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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 15 Dec 2014 12:47:24 +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/2014/Dec/15/t14186476968z28m4gjil9fc0m.htm/, Retrieved Sun, 19 May 2024 14:56:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268273, Retrieved Sun, 19 May 2024 14:56:52 +0000

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-       [Multiple Regression] [] [2014-12-15 12:47:24] [624214a256768d6065ce8a528542dcc5] [Current]

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12,9	26	50	13	12	13
12,8	37	54	14	11	11
14,8	52	73	15	7	11
12	58	73	17	14	13
6,3	68	75	11	6	14
11,3	62	72	16	12	11
9,3	56	70	15	11	14
10	74	81	14	12	12
10,8	58	71	17	11	14
13,4	51	61	16	12	15
11,5	53	76	17	13	16
8,3	29	70	14	9	12
11,7	54	60	16	11	12
10,4	54	70	8	5	14
11,8	47	76	10	6	16
11,3	68	67	8	6	16
12,7	67	76	8	8	16
5,7	41	75	15	6	11
8	45	63	10	10	12
12,5	56	70	11	10	14
7,6	41	75	9	7	14
9,2	53	60	12	7	15
11,1	66	73	14	12	13
12,2	37	64	15	12	12
12,3	51	59	17	12	15
11,4	51	64	14	12	14
8,8	56	60	11	8	15
12,6	37	78	7	5	13
13	42	67	15	10	12
13,2	66	66	15	11	14
9,9	34	68	14	9	14
10,5	49	66	14	11	11
13,4	55	73	13	10	13
10,9	49	72	16	12	14
10,3	40	59	16	9	13
11,4	63	78	10	7	11
8,6	56	68	12	11	13
13,2	54	73	12	12	13
8,8	32	65	14	10	12
9	67	71	16	12	14
10,3	66	76	16	12	15
8,5	51	63	16	10	12
13,5	55	59	10	9	15
4,9	50	73	14	10	12
6,4	60	66	12	9	13
9,6	56	62	11	10	11
11,6	63	69	15	12	14

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268273&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]5 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=268273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268273&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	5 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT.V[t] = + 8.16621 -0.00704044AMS.I.V[t] -0.0275831AMS.E.V[t] -0.154466CONFSTAT.V[t] + 0.365065CONFSOFT.V[t] + 0.239162STRESS.V[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT.V[t] =  +  8.16621 -0.00704044AMS.I.V[t] -0.0275831AMS.E.V[t] -0.154466CONFSTAT.V[t] +  0.365065CONFSOFT.V[t] +  0.239162STRESS.V[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268273&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT.V[t] =  +  8.16621 -0.00704044AMS.I.V[t] -0.0275831AMS.E.V[t] -0.154466CONFSTAT.V[t] +  0.365065CONFSOFT.V[t] +  0.239162STRESS.V[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268273&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
TOT.V[t] = + 8.16621 -0.00704044AMS.I.V[t] -0.0275831AMS.E.V[t] -0.154466CONFSTAT.V[t] + 0.365065CONFSOFT.V[t] + 0.239162STRESS.V[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)	8.16621	5.2098	1.567	0.124691	0.0623455
AMS.I.V	-0.00704044	0.0348736	-0.2019	0.841005	0.420503
AMS.E.V	-0.0275831	0.0555802	-0.4963	0.622351	0.311175
CONFSTAT.V	-0.154466	0.176237	-0.8765	0.385882	0.192941
