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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 20 Nov 2013 12:05:14 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/20/t1384967229f6qemn7nmvmu3tm.htm/, Retrieved Wed, 01 May 2024 23:11:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=226672, Retrieved Wed, 01 May 2024 23:11:57 +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] [W7_opgave1] [2013-11-20 17:05:14] [6ca235be63c25d1ede0161784935ca53] [Current]

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9,2	2,07	102,06	0,44	0,67
11,7	1,92	81,65	0,44	0,80
15,8	1,95	86,18	0,46	0,76
8,6	1,89	81,65	0,42	0,65
23,2	2,10	92,99	0,45	0,90
27,4	1,95	102,06	0,43	0,78
9,3	1,92	83,91	0,49	0,77
16	2,07	106,59	0,47	0,75
4,7	2,10	106,59	0,44	0,82
12,5	2,04	95,25	0,48	0,83
20,1	2,10	111,13	0,52	0,63
9,1	2,10	111,13	0,49	0,76
8,1	1,92	83,91	0,37	0,71
8,6	1,86	83,91	0,42	0,78
20,3	1,89	81,65	0,44	0,78
25	2,07	99,79	0,50	0,88
19,2	1,98	88,00	0,50	0,83
3,3	2,32	102,06	0,43	0,57
11,2	1,92	95,25	0,37	0,82
10,5	2,16	108,86	0,50	0,71
10,1	2,07	102,06	0,40	0,77
7,2	2,23	119,29	0,48	0,66
13,6	1,95	95,25	0,48	0,24
9	2,07	106,59	0,43	0,73
24,6	2,19	104,33	0,56	0,72
12,6	1,95	86,18	0,44	0,76
5,6	2,01	99,79	0,49	0,75
8,7	2,07	95,25	0,40	0,74
7,7	1,86	81,65	0,42	0,71
24,1	1,98	106,59	0,49	0,74
11,7	1,95	83,91	0,48	0,86
7,7	1,83	79,38	0,39	0,72
9,6	1,83	87,09	0,44	0,79
7,2	2,23	119,29	0,48	0,66
12,3	1,86	81,65	0,34	0,82
8,9	2,04	108,86	0,52	0,73
13,6	1,95	95,25	0,48	0,85
11,2	1,77	72,57	0,41	0,81
2,8	2,10	104,33	0,41	0,60
3,2	2,13	111,13	0,41	0,57
9,4	2,23	103,42	0,45	0,73
11,9	1,80	70,31	0,29	0,71
15,4	1,89	90,72	0,45	0,80
7,4	2,07	106,59	0,55	0,78
18,9	2,13	106,59	0,48	0,74
7,9	1,80	47,63	0,36	0,84
12,2	1,86	81,65	0,53	0,79
11	1,74	83,91	0,35	0,70
2,8	2,16	111,13	0,41	0,78
11,8	1,77	81,65	0,43	0,87
17,1	2,26	108,86	0,60	0,71
11,6	2,07	102,06	0,48	0,70
5,8	2,07	97,52	0,46	0,73
8,3	2,13	104,33	0,44	0,76

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	'Gertrude Mary Cox' @ cox.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 & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226672&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226672&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226672&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	10 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
V1[t] = + 4.56113 -12.2915V2[t] + 0.0249683V3[t] + 46.7043V4[t] + 11.4732V5[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
V1[t] =  +  4.56113 -12.2915V2[t] +  0.0249683V3[t] +  46.7043V4[t] +  11.4732V5[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226672&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]V1[t] =  +  4.56113 -12.2915V2[t] +  0.0249683V3[t] +  46.7043V4[t] +  11.4732V5[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226672&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226672&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
