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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 18 Dec 2014 13:09:25 +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/18/t1418908217x0x4xtfnthid4jb.htm/, Retrieved Fri, 17 May 2024 16:57:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270892, Retrieved Fri, 17 May 2024 16:57:26 +0000

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

-       [Multiple Regression] [] [2014-12-18 13:09:25] [dc060611fd89d91eb1d5c55ae338991b] [Current]

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12 13 26 7.5
11 11 37 6.5
13 14 67 1.0
11 15 43 1.0
10 14 52 5.5
7 11 52 8.5
10 13 43 6.5
15 16 84 4.5
12 14 67 2.0
12 14 49 5.0
10 15 70 0.5
14 13 58 5.0
6 14 68 2.5
12 11 62 5.0
14 12 43 5.5
11 14 56 3.5
12 12 74 4.0
13 15 63 6.5
11 14 58 4.5
7 12 63 5.5
11 12 53 4.0
7 12 57 7.5
12 14 64 4.0
13 16 53 5.5
9 12 29 2.5
11 12 54 5.5
12 14 58 3.5
12 15 51 4.5
5 14 54 4.5
13 13 56 6.0
6 16 47 5.0
6 15 50 6.5
12 13 35 5.0
11 16 30 6.0
6 16 68 4.5
11 15 56 5.0
12 13 43 5.0
13 12 67 6.5
14 14 62 7.0
12 14 57 4.5
14 10 54 8.5
11 16 61 3.5
10 14 56 6.0
7 14 41 1.5
7 15 53 3.5
10 16 46 7.5
12 15 51 5.0
5 13 37 6.5
10 12 42 6.5
12 12 38 6.5
11 14 66 7.0
12 15 53 1.5
11 11 49 4.0
12 14 49 4.5
10 16 59 0.0
9 13 40 3.5
7 11 63 4.5
9 12 34 0.0
10 12 32 3.0
12 14 67 3.5
14 12 61 3.0
9 13 60 1.0
12 14 63 5.5

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=270892&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=270892&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270892&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
Ex[t] = + 8.57311 + 0.0603715CONFSOFTTOT[t] -0.273106STRESSTOT[t] -0.0174163AMS.I[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  8.57311 +  0.0603715CONFSOFTTOT[t] -0.273106STRESSTOT[t] -0.0174163AMS.I[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270892&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  8.57311 +  0.0603715CONFSOFTTOT[t] -0.273106STRESSTOT[t] -0.0174163AMS.I[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270892&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270892&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
Ex[t] = + 8.57311 + 0.0603715CONFSOFTTOT[t] -0.273106STRESSTOT[t] -0.0174163AMS.I[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)	8.57311	2.57356	3.331	0.00149585	0.000747926
CONFSOFTTOT	0.0603715	0.104989	0.575	0.567459	0.28373
STRESSTOT	-0.273106	0.168048	-1.625	0.109458	0.0547288
AMS.I	-0.0174163	0.0222231	-0.7837	0.43635	0.218175

\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) & 8.57311 & 2.57356 & 3.331 & 0.00149585 & 0.000747926 \tabularnewline
CONFSOFTTOT & 0.0603715 & 0.104989 & 0.575 & 0.567459 & 0.28373 \tabularnewline
STRESSTOT & -0.273106 & 0.168048 & -1.625 & 0.109458 & 0.0547288 \tabularnewline
