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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 15 Dec 2014 12:59:42 +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/t1418648389srivmqxrn09r07z.htm/, Retrieved Thu, 16 May 2024 13:57:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268296, Retrieved Thu, 16 May 2024 13:57:07 +0000

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-       [Multiple Regression] [] [2014-12-15 12:59:42] [624214a256768d6065ce8a528542dcc5] [Current]

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12,9	26	50	13	12	13
12,2	57	62	8	8	13
12,8	37	54	14	11	11
7,4	67	71	16	13	14
6,7	43	54	14	11	15
12,6	52	65	13	10	14
14,8	52	73	15	7	11
13,3	43	52	13	10	13
11,1	84	84	20	15	16
8,2	67	42	17	12	14
11,4	49	66	15	12	14
6,4	70	65	16	10	15
10,6	52	78	12	10	15
12	58	73	17	14	13
6,3	68	75	11	6	14
11,9	43	66	16	14	12
9,3	56	70	15	11	14
10	74	81	14	12	12
6,4	65	71	19	15	15
13,8	63	69	16	13	15
10,8	58	71	17	11	14
13,8	57	72	10	12	14
11,7	63	68	15	7	12
10,9	53	70	14	11	12
9,9	64	67	15	12	14
11,5	53	76	17	13	16
8,3	29	70	14	9	12
11,7	54	60	16	11	12
9	58	72	15	12	14
9,7	43	69	16	15	16
10,8	51	71	16	12	15
10,3	53	62	10	6	12
10,4	54	70	8	5	14
9,3	61	58	14	11	14
11,8	47	76	10	6	16
5,9	39	52	14	12	12
11,4	48	59	12	10	14
13	50	68	16	6	15
10,8	35	76	16	12	13
11,3	68	67	8	6	16
11,8	49	59	16	12	12
12,7	67	76	8	8	16
10,9	43	60	16	12	13
13,3	62	63	19	14	14
10,1	57	70	14	12	14
14,3	54	66	13	14	10
9,3	61	64	15	11	16
12,5	56	70	11	10	14
7,6	41	75	9	7	14
15,9	43	61	16	12	15
9,2	53	60	12	7	15
11,1	66	73	14	12	13
13	58	61	14	10	11
14,5	46	66	13	10	16
12,3	51	59	17	12	15
11,4	51	64	14	12	14
12,6	37	78	7	5	13
13	42	67	15	10	12
13,2	66	66	15	11	14
7,7	53	71	16	12	15

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=268296&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=268296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268296&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.S[t] = + 14.7809 -0.0269374AMS.I.S[t] + 0.020944AMS.E.S[t] -0.0810944CONFSTAT.S[t] + 0.063917CONFSOFT.S[t] -0.239411STRESS.S[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT.S[t] =  +  14.7809 -0.0269374AMS.I.S[t] +  0.020944AMS.E.S[t] -0.0810944CONFSTAT.S[t] +  0.063917CONFSOFT.S[t] -0.239411STRESS.S[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268296&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT.S[t] =  +  14.7809 -0.0269374AMS.I.S[t] +  0.020944AMS.E.S[t] -0.0810944CONFSTAT.S[t] +  0.063917CONFSOFT.S[t] -0.239411STRESS.S[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268296&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.S[t] = + 14.7809 -0.0269374AMS.I.S[t] + 0.020944AMS.E.S[t] -0.0810944CONFSTAT.S[t] + 0.063917CONFSOFT.S[t] -0.239411STRESS.S[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)	14.7809	3.80786	3.882	0.000284562	0.000142281
AMS.I.S	-0.0269374	0.0301677	-0.8929	0.375861	0.187931
AMS.E.S	0.020944	0.040946	0.5115	0.611084	0.305542
CONFSTAT.S	-0.0810944	0.156831	-0.5171	0.607211	0.303606
CONFSOFT.S	0.063917	0.172485	0.3706	0.71241	0.356205
STRESS.S	-0.239411	0.214425	-1.117	0.269142	0.134571