V1[t] = + 4.56113 -12.2915V2[t] + 0.0249683V3[t] + 46.7043V4[t] + 11.4732V5[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)	4.56113	14.9477	0.3051	0.761553	0.380776
V2	-12.2915	9.77056	-1.258	0.21435	0.107175
V3	0.0249683	0.101644	0.2456	0.806984	0.403492
V4	46.7043	15.5934	2.995	0.00429341	0.00214671
V5	11.4732	7.83009	1.465	0.149236	0.074618

\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) & 4.56113 & 14.9477 & 0.3051 & 0.761553 & 0.380776 \tabularnewline
V2 & -12.2915 & 9.77056 & -1.258 & 0.21435 & 0.107175 \tabularnewline
V3 & 0.0249683 & 0.101644 & 0.2456 & 0.806984 & 0.403492 \tabularnewline
V4 & 46.7043 & 15.5934 & 2.995 & 0.00429341 & 0.00214671 \tabularnewline
V5 & 11.4732 & 7.83009 & 1.465 & 0.149236 & 0.074618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226672&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]4.56113[/C][C]14.9477[/C][C]0.3051[/C][C]0.761553[/C][C]0.380776[/C][/ROW]
[ROW][C]V2[/C][C]-12.2915[/C][C]9.77056[/C][C]-1.258[/C][C]0.21435[/C][C]0.107175[/C][/ROW]
[ROW][C]V3[/C][C]0.0249683[/C][C]0.101644[/C][C]0.2456[/C][C]0.806984[/C][C]0.403492[/C][/ROW]
[ROW][C]V4[/C][C]46.7043[/C][C]15.5934[/C][C]2.995[/C][C]0.00429341[/C][C]0.00214671[/C][/ROW]
[ROW][C]V5[/C][C]11.4732[/C][C]7.83009[/C][C]1.465[/C][C]0.149236[/C][C]0.074618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226672&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226672&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)	4.56113	14.9477	0.3051	0.761553	0.380776
V2	-12.2915	9.77056	-1.258	0.21435	0.107175
V3	0.0249683	0.101644	0.2456	0.806984	0.403492
V4	46.7043	15.5934	2.995	0.00429341	0.00214671
V5	11.4732	7.83009	1.465	0.149236	0.074618

Multiple Linear Regression - Regression Statistics
Multiple R	0.464044
R-squared	0.215337
Adjusted R-squared	0.151283
F-TEST (value)	3.3618
F-TEST (DF numerator)	4
F-TEST (DF denominator)	49
p-value	0.0164969
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.43474
Sum Squared Residuals	1447.28

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.464044 \tabularnewline
R-squared & 0.215337 \tabularnewline
Adjusted R-squared & 0.151283 \tabularnewline
F-TEST (value) & 3.3618 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 49 \tabularnewline
p-value & 0.0164969 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.43474 \tabularnewline
Sum Squared Residuals & 1447.28 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226672&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.464044[/C][/ROW]
[ROW][C]R-squared[/C][C]0.215337[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.151283[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.3618[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]49[/C][/ROW]