AMS.I & -0.0174163 & 0.0222231 & -0.7837 & 0.43635 & 0.218175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270892&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]8.57311[/C][C]2.57356[/C][C]3.331[/C][C]0.00149585[/C][C]0.000747926[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.0603715[/C][C]0.104989[/C][C]0.575[/C][C]0.567459[/C][C]0.28373[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]-0.273106[/C][C]0.168048[/C][C]-1.625[/C][C]0.109458[/C][C]0.0547288[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.0174163[/C][C]0.0222231[/C][C]-0.7837[/C][C]0.43635[/C][C]0.218175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270892&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270892&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)	8.57311	2.57356	3.331	0.00149585	0.000747926
CONFSOFTTOT	0.0603715	0.104989	0.575	0.567459	0.28373
STRESSTOT	-0.273106	0.168048	-1.625	0.109458	0.0547288
AMS.I	-0.0174163	0.0222231	-0.7837	0.43635	0.218175

Multiple Linear Regression - Regression Statistics
Multiple R	0.258743
R-squared	0.0669478
Adjusted R-squared	0.0195045
F-TEST (value)	1.41111
F-TEST (DF numerator)	3
F-TEST (DF denominator)	59
p-value	0.24853
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.00725
Sum Squared Residuals	237.714

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.258743 \tabularnewline
R-squared & 0.0669478 \tabularnewline
Adjusted R-squared & 0.0195045 \tabularnewline
F-TEST (value) & 1.41111 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.24853 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.00725 \tabularnewline
Sum Squared Residuals & 237.714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270892&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.258743[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0669478[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0195045[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.41111[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.24853[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.00725[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]237.714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270892&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270892&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	0.258743
R-squared	0.0669478
Adjusted R-squared	0.0195045
F-TEST (value)	1.41111
F-TEST (DF numerator)	3
F-TEST (DF denominator)	59
p-value	0.24853
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.00725
Sum Squared Residuals	237.714

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	7.5	5.29437	2.20563
2	6.5	5.58863	0.911374
3	1	4.36756	-3.36756
4	1	4.39171	-3.39171
5	5.5	4.44769	1.05231
6	8.5	5.0859	3.4141
7	6.5	4.87755	1.62245
8	4.5	3.64602	0.853983
9	2	4.30719	-2.30719
10	5	4.62069	0.379315
11	0.5	3.86109	-3.36109
12	5	4.85779	0.142213
13	2.5	3.92755	-1.42755
14	5	5.21359	-0.21359
15	5.5	5.39214	0.107863
16	3.5	4.4384	-0.938399
17	4	4.73149	-0.731489
18	6.5	4.16412	2.33588
19	4.5	4.40357	0.0964333
20	5.5	4.62121	0.87879
21	4	5.03686	-1.03686
22	7.5	4.72571	2.77429
23	4	4.35944	-0.35944
24	5.5	4.06518	1.43482
25	2.5	5.33411	-2.83411
26	5.5	5.01944	0.480557
27	3.5	4.46394	-0.963938
28	4.5	4.31275	0.187253
29	4.5	4.111	0.388997
30	6	4.83225	1.16775
31	5	3.74708	1.25292
32	6.5	3.96793	2.53207
33	5	5.13762	-0.137619
34	6	4.34501	1.65499
35	4.5	3.38133	1.11867
36	5	4.16529	0.834706
37	5	4.99829	0.00171137
38	6.5	4.91377	1.58623
39	7	4.51502	2.48498
40	4.5	4.48135	0.0186455
41	8.5	5.74677	2.75323
42	3.5	3.80511	-0.305106
43	6	4.37803	1.62197