CONFSOFT.V	0.365065	0.215015	1.698	0.0971128	0.0485564
STRESS.V	0.239162	0.233918	1.022	0.312579	0.156289

\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) & 8.16621 & 5.2098 & 1.567 & 0.124691 & 0.0623455 \tabularnewline
AMS.I.V & -0.00704044 & 0.0348736 & -0.2019 & 0.841005 & 0.420503 \tabularnewline
AMS.E.V & -0.0275831 & 0.0555802 & -0.4963 & 0.622351 & 0.311175 \tabularnewline
CONFSTAT.V & -0.154466 & 0.176237 & -0.8765 & 0.385882 & 0.192941 \tabularnewline
CONFSOFT.V & 0.365065 & 0.215015 & 1.698 & 0.0971128 & 0.0485564 \tabularnewline
STRESS.V & 0.239162 & 0.233918 & 1.022 & 0.312579 & 0.156289 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268273&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]8.16621[/C][C]5.2098[/C][C]1.567[/C][C]0.124691[/C][C]0.0623455[/C][/ROW]
[ROW][C]AMS.I.V[/C][C]-0.00704044[/C][C]0.0348736[/C][C]-0.2019[/C][C]0.841005[/C][C]0.420503[/C][/ROW]
[ROW][C]AMS.E.V[/C][C]-0.0275831[/C][C]0.0555802[/C][C]-0.4963[/C][C]0.622351[/C][C]0.311175[/C][/ROW]
[ROW][C]CONFSTAT.V[/C][C]-0.154466[/C][C]0.176237[/C][C]-0.8765[/C][C]0.385882[/C][C]0.192941[/C][/ROW]
[ROW][C]CONFSOFT.V[/C][C]0.365065[/C][C]0.215015[/C][C]1.698[/C][C]0.0971128[/C][C]0.0485564[/C][/ROW]
[ROW][C]STRESS.V[/C][C]0.239162[/C][C]0.233918[/C][C]1.022[/C][C]0.312579[/C][C]0.156289[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268273&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268273&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)	8.16621	5.2098	1.567	0.124691	0.0623455
AMS.I.V	-0.00704044	0.0348736	-0.2019	0.841005	0.420503
AMS.E.V	-0.0275831	0.0555802	-0.4963	0.622351	0.311175
CONFSTAT.V	-0.154466	0.176237	-0.8765	0.385882	0.192941
CONFSOFT.V	0.365065	0.215015	1.698	0.0971128	0.0485564
STRESS.V	0.239162	0.233918	1.022	0.312579	0.156289

Multiple Linear Regression - Regression Statistics
Multiple R	0.335899
R-squared	0.112828
Adjusted R-squared	0.00463644
F-TEST (value)	1.04285
F-TEST (DF numerator)	5
F-TEST (DF denominator)	41
p-value	0.405751
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.25251
Sum Squared Residuals	208.026

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.335899 \tabularnewline
R-squared & 0.112828 \tabularnewline
Adjusted R-squared & 0.00463644 \tabularnewline
F-TEST (value) & 1.04285 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 0.405751 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.25251 \tabularnewline
Sum Squared Residuals & 208.026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268273&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.335899[/C][/ROW]
[ROW][C]R-squared[/C][C]0.112828[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00463644[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.04285[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/C][/ROW]
