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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 08 Dec 2015 10:25:59 +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/2015/Dec/08/t1449570437vo1cvmdczvk1buz.htm/, Retrieved Thu, 16 May 2024 08:57:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285454, Retrieved Thu, 16 May 2024 08:57:23 +0000

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140

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-       [Multiple Regression] [Multiple Regressi...] [2015-12-08 10:25:59] [de1999c3654db5e2fc07ea44f4628001] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285454&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285454&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285454&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	4 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
overall_crime[t] = + 397.706 -7.22207not_in_school[t] -0.0405206`overall_crime(t-1)`[t] + 0.0702034`overall_crime(t-2)`[t] + 0.566782`overall_crime(t-3)`[t] -0.318956`overall_crime(t-4)`[t] -0.277972`overall_crime(t-5)`[t] -0.312986`overall_crime(t-6)`[t] + 0.427323`overall_crime(t-7)`[t] + 17.5157t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
overall_crime[t] =  +  397.706 -7.22207not_in_school[t] -0.0405206`overall_crime(t-1)`[t] +  0.0702034`overall_crime(t-2)`[t] +  0.566782`overall_crime(t-3)`[t] -0.318956`overall_crime(t-4)`[t] -0.277972`overall_crime(t-5)`[t] -0.312986`overall_crime(t-6)`[t] +  0.427323`overall_crime(t-7)`[t] +  17.5157t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285454&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]overall_crime[t] =  +  397.706 -7.22207not_in_school[t] -0.0405206`overall_crime(t-1)`[t] +  0.0702034`overall_crime(t-2)`[t] +  0.566782`overall_crime(t-3)`[t] -0.318956`overall_crime(t-4)`[t] -0.277972`overall_crime(t-5)`[t] -0.312986`overall_crime(t-6)`[t] +  0.427323`overall_crime(t-7)`[t] +  17.5157t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285454&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285454&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
overall_crime[t] = + 397.706 -7.22207not_in_school[t] -0.0405206`overall_crime(t-1)`[t] + 0.0702034`overall_crime(t-2)`[t] + 0.566782`overall_crime(t-3)`[t] -0.318956`overall_crime(t-4)`[t] -0.277972`overall_crime(t-5)`[t] -0.312986`overall_crime(t-6)`[t] + 0.427323`overall_crime(t-7)`[t] + 17.5157t + 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)	+397.7	180.4	+2.2040e+00	0.03458	0.01729
not_in_school	-7.222	7.226	-9.9950e-01	0.3248	0.1624
`overall_crime(t-1)`	-0.04052	0.1602	-2.5290e-01	0.8019	0.401
`overall_crime(t-2)`	+0.0702	0.1494	+4.7000e-01	0.6414	0.3207
`overall_crime(t-3)`	+0.5668	0.1554	+3.6470e+00	0.0009049	0.0004524
`overall_crime(t-4)`	-0.319	0.1767	-1.8050e+00	0.08025	0.04012
`overall_crime(t-5)`	-0.278	0.1527	-1.8200e+00	0.07777	0.03888
`overall_crime(t-6)`	-0.313	0.1654	-1.8920e+00	0.06725	0.03363
`overall_crime(t-7)`	+0.4273	0.1806	+2.3660e+00	0.02398	0.01199
t	+17.52	6.431	+2.7230e+00	0.01025	0.005123