44	1.5	4.45816	-2.95816
45	3.5	3.97606	-0.476056
46	7.5	4.00598	3.49402
47	5	4.31275	0.687253
48	6.5	4.68019	1.81981
49	6.5	5.16807	1.33193
50	6.5	5.35848	1.14152
51	7	4.26424	2.73576
52	1.5	4.27791	-2.77791
53	4	5.37963	-1.37963
54	4.5	4.62069	-0.120685
55	0	3.77957	-3.77957
56	3.5	4.86942	-1.36942
57	4.5	4.89432	-0.394316
58	0	5.24703	-5.24703
59	3	5.34223	-2.34223
60	3.5	4.30719	-0.807191
61	3	5.07864	-2.07864
62	1	4.5211	-3.5211
63	5.5	4.37686	1.12314

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 5.29437 & 2.20563 \tabularnewline
2 & 6.5 & 5.58863 & 0.911374 \tabularnewline
3 & 1 & 4.36756 & -3.36756 \tabularnewline
4 & 1 & 4.39171 & -3.39171 \tabularnewline
5 & 5.5 & 4.44769 & 1.05231 \tabularnewline
6 & 8.5 & 5.0859 & 3.4141 \tabularnewline
7 & 6.5 & 4.87755 & 1.62245 \tabularnewline
8 & 4.5 & 3.64602 & 0.853983 \tabularnewline
9 & 2 & 4.30719 & -2.30719 \tabularnewline
10 & 5 & 4.62069 & 0.379315 \tabularnewline
11 & 0.5 & 3.86109 & -3.36109 \tabularnewline
12 & 5 & 4.85779 & 0.142213 \tabularnewline
13 & 2.5 & 3.92755 & -1.42755 \tabularnewline
14 & 5 & 5.21359 & -0.21359 \tabularnewline
15 & 5.5 & 5.39214 & 0.107863 \tabularnewline
16 & 3.5 & 4.4384 & -0.938399 \tabularnewline
17 & 4 & 4.73149 & -0.731489 \tabularnewline
18 & 6.5 & 4.16412 & 2.33588 \tabularnewline
19 & 4.5 & 4.40357 & 0.0964333 \tabularnewline
20 & 5.5 & 4.62121 & 0.87879 \tabularnewline
21 & 4 & 5.03686 & -1.03686 \tabularnewline
22 & 7.5 & 4.72571 & 2.77429 \tabularnewline
23 & 4 & 4.35944 & -0.35944 \tabularnewline
24 & 5.5 & 4.06518 & 1.43482 \tabularnewline
25 & 2.5 & 5.33411 & -2.83411 \tabularnewline
26 & 5.5 & 5.01944 & 0.480557 \tabularnewline
27 & 3.5 & 4.46394 & -0.963938 \tabularnewline
28 & 4.5 & 4.31275 & 0.187253 \tabularnewline
29 & 4.5 & 4.111 & 0.388997 \tabularnewline
30 & 6 & 4.83225 & 1.16775 \tabularnewline
31 & 5 & 3.74708 & 1.25292 \tabularnewline
32 & 6.5 & 3.96793 & 2.53207 \tabularnewline
33 & 5 & 5.13762 & -0.137619 \tabularnewline
34 & 6 & 4.34501 & 1.65499 \tabularnewline
35 & 4.5 & 3.38133 & 1.11867 \tabularnewline
36 & 5 & 4.16529 & 0.834706 \tabularnewline
37 & 5 & 4.99829 & 0.00171137 \tabularnewline
38 & 6.5 & 4.91377 & 1.58623 \tabularnewline
39 & 7 & 4.51502 & 2.48498 \tabularnewline
40 & 4.5 & 4.48135 & 0.0186455 \tabularnewline
41 & 8.5 & 5.74677 & 2.75323 \tabularnewline
42 & 3.5 & 3.80511 & -0.305106 \tabularnewline
43 & 6 & 4.37803 & 1.62197 \tabularnewline
44 & 1.5 & 4.45816 & -2.95816 \tabularnewline
45 & 3.5 & 3.97606 & -0.476056 \tabularnewline
46 & 7.5 & 4.00598 & 3.49402 \tabularnewline
47 & 5 & 4.31275 & 0.687253 \tabularnewline
48 & 6.5 & 4.68019 & 1.81981 \tabularnewline
49 & 6.5 & 5.16807 & 1.33193 \tabularnewline
50 & 6.5 & 5.35848 & 1.14152 \tabularnewline
51 & 7 & 4.26424 & 2.73576 \tabularnewline
52 & 1.5 & 4.27791 & -2.77791 \tabularnewline
53 & 4 & 5.37963 & -1.37963 \tabularnewline
54 & 4.5 & 4.62069 & -0.120685 \tabularnewline
55 & 0 & 3.77957 & -3.77957 \tabularnewline
56 & 3.5 & 4.86942 & -1.36942 \tabularnewline
57 & 4.5 & 4.89432 & -0.394316 \tabularnewline
58 & 0 & 5.24703 & -5.24703 \tabularnewline
59 & 3 & 5.34223 & -2.34223 \tabularnewline
60 & 3.5 & 4.30719 & -0.807191 \tabularnewline
