1.54656784822216[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]24.3777109480598[/C][C]-22.3777109480598[/C][/ROW]
[ROW][C]5[/C][C]40[/C][C]28.3745577336477[/C][C]11.6254422663523[/C][/ROW]
[ROW][C]6[/C][C]37[/C][C]23.87984699706[/C][C]13.1201530029399[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]24.8356087243191[/C][C]-8.83560872431915[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.1843789093607[/C][C]1.81562109063934[/C][/ROW]
[ROW][C]9[/C][C]33[/C][C]21.1811709857444[/C][C]11.8188290142556[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]16.9448033423599[/C][C]0.055196657640077[/C][/ROW]
[ROW][C]11[/C][C]28[/C][C]18.4756990437513[/C][C]9.52430095624872[/C][/ROW]
[ROW][C]12[/C][C]27[/C][C]15.9068010043778[/C][C]11.0931989956222[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]17.1533046707181[/C][C]-3.15330467071809[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]14.1556339406912[/C][C]-1.15563394069125[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]15.9555481499098[/C][C]-11.9555481499098[/C][/ROW]
[ROW][C]16[/C][C]21[/C][C]13.2861311082879[/C][C]7.71386889171213[/C][/ROW]
[ROW][C]17[/C][C]23[/C][C]24.27206003015[/C][C]-1.27206003014995[/C][/ROW]
[ROW][C]18[/C][C]35[/C][C]25.9104891798774[/C][C]9.08951082012257[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]28.8648791386409[/C][C]-9.86487913864087[/C][/ROW]
[ROW][C]20[/C][C]34[/C][C]18.4681951545627[/C][C]15.5318048454373[/C][/ROW]
[ROW][C]21[/C][C]31[/C][C]24.8754357470613[/C][C]6.12456425293871[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]23.1137889174103[/C][C]-14.1137889174103[/C][/ROW]
[ROW][C]23[/C][C]38[/C][C]20.3155007495907[/C][C]17.6844992504093[/C][/ROW]
[ROW][C]24[/C][C]15[/C][C]14.2387433285472[/C][C]0.761256671452779[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]26.3452139970089[/C][C]12.6547860029911[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]20.465169925779[/C][C]-12.465169925779[/C][/ROW]
[ROW][C]27[/C][C]26[/C][C]22.5845925006498[/C][C]3.4154074993502[/C][/ROW]
[ROW][C]28[/C][C]11[/C][C]18.9144103073261[/C][C]-7.91441030732609[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]19.9351265599296[/C][C]-13.9351265599296[/C][/ROW]
[ROW][C]30[/C][C]20[/C][C]17.5580256826278[/C][C]2.44197431737223[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]18.8733310457819[/C][C]-8.8733310457819[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]18.6152745999646[/C][C]13.3847254000354[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]17.0220159108372[/C][C]-16.0220159108372[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]16.820222279018[/C][C]-13.820222279018[/C][/ROW]
[ROW][C]35[/C][C]5[/C][C]16.8855511526285[/C][C]-11.8855511526285[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]17.4807697788578[/C][C]-10.4807697788578[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]17.2470375866068[/C][C]-5.24703758660677[/C][/ROW]
[ROW][C]38[/C][C]18[/C][C]16.5197094604095[/C][C]1.48029053959045[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]16.6053628724767[/C][C]12.3946371275233[/C][/ROW]
[ROW][C]40[/C][C]36[/C][C]17.7188572293495[/C][C]18.2811427706505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199720&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199720&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	25	28.8547756362267	-3.85477563622669
2	24	28.3308335186157	-4.33083351861573
3	30	28.4534321517778	1.54656784822216
4	2	24.3777109480598	-22.3777109480598
5	40	28.3745577336477	11.6254422663523
6	37	23.87984699706	13.1201530029399
7	16	24.8356087243191	-8.83560872431915
8	22	20.1843789093607	1.81562109063934