[ROW][C]p-value[/C][C]0.0164969[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.43474[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1447.28[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226672&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226672&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	0.464044
R-squared	0.215337
Adjusted R-squared	0.151283
F-TEST (value)	3.3618
F-TEST (DF numerator)	4
F-TEST (DF denominator)	49
p-value	0.0164969
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.43474
Sum Squared Residuals	1447.28

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	9.2	9.90294	-0.702938
2	11.7	12.7286	-1.02857
3	15.8	12.9481	2.85191
4	8.6	10.4423	-1.84225
5	23.2	12.4136	10.7864
6	27.4	12.1729	15.2271
7	9.3	14.776	-5.47602
8	16	12.335	3.66497
9	4.7	11.3683	-6.66827
10	12.5	13.8055	-1.30553
11	20.1	13.0381	7.06193
12	9.1	13.1285	-4.02846
13	8.1	8.48311	-0.38311
14	8.6	12.3589	-3.75894
15	20.3	12.8678	7.43215
16	25	15.0579	9.94212
17	19.2	15.2961	3.90392
18	3.3	5.21571	-1.91571
19	11.2	10.0283	1.1717
20	10.5	12.2277	-1.72767
21	10.1	9.18208	0.917919
22	7.2	10.1199	-2.91995
23	13.6	8.14259	5.45741
24	9	10.2374	-1.23739
25	24.6	14.6628	9.93719
26	12.6	12.014	0.585997
27	5.6	13.8368	-8.23682
28	8.7	8.66785	0.0321478
29	7.7	11.4994	-3.79939
30	24.1	14.2606	9.83939
31	11.7	14.9728	-3.27281
32	7.7	10.5251	-2.82505
33	9.6	13.8559	-4.2559
34	7.2	10.1199	-2.91995
35	12.3	9.02509	3.27491
36	8.9	14.8662	-5.9662
37	13.6	15.1412	-1.54122
38	11.2	13.0592	-1.85918
39	2.8	7.38662	-4.58662
40	3.2	6.84347	-3.64347
41	9.4	9.12569	0.27431
42	11.9	5.88217	6.01783
43	15.4	13.7908	1.60918
44	7.4	16.4156	-9.01556
45	18.9	11.9498	6.95015
46	7.9	10.0767	-2.1767
47	12.2	17.5547	-5.35471
48	11	9.64676	1.35324
49	2.8	8.88409	-6.08409
50	11.8	14.9084	-3.10837
51	17.1	15.669	1.43104
52	11.6	12.1153	-0.515304
53	5.8	11.4121	-5.61206
54	8.3	10.2547	-1.95471

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.2 & 9.90294 & -0.702938 \tabularnewline
2 & 11.7 & 12.7286 & -1.02857 \tabularnewline
3 & 15.8 & 12.9481 & 2.85191 \tabularnewline
4 & 8.6 & 10.4423 & -1.84225 \tabularnewline
5 & 23.2 & 12.4136 & 10.7864 \tabularnewline
6 & 27.4 & 12.1729 & 15.2271 \tabularnewline
7 & 9.3 & 14.776 & -5.47602 \tabularnewline
8 & 16 & 12.335 & 3.66497 \tabularnewline
9 & 4.7 & 11.3683 & -6.66827 \tabularnewline
10 & 12.5 & 13.8055 & -1.30553 \tabularnewline
11 & 20.1 & 13.0381 & 7.06193 \tabularnewline
12 & 9.1 & 13.1285 & -4.02846 \tabularnewline
13 & 8.1 & 8.48311 & -0.38311 \tabularnewline
14 & 8.6 & 12.3589 & -3.75894 \tabularnewline
15 & 20.3 & 12.8678 & 7.43215 \tabularnewline
16 & 25 & 15.0579 & 9.94212 \tabularnewline
17 & 19.2 & 15.2961 & 3.90392 \tabularnewline
18 & 3.3 & 5.21571 & -1.91571 \tabularnewline
19 & 11.2 & 10.0283 & 1.1717 \tabularnewline
20 & 10.5 & 12.2277 & -1.72767 \tabularnewline
21 & 10.1 & 9.18208 & 0.917919 \tabularnewline