61 & 3 & 5.07864 & -2.07864 \tabularnewline
62 & 1 & 4.5211 & -3.5211 \tabularnewline
63 & 5.5 & 4.37686 & 1.12314 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270892&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]7.5[/C][C]5.29437[/C][C]2.20563[/C][/ROW]
[ROW][C]2[/C][C]6.5[/C][C]5.58863[/C][C]0.911374[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]4.36756[/C][C]-3.36756[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]4.39171[/C][C]-3.39171[/C][/ROW]
[ROW][C]5[/C][C]5.5[/C][C]4.44769[/C][C]1.05231[/C][/ROW]
[ROW][C]6[/C][C]8.5[/C][C]5.0859[/C][C]3.4141[/C][/ROW]
[ROW][C]7[/C][C]6.5[/C][C]4.87755[/C][C]1.62245[/C][/ROW]
[ROW][C]8[/C][C]4.5[/C][C]3.64602[/C][C]0.853983[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]4.30719[/C][C]-2.30719[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]4.62069[/C][C]0.379315[/C][/ROW]
[ROW][C]11[/C][C]0.5[/C][C]3.86109[/C][C]-3.36109[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]4.85779[/C][C]0.142213[/C][/ROW]
[ROW][C]13[/C][C]2.5[/C][C]3.92755[/C][C]-1.42755[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]5.21359[/C][C]-0.21359[/C][/ROW]
[ROW][C]15[/C][C]5.5[/C][C]5.39214[/C][C]0.107863[/C][/ROW]
[ROW][C]16[/C][C]3.5[/C][C]4.4384[/C][C]-0.938399[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]4.73149[/C][C]-0.731489[/C][/ROW]
[ROW][C]18[/C][C]6.5[/C][C]4.16412[/C][C]2.33588[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]4.40357[/C][C]0.0964333[/C][/ROW]
[ROW][C]20[/C][C]5.5[/C][C]4.62121[/C][C]0.87879[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]5.03686[/C][C]-1.03686[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]4.72571[/C][C]2.77429[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]4.35944[/C][C]-0.35944[/C][/ROW]
[ROW][C]24[/C][C]5.5[/C][C]4.06518[/C][C]1.43482[/C][/ROW]
[ROW][C]25[/C][C]2.5[/C][C]5.33411[/C][C]-2.83411[/C][/ROW]
[ROW][C]26[/C][C]5.5[/C][C]5.01944[/C][C]0.480557[/C][/ROW]
[ROW][C]27[/C][C]3.5[/C][C]4.46394[/C][C]-0.963938[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]4.31275[/C][C]0.187253[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]4.111[/C][C]0.388997[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]4.83225[/C][C]1.16775[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]3.74708[/C][C]1.25292[/C][/ROW]
[ROW][C]32[/C][C]6.5[/C][C]3.96793[/C][C]2.53207[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]5.13762[/C][C]-0.137619[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]4.34501[/C][C]1.65499[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]3.38133[/C][C]1.11867[/C][/ROW]
[ROW][C]36[/C][C]5[/C][C]4.16529[/C][C]0.834706[/C][/ROW]
[ROW][C]37[/C][C]5[/C][C]4.99829[/C][C]0.00171137[/C][/ROW]
[ROW][C]38[/C][C]6.5[/C][C]4.91377[/C][C]1.58623[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]4.51502[/C][C]2.48498[/C][/ROW]
[ROW][C]40[/C][C]4.5[/C][C]4.48135[/C][C]0.0186455[/C][/ROW]
[ROW][C]41[/C][C]8.5[/C][C]5.74677[/C][C]2.75323[/C][/ROW]
[ROW][C]42[/C][C]3.5[/C][C]3.80511[/C][C]-0.305106[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]4.37803[/C][C]1.62197[/C][/ROW]
[ROW][C]44[/C][C]1.5[/C][C]4.45816[/C][C]-2.95816[/C][/ROW]