9	33	21.1811709857444	11.8188290142556
10	17	16.9448033423599	0.055196657640077
11	28	18.4756990437513	9.52430095624872
12	27	15.9068010043778	11.0931989956222
13	14	17.1533046707181	-3.15330467071809
14	13	14.1556339406912	-1.15563394069125
15	4	15.9555481499098	-11.9555481499098
16	21	13.2861311082879	7.71386889171213
17	23	24.27206003015	-1.27206003014995
18	35	25.9104891798774	9.08951082012257
19	19	28.8648791386409	-9.86487913864087
20	34	18.4681951545627	15.5318048454373
21	31	24.8754357470613	6.12456425293871
22	9	23.1137889174103	-14.1137889174103
23	38	20.3155007495907	17.6844992504093
24	15	14.2387433285472	0.761256671452779
25	39	26.3452139970089	12.6547860029911
26	8	20.465169925779	-12.465169925779
27	26	22.5845925006498	3.4154074993502
28	11	18.9144103073261	-7.91441030732609
29	6	19.9351265599296	-13.9351265599296
30	20	17.5580256826278	2.44197431737223
31	10	18.8733310457819	-8.8733310457819
32	32	18.6152745999646	13.3847254000354
33	1	17.0220159108372	-16.0220159108372
34	3	16.820222279018	-13.820222279018
35	5	16.8855511526285	-11.8855511526285
36	7	17.4807697788578	-10.4807697788578
37	12	17.2470375866068	-5.24703758660677
38	18	16.5197094604095	1.48029053959045
39	29	16.6053628724767	12.3946371275233
40	36	17.7188572293495	18.2811427706505

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.727637757049513	0.544724485900974	0.272362242950487
12	0.651902047908288	0.696195904183425	0.348097952091712
13	0.728284546511304	0.543430906977392	0.271715453488696
14	0.674198149405137	0.651603701189726	0.325801850594863
15	0.740584951354128	0.518830097291743	0.259415048645872
16	0.653059012699293	0.693881974601414	0.346940987300707
17	0.591398646045767	0.817202707908466	0.408601353954233
18	0.544558063313144	0.910883873373711	0.455441936686856
19	0.500592956220429	0.998814087559141	0.499407043779571
20	0.531876222462164	0.936247555075671	0.468123777537836
21	0.42340486621388	0.846809732427761	0.57659513378612
22	0.503582116901811	0.992835766196379	0.496417883098189
23	0.455805505213517	0.911611010427034	0.544194494786483
24	0.353754920213109	0.707509840426218	0.646245079786891
25	0.266560295669875	0.533120591339751	0.733439704330125
26	0.265760045214128	0.531520090428257	0.734239954785872
27	0.190126858870233	0.380253717740466	0.809873141129767
28	0.11098259370598	0.221965187411959	0.88901740629402
29	0.0677210513524301	0.13544210270486	0.93227894864757

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.727637757049513 & 0.544724485900974 & 0.272362242950487 \tabularnewline
12 & 0.651902047908288 & 0.696195904183425 & 0.348097952091712 \tabularnewline
13 & 0.728284546511304 & 0.543430906977392 & 0.271715453488696 \tabularnewline
14 & 0.674198149405137 & 0.651603701189726 & 0.325801850594863 \tabularnewline
15 & 0.740584951354128 & 0.518830097291743 & 0.259415048645872 \tabularnewline
16 & 0.653059012699293 & 0.693881974601414 & 0.346940987300707 \tabularnewline
17 & 0.591398646045767 & 0.817202707908466 & 0.408601353954233 \tabularnewline
18 & 0.544558063313144 & 0.910883873373711 & 0.455441936686856 \tabularnewline
19 & 0.500592956220429 & 0.998814087559141 & 0.499407043779571 \tabularnewline
20 & 0.531876222462164 & 0.936247555075671 & 0.468123777537836 \tabularnewline
21 & 0.42340486621388 & 0.846809732427761 & 0.57659513378612 \tabularnewline
22 & 0.503582116901811 & 0.992835766196379 & 0.496417883098189 \tabularnewline
23 & 0.455805505213517 & 0.911611010427034 & 0.544194494786483 \tabularnewline
24 & 0.353754920213109 & 0.707509840426218 & 0.646245079786891 \tabularnewline
25 & 0.266560295669875 & 0.533120591339751 & 0.733439704330125 \tabularnewline