22 & 7.2 & 10.1199 & -2.91995 \tabularnewline
23 & 13.6 & 8.14259 & 5.45741 \tabularnewline
24 & 9 & 10.2374 & -1.23739 \tabularnewline
25 & 24.6 & 14.6628 & 9.93719 \tabularnewline
26 & 12.6 & 12.014 & 0.585997 \tabularnewline
27 & 5.6 & 13.8368 & -8.23682 \tabularnewline
28 & 8.7 & 8.66785 & 0.0321478 \tabularnewline
29 & 7.7 & 11.4994 & -3.79939 \tabularnewline
30 & 24.1 & 14.2606 & 9.83939 \tabularnewline
31 & 11.7 & 14.9728 & -3.27281 \tabularnewline
32 & 7.7 & 10.5251 & -2.82505 \tabularnewline
33 & 9.6 & 13.8559 & -4.2559 \tabularnewline
34 & 7.2 & 10.1199 & -2.91995 \tabularnewline
35 & 12.3 & 9.02509 & 3.27491 \tabularnewline
36 & 8.9 & 14.8662 & -5.9662 \tabularnewline
37 & 13.6 & 15.1412 & -1.54122 \tabularnewline
38 & 11.2 & 13.0592 & -1.85918 \tabularnewline
39 & 2.8 & 7.38662 & -4.58662 \tabularnewline
40 & 3.2 & 6.84347 & -3.64347 \tabularnewline
41 & 9.4 & 9.12569 & 0.27431 \tabularnewline
42 & 11.9 & 5.88217 & 6.01783 \tabularnewline
43 & 15.4 & 13.7908 & 1.60918 \tabularnewline
44 & 7.4 & 16.4156 & -9.01556 \tabularnewline
45 & 18.9 & 11.9498 & 6.95015 \tabularnewline
46 & 7.9 & 10.0767 & -2.1767 \tabularnewline
47 & 12.2 & 17.5547 & -5.35471 \tabularnewline
48 & 11 & 9.64676 & 1.35324 \tabularnewline
49 & 2.8 & 8.88409 & -6.08409 \tabularnewline
50 & 11.8 & 14.9084 & -3.10837 \tabularnewline
51 & 17.1 & 15.669 & 1.43104 \tabularnewline
52 & 11.6 & 12.1153 & -0.515304 \tabularnewline
53 & 5.8 & 11.4121 & -5.61206 \tabularnewline
54 & 8.3 & 10.2547 & -1.95471 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226672&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]9.2[/C][C]9.90294[/C][C]-0.702938[/C][/ROW]
[ROW][C]2[/C][C]11.7[/C][C]12.7286[/C][C]-1.02857[/C][/ROW]
[ROW][C]3[/C][C]15.8[/C][C]12.9481[/C][C]2.85191[/C][/ROW]
[ROW][C]4[/C][C]8.6[/C][C]10.4423[/C][C]-1.84225[/C][/ROW]
[ROW][C]5[/C][C]23.2[/C][C]12.4136[/C][C]10.7864[/C][/ROW]
[ROW][C]6[/C][C]27.4[/C][C]12.1729[/C][C]15.2271[/C][/ROW]
[ROW][C]7[/C][C]9.3[/C][C]14.776[/C][C]-5.47602[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]12.335[/C][C]3.66497[/C][/ROW]
[ROW][C]9[/C][C]4.7[/C][C]11.3683[/C][C]-6.66827[/C][/ROW]
[ROW][C]10[/C][C]12.5[/C][C]13.8055[/C][C]-1.30553[/C][/ROW]
[ROW][C]11[/C][C]20.1[/C][C]13.0381[/C][C]7.06193[/C][/ROW]
[ROW][C]12[/C][C]9.1[/C][C]13.1285[/C][C]-4.02846[/C][/ROW]
[ROW][C]13[/C][C]8.1[/C][C]8.48311[/C][C]-0.38311[/C][/ROW]
[ROW][C]14[/C][C]8.6[/C][C]12.3589[/C][C]-3.75894[/C][/ROW]
[ROW][C]15[/C][C]20.3[/C][C]12.8678[/C][C]7.43215[/C][/ROW]
[ROW][C]16[/C][C]25[/C][C]15.0579[/C][C]9.94212[/C][/ROW]
[ROW][C]17[/C][C]19.2[/C][C]15.2961[/C][C]3.90392[/C][/ROW]
[ROW][C]18[/C][C]3.3[/C][C]5.21571[/C][C]-1.91571[/C][/ROW]
[ROW][C]19[/C][C]11.2[/C][C]10.0283[/C][C]1.1717[/C][/ROW]
[ROW][C]20[/C][C]10.5[/C][C]12.2277[/C][C]-1.72767[/C][/ROW]
[ROW][C]21[/C][C]10.1[/C][C]9.18208[/C][C]0.917919[/C][/ROW]