[ROW][C]45[/C][C]3.5[/C][C]3.97606[/C][C]-0.476056[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]4.00598[/C][C]3.49402[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]4.31275[/C][C]0.687253[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]4.68019[/C][C]1.81981[/C][/ROW]
[ROW][C]49[/C][C]6.5[/C][C]5.16807[/C][C]1.33193[/C][/ROW]
[ROW][C]50[/C][C]6.5[/C][C]5.35848[/C][C]1.14152[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]4.26424[/C][C]2.73576[/C][/ROW]
[ROW][C]52[/C][C]1.5[/C][C]4.27791[/C][C]-2.77791[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]5.37963[/C][C]-1.37963[/C][/ROW]
[ROW][C]54[/C][C]4.5[/C][C]4.62069[/C][C]-0.120685[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]3.77957[/C][C]-3.77957[/C][/ROW]
[ROW][C]56[/C][C]3.5[/C][C]4.86942[/C][C]-1.36942[/C][/ROW]
[ROW][C]57[/C][C]4.5[/C][C]4.89432[/C][C]-0.394316[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]5.24703[/C][C]-5.24703[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]5.34223[/C][C]-2.34223[/C][/ROW]
[ROW][C]60[/C][C]3.5[/C][C]4.30719[/C][C]-0.807191[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]5.07864[/C][C]-2.07864[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]4.5211[/C][C]-3.5211[/C][/ROW]
[ROW][C]63[/C][C]5.5[/C][C]4.37686[/C][C]1.12314[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270892&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270892&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	7.5	5.29437	2.20563
2	6.5	5.58863	0.911374
3	1	4.36756	-3.36756
4	1	4.39171	-3.39171
5	5.5	4.44769	1.05231
6	8.5	5.0859	3.4141
7	6.5	4.87755	1.62245
8	4.5	3.64602	0.853983
9	2	4.30719	-2.30719
10	5	4.62069	0.379315
11	0.5	3.86109	-3.36109
12	5	4.85779	0.142213
13	2.5	3.92755	-1.42755
14	5	5.21359	-0.21359
15	5.5	5.39214	0.107863
16	3.5	4.4384	-0.938399
17	4	4.73149	-0.731489
18	6.5	4.16412	2.33588
19	4.5	4.40357	0.0964333
20	5.5	4.62121	0.87879
21	4	5.03686	-1.03686
22	7.5	4.72571	2.77429
23	4	4.35944	-0.35944
24	5.5	4.06518	1.43482
25	2.5	5.33411	-2.83411
26	5.5	5.01944	0.480557
27	3.5	4.46394	-0.963938
28	4.5	4.31275	0.187253
29	4.5	4.111	0.388997
30	6	4.83225	1.16775
31	5	3.74708	1.25292
32	6.5	3.96793	2.53207
33	5	5.13762	-0.137619
34	6	4.34501	1.65499
35	4.5	3.38133	1.11867
36	5	4.16529	0.834706
37	5	4.99829	0.00171137
38	6.5	4.91377	1.58623
39	7	4.51502	2.48498
40	4.5	4.48135	0.0186455
41	8.5	5.74677	2.75323
42	3.5	3.80511	-0.305106
43	6	4.37803	1.62197
44	1.5	4.45816	-2.95816
45	3.5	3.97606	-0.476056
46	7.5	4.00598	3.49402
47	5	4.31275	0.687253
48	6.5	4.68019	1.81981
49	6.5	5.16807	1.33193
50	6.5	5.35848	1.14152
51	7	4.26424	2.73576
52	1.5	4.27791	-2.77791
53	4	5.37963	-1.37963
54	4.5	4.62069	-0.120685
55	0	3.77957	-3.77957
56	3.5	4.86942	-1.36942
57	4.5	4.89432	-0.394316
58	0	5.24703	-5.24703
59	3	5.34223	-2.34223
60	3.5	4.30719	-0.807191
61	3	5.07864	-2.07864
62	1	4.5211	-3.5211
63	5.5	4.37686	1.12314

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.608572	0.782856	0.391428
8	0.897837	0.204326	0.102163
9	0.892167	0.215665	0.107833
10	0.829827	0.340346	0.170173
11	0.85087	0.298261	0.14913
12	0.780121	0.439757	0.219879
13	0.709137	0.581726	0.290863
14	0.667912	0.664177	0.332088
15	0.590263	0.819475	0.409737