26 & 0.265760045214128 & 0.531520090428257 & 0.734239954785872 \tabularnewline
27 & 0.190126858870233 & 0.380253717740466 & 0.809873141129767 \tabularnewline
28 & 0.11098259370598 & 0.221965187411959 & 0.88901740629402 \tabularnewline
29 & 0.0677210513524301 & 0.13544210270486 & 0.93227894864757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199720&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.727637757049513[/C][C]0.544724485900974[/C][C]0.272362242950487[/C][/ROW]
[ROW][C]12[/C][C]0.651902047908288[/C][C]0.696195904183425[/C][C]0.348097952091712[/C][/ROW]
[ROW][C]13[/C][C]0.728284546511304[/C][C]0.543430906977392[/C][C]0.271715453488696[/C][/ROW]
[ROW][C]14[/C][C]0.674198149405137[/C][C]0.651603701189726[/C][C]0.325801850594863[/C][/ROW]
[ROW][C]15[/C][C]0.740584951354128[/C][C]0.518830097291743[/C][C]0.259415048645872[/C][/ROW]
[ROW][C]16[/C][C]0.653059012699293[/C][C]0.693881974601414[/C][C]0.346940987300707[/C][/ROW]
[ROW][C]17[/C][C]0.591398646045767[/C][C]0.817202707908466[/C][C]0.408601353954233[/C][/ROW]
[ROW][C]18[/C][C]0.544558063313144[/C][C]0.910883873373711[/C][C]0.455441936686856[/C][/ROW]
[ROW][C]19[/C][C]0.500592956220429[/C][C]0.998814087559141[/C][C]0.499407043779571[/C][/ROW]
[ROW][C]20[/C][C]0.531876222462164[/C][C]0.936247555075671[/C][C]0.468123777537836[/C][/ROW]
[ROW][C]21[/C][C]0.42340486621388[/C][C]0.846809732427761[/C][C]0.57659513378612[/C][/ROW]
[ROW][C]22[/C][C]0.503582116901811[/C][C]0.992835766196379[/C][C]0.496417883098189[/C][/ROW]
[ROW][C]23[/C][C]0.455805505213517[/C][C]0.911611010427034[/C][C]0.544194494786483[/C][/ROW]
[ROW][C]24[/C][C]0.353754920213109[/C][C]0.707509840426218[/C][C]0.646245079786891[/C][/ROW]
[ROW][C]25[/C][C]0.266560295669875[/C][C]0.533120591339751[/C][C]0.733439704330125[/C][/ROW]
[ROW][C]26[/C][C]0.265760045214128[/C][C]0.531520090428257[/C][C]0.734239954785872[/C][/ROW]
[ROW][C]27[/C][C]0.190126858870233[/C][C]0.380253717740466[/C][C]0.809873141129767[/C][/ROW]
[ROW][C]28[/C][C]0.11098259370598[/C][C]0.221965187411959[/C][C]0.88901740629402[/C][/ROW]
[ROW][C]29[/C][C]0.0677210513524301[/C][C]0.13544210270486[/C][C]0.93227894864757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199720&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199720&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.727637757049513	0.544724485900974	0.272362242950487
12	0.651902047908288	0.696195904183425	0.348097952091712
13	0.728284546511304	0.543430906977392	0.271715453488696
14	0.674198149405137	0.651603701189726	0.325801850594863
15	0.740584951354128	0.518830097291743	0.259415048645872
16	0.653059012699293	0.693881974601414	0.346940987300707
17	0.591398646045767	0.817202707908466	0.408601353954233
18	0.544558063313144	0.910883873373711	0.455441936686856
19	0.500592956220429	0.998814087559141	0.499407043779571
20	0.531876222462164	0.936247555075671	0.468123777537836
21	0.42340486621388	0.846809732427761	0.57659513378612
22	0.503582116901811	0.992835766196379	0.496417883098189
23	0.455805505213517	0.911611010427034	0.544194494786483
24	0.353754920213109	0.707509840426218	0.646245079786891
25	0.266560295669875	0.533120591339751	0.733439704330125
26	0.265760045214128	0.531520090428257	0.734239954785872
27	0.190126858870233	0.380253717740466	0.809873141129767
28	0.11098259370598	0.221965187411959	0.88901740629402
29	0.0677210513524301	0.13544210270486	0.93227894864757

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=199720&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=199720&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199720&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 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code