[ROW][C]22[/C][C]7.2[/C][C]10.1199[/C][C]-2.91995[/C][/ROW]
[ROW][C]23[/C][C]13.6[/C][C]8.14259[/C][C]5.45741[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]10.2374[/C][C]-1.23739[/C][/ROW]
[ROW][C]25[/C][C]24.6[/C][C]14.6628[/C][C]9.93719[/C][/ROW]
[ROW][C]26[/C][C]12.6[/C][C]12.014[/C][C]0.585997[/C][/ROW]
[ROW][C]27[/C][C]5.6[/C][C]13.8368[/C][C]-8.23682[/C][/ROW]
[ROW][C]28[/C][C]8.7[/C][C]8.66785[/C][C]0.0321478[/C][/ROW]
[ROW][C]29[/C][C]7.7[/C][C]11.4994[/C][C]-3.79939[/C][/ROW]
[ROW][C]30[/C][C]24.1[/C][C]14.2606[/C][C]9.83939[/C][/ROW]
[ROW][C]31[/C][C]11.7[/C][C]14.9728[/C][C]-3.27281[/C][/ROW]
[ROW][C]32[/C][C]7.7[/C][C]10.5251[/C][C]-2.82505[/C][/ROW]
[ROW][C]33[/C][C]9.6[/C][C]13.8559[/C][C]-4.2559[/C][/ROW]
[ROW][C]34[/C][C]7.2[/C][C]10.1199[/C][C]-2.91995[/C][/ROW]
[ROW][C]35[/C][C]12.3[/C][C]9.02509[/C][C]3.27491[/C][/ROW]
[ROW][C]36[/C][C]8.9[/C][C]14.8662[/C][C]-5.9662[/C][/ROW]
[ROW][C]37[/C][C]13.6[/C][C]15.1412[/C][C]-1.54122[/C][/ROW]
[ROW][C]38[/C][C]11.2[/C][C]13.0592[/C][C]-1.85918[/C][/ROW]
[ROW][C]39[/C][C]2.8[/C][C]7.38662[/C][C]-4.58662[/C][/ROW]
[ROW][C]40[/C][C]3.2[/C][C]6.84347[/C][C]-3.64347[/C][/ROW]
[ROW][C]41[/C][C]9.4[/C][C]9.12569[/C][C]0.27431[/C][/ROW]
[ROW][C]42[/C][C]11.9[/C][C]5.88217[/C][C]6.01783[/C][/ROW]
[ROW][C]43[/C][C]15.4[/C][C]13.7908[/C][C]1.60918[/C][/ROW]
[ROW][C]44[/C][C]7.4[/C][C]16.4156[/C][C]-9.01556[/C][/ROW]
[ROW][C]45[/C][C]18.9[/C][C]11.9498[/C][C]6.95015[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]10.0767[/C][C]-2.1767[/C][/ROW]
[ROW][C]47[/C][C]12.2[/C][C]17.5547[/C][C]-5.35471[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]9.64676[/C][C]1.35324[/C][/ROW]
[ROW][C]49[/C][C]2.8[/C][C]8.88409[/C][C]-6.08409[/C][/ROW]
[ROW][C]50[/C][C]11.8[/C][C]14.9084[/C][C]-3.10837[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]15.669[/C][C]1.43104[/C][/ROW]
[ROW][C]52[/C][C]11.6[/C][C]12.1153[/C][C]-0.515304[/C][/ROW]
[ROW][C]53[/C][C]5.8[/C][C]11.4121[/C][C]-5.61206[/C][/ROW]
[ROW][C]54[/C][C]8.3[/C][C]10.2547[/C][C]-1.95471[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226672&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226672&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	9.2	9.90294	-0.702938
2	11.7	12.7286	-1.02857
3	15.8	12.9481	2.85191
4	8.6	10.4423	-1.84225
5	23.2	12.4136	10.7864
6	27.4	12.1729	15.2271
7	9.3	14.776	-5.47602
8	16	12.335	3.66497
9	4.7	11.3683	-6.66827
10	12.5	13.8055	-1.30553
11	20.1	13.0381	7.06193
12	9.1	13.1285	-4.02846
13	8.1	8.48311	-0.38311
14	8.6	12.3589	-3.75894
15	20.3	12.8678	7.43215
16	25	15.0579	9.94212
17	19.2	15.2961	3.90392
18	3.3	5.21571	-1.91571
19	11.2	10.0283	1.1717
20	10.5	12.2277	-1.72767
21	10.1	9.18208	0.917919
22	7.2	10.1199	-2.91995
23	13.6	8.14259	5.45741
24	9	10.2374	-1.23739
25	24.6	14.6628	9.93719
26	12.6	12.014	0.585997
27	5.6	13.8368	-8.23682
28	8.7	8.66785	0.0321478