\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) & 14.7809 & 3.80786 & 3.882 & 0.000284562 & 0.000142281 \tabularnewline
AMS.I.S & -0.0269374 & 0.0301677 & -0.8929 & 0.375861 & 0.187931 \tabularnewline
AMS.E.S & 0.020944 & 0.040946 & 0.5115 & 0.611084 & 0.305542 \tabularnewline
CONFSTAT.S & -0.0810944 & 0.156831 & -0.5171 & 0.607211 & 0.303606 \tabularnewline
CONFSOFT.S & 0.063917 & 0.172485 & 0.3706 & 0.71241 & 0.356205 \tabularnewline
STRESS.S & -0.239411 & 0.214425 & -1.117 & 0.269142 & 0.134571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268296&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]14.7809[/C][C]3.80786[/C][C]3.882[/C][C]0.000284562[/C][C]0.000142281[/C][/ROW]
[ROW][C]AMS.I.S[/C][C]-0.0269374[/C][C]0.0301677[/C][C]-0.8929[/C][C]0.375861[/C][C]0.187931[/C][/ROW]
[ROW][C]AMS.E.S[/C][C]0.020944[/C][C]0.040946[/C][C]0.5115[/C][C]0.611084[/C][C]0.305542[/C][/ROW]
[ROW][C]CONFSTAT.S[/C][C]-0.0810944[/C][C]0.156831[/C][C]-0.5171[/C][C]0.607211[/C][C]0.303606[/C][/ROW]
[ROW][C]CONFSOFT.S[/C][C]0.063917[/C][C]0.172485[/C][C]0.3706[/C][C]0.71241[/C][C]0.356205[/C][/ROW]
[ROW][C]STRESS.S[/C][C]-0.239411[/C][C]0.214425[/C][C]-1.117[/C][C]0.269142[/C][C]0.134571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268296&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268296&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)	14.7809	3.80786	3.882	0.000284562	0.000142281
AMS.I.S	-0.0269374	0.0301677	-0.8929	0.375861	0.187931
AMS.E.S	0.020944	0.040946	0.5115	0.611084	0.305542
CONFSTAT.S	-0.0810944	0.156831	-0.5171	0.607211	0.303606
CONFSOFT.S	0.063917	0.172485	0.3706	0.71241	0.356205
STRESS.S	-0.239411	0.214425	-1.117	0.269142	0.134571

Multiple Linear Regression - Regression Statistics
Multiple R	0.233453
R-squared	0.0545004
Adjusted R-squared	-0.0330458
F-TEST (value)	0.622533
F-TEST (DF numerator)	5
F-TEST (DF denominator)	54
p-value	0.683178
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.33367
Sum Squared Residuals	294.084

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.233453 \tabularnewline
R-squared & 0.0545004 \tabularnewline
Adjusted R-squared & -0.0330458 \tabularnewline
F-TEST (value) & 0.622533 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0.683178 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.33367 \tabularnewline
Sum Squared Residuals & 294.084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268296&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.233453[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0545004[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0330458[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.622533[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0.683178[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.33367[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]294.084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268296&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268296&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	0.233453
R-squared	0.0545004
Adjusted R-squared	-0.0330458
F-TEST (value)	0.622533
F-TEST (DF numerator)	5
F-TEST (DF denominator)	54
p-value	0.683178