[ROW][C]p-value[/C][C]0.405751[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.25251[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]208.026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268273&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268273&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	0.335899
R-squared	0.112828
Adjusted R-squared	0.00463644
F-TEST (value)	1.04285
F-TEST (DF numerator)	5
F-TEST (DF denominator)	41
p-value	0.405751
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.25251
Sum Squared Residuals	208.026

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	12.0858	0.814164
2	12.8	10.9002	1.8998
3	14.8	8.65579	6.14421
4	12	11.3384	0.661602
5	6.3	9.45827	-3.15827
6	11.3	10.2838	1.01617
7	9.3	10.8881	-1.58813
8	10	10.4992	-0.499192
9	10.8	10.5375	0.262468
10	13.4	11.6213	1.77866
11	11.5	11.6433	-0.143273
12	8.3	10.0242	-1.72423
13	11.7	10.5452	1.15475
14	10.4	9.79308	0.606921
15	11.8	10.2113	1.58868
16	11.3	10.6207	0.679349
17	12.7	11.1096	1.59043
18	5.7	8.31301	-2.61301
19	8	11.0876	-3.08759
20	12.5	11.1409	1.35907
21	7.6	10.3224	-2.72235
22	9.2	10.4274	-1.22738
23	11.1	11.0153	0.0846576
24	12.2	11.0741	1.12587
25	12.3	11.522	0.777961
26	11.4	11.6084	-0.208359
27	8.8	10.9258	-2.12579
28	12.6	9.60741	2.99259
29	13	10.2261	2.77395
30	13.2	10.9281	2.27194
31	9.9	10.5225	-0.622519
32	10.5	10.4847	0.0152776
33	13.4	10.5171	2.88288
34	10.9	11.0928	-0.192844
35	10.3	10.1804	0.119569
36	11.4	9.21276	2.18724
37	8.6	11.1675	-2.56753
38	13.2	11.4088	1.79124
39	8.8	10.5061	-1.70609
40	9	10.9937	-1.9937
41	10.3	11.102	-0.801986
42	8.5	10.1186	-1.61856
43	13.5	11.4799	2.02006
44	4.9	10.1587	-5.2587
45	6.4	10.4644	-4.0644
46	9.6	10.6441	-1.0441
47	11.6	11.2315	0.368508

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.0858 & 0.814164 \tabularnewline
2 & 12.8 & 10.9002 & 1.8998 \tabularnewline
3 & 14.8 & 8.65579 & 6.14421 \tabularnewline
4 & 12 & 11.3384 & 0.661602 \tabularnewline
5 & 6.3 & 9.45827 & -3.15827 \tabularnewline
6 & 11.3 & 10.2838 & 1.01617 \tabularnewline
7 & 9.3 & 10.8881 & -1.58813 \tabularnewline
8 & 10 & 10.4992 & -0.499192 \tabularnewline
9 & 10.8 & 10.5375 & 0.262468 \tabularnewline
10 & 13.4 & 11.6213 & 1.77866 \tabularnewline
11 & 11.5 & 11.6433 & -0.143273 \tabularnewline
12 & 8.3 & 10.0242 & -1.72423 \tabularnewline
13 & 11.7 & 10.5452 & 1.15475 \tabularnewline
14 & 10.4 & 9.79308 & 0.606921 \tabularnewline
15 & 11.8 & 10.2113 & 1.58868 \tabularnewline
16 & 11.3 & 10.6207 & 0.679349 \tabularnewline
17 & 12.7 & 11.1096 & 1.59043 \tabularnewline
18 & 5.7 & 8.31301 & -2.61301 \tabularnewline
19 & 8 & 11.0876 & -3.08759 \tabularnewline
20 & 12.5 & 11.1409 & 1.35907 \tabularnewline
21 & 7.6 & 10.3224 & -2.72235 \tabularnewline
22 & 9.2 & 10.4274 & -1.22738 \tabularnewline
23 & 11.1 & 11.0153 & 0.0846576 \tabularnewline
24 & 12.2 & 11.0741 & 1.12587 \tabularnewline
25 & 12.3 & 11.522 & 0.777961 \tabularnewline
26 & 11.4 & 11.6084 & -0.208359 \tabularnewline