29	7.7	11.4994	-3.79939
30	24.1	14.2606	9.83939
31	11.7	14.9728	-3.27281
32	7.7	10.5251	-2.82505
33	9.6	13.8559	-4.2559
34	7.2	10.1199	-2.91995
35	12.3	9.02509	3.27491
36	8.9	14.8662	-5.9662
37	13.6	15.1412	-1.54122
38	11.2	13.0592	-1.85918
39	2.8	7.38662	-4.58662
40	3.2	6.84347	-3.64347
41	9.4	9.12569	0.27431
42	11.9	5.88217	6.01783
43	15.4	13.7908	1.60918
44	7.4	16.4156	-9.01556
45	18.9	11.9498	6.95015
46	7.9	10.0767	-2.1767
47	12.2	17.5547	-5.35471
48	11	9.64676	1.35324
49	2.8	8.88409	-6.08409
50	11.8	14.9084	-3.10837
51	17.1	15.669	1.43104
52	11.6	12.1153	-0.515304
53	5.8	11.4121	-5.61206
54	8.3	10.2547	-1.95471

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.361303	0.722607	0.638697
9	0.968222	0.063557	0.0317785
10	0.939244	0.121511	0.0607555
11	0.965652	0.0686954	0.0343477
12	0.970222	0.0595557	0.0297778
13	0.947983	0.104035	0.0520174
14	0.937827	0.124345	0.0621725
15	0.94997	0.10006	0.0500298
16	0.972954	0.0540914	0.0270457
17	0.963554	0.072891	0.0364455
18	0.946994	0.106012	0.0530059
19	0.923606	0.152788	0.0763941
20	0.896194	0.207612	0.103806
21	0.855238	0.289523	0.144762
22	0.815655	0.36869	0.184345
23	0.816671	0.366658	0.183329
24	0.759579	0.480843	0.240421
25	0.894742	0.210517	0.105258
26	0.856074	0.287851	0.143926
27	0.918771	0.162458	0.081229
28	0.881106	0.237787	0.118894
29	0.853092	0.293815	0.146908
30	0.970306	0.0593874	0.0296937
31	0.958454	0.0830928	0.0415464
32	0.938264	0.123472	0.0617358
33	0.920508	0.158985	0.0794924
34	0.889844	0.220312	0.110156
35	0.86688	0.266241	0.13312
36	0.853979	0.292042	0.146021
37	0.796837	0.406326	0.203163
38	0.719822	0.560357	0.280178
39	0.699258	0.601485	0.300742
40	0.751988	0.496025	0.248012
41	0.652543	0.694915	0.347457
42	0.592074	0.815851	0.407926
43	0.558618	0.882765	0.441382
44	0.598931	0.802137	0.401069
45	0.855758	0.288483	0.144242
46	0.822454	0.355093	0.177546

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.361303 & 0.722607 & 0.638697 \tabularnewline
9 & 0.968222 & 0.063557 & 0.0317785 \tabularnewline
10 & 0.939244 & 0.121511 & 0.0607555 \tabularnewline
11 & 0.965652 & 0.0686954 & 0.0343477 \tabularnewline
12 & 0.970222 & 0.0595557 & 0.0297778 \tabularnewline
13 & 0.947983 & 0.104035 & 0.0520174 \tabularnewline
14 & 0.937827 & 0.124345 & 0.0621725 \tabularnewline
15 & 0.94997 & 0.10006 & 0.0500298 \tabularnewline
16 & 0.972954 & 0.0540914 & 0.0270457 \tabularnewline
17 & 0.963554 & 0.072891 & 0.0364455 \tabularnewline
18 & 0.946994 & 0.106012 & 0.0530059 \tabularnewline
19 & 0.923606 & 0.152788 & 0.0763941 \tabularnewline
20 & 0.896194 & 0.207612 & 0.103806 \tabularnewline
21 & 0.855238 & 0.289523 & 0.144762 \tabularnewline
22 & 0.815655 & 0.36869 & 0.184345 \tabularnewline
23 & 0.816671 & 0.366658 & 0.183329 \tabularnewline
24 & 0.759579 & 0.480843 & 0.240421 \tabularnewline
25 & 0.894742 & 0.210517 & 0.105258 \tabularnewline