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.33367
Sum Squared Residuals	294.084

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	11.7282	1.17185
2	12.2	11.2942	0.905775
3	12.8	11.8494	0.950573
4	7.4	10.6448	-3.24477
5	6.7	10.7302	-4.03016
6	12.6	10.9747	1.6253
7	14.8	11.5065	3.29346
8	13.3	11.1843	2.11573
9	11.1	9.78374	1.31626
10	8.2	9.89238	-1.69238
11	11.4	11.0421	0.357903
12	6.4	10.0071	-3.60713
13	10.6	11.0887	-0.488651
14	12	11.1513	0.848676
15	6.3	10.6597	-4.35966
16	11.9	11.7293	0.170718
17	9.3	10.8734	-1.57339
18	10	11.2427	-1.24274
19	6.4	10.3438	-3.94378
20	13.8	10.4712	3.32878
21	10.8	10.6783	0.121725
22	13.8	11.3577	2.44227
23	11.7	10.8661	0.833902
24	10.9	11.5141	-0.614122
25	9.9	10.659	-0.75898
26	11.5	10.5667	0.933306
27	8.3	12.0328	-3.73279
28	11.7	11.1156	0.584444
29	9	10.9253	-1.92532
30	9.7	10.8984	-1.19839
31	10.8	10.7724	0.0275629
32	10.3	11.3514	-1.05136
33	10.4	11.1114	-0.711428
34	9.3	10.5685	-1.26847
35	11.8	10.8486	0.95144
36	5.9	11.5782	-5.67817
37	11.4	11.0379	0.362125
38	13	10.353	2.64696
39	10.8	11.787	-0.986977
40	11.3	10.2566	1.04343
41	11.8	11.2932	0.506784
42	12.7	10.5998	2.10016
43	10.9	11.2364	-0.336373
44	13.3	10.4325	2.86746
45	10.1	10.9915	-0.891468
46	14.3	12.1551	2.14492
47	9.3	10.1342	-0.834222
48	12.5	11.1339	1.36615
49	7.6	11.6131	-4.01307
50	15.9	10.7785	5.1215
51	9.2	10.493	-1.29297
52	11.1	11.0513	0.0487256
53	13	11.3664	1.63357
54	14.5	10.6784	3.82156
55	12.3	10.44	1.85999
56	11.4	11.0274	0.372572
57	12.6	12.0574	0.542579
58	13	11.6026	1.39741
59	13.2	10.5202	2.67976
60	7.7	10.7186	-3.01856

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 11.7282 & 1.17185 \tabularnewline
2 & 12.2 & 11.2942 & 0.905775 \tabularnewline
3 & 12.8 & 11.8494 & 0.950573 \tabularnewline
4 & 7.4 & 10.6448 & -3.24477 \tabularnewline
5 & 6.7 & 10.7302 & -4.03016 \tabularnewline
6 & 12.6 & 10.9747 & 1.6253 \tabularnewline
7 & 14.8 & 11.5065 & 3.29346 \tabularnewline
8 & 13.3 & 11.1843 & 2.11573 \tabularnewline
9 & 11.1 & 9.78374 & 1.31626 \tabularnewline
10 & 8.2 & 9.89238 & -1.69238 \tabularnewline
11 & 11.4 & 11.0421 & 0.357903 \tabularnewline
12 & 6.4 & 10.0071 & -3.60713 \tabularnewline
13 & 10.6 & 11.0887 & -0.488651 \tabularnewline
14 & 12 & 11.1513 & 0.848676 \tabularnewline
15 & 6.3 & 10.6597 & -4.35966 \tabularnewline
16 & 11.9 & 11.7293 & 0.170718 \tabularnewline
17 & 9.3 & 10.8734 & -1.57339 \tabularnewline
18 & 10 & 11.2427 & -1.24274 \tabularnewline
19 & 6.4 & 10.3438 & -3.94378 \tabularnewline
20 & 13.8 & 10.4712 & 3.32878 \tabularnewline
21 & 10.8 & 10.6783 & 0.121725 \tabularnewline
22 & 13.8 & 11.3577 & 2.44227 \tabularnewline
23 & 11.7 & 10.8661 & 0.833902 \tabularnewline
24 & 10.9 & 11.5141 & -0.614122 \tabularnewline
25 & 9.9 & 10.659 & -0.75898 \tabularnewline
26 & 11.5 & 10.5667 & 0.933306 \tabularnewline
27 & 8.3 & 12.0328 & -3.73279 \tabularnewline
28 & 11.7 & 11.1156 & 0.584444 \tabularnewline
29 & 9 & 10.9253 & -1.92532 \tabularnewline
30 & 9.7 & 10.8984 & -1.19839 \tabularnewline
31 & 10.8 & 10.7724 & 0.0275629 \tabularnewline
32 & 10.3 & 11.3514 & -1.05136 \tabularnewline
33 & 10.4 & 11.1114 & -0.711428 \tabularnewline