\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) & +397.7 &  180.4 & +2.2040e+00 &  0.03458 &  0.01729 \tabularnewline
not_in_school & -7.222 &  7.226 & -9.9950e-01 &  0.3248 &  0.1624 \tabularnewline
`overall_crime(t-1)` & -0.04052 &  0.1602 & -2.5290e-01 &  0.8019 &  0.401 \tabularnewline
`overall_crime(t-2)` & +0.0702 &  0.1494 & +4.7000e-01 &  0.6414 &  0.3207 \tabularnewline
`overall_crime(t-3)` & +0.5668 &  0.1554 & +3.6470e+00 &  0.0009049 &  0.0004524 \tabularnewline
`overall_crime(t-4)` & -0.319 &  0.1767 & -1.8050e+00 &  0.08025 &  0.04012 \tabularnewline
`overall_crime(t-5)` & -0.278 &  0.1527 & -1.8200e+00 &  0.07777 &  0.03888 \tabularnewline
`overall_crime(t-6)` & -0.313 &  0.1654 & -1.8920e+00 &  0.06725 &  0.03363 \tabularnewline
`overall_crime(t-7)` & +0.4273 &  0.1806 & +2.3660e+00 &  0.02398 &  0.01199 \tabularnewline
t & +17.52 &  6.431 & +2.7230e+00 &  0.01025 &  0.005123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285454&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]+397.7[/C][C] 180.4[/C][C]+2.2040e+00[/C][C] 0.03458[/C][C] 0.01729[/C][/ROW]
[ROW][C]not_in_school[/C][C]-7.222[/C][C] 7.226[/C][C]-9.9950e-01[/C][C] 0.3248[/C][C] 0.1624[/C][/ROW]
[ROW][C]`overall_crime(t-1)`[/C][C]-0.04052[/C][C] 0.1602[/C][C]-2.5290e-01[/C][C] 0.8019[/C][C] 0.401[/C][/ROW]
[ROW][C]`overall_crime(t-2)`[/C][C]+0.0702[/C][C] 0.1494[/C][C]+4.7000e-01[/C][C] 0.6414[/C][C] 0.3207[/C][/ROW]
[ROW][C]`overall_crime(t-3)`[/C][C]+0.5668[/C][C] 0.1554[/C][C]+3.6470e+00[/C][C] 0.0009049[/C][C] 0.0004524[/C][/ROW]
[ROW][C]`overall_crime(t-4)`[/C][C]-0.319[/C][C] 0.1767[/C][C]-1.8050e+00[/C][C] 0.08025[/C][C] 0.04012[/C][/ROW]
[ROW][C]`overall_crime(t-5)`[/C][C]-0.278[/C][C] 0.1527[/C][C]-1.8200e+00[/C][C] 0.07777[/C][C] 0.03888[/C][/ROW]
[ROW][C]`overall_crime(t-6)`[/C][C]-0.313[/C][C] 0.1654[/C][C]-1.8920e+00[/C][C] 0.06725[/C][C] 0.03363[/C][/ROW]
[ROW][C]`overall_crime(t-7)`[/C][C]+0.4273[/C][C] 0.1806[/C][C]+2.3660e+00[/C][C] 0.02398[/C][C] 0.01199[/C][/ROW]
[ROW][C]t[/C][C]+17.52[/C][C] 6.431[/C][C]+2.7230e+00[/C][C] 0.01025[/C][C] 0.005123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285454&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285454&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)	+397.7	180.4	+2.2040e+00	0.03458	0.01729
not_in_school	-7.222	7.226	-9.9950e-01	0.3248	0.1624
`overall_crime(t-1)`	-0.04052	0.1602	-2.5290e-01	0.8019	0.401
`overall_crime(t-2)`	+0.0702	0.1494	+4.7000e-01	0.6414	0.3207
`overall_crime(t-3)`	+0.5668	0.1554	+3.6470e+00	0.0009049	0.0004524
`overall_crime(t-4)`	-0.319	0.1767	-1.8050e+00	0.08025	0.04012
`overall_crime(t-5)`	-0.278	0.1527	-1.8200e+00	0.07777	0.03888
`overall_crime(t-6)`	-0.313	0.1654	-1.8920e+00	0.06725	0.03363
`overall_crime(t-7)`	+0.4273	0.1806	+2.3660e+00	0.02398	0.01199
t	+17.52	6.431	+2.7230e+00	0.01025	0.005123