26 & 0.856074 & 0.287851 & 0.143926 \tabularnewline
27 & 0.918771 & 0.162458 & 0.081229 \tabularnewline
28 & 0.881106 & 0.237787 & 0.118894 \tabularnewline
29 & 0.853092 & 0.293815 & 0.146908 \tabularnewline
30 & 0.970306 & 0.0593874 & 0.0296937 \tabularnewline
31 & 0.958454 & 0.0830928 & 0.0415464 \tabularnewline
32 & 0.938264 & 0.123472 & 0.0617358 \tabularnewline
33 & 0.920508 & 0.158985 & 0.0794924 \tabularnewline
34 & 0.889844 & 0.220312 & 0.110156 \tabularnewline
35 & 0.86688 & 0.266241 & 0.13312 \tabularnewline
36 & 0.853979 & 0.292042 & 0.146021 \tabularnewline
37 & 0.796837 & 0.406326 & 0.203163 \tabularnewline
38 & 0.719822 & 0.560357 & 0.280178 \tabularnewline
39 & 0.699258 & 0.601485 & 0.300742 \tabularnewline
40 & 0.751988 & 0.496025 & 0.248012 \tabularnewline
41 & 0.652543 & 0.694915 & 0.347457 \tabularnewline
42 & 0.592074 & 0.815851 & 0.407926 \tabularnewline
43 & 0.558618 & 0.882765 & 0.441382 \tabularnewline
44 & 0.598931 & 0.802137 & 0.401069 \tabularnewline
45 & 0.855758 & 0.288483 & 0.144242 \tabularnewline
46 & 0.822454 & 0.355093 & 0.177546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226672&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]8[/C][C]0.361303[/C][C]0.722607[/C][C]0.638697[/C][/ROW]
[ROW][C]9[/C][C]0.968222[/C][C]0.063557[/C][C]0.0317785[/C][/ROW]
[ROW][C]10[/C][C]0.939244[/C][C]0.121511[/C][C]0.0607555[/C][/ROW]
[ROW][C]11[/C][C]0.965652[/C][C]0.0686954[/C][C]0.0343477[/C][/ROW]
[ROW][C]12[/C][C]0.970222[/C][C]0.0595557[/C][C]0.0297778[/C][/ROW]
[ROW][C]13[/C][C]0.947983[/C][C]0.104035[/C][C]0.0520174[/C][/ROW]
[ROW][C]14[/C][C]0.937827[/C][C]0.124345[/C][C]0.0621725[/C][/ROW]
[ROW][C]15[/C][C]0.94997[/C][C]0.10006[/C][C]0.0500298[/C][/ROW]
[ROW][C]16[/C][C]0.972954[/C][C]0.0540914[/C][C]0.0270457[/C][/ROW]
[ROW][C]17[/C][C]0.963554[/C][C]0.072891[/C][C]0.0364455[/C][/ROW]
[ROW][C]18[/C][C]0.946994[/C][C]0.106012[/C][C]0.0530059[/C][/ROW]
[ROW][C]19[/C][C]0.923606[/C][C]0.152788[/C][C]0.0763941[/C][/ROW]
[ROW][C]20[/C][C]0.896194[/C][C]0.207612[/C][C]0.103806[/C][/ROW]
[ROW][C]21[/C][C]0.855238[/C][C]0.289523[/C][C]0.144762[/C][/ROW]
[ROW][C]22[/C][C]0.815655[/C][C]0.36869[/C][C]0.184345[/C][/ROW]
[ROW][C]23[/C][C]0.816671[/C][C]0.366658[/C][C]0.183329[/C][/ROW]
[ROW][C]24[/C][C]0.759579[/C][C]0.480843[/C][C]0.240421[/C][/ROW]
[ROW][C]25[/C][C]0.894742[/C][C]0.210517[/C][C]0.105258[/C][/ROW]
[ROW][C]26[/C][C]0.856074[/C][C]0.287851[/C][C]0.143926[/C][/ROW]
[ROW][C]27[/C][C]0.918771[/C][C]0.162458[/C][C]0.081229[/C][/ROW]
[ROW][C]28[/C][C]0.881106[/C][C]0.237787[/C][C]0.118894[/C][/ROW]
[ROW][C]29[/C][C]0.853092[/C][C]0.293815[/C][C]0.146908[/C][/ROW]
[ROW][C]30[/C][C]0.970306[/C][C]0.0593874[/C][C]0.0296937[/C][/ROW]
[ROW][C]31[/C][C]0.958454[/C][C]0.0830928[/C][C]0.0415464[/C][/ROW]
[ROW][C]32[/C][C]0.938264[/C][C]0.123472[/C][C]0.0617358[/C][/ROW]