34 & 9.3 & 10.5685 & -1.26847 \tabularnewline
35 & 11.8 & 10.8486 & 0.95144 \tabularnewline
36 & 5.9 & 11.5782 & -5.67817 \tabularnewline
37 & 11.4 & 11.0379 & 0.362125 \tabularnewline
38 & 13 & 10.353 & 2.64696 \tabularnewline
39 & 10.8 & 11.787 & -0.986977 \tabularnewline
40 & 11.3 & 10.2566 & 1.04343 \tabularnewline
41 & 11.8 & 11.2932 & 0.506784 \tabularnewline
42 & 12.7 & 10.5998 & 2.10016 \tabularnewline
43 & 10.9 & 11.2364 & -0.336373 \tabularnewline
44 & 13.3 & 10.4325 & 2.86746 \tabularnewline
45 & 10.1 & 10.9915 & -0.891468 \tabularnewline
46 & 14.3 & 12.1551 & 2.14492 \tabularnewline
47 & 9.3 & 10.1342 & -0.834222 \tabularnewline
48 & 12.5 & 11.1339 & 1.36615 \tabularnewline
49 & 7.6 & 11.6131 & -4.01307 \tabularnewline
50 & 15.9 & 10.7785 & 5.1215 \tabularnewline
51 & 9.2 & 10.493 & -1.29297 \tabularnewline
52 & 11.1 & 11.0513 & 0.0487256 \tabularnewline
53 & 13 & 11.3664 & 1.63357 \tabularnewline
54 & 14.5 & 10.6784 & 3.82156 \tabularnewline
55 & 12.3 & 10.44 & 1.85999 \tabularnewline
56 & 11.4 & 11.0274 & 0.372572 \tabularnewline
57 & 12.6 & 12.0574 & 0.542579 \tabularnewline
58 & 13 & 11.6026 & 1.39741 \tabularnewline
59 & 13.2 & 10.5202 & 2.67976 \tabularnewline
60 & 7.7 & 10.7186 & -3.01856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268296&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]11.7282[/C][C]1.17185[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]11.2942[/C][C]0.905775[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.8494[/C][C]0.950573[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]10.6448[/C][C]-3.24477[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.7302[/C][C]-4.03016[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]10.9747[/C][C]1.6253[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.5065[/C][C]3.29346[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]11.1843[/C][C]2.11573[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]9.78374[/C][C]1.31626[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]9.89238[/C][C]-1.69238[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.0421[/C][C]0.357903[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.0071[/C][C]-3.60713[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]11.0887[/C][C]-0.488651[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.1513[/C][C]0.848676[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]10.6597[/C][C]-4.35966[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]11.7293[/C][C]0.170718[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.8734[/C][C]-1.57339[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]11.2427[/C][C]-1.24274[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]10.3438[/C][C]-3.94378[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]10.4712[/C][C]3.32878[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.6783[/C][C]0.121725[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.3577[/C][C]2.44227[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]10.8661[/C][C]0.833902[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.5141[/C][C]-0.614122[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