16	0.504401	0.991198	0.495599
17	0.427037	0.854075	0.572963
18	0.570599	0.858802	0.429401
19	0.4878	0.975601	0.5122
20	0.413872	0.827744	0.586128
21	0.372516	0.745032	0.627484
22	0.420405	0.840809	0.579595
23	0.347375	0.694751	0.652625
24	0.342883	0.685767	0.657117
25	0.478832	0.957663	0.521168
26	0.403754	0.807507	0.596246
27	0.345569	0.691138	0.654431
28	0.280412	0.560825	0.719588
29	0.222023	0.444046	0.777977
30	0.183442	0.366884	0.816558
31	0.156173	0.312347	0.843827
32	0.184355	0.368709	0.815645
33	0.138928	0.277856	0.861072
34	0.125562	0.251125	0.874438
35	0.100377	0.200754	0.899623
36	0.0747298	0.14946	0.92527
37	0.0512089	0.102418	0.948791
38	0.0437567	0.0875135	0.956243
39	0.0518607	0.103721	0.948139
40	0.0340555	0.068111	0.965944
41	0.0513648	0.10273	0.948635
42	0.0337679	0.0675357	0.966232
43	0.0298838	0.0597677	0.970116
44	0.0462579	0.0925158	0.953742
45	0.0302228	0.0604456	0.969777
46	0.0746812	0.149362	0.925319
47	0.061558	0.123116	0.938442
48	0.117953	0.235906	0.882047
49	0.150722	0.301445	0.849278
50	0.204029	0.408059	0.795971
51	0.410325	0.820649	0.589675
52	0.359301	0.718602	0.640699
53	0.269411	0.538822	0.730589
54	0.258967	0.517934	0.741033
55	0.303912	0.607824	0.696088
56	0.235064	0.470127	0.764936

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.608572 & 0.782856 & 0.391428 \tabularnewline
8 & 0.897837 & 0.204326 & 0.102163 \tabularnewline
9 & 0.892167 & 0.215665 & 0.107833 \tabularnewline
10 & 0.829827 & 0.340346 & 0.170173 \tabularnewline
11 & 0.85087 & 0.298261 & 0.14913 \tabularnewline
12 & 0.780121 & 0.439757 & 0.219879 \tabularnewline
13 & 0.709137 & 0.581726 & 0.290863 \tabularnewline
14 & 0.667912 & 0.664177 & 0.332088 \tabularnewline
15 & 0.590263 & 0.819475 & 0.409737 \tabularnewline
16 & 0.504401 & 0.991198 & 0.495599 \tabularnewline
17 & 0.427037 & 0.854075 & 0.572963 \tabularnewline
18 & 0.570599 & 0.858802 & 0.429401 \tabularnewline
19 & 0.4878 & 0.975601 & 0.5122 \tabularnewline
20 & 0.413872 & 0.827744 & 0.586128 \tabularnewline
21 & 0.372516 & 0.745032 & 0.627484 \tabularnewline
22 & 0.420405 & 0.840809 & 0.579595 \tabularnewline
23 & 0.347375 & 0.694751 & 0.652625 \tabularnewline
24 & 0.342883 & 0.685767 & 0.657117 \tabularnewline
25 & 0.478832 & 0.957663 & 0.521168 \tabularnewline
26 & 0.403754 & 0.807507 & 0.596246 \tabularnewline
27 & 0.345569 & 0.691138 & 0.654431 \tabularnewline
28 & 0.280412 & 0.560825 & 0.719588 \tabularnewline
29 & 0.222023 & 0.444046 & 0.777977 \tabularnewline
30 & 0.183442 & 0.366884 & 0.816558 \tabularnewline
31 & 0.156173 & 0.312347 & 0.843827 \tabularnewline
32 & 0.184355 & 0.368709 & 0.815645 \tabularnewline
33 & 0.138928 & 0.277856 & 0.861072 \tabularnewline
34 & 0.125562 & 0.251125 & 0.874438 \tabularnewline
35 & 0.100377 & 0.200754 & 0.899623 \tabularnewline
36 & 0.0747298 & 0.14946 & 0.92527 \tabularnewline
37 & 0.0512089 & 0.102418 & 0.948791 \tabularnewline
38 & 0.0437567 & 0.0875135 & 0.956243 \tabularnewline
39 & 0.0518607 & 0.103721 & 0.948139 \tabularnewline
40 & 0.0340555 & 0.068111 & 0.965944 \tabularnewline
41 & 0.0513648 & 0.10273 & 0.948635 \tabularnewline
42 & 0.0337679 & 0.0675357 & 0.966232 \tabularnewline