Multiple Linear Regression - Regression Statistics
Multiple R	0.8324
R-squared	0.6928
Adjusted R-squared	0.609
F-TEST (value)	8.27
F-TEST (DF numerator)	9
F-TEST (DF denominator)	33
p-value	2.577e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	189.9
Sum Squared Residuals	1.19e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8324 \tabularnewline
R-squared &  0.6928 \tabularnewline
Adjusted R-squared &  0.609 \tabularnewline
F-TEST (value) &  8.27 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 33 \tabularnewline
p-value &  2.577e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  189.9 \tabularnewline
Sum Squared Residuals &  1.19e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285454&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8324[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6928[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.609[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 8.27[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]33[/C][/ROW]
[ROW][C]p-value[/C][C] 2.577e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 189.9[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.19e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285454&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285454&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	0.8324
R-squared	0.6928
Adjusted R-squared	0.609
F-TEST (value)	8.27
F-TEST (DF numerator)	9
F-TEST (DF denominator)	33
p-value	2.577e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	189.9
Sum Squared Residuals	1.19e+06

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	546	544.3	1.677
2	424	404.3	19.69
3	548	441.6	106.4
4	506	272.7	233.3
5	819	547.6	271.4
6	541	569.9	-28.92
7	491	506.5	-15.53
8	514	720.3	-206.3
9	371	411.2	-40.19
10	457	461.1	-4.128
11	437	491.6	-54.58
12	570	604.4	-34.44
13	432	600	-168
14	619	554.3	64.71
15	357	724.8	-367.8
16	623	519.6	103.4
17	547	620.6	-73.63
18	792	541.1	250.9
19	799	847.4	-48.35
20	439	637	-198
21	867	935.1	-68.09
22	912	632.8	279.2
23	462	563.3	-101.3
24	859	841.3	17.69
25	805	938.8	-133.8
26	652	677.6	-25.57
27	776	765.7	10.26
28	919	925.8	-6.82
29	732	948.1	-216.1
30	657	800.8	-143.8
31	1419	1072	347.4
32	989	927.8	61.18
33	821	860.6	-39.56
34	1740	1306	434.3
35	815	964	-149
36	760	734.5	25.54
37	936	1170	-234.5
38	863	888.2	-25.18
39	783	798.2	-15.25
40	715	865	-150
41	1504	1501	2.694
42	1324	982.5	341.5
43	940	962.5	-22.47