]10.659[/C][C]-0.75898[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]10.5667[/C][C]0.933306[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]12.0328[/C][C]-3.73279[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]11.1156[/C][C]0.584444[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.9253[/C][C]-1.92532[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]10.8984[/C][C]-1.19839[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]10.7724[/C][C]0.0275629[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]11.3514[/C][C]-1.05136[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]11.1114[/C][C]-0.711428[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]10.5685[/C][C]-1.26847[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]10.8486[/C][C]0.95144[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]11.5782[/C][C]-5.67817[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.0379[/C][C]0.362125[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]10.353[/C][C]2.64696[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]11.787[/C][C]-0.986977[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.2566[/C][C]1.04343[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.2932[/C][C]0.506784[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]10.5998[/C][C]2.10016[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]11.2364[/C][C]-0.336373[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]10.4325[/C][C]2.86746[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.9915[/C][C]-0.891468[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]12.1551[/C][C]2.14492[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]10.1342[/C][C]-0.834222[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]11.1339[/C][C]1.36615[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]11.6131[/C][C]-4.01307[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]10.7785[/C][C]5.1215[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.493[/C][C]-1.29297[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]11.0513[/C][C]0.0487256[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]11.3664[/C][C]1.63357[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]10.6784[/C][C]3.82156[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]10.44[/C][C]1.85999[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]11.0274[/C][C]0.372572[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]12.0574[/C][C]0.542579[/C][/ROW]
[ROW][C]58[/C][C]13[/C][C]11.6026[/C][C]1.39741[/C][/ROW]
[ROW][C]59[/C][C]13.2[/C][C]10.5202[/C][C]2.67976[/C][/ROW]
[ROW][C]60[/C][C]7.7[/C][C]10.7186[/C][C]-3.01856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268296&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268296&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	11.7282	1.17185
2	12.2	11.2942	0.905775
3	12.8	11.8494	0.950573
4	7.4	10.6448	-3.24477
5	6.7	10.7302	-4.03016
6	12.6	10.9747	1.6253
7	14.8	11.5065	3.29346
8	13.3	11.1843	2.11573
9	11.1	9.78374	1.31626
10	8.2	9.89238	-1.69238
11	11.4	11.0421	0.357903
12	6.4	10.0071	-3.60713
13	10.6	11.0887	-0.488651
14	12	11.1513	0.848676