43 & 0.0298838 & 0.0597677 & 0.970116 \tabularnewline
44 & 0.0462579 & 0.0925158 & 0.953742 \tabularnewline
45 & 0.0302228 & 0.0604456 & 0.969777 \tabularnewline
46 & 0.0746812 & 0.149362 & 0.925319 \tabularnewline
47 & 0.061558 & 0.123116 & 0.938442 \tabularnewline
48 & 0.117953 & 0.235906 & 0.882047 \tabularnewline
49 & 0.150722 & 0.301445 & 0.849278 \tabularnewline
50 & 0.204029 & 0.408059 & 0.795971 \tabularnewline
51 & 0.410325 & 0.820649 & 0.589675 \tabularnewline
52 & 0.359301 & 0.718602 & 0.640699 \tabularnewline
53 & 0.269411 & 0.538822 & 0.730589 \tabularnewline
54 & 0.258967 & 0.517934 & 0.741033 \tabularnewline
55 & 0.303912 & 0.607824 & 0.696088 \tabularnewline
56 & 0.235064 & 0.470127 & 0.764936 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270892&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]7[/C][C]0.608572[/C][C]0.782856[/C][C]0.391428[/C][/ROW]
[ROW][C]8[/C][C]0.897837[/C][C]0.204326[/C][C]0.102163[/C][/ROW]
[ROW][C]9[/C][C]0.892167[/C][C]0.215665[/C][C]0.107833[/C][/ROW]
[ROW][C]10[/C][C]0.829827[/C][C]0.340346[/C][C]0.170173[/C][/ROW]
[ROW][C]11[/C][C]0.85087[/C][C]0.298261[/C][C]0.14913[/C][/ROW]
[ROW][C]12[/C][C]0.780121[/C][C]0.439757[/C][C]0.219879[/C][/ROW]
[ROW][C]13[/C][C]0.709137[/C][C]0.581726[/C][C]0.290863[/C][/ROW]
[ROW][C]14[/C][C]0.667912[/C][C]0.664177[/C][C]0.332088[/C][/ROW]
[ROW][C]15[/C][C]0.590263[/C][C]0.819475[/C][C]0.409737[/C][/ROW]
[ROW][C]16[/C][C]0.504401[/C][C]0.991198[/C][C]0.495599[/C][/ROW]
[ROW][C]17[/C][C]0.427037[/C][C]0.854075[/C][C]0.572963[/C][/ROW]
[ROW][C]18[/C][C]0.570599[/C][C]0.858802[/C][C]0.429401[/C][/ROW]
[ROW][C]19[/C][C]0.4878[/C][C]0.975601[/C][C]0.5122[/C][/ROW]
[ROW][C]20[/C][C]0.413872[/C][C]0.827744[/C][C]0.586128[/C][/ROW]
[ROW][C]21[/C][C]0.372516[/C][C]0.745032[/C][C]0.627484[/C][/ROW]
[ROW][C]22[/C][C]0.420405[/C][C]0.840809[/C][C]0.579595[/C][/ROW]
[ROW][C]23[/C][C]0.347375[/C][C]0.694751[/C][C]0.652625[/C][/ROW]
[ROW][C]24[/C][C]0.342883[/C][C]0.685767[/C][C]0.657117[/C][/ROW]
[ROW][C]25[/C][C]0.478832[/C][C]0.957663[/C][C]0.521168[/C][/ROW]
[ROW][C]26[/C][C]0.403754[/C][C]0.807507[/C][C]0.596246[/C][/ROW]
[ROW][C]27[/C][C]0.345569[/C][C]0.691138[/C][C]0.654431[/C][/ROW]
[ROW][C]28[/C][C]0.280412[/C][C]0.560825[/C][C]0.719588[/C][/ROW]
[ROW][C]29[/C][C]0.222023[/C][C]0.444046[/C][C]0.777977[/C][/ROW]
[ROW][C]30[/C][C]0.183442[/C][C]0.366884[/C][C]0.816558[/C][/ROW]
[ROW][C]31[/C][C]0.156173[/C][C]0.312347[/C][C]0.843827[/C][/ROW]
[ROW][C]32[/C][C]0.184355[/C][C]0.368709[/C][C]0.815645[/C][/ROW]
[ROW][C]33[/C][C]0.138928[/C][C]0.277856[/C][C]0.861072[/C][/ROW]
[ROW][C]34[/C][C]0.125562[/C][C]0.251125[/C][C]0.874438[/C][/ROW]
[ROW][C]35[/C][C]0.100377[/C][C]0.200754[/C][C]0.899623[/C][/ROW]
[ROW][C]36[/C][C]0.0747298[/C][C]0.14946[/C][C]0.92527[/C][/ROW]
[ROW][C]37[/C][C]0.0512089[/C][C]0.102418[/C][C]0.948791[/C][/ROW]
[ROW][C]38[/C][C]0.0437567[/C][C]0.0875135[/C][C]0.956243[/C][/ROW]
[ROW][C]39[/C][C]0.0518607[/C][C]0.103721[/C][C]0.948139[/C][/ROW]
[ROW][C]40[/C][C]0.0340555[/C][C]0.068111[/C][C]0.965944[/C][/ROW]
[ROW][C]41[/C][C]0.0513648[/C][C]0.10273[/C][C]0.948635[/C][/ROW]
[ROW][C]42[/C][C]0.0337679[/C][C]0.0675357[/C][C]0.966232[/C][/ROW]