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  546 &  544.3 &  1.677 \tabularnewline
2 &  424 &  404.3 &  19.69 \tabularnewline
3 &  548 &  441.6 &  106.4 \tabularnewline
4 &  506 &  272.7 &  233.3 \tabularnewline
5 &  819 &  547.6 &  271.4 \tabularnewline
6 &  541 &  569.9 & -28.92 \tabularnewline
7 &  491 &  506.5 & -15.53 \tabularnewline
8 &  514 &  720.3 & -206.3 \tabularnewline
9 &  371 &  411.2 & -40.19 \tabularnewline
10 &  457 &  461.1 & -4.128 \tabularnewline
11 &  437 &  491.6 & -54.58 \tabularnewline
12 &  570 &  604.4 & -34.44 \tabularnewline
13 &  432 &  600 & -168 \tabularnewline
14 &  619 &  554.3 &  64.71 \tabularnewline
15 &  357 &  724.8 & -367.8 \tabularnewline
16 &  623 &  519.6 &  103.4 \tabularnewline
17 &  547 &  620.6 & -73.63 \tabularnewline
18 &  792 &  541.1 &  250.9 \tabularnewline
19 &  799 &  847.4 & -48.35 \tabularnewline
20 &  439 &  637 & -198 \tabularnewline
21 &  867 &  935.1 & -68.09 \tabularnewline
22 &  912 &  632.8 &  279.2 \tabularnewline
23 &  462 &  563.3 & -101.3 \tabularnewline
24 &  859 &  841.3 &  17.69 \tabularnewline
25 &  805 &  938.8 & -133.8 \tabularnewline
26 &  652 &  677.6 & -25.57 \tabularnewline
27 &  776 &  765.7 &  10.26 \tabularnewline
28 &  919 &  925.8 & -6.82 \tabularnewline
29 &  732 &  948.1 & -216.1 \tabularnewline
30 &  657 &  800.8 & -143.8 \tabularnewline
31 &  1419 &  1072 &  347.4 \tabularnewline
32 &  989 &  927.8 &  61.18 \tabularnewline
33 &  821 &  860.6 & -39.56 \tabularnewline
34 &  1740 &  1306 &  434.3 \tabularnewline
35 &  815 &  964 & -149 \tabularnewline
36 &  760 &  734.5 &  25.54 \tabularnewline
37 &  936 &  1170 & -234.5 \tabularnewline
38 &  863 &  888.2 & -25.18 \tabularnewline
39 &  783 &  798.2 & -15.25 \tabularnewline
40 &  715 &  865 & -150 \tabularnewline
41 &  1504 &  1501 &  2.694 \tabularnewline
42 &  1324 &  982.5 &  341.5 \tabularnewline
43 &  940 &  962.5 & -22.47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285454&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] 546[/C][C] 544.3[/C][C] 1.677[/C][/ROW]
[ROW][C]2[/C][C] 424[/C][C] 404.3[/C][C] 19.69[/C][/ROW]
[ROW][C]3[/C][C] 548[/C][C] 441.6[/C][C] 106.4[/C][/ROW]
[ROW][C]4[/C][C] 506[/C][C] 272.7[/C][C] 233.3[/C][/ROW]
[ROW][C]5[/C][C] 819[/C][C] 547.6[/C][C] 271.4[/C][/ROW]
[ROW][C]6[/C][C] 541[/C][C] 569.9[/C][C]-28.92[/C][/ROW]
[ROW][C]7[/C][C] 491[/C][C] 506.5[/C][C]-15.53[/C][/ROW]
[ROW][C]8[/C][C] 514[/C][C] 720.3[/C][C]-206.3[/C][/ROW]
[ROW][C]9[/C][C] 371[/C][C] 411.2[/C][C]-40.19[/C][/ROW]
[ROW][C]10[/C][C] 457[/C][C] 461.1[/C][C]-4.128[/C][/ROW]
[ROW][C]11[/C][C] 437[/C][C] 491.6[/C][C]-54.58[/C][/ROW]
[ROW][C]12[/C][C] 570[/C][C] 604.4[/C][C]-34.44[/C][/ROW]
[ROW][C]13[/C][C] 432[/C][C] 600[/C][C]-168[/C][/ROW]