27 & 8.8 & 10.9258 & -2.12579 \tabularnewline
28 & 12.6 & 9.60741 & 2.99259 \tabularnewline
29 & 13 & 10.2261 & 2.77395 \tabularnewline
30 & 13.2 & 10.9281 & 2.27194 \tabularnewline
31 & 9.9 & 10.5225 & -0.622519 \tabularnewline
32 & 10.5 & 10.4847 & 0.0152776 \tabularnewline
33 & 13.4 & 10.5171 & 2.88288 \tabularnewline
34 & 10.9 & 11.0928 & -0.192844 \tabularnewline
35 & 10.3 & 10.1804 & 0.119569 \tabularnewline
36 & 11.4 & 9.21276 & 2.18724 \tabularnewline
37 & 8.6 & 11.1675 & -2.56753 \tabularnewline
38 & 13.2 & 11.4088 & 1.79124 \tabularnewline
39 & 8.8 & 10.5061 & -1.70609 \tabularnewline
40 & 9 & 10.9937 & -1.9937 \tabularnewline
41 & 10.3 & 11.102 & -0.801986 \tabularnewline
42 & 8.5 & 10.1186 & -1.61856 \tabularnewline
43 & 13.5 & 11.4799 & 2.02006 \tabularnewline
44 & 4.9 & 10.1587 & -5.2587 \tabularnewline
45 & 6.4 & 10.4644 & -4.0644 \tabularnewline
46 & 9.6 & 10.6441 & -1.0441 \tabularnewline
47 & 11.6 & 11.2315 & 0.368508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268273&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]12.9[/C][C]12.0858[/C][C]0.814164[/C][/ROW]
[ROW][C]2[/C][C]12.8[/C][C]10.9002[/C][C]1.8998[/C][/ROW]
[ROW][C]3[/C][C]14.8[/C][C]8.65579[/C][C]6.14421[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.3384[/C][C]0.661602[/C][/ROW]
[ROW][C]5[/C][C]6.3[/C][C]9.45827[/C][C]-3.15827[/C][/ROW]
[ROW][C]6[/C][C]11.3[/C][C]10.2838[/C][C]1.01617[/C][/ROW]
[ROW][C]7[/C][C]9.3[/C][C]10.8881[/C][C]-1.58813[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]10.4992[/C][C]-0.499192[/C][/ROW]
[ROW][C]9[/C][C]10.8[/C][C]10.5375[/C][C]0.262468[/C][/ROW]
[ROW][C]10[/C][C]13.4[/C][C]11.6213[/C][C]1.77866[/C][/ROW]
[ROW][C]11[/C][C]11.5[/C][C]11.6433[/C][C]-0.143273[/C][/ROW]
[ROW][C]12[/C][C]8.3[/C][C]10.0242[/C][C]-1.72423[/C][/ROW]
[ROW][C]13[/C][C]11.7[/C][C]10.5452[/C][C]1.15475[/C][/ROW]
[ROW][C]14[/C][C]10.4[/C][C]9.79308[/C][C]0.606921[/C][/ROW]
[ROW][C]15[/C][C]11.8[/C][C]10.2113[/C][C]1.58868[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.6207[/C][C]0.679349[/C][/ROW]
[ROW][C]17[/C][C]12.7[/C][C]11.1096[/C][C]1.59043[/C][/ROW]
[ROW][C]18[/C][C]5.7[/C][C]8.31301[/C][C]-2.61301[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]11.0876[/C][C]-3.08759[/C][/ROW]
[ROW][C]20[/C][C]12.5[/C][C]11.1409[/C][C]1.35907[/C][/ROW]
[ROW][C]21[/C][C]7.6[/C][C]10.3224[/C][C]-2.72235[/C][/ROW]
[ROW][C]22[/C][C]9.2[/C][C]10.4274[/C][C]-1.22738[/C][/ROW]
[ROW][C]23[/C][C]11.1[/C][C]11.0153[/C][C]0.0846576[/C][/ROW]
[ROW][C]24[/C][C]12.2[/C][C]11.0741[/C][C]1.12587[/C][/ROW]
[ROW][C]25[/C][C]12.3[/C][C]11.522[/C][C]0.777961[/C][/ROW]
[ROW][C]26[/C][C]11.4[/C][C]11.6084[/C][C]-0.208359[/C][/ROW]
[ROW][C]27[/C][C]8.8[/C][C]10.9258[/C][C]-2.12579[/C][/ROW]
[ROW][C]28[/C][C]12.6[/C][C]9.60741[/C][C]2.99259[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]10.2261[/C][C]2.77395[/C][/ROW]