15	6.3	10.6597	-4.35966
16	11.9	11.7293	0.170718
17	9.3	10.8734	-1.57339
18	10	11.2427	-1.24274
19	6.4	10.3438	-3.94378
20	13.8	10.4712	3.32878
21	10.8	10.6783	0.121725
22	13.8	11.3577	2.44227
23	11.7	10.8661	0.833902
24	10.9	11.5141	-0.614122
25	9.9	10.659	-0.75898
26	11.5	10.5667	0.933306
27	8.3	12.0328	-3.73279
28	11.7	11.1156	0.584444
29	9	10.9253	-1.92532
30	9.7	10.8984	-1.19839
31	10.8	10.7724	0.0275629
32	10.3	11.3514	-1.05136
33	10.4	11.1114	-0.711428
34	9.3	10.5685	-1.26847
35	11.8	10.8486	0.95144
36	5.9	11.5782	-5.67817
37	11.4	11.0379	0.362125
38	13	10.353	2.64696
39	10.8	11.787	-0.986977
40	11.3	10.2566	1.04343
41	11.8	11.2932	0.506784
42	12.7	10.5998	2.10016
43	10.9	11.2364	-0.336373
44	13.3	10.4325	2.86746
45	10.1	10.9915	-0.891468
46	14.3	12.1551	2.14492
47	9.3	10.1342	-0.834222
48	12.5	11.1339	1.36615
49	7.6	11.6131	-4.01307
50	15.9	10.7785	5.1215
51	9.2	10.493	-1.29297
52	11.1	11.0513	0.0487256
53	13	11.3664	1.63357
54	14.5	10.6784	3.82156
55	12.3	10.44	1.85999
56	11.4	11.0274	0.372572
57	12.6	12.0574	0.542579
58	13	11.6026	1.39741
59	13.2	10.5202	2.67976
60	7.7	10.7186	-3.01856

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.85354	0.292919	0.14646
10	0.771044	0.457912	0.228956
11	0.649306	0.701388	0.350694
12	0.672517	0.654965	0.327483
13	0.560024	0.879952	0.439976
14	0.453773	0.907546	0.546227
15	0.602634	0.794733	0.397366
16	0.587848	0.824305	0.412152
17	0.519113	0.961774	0.480887
18	0.486724	0.973448	0.513276
19	0.625428	0.749144	0.374572
20	0.792349	0.415301	0.207651
21	0.728381	0.543237	0.271619
22	0.736263	0.527474	0.263737
23	0.675746	0.648508	0.324254
24	0.621123	0.757754	0.378877
25	0.560354	0.879292	0.439646
26	0.496598	0.993197	0.503402
27	0.646232	0.707536	0.353768
28	0.569231	0.861539	0.430769
29	0.567649	0.864702	0.432351
30	0.499275	0.998549	0.500725
31	0.434153	0.868305	0.565847
32	0.361353	0.722706	0.638647
33	0.291441	0.582882	0.708559
34	0.252164	0.504329	0.747836
35	0.215509	0.431018	0.784491
36	0.598589	0.802823	0.401411
37	0.53017	0.939661	0.46983
38	0.593131	0.813739	0.406869
39	0.512882	0.974235	0.487118
40	0.430935	0.86187	0.569065
41	0.363923	0.727846	0.636077
42	0.36527	0.73054	0.63473
43	0.389255	0.77851	0.610745
44	0.375147	0.750294	0.624853
45	0.299435	0.598871	0.700565
46	0.260909	0.521819	0.739091
47	0.190329	0.380658	0.809671
48	0.127078	0.254156	0.872922
49	0.22368	0.44736	0.77632
50	0.282611	0.565221	0.717389
51	0.523407	0.953186	0.476593

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.85354 & 0.292919 & 0.14646 \tabularnewline
10 & 0.771044 & 0.457912 & 0.228956 \tabularnewline
11 & 0.649306 & 0.701388 & 0.350694 \tabularnewline
12 & 0.672517 & 0.654965 & 0.327483 \tabularnewline
13 & 0.560024 & 0.879952 & 0.439976 \tabularnewline
14 & 0.453773 & 0.907546 & 0.546227 \tabularnewline
15 & 0.602634 & 0.794733 & 0.397366 \tabularnewline
16 & 0.587848 & 0.824305 & 0.412152 \tabularnewline
17 & 0.519113 & 0.961774 & 0.480887 \tabularnewline
18 & 0.486724 & 0.973448 & 0.513276 \tabularnewline
19 & 0.625428 & 0.749144 & 0.374572 \tabularnewline
20 & 0.792349 & 0.415301 & 0.207651 \tabularnewline