[ROW][C]14[/C][C] 619[/C][C] 554.3[/C][C] 64.71[/C][/ROW]
[ROW][C]15[/C][C] 357[/C][C] 724.8[/C][C]-367.8[/C][/ROW]
[ROW][C]16[/C][C] 623[/C][C] 519.6[/C][C] 103.4[/C][/ROW]
[ROW][C]17[/C][C] 547[/C][C] 620.6[/C][C]-73.63[/C][/ROW]
[ROW][C]18[/C][C] 792[/C][C] 541.1[/C][C] 250.9[/C][/ROW]
[ROW][C]19[/C][C] 799[/C][C] 847.4[/C][C]-48.35[/C][/ROW]
[ROW][C]20[/C][C] 439[/C][C] 637[/C][C]-198[/C][/ROW]
[ROW][C]21[/C][C] 867[/C][C] 935.1[/C][C]-68.09[/C][/ROW]
[ROW][C]22[/C][C] 912[/C][C] 632.8[/C][C] 279.2[/C][/ROW]
[ROW][C]23[/C][C] 462[/C][C] 563.3[/C][C]-101.3[/C][/ROW]
[ROW][C]24[/C][C] 859[/C][C] 841.3[/C][C] 17.69[/C][/ROW]
[ROW][C]25[/C][C] 805[/C][C] 938.8[/C][C]-133.8[/C][/ROW]
[ROW][C]26[/C][C] 652[/C][C] 677.6[/C][C]-25.57[/C][/ROW]
[ROW][C]27[/C][C] 776[/C][C] 765.7[/C][C] 10.26[/C][/ROW]
[ROW][C]28[/C][C] 919[/C][C] 925.8[/C][C]-6.82[/C][/ROW]
[ROW][C]29[/C][C] 732[/C][C] 948.1[/C][C]-216.1[/C][/ROW]
[ROW][C]30[/C][C] 657[/C][C] 800.8[/C][C]-143.8[/C][/ROW]
[ROW][C]31[/C][C] 1419[/C][C] 1072[/C][C] 347.4[/C][/ROW]
[ROW][C]32[/C][C] 989[/C][C] 927.8[/C][C] 61.18[/C][/ROW]
[ROW][C]33[/C][C] 821[/C][C] 860.6[/C][C]-39.56[/C][/ROW]
[ROW][C]34[/C][C] 1740[/C][C] 1306[/C][C] 434.3[/C][/ROW]
[ROW][C]35[/C][C] 815[/C][C] 964[/C][C]-149[/C][/ROW]
[ROW][C]36[/C][C] 760[/C][C] 734.5[/C][C] 25.54[/C][/ROW]
[ROW][C]37[/C][C] 936[/C][C] 1170[/C][C]-234.5[/C][/ROW]
[ROW][C]38[/C][C] 863[/C][C] 888.2[/C][C]-25.18[/C][/ROW]
[ROW][C]39[/C][C] 783[/C][C] 798.2[/C][C]-15.25[/C][/ROW]
[ROW][C]40[/C][C] 715[/C][C] 865[/C][C]-150[/C][/ROW]
[ROW][C]41[/C][C] 1504[/C][C] 1501[/C][C] 2.694[/C][/ROW]
[ROW][C]42[/C][C] 1324[/C][C] 982.5[/C][C] 341.5[/C][/ROW]
[ROW][C]43[/C][C] 940[/C][C] 962.5[/C][C]-22.47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285454&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285454&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	546	544.3	1.677
2	424	404.3	19.69
3	548	441.6	106.4
4	506	272.7	233.3
5	819	547.6	271.4
6	541	569.9	-28.92
7	491	506.5	-15.53
8	514	720.3	-206.3
9	371	411.2	-40.19
10	457	461.1	-4.128
11	437	491.6	-54.58
12	570	604.4	-34.44
13	432	600	-168
14	619	554.3	64.71
15	357	724.8	-367.8
16	623	519.6	103.4
17	547	620.6	-73.63
18	792	541.1	250.9
19	799	847.4	-48.35
20	439	637	-198
21	867	935.1	-68.09
22	912	632.8	279.2
23	462	563.3	-101.3
24	859	841.3	17.69
25	805	938.8	-133.8
26	652	677.6	-25.57
27	776	765.7	10.26
28	919	925.8	-6.82
29	732	948.1	-216.1
30	657	800.8	-143.8
31	1419	1072	347.4
32	989	927.8	61.18
33	821	860.6	-39.56
34	1740	1306	434.3
35	815	964	-149
36	760	734.5	25.54
37	936	1170	-234.5
38	863	888.2	-25.18
39	783	798.2	-15.25
40	715	865	-150
41	1504	1501	2.694
42	1324	982.5	341.5
43	940	962.5	-22.47