[ROW][C]30[/C][C]13.2[/C][C]10.9281[/C][C]2.27194[/C][/ROW]
[ROW][C]31[/C][C]9.9[/C][C]10.5225[/C][C]-0.622519[/C][/ROW]
[ROW][C]32[/C][C]10.5[/C][C]10.4847[/C][C]0.0152776[/C][/ROW]
[ROW][C]33[/C][C]13.4[/C][C]10.5171[/C][C]2.88288[/C][/ROW]
[ROW][C]34[/C][C]10.9[/C][C]11.0928[/C][C]-0.192844[/C][/ROW]
[ROW][C]35[/C][C]10.3[/C][C]10.1804[/C][C]0.119569[/C][/ROW]
[ROW][C]36[/C][C]11.4[/C][C]9.21276[/C][C]2.18724[/C][/ROW]
[ROW][C]37[/C][C]8.6[/C][C]11.1675[/C][C]-2.56753[/C][/ROW]
[ROW][C]38[/C][C]13.2[/C][C]11.4088[/C][C]1.79124[/C][/ROW]
[ROW][C]39[/C][C]8.8[/C][C]10.5061[/C][C]-1.70609[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]10.9937[/C][C]-1.9937[/C][/ROW]
[ROW][C]41[/C][C]10.3[/C][C]11.102[/C][C]-0.801986[/C][/ROW]
[ROW][C]42[/C][C]8.5[/C][C]10.1186[/C][C]-1.61856[/C][/ROW]
[ROW][C]43[/C][C]13.5[/C][C]11.4799[/C][C]2.02006[/C][/ROW]
[ROW][C]44[/C][C]4.9[/C][C]10.1587[/C][C]-5.2587[/C][/ROW]
[ROW][C]45[/C][C]6.4[/C][C]10.4644[/C][C]-4.0644[/C][/ROW]
[ROW][C]46[/C][C]9.6[/C][C]10.6441[/C][C]-1.0441[/C][/ROW]
[ROW][C]47[/C][C]11.6[/C][C]11.2315[/C][C]0.368508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268273&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268273&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	12.9	12.0858	0.814164
2	12.8	10.9002	1.8998
3	14.8	8.65579	6.14421
4	12	11.3384	0.661602
5	6.3	9.45827	-3.15827
6	11.3	10.2838	1.01617
7	9.3	10.8881	-1.58813
8	10	10.4992	-0.499192
9	10.8	10.5375	0.262468
10	13.4	11.6213	1.77866
11	11.5	11.6433	-0.143273
12	8.3	10.0242	-1.72423
13	11.7	10.5452	1.15475
14	10.4	9.79308	0.606921
15	11.8	10.2113	1.58868
16	11.3	10.6207	0.679349
17	12.7	11.1096	1.59043
18	5.7	8.31301	-2.61301
19	8	11.0876	-3.08759
20	12.5	11.1409	1.35907
21	7.6	10.3224	-2.72235
22	9.2	10.4274	-1.22738
23	11.1	11.0153	0.0846576
24	12.2	11.0741	1.12587
25	12.3	11.522	0.777961
26	11.4	11.6084	-0.208359
27	8.8	10.9258	-2.12579
28	12.6	9.60741	2.99259
29	13	10.2261	2.77395
30	13.2	10.9281	2.27194
31	9.9	10.5225	-0.622519
32	10.5	10.4847	0.0152776
33	13.4	10.5171	2.88288
34	10.9	11.0928	-0.192844
35	10.3	10.1804	0.119569
36	11.4	9.21276	2.18724
37	8.6	11.1675	-2.56753
38	13.2	11.4088	1.79124
39	8.8	10.5061	-1.70609
40	9	10.9937	-1.9937
41	10.3	11.102	-0.801986
42	8.5	10.1186	-1.61856
43	13.5	11.4799	2.02006
44	4.9	10.1587	-5.2587
45	6.4	10.4644	-4.0644
46	9.6	10.6441	-1.0441
47	11.6	11.2315	0.368508

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0305566	0.0611132	0.969443
10	0.428694	0.857389	0.571306
11	0.281586	0.563172	0.718414
12	0.440149	0.880297	0.559851
13	0.445756	0.891512	0.554244
14	0.531499	0.937002	0.468501
15	0.555596	0.888809	0.444404
16	0.473147	0.946294	0.526853
17	0.477608	0.955216	0.522392
18	0.540227	0.919547	0.459773
19	0.598647	0.802705	0.401353
20	0.534175	0.931649	0.465825
21	0.547868	0.904265	0.452132
22	0.51763	0.96474	0.48237
23	0.42192	0.84384	0.57808