[ROW][C]33[/C][C]0.920508[/C][C]0.158985[/C][C]0.0794924[/C][/ROW]
[ROW][C]34[/C][C]0.889844[/C][C]0.220312[/C][C]0.110156[/C][/ROW]
[ROW][C]35[/C][C]0.86688[/C][C]0.266241[/C][C]0.13312[/C][/ROW]
[ROW][C]36[/C][C]0.853979[/C][C]0.292042[/C][C]0.146021[/C][/ROW]
[ROW][C]37[/C][C]0.796837[/C][C]0.406326[/C][C]0.203163[/C][/ROW]
[ROW][C]38[/C][C]0.719822[/C][C]0.560357[/C][C]0.280178[/C][/ROW]
[ROW][C]39[/C][C]0.699258[/C][C]0.601485[/C][C]0.300742[/C][/ROW]
[ROW][C]40[/C][C]0.751988[/C][C]0.496025[/C][C]0.248012[/C][/ROW]
[ROW][C]41[/C][C]0.652543[/C][C]0.694915[/C][C]0.347457[/C][/ROW]
[ROW][C]42[/C][C]0.592074[/C][C]0.815851[/C][C]0.407926[/C][/ROW]
[ROW][C]43[/C][C]0.558618[/C][C]0.882765[/C][C]0.441382[/C][/ROW]
[ROW][C]44[/C][C]0.598931[/C][C]0.802137[/C][C]0.401069[/C][/ROW]
[ROW][C]45[/C][C]0.855758[/C][C]0.288483[/C][C]0.144242[/C][/ROW]
[ROW][C]46[/C][C]0.822454[/C][C]0.355093[/C][C]0.177546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226672&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226672&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
8	0.361303	0.722607	0.638697
9	0.968222	0.063557	0.0317785
10	0.939244	0.121511	0.0607555
11	0.965652	0.0686954	0.0343477
12	0.970222	0.0595557	0.0297778
13	0.947983	0.104035	0.0520174
14	0.937827	0.124345	0.0621725
15	0.94997	0.10006	0.0500298
16	0.972954	0.0540914	0.0270457
17	0.963554	0.072891	0.0364455
18	0.946994	0.106012	0.0530059
19	0.923606	0.152788	0.0763941
20	0.896194	0.207612	0.103806
21	0.855238	0.289523	0.144762
22	0.815655	0.36869	0.184345
23	0.816671	0.366658	0.183329
24	0.759579	0.480843	0.240421
25	0.894742	0.210517	0.105258
26	0.856074	0.287851	0.143926
27	0.918771	0.162458	0.081229
28	0.881106	0.237787	0.118894
29	0.853092	0.293815	0.146908
30	0.970306	0.0593874	0.0296937
31	0.958454	0.0830928	0.0415464
32	0.938264	0.123472	0.0617358
33	0.920508	0.158985	0.0794924
34	0.889844	0.220312	0.110156
35	0.86688	0.266241	0.13312
36	0.853979	0.292042	0.146021
37	0.796837	0.406326	0.203163
38	0.719822	0.560357	0.280178
39	0.699258	0.601485	0.300742
40	0.751988	0.496025	0.248012
41	0.652543	0.694915	0.347457
42	0.592074	0.815851	0.407926
43	0.558618	0.882765	0.441382
44	0.598931	0.802137	0.401069
45	0.855758	0.288483	0.144242
46	0.822454	0.355093	0.177546

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	7	0.179487	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 & 7 & 0.179487 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226672&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]7[/C][C]0.179487[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226672&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226672&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	7	0.179487	NOK

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):

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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