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	0.1582	0.3165	0.8418
14	0.1948	0.3896	0.8052
15	0.1293	0.2586	0.8707
16	0.1393	0.2786	0.8607
17	0.1199	0.2397	0.8801
18	0.2635	0.5271	0.7365
19	0.2132	0.4263	0.7868
20	0.2637	0.5275	0.7363
21	0.2721	0.5442	0.7279
22	0.3778	0.7556	0.6222
23	0.3828	0.7656	0.6172
24	0.2772	0.5544	0.7228
25	0.1993	0.3987	0.8007
26	0.1239	0.2477	0.8761
27	0.06972	0.1394	0.9303
28	0.03496	0.06992	0.965
29	0.1388	0.2776	0.8612
30	0.3347	0.6693	0.6653

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 &  0.1582 &  0.3165 &  0.8418 \tabularnewline
14 &  0.1948 &  0.3896 &  0.8052 \tabularnewline
15 &  0.1293 &  0.2586 &  0.8707 \tabularnewline
16 &  0.1393 &  0.2786 &  0.8607 \tabularnewline
17 &  0.1199 &  0.2397 &  0.8801 \tabularnewline
18 &  0.2635 &  0.5271 &  0.7365 \tabularnewline
19 &  0.2132 &  0.4263 &  0.7868 \tabularnewline
20 &  0.2637 &  0.5275 &  0.7363 \tabularnewline
21 &  0.2721 &  0.5442 &  0.7279 \tabularnewline
22 &  0.3778 &  0.7556 &  0.6222 \tabularnewline
23 &  0.3828 &  0.7656 &  0.6172 \tabularnewline
24 &  0.2772 &  0.5544 &  0.7228 \tabularnewline
25 &  0.1993 &  0.3987 &  0.8007 \tabularnewline
26 &  0.1239 &  0.2477 &  0.8761 \tabularnewline
27 &  0.06972 &  0.1394 &  0.9303 \tabularnewline
28 &  0.03496 &  0.06992 &  0.965 \tabularnewline
29 &  0.1388 &  0.2776 &  0.8612 \tabularnewline
30 &  0.3347 &  0.6693 &  0.6653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285454&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] 0.1582[/C][C] 0.3165[/C][C] 0.8418[/C][/ROW]
[ROW][C]14[/C][C] 0.1948[/C][C] 0.3896[/C][C] 0.8052[/C][/ROW]
[ROW][C]15[/C][C] 0.1293[/C][C] 0.2586[/C][C] 0.8707[/C][/ROW]
[ROW][C]16[/C][C] 0.1393[/C][C] 0.2786[/C][C] 0.8607[/C][/ROW]
[ROW][C]17[/C][C] 0.1199[/C][C] 0.2397[/C][C] 0.8801[/C][/ROW]
[ROW][C]18[/C][C] 0.2635[/C][C] 0.5271[/C][C] 0.7365[/C][/ROW]
[ROW][C]19[/C][C] 0.2132[/C][C] 0.4263[/C][C] 0.7868[/C][/ROW]
[ROW][C]20[/C][C] 0.2637[/C][C] 0.5275[/C][C] 0.7363[/C][/ROW]
[ROW][C]21[/C][C] 0.2721[/C][C] 0.5442[/C][C] 0.7279[/C][/ROW]
[ROW][C]22[/C][C] 0.3778[/C][C] 0.7556[/C][C] 0.6222[/C][/ROW]
[ROW][C]23[/C][C] 0.3828[/C][C] 0.7656[/C][C] 0.6172[/C][/ROW]
[ROW][C]24[/C][C] 0.2772[/C][C] 0.5544[/C][C] 0.7228[/C][/ROW]
[ROW][C]25[/C][C] 0.1993[/C][C] 0.3987[/C][C] 0.8007[/C][/ROW]
[ROW][C]26[/C][C] 0.1239[/C][C] 0.2477[/C][C] 0.8761[/C][/ROW]
[ROW][C]27[/C][C] 0.06972[/C][C] 0.1394[/C][C] 0.9303[/C][/ROW]
[ROW][C]28[/C][C] 0.03496[/C][C] 0.06992[/C][C] 0.965[/C][/ROW]
[ROW][C]29[/C][C] 0.1388[/C][C] 0.2776[/C][C] 0.8612[/C][/ROW]
[ROW][C]30[/C][C] 0.3347[/C][C] 0.6693[/C][C] 0.6653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285454&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285454&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	0.1582	0.3165	0.8418
14	0.1948	0.3896	0.8052
15	0.1293	0.2586	0.8707
16	0.1393	0.2786	0.8607
17	0.1199	0.2397	0.8801
18	0.2635	0.5271	0.7365
19	0.2132	0.4263	0.7868
20	0.2637	0.5275	0.7363
21	0.2721	0.5442	0.7279
22	0.3778	0.7556	0.6222
23	0.3828	0.7656	0.6172
24	0.2772	0.5544	0.7228
25	0.1993	0.3987	0.8007
26	0.1239	0.2477	0.8761
27	0.06972	0.1394	0.9303
28	0.03496	0.06992	0.965
29	0.1388	0.2776	0.8612
30	0.3347	0.6693	0.6653

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	1	0.0555556	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 & 1 & 0.0555556 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285454&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]1[/C][C]0.0555556[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285454&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285454&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	1	0.0555556	OK

Figure 1

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Figure 2

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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 7 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 7 ; par5 = ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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