24	0.356504	0.713008	0.643496
25	0.294778	0.589557	0.705222
26	0.219213	0.438425	0.780787
27	0.230572	0.461144	0.769428
28	0.241306	0.482613	0.758694
29	0.308344	0.616688	0.691656
30	0.321892	0.643783	0.678108
31	0.244393	0.488786	0.755607
32	0.205819	0.411637	0.794181
33	0.246352	0.492704	0.753648
34	0.174179	0.348357	0.825821
35	0.156798	0.313595	0.843202
36	0.730267	0.539466	0.269733
37	0.877449	0.245103	0.122551
38	0.854493	0.291013	0.145507

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0305566 & 0.0611132 & 0.969443 \tabularnewline
10 & 0.428694 & 0.857389 & 0.571306 \tabularnewline
11 & 0.281586 & 0.563172 & 0.718414 \tabularnewline
12 & 0.440149 & 0.880297 & 0.559851 \tabularnewline
13 & 0.445756 & 0.891512 & 0.554244 \tabularnewline
14 & 0.531499 & 0.937002 & 0.468501 \tabularnewline
15 & 0.555596 & 0.888809 & 0.444404 \tabularnewline
16 & 0.473147 & 0.946294 & 0.526853 \tabularnewline
17 & 0.477608 & 0.955216 & 0.522392 \tabularnewline
18 & 0.540227 & 0.919547 & 0.459773 \tabularnewline
19 & 0.598647 & 0.802705 & 0.401353 \tabularnewline
20 & 0.534175 & 0.931649 & 0.465825 \tabularnewline
21 & 0.547868 & 0.904265 & 0.452132 \tabularnewline
22 & 0.51763 & 0.96474 & 0.48237 \tabularnewline
23 & 0.42192 & 0.84384 & 0.57808 \tabularnewline
24 & 0.356504 & 0.713008 & 0.643496 \tabularnewline
25 & 0.294778 & 0.589557 & 0.705222 \tabularnewline
26 & 0.219213 & 0.438425 & 0.780787 \tabularnewline
27 & 0.230572 & 0.461144 & 0.769428 \tabularnewline
28 & 0.241306 & 0.482613 & 0.758694 \tabularnewline
29 & 0.308344 & 0.616688 & 0.691656 \tabularnewline
30 & 0.321892 & 0.643783 & 0.678108 \tabularnewline
31 & 0.244393 & 0.488786 & 0.755607 \tabularnewline
32 & 0.205819 & 0.411637 & 0.794181 \tabularnewline
33 & 0.246352 & 0.492704 & 0.753648 \tabularnewline
34 & 0.174179 & 0.348357 & 0.825821 \tabularnewline
35 & 0.156798 & 0.313595 & 0.843202 \tabularnewline
36 & 0.730267 & 0.539466 & 0.269733 \tabularnewline
37 & 0.877449 & 0.245103 & 0.122551 \tabularnewline
38 & 0.854493 & 0.291013 & 0.145507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268273&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]9[/C][C]0.0305566[/C][C]0.0611132[/C][C]0.969443[/C][/ROW]
[ROW][C]10[/C][C]0.428694[/C][C]0.857389[/C][C]0.571306[/C][/ROW]
[ROW][C]11[/C][C]0.281586[/C][C]0.563172[/C][C]0.718414[/C][/ROW]
[ROW][C]12[/C][C]0.440149[/C][C]0.880297[/C][C]0.559851[/C][/ROW]
[ROW][C]13[/C][C]0.445756[/C][C]0.891512[/C][C]0.554244[/C][/ROW]
[ROW][C]14[/C][C]0.531499[/C][C]0.937002[/C][C]0.468501[/C][/ROW]
[ROW][C]15[/C][C]0.555596[/C][C]0.888809[/C][C]0.444404[/C][/ROW]
[ROW][C]16[/C][C]0.473147[/C][C]0.946294[/C][C]0.526853[/C][/ROW]
[ROW][C]17[/C][C]0.477608[/C][C]0.955216[/C][C]0.522392[/C][/ROW]
[ROW][C]18[/C][C]0.540227[/C][C]0.919547[/C][C]0.459773[/C][/ROW]
[ROW][C]19[/C][C]0.598647[/C][C]0.802705[/C][C]0.401353[/C][/ROW]