[ROW][C]43[/C][C]0.0298838[/C][C]0.0597677[/C][C]0.970116[/C][/ROW]
[ROW][C]44[/C][C]0.0462579[/C][C]0.0925158[/C][C]0.953742[/C][/ROW]
[ROW][C]45[/C][C]0.0302228[/C][C]0.0604456[/C][C]0.969777[/C][/ROW]
[ROW][C]46[/C][C]0.0746812[/C][C]0.149362[/C][C]0.925319[/C][/ROW]
[ROW][C]47[/C][C]0.061558[/C][C]0.123116[/C][C]0.938442[/C][/ROW]
[ROW][C]48[/C][C]0.117953[/C][C]0.235906[/C][C]0.882047[/C][/ROW]
[ROW][C]49[/C][C]0.150722[/C][C]0.301445[/C][C]0.849278[/C][/ROW]
[ROW][C]50[/C][C]0.204029[/C][C]0.408059[/C][C]0.795971[/C][/ROW]
[ROW][C]51[/C][C]0.410325[/C][C]0.820649[/C][C]0.589675[/C][/ROW]
[ROW][C]52[/C][C]0.359301[/C][C]0.718602[/C][C]0.640699[/C][/ROW]
[ROW][C]53[/C][C]0.269411[/C][C]0.538822[/C][C]0.730589[/C][/ROW]
[ROW][C]54[/C][C]0.258967[/C][C]0.517934[/C][C]0.741033[/C][/ROW]
[ROW][C]55[/C][C]0.303912[/C][C]0.607824[/C][C]0.696088[/C][/ROW]
[ROW][C]56[/C][C]0.235064[/C][C]0.470127[/C][C]0.764936[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270892&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270892&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
7	0.608572	0.782856	0.391428
8	0.897837	0.204326	0.102163
9	0.892167	0.215665	0.107833
10	0.829827	0.340346	0.170173
11	0.85087	0.298261	0.14913
12	0.780121	0.439757	0.219879
13	0.709137	0.581726	0.290863
14	0.667912	0.664177	0.332088
15	0.590263	0.819475	0.409737
16	0.504401	0.991198	0.495599
17	0.427037	0.854075	0.572963
18	0.570599	0.858802	0.429401
19	0.4878	0.975601	0.5122
20	0.413872	0.827744	0.586128
21	0.372516	0.745032	0.627484
22	0.420405	0.840809	0.579595
23	0.347375	0.694751	0.652625
24	0.342883	0.685767	0.657117
25	0.478832	0.957663	0.521168
26	0.403754	0.807507	0.596246
27	0.345569	0.691138	0.654431
28	0.280412	0.560825	0.719588
29	0.222023	0.444046	0.777977
30	0.183442	0.366884	0.816558
31	0.156173	0.312347	0.843827
32	0.184355	0.368709	0.815645
33	0.138928	0.277856	0.861072
34	0.125562	0.251125	0.874438
35	0.100377	0.200754	0.899623
36	0.0747298	0.14946	0.92527
37	0.0512089	0.102418	0.948791
38	0.0437567	0.0875135	0.956243
39	0.0518607	0.103721	0.948139
40	0.0340555	0.068111	0.965944
41	0.0513648	0.10273	0.948635
42	0.0337679	0.0675357	0.966232
43	0.0298838	0.0597677	0.970116
44	0.0462579	0.0925158	0.953742
45	0.0302228	0.0604456	0.969777
46	0.0746812	0.149362	0.925319
47	0.061558	0.123116	0.938442
48	0.117953	0.235906	0.882047
49	0.150722	0.301445	0.849278
50	0.204029	0.408059	0.795971
51	0.410325	0.820649	0.589675
52	0.359301	0.718602	0.640699
53	0.269411	0.538822	0.730589
54	0.258967	0.517934	0.741033
55	0.303912	0.607824	0.696088
56	0.235064	0.470127	0.764936

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	6	0.12	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 6 & 0.12 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270892&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]6[/C][C]0.12[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270892&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270892&T=6

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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	6	0.12	NOK

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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