21 & 0.728381 & 0.543237 & 0.271619 \tabularnewline
22 & 0.736263 & 0.527474 & 0.263737 \tabularnewline
23 & 0.675746 & 0.648508 & 0.324254 \tabularnewline
24 & 0.621123 & 0.757754 & 0.378877 \tabularnewline
25 & 0.560354 & 0.879292 & 0.439646 \tabularnewline
26 & 0.496598 & 0.993197 & 0.503402 \tabularnewline
27 & 0.646232 & 0.707536 & 0.353768 \tabularnewline
28 & 0.569231 & 0.861539 & 0.430769 \tabularnewline
29 & 0.567649 & 0.864702 & 0.432351 \tabularnewline
30 & 0.499275 & 0.998549 & 0.500725 \tabularnewline
31 & 0.434153 & 0.868305 & 0.565847 \tabularnewline
32 & 0.361353 & 0.722706 & 0.638647 \tabularnewline
33 & 0.291441 & 0.582882 & 0.708559 \tabularnewline
34 & 0.252164 & 0.504329 & 0.747836 \tabularnewline
35 & 0.215509 & 0.431018 & 0.784491 \tabularnewline
36 & 0.598589 & 0.802823 & 0.401411 \tabularnewline
37 & 0.53017 & 0.939661 & 0.46983 \tabularnewline
38 & 0.593131 & 0.813739 & 0.406869 \tabularnewline
39 & 0.512882 & 0.974235 & 0.487118 \tabularnewline
40 & 0.430935 & 0.86187 & 0.569065 \tabularnewline
41 & 0.363923 & 0.727846 & 0.636077 \tabularnewline
42 & 0.36527 & 0.73054 & 0.63473 \tabularnewline
43 & 0.389255 & 0.77851 & 0.610745 \tabularnewline
44 & 0.375147 & 0.750294 & 0.624853 \tabularnewline
45 & 0.299435 & 0.598871 & 0.700565 \tabularnewline
46 & 0.260909 & 0.521819 & 0.739091 \tabularnewline
47 & 0.190329 & 0.380658 & 0.809671 \tabularnewline
48 & 0.127078 & 0.254156 & 0.872922 \tabularnewline
49 & 0.22368 & 0.44736 & 0.77632 \tabularnewline
50 & 0.282611 & 0.565221 & 0.717389 \tabularnewline
51 & 0.523407 & 0.953186 & 0.476593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268296&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]0.85354[/C][C]0.292919[/C][C]0.14646[/C][/ROW]
[ROW][C]10[/C][C]0.771044[/C][C]0.457912[/C][C]0.228956[/C][/ROW]
[ROW][C]11[/C][C]0.649306[/C][C]0.701388[/C][C]0.350694[/C][/ROW]
[ROW][C]12[/C][C]0.672517[/C][C]0.654965[/C][C]0.327483[/C][/ROW]
[ROW][C]13[/C][C]0.560024[/C][C]0.879952[/C][C]0.439976[/C][/ROW]
[ROW][C]14[/C][C]0.453773[/C][C]0.907546[/C][C]0.546227[/C][/ROW]
[ROW][C]15[/C][C]0.602634[/C][C]0.794733[/C][C]0.397366[/C][/ROW]
[ROW][C]16[/C][C]0.587848[/C][C]0.824305[/C][C]0.412152[/C][/ROW]
[ROW][C]17[/C][C]0.519113[/C][C]0.961774[/C][C]0.480887[/C][/ROW]
[ROW][C]18[/C][C]0.486724[/C][C]0.973448[/C][C]0.513276[/C][/ROW]
[ROW][C]19[/C][C]0.625428[/C][C]0.749144[/C][C]0.374572[/C][/ROW]
[ROW][C]20[/C][C]0.792349[/C][C]0.415301[/C][C]0.207651[/C][/ROW]
[ROW][C]21[/C][C]0.728381[/C][C]0.543237[/C][C]0.271619[/C][/ROW]
[ROW][C]22[/C][C]0.736263[/C][C]0.527474[/C][C]0.263737[/C][/ROW]
[ROW][C]23[/C][C]0.675746[/C][C]0.648508[/C][C]0.324254[/C][/ROW]
[ROW][C]24[/C][C]0.621123[/C][C]0.757754[/C][C]0.378877[/C][/ROW]
[ROW][C]25[/C][C]0.560354[/C][C]0.879292[/C][C]0.439646[/C][/ROW]
[ROW][C]26[/C][C]0.496598[/C][C]0.993197[/C][C]0.503402[/C][/ROW]
[ROW][C]27[/C][C]0.646232[/C][C]0.707536[/C][C]0.353768[/C][/ROW]
[ROW][C]28[/C][C]0.569231[/C][C]0.861539[/C][C]0.430769[/C][/ROW]
[ROW][C]29[/C][C]0.567649[/C][C]0.864702[/C][C]0.432351[/C][/ROW]
[ROW][C]30[/C][C]0.499275[/C][C]0.998549[/C][C]0.500725[/C][/ROW]