[ROW][C]20[/C][C]0.534175[/C][C]0.931649[/C][C]0.465825[/C][/ROW]
[ROW][C]21[/C][C]0.547868[/C][C]0.904265[/C][C]0.452132[/C][/ROW]
[ROW][C]22[/C][C]0.51763[/C][C]0.96474[/C][C]0.48237[/C][/ROW]
[ROW][C]23[/C][C]0.42192[/C][C]0.84384[/C][C]0.57808[/C][/ROW]
[ROW][C]24[/C][C]0.356504[/C][C]0.713008[/C][C]0.643496[/C][/ROW]
[ROW][C]25[/C][C]0.294778[/C][C]0.589557[/C][C]0.705222[/C][/ROW]
[ROW][C]26[/C][C]0.219213[/C][C]0.438425[/C][C]0.780787[/C][/ROW]
[ROW][C]27[/C][C]0.230572[/C][C]0.461144[/C][C]0.769428[/C][/ROW]
[ROW][C]28[/C][C]0.241306[/C][C]0.482613[/C][C]0.758694[/C][/ROW]
[ROW][C]29[/C][C]0.308344[/C][C]0.616688[/C][C]0.691656[/C][/ROW]
[ROW][C]30[/C][C]0.321892[/C][C]0.643783[/C][C]0.678108[/C][/ROW]
[ROW][C]31[/C][C]0.244393[/C][C]0.488786[/C][C]0.755607[/C][/ROW]
[ROW][C]32[/C][C]0.205819[/C][C]0.411637[/C][C]0.794181[/C][/ROW]
[ROW][C]33[/C][C]0.246352[/C][C]0.492704[/C][C]0.753648[/C][/ROW]
[ROW][C]34[/C][C]0.174179[/C][C]0.348357[/C][C]0.825821[/C][/ROW]
[ROW][C]35[/C][C]0.156798[/C][C]0.313595[/C][C]0.843202[/C][/ROW]
[ROW][C]36[/C][C]0.730267[/C][C]0.539466[/C][C]0.269733[/C][/ROW]
[ROW][C]37[/C][C]0.877449[/C][C]0.245103[/C][C]0.122551[/C][/ROW]
[ROW][C]38[/C][C]0.854493[/C][C]0.291013[/C][C]0.145507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268273&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268273&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
9	0.0305566	0.0611132	0.969443
10	0.428694	0.857389	0.571306
11	0.281586	0.563172	0.718414
12	0.440149	0.880297	0.559851
13	0.445756	0.891512	0.554244
14	0.531499	0.937002	0.468501
15	0.555596	0.888809	0.444404
16	0.473147	0.946294	0.526853
17	0.477608	0.955216	0.522392
18	0.540227	0.919547	0.459773
19	0.598647	0.802705	0.401353
20	0.534175	0.931649	0.465825
21	0.547868	0.904265	0.452132
22	0.51763	0.96474	0.48237
23	0.42192	0.84384	0.57808
24	0.356504	0.713008	0.643496
25	0.294778	0.589557	0.705222
26	0.219213	0.438425	0.780787
27	0.230572	0.461144	0.769428
28	0.241306	0.482613	0.758694
29	0.308344	0.616688	0.691656
30	0.321892	0.643783	0.678108
31	0.244393	0.488786	0.755607
32	0.205819	0.411637	0.794181
33	0.246352	0.492704	0.753648
34	0.174179	0.348357	0.825821
35	0.156798	0.313595	0.843202
36	0.730267	0.539466	0.269733
37	0.877449	0.245103	0.122551
38	0.854493	0.291013	0.145507

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	0.0333333	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 & 0.0333333 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268273&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]0.0333333[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268273&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268273&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	0.0333333	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 = 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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