[ROW][C]31[/C][C]0.434153[/C][C]0.868305[/C][C]0.565847[/C][/ROW]
[ROW][C]32[/C][C]0.361353[/C][C]0.722706[/C][C]0.638647[/C][/ROW]
[ROW][C]33[/C][C]0.291441[/C][C]0.582882[/C][C]0.708559[/C][/ROW]
[ROW][C]34[/C][C]0.252164[/C][C]0.504329[/C][C]0.747836[/C][/ROW]
[ROW][C]35[/C][C]0.215509[/C][C]0.431018[/C][C]0.784491[/C][/ROW]
[ROW][C]36[/C][C]0.598589[/C][C]0.802823[/C][C]0.401411[/C][/ROW]
[ROW][C]37[/C][C]0.53017[/C][C]0.939661[/C][C]0.46983[/C][/ROW]
[ROW][C]38[/C][C]0.593131[/C][C]0.813739[/C][C]0.406869[/C][/ROW]
[ROW][C]39[/C][C]0.512882[/C][C]0.974235[/C][C]0.487118[/C][/ROW]
[ROW][C]40[/C][C]0.430935[/C][C]0.86187[/C][C]0.569065[/C][/ROW]
[ROW][C]41[/C][C]0.363923[/C][C]0.727846[/C][C]0.636077[/C][/ROW]
[ROW][C]42[/C][C]0.36527[/C][C]0.73054[/C][C]0.63473[/C][/ROW]
[ROW][C]43[/C][C]0.389255[/C][C]0.77851[/C][C]0.610745[/C][/ROW]
[ROW][C]44[/C][C]0.375147[/C][C]0.750294[/C][C]0.624853[/C][/ROW]
[ROW][C]45[/C][C]0.299435[/C][C]0.598871[/C][C]0.700565[/C][/ROW]
[ROW][C]46[/C][C]0.260909[/C][C]0.521819[/C][C]0.739091[/C][/ROW]
[ROW][C]47[/C][C]0.190329[/C][C]0.380658[/C][C]0.809671[/C][/ROW]
[ROW][C]48[/C][C]0.127078[/C][C]0.254156[/C][C]0.872922[/C][/ROW]
[ROW][C]49[/C][C]0.22368[/C][C]0.44736[/C][C]0.77632[/C][/ROW]
[ROW][C]50[/C][C]0.282611[/C][C]0.565221[/C][C]0.717389[/C][/ROW]
[ROW][C]51[/C][C]0.523407[/C][C]0.953186[/C][C]0.476593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268296&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268296&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	0.85354	0.292919	0.14646
10	0.771044	0.457912	0.228956
11	0.649306	0.701388	0.350694
12	0.672517	0.654965	0.327483
13	0.560024	0.879952	0.439976
14	0.453773	0.907546	0.546227
15	0.602634	0.794733	0.397366
16	0.587848	0.824305	0.412152
17	0.519113	0.961774	0.480887
18	0.486724	0.973448	0.513276
19	0.625428	0.749144	0.374572
20	0.792349	0.415301	0.207651
21	0.728381	0.543237	0.271619
22	0.736263	0.527474	0.263737
23	0.675746	0.648508	0.324254
24	0.621123	0.757754	0.378877
25	0.560354	0.879292	0.439646
26	0.496598	0.993197	0.503402
27	0.646232	0.707536	0.353768
28	0.569231	0.861539	0.430769
29	0.567649	0.864702	0.432351
30	0.499275	0.998549	0.500725
31	0.434153	0.868305	0.565847
32	0.361353	0.722706	0.638647
33	0.291441	0.582882	0.708559
34	0.252164	0.504329	0.747836
35	0.215509	0.431018	0.784491
36	0.598589	0.802823	0.401411
37	0.53017	0.939661	0.46983
38	0.593131	0.813739	0.406869
39	0.512882	0.974235	0.487118
40	0.430935	0.86187	0.569065
41	0.363923	0.727846	0.636077
42	0.36527	0.73054	0.63473
43	0.389255	0.77851	0.610745
44	0.375147	0.750294	0.624853
45	0.299435	0.598871	0.700565
46	0.260909	0.521819	0.739091
47	0.190329	0.380658	0.809671
48	0.127078	0.254156	0.872922
49	0.22368	0.44736	0.77632
50	0.282611	0.565221	0.717389
51	0.523407	0.953186	0.476593

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268296&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268296&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268296&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	0	0	OK

Figure 1

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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