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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 07 Dec 2016 10:44:23 +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/2016/Dec/07/t148111397392eia8tvaoyi8u5.htm/, Retrieved Tue, 07 May 2024 23:16:22 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Tue, 07 May 2024 23:16:22 +0200

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Rupture

IsPrivate?

No (this computation is public)

User-defined keywords

Rupture

Estimated Impact

Dataseries X:

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0.56	1	0.6	110	0.3	0.25	80
0.56	1	0.8	110	0.3	0.25	80
0.60	1	0.8	88	0.3	0.25	60
0.60	1	0.8	88	0.3	0.75	60
0.55	1	0.8	88	0.6	0.75	60
0.55	1	0.8	88	0.6	0.25	60
0.55	1	0.8	88	0.6	0.25	60
0.70	2	0.3	100	0.4	0.5	50
0.70	2	0.3	100	0.4	0.5	50
0.71	2	0.3	65	0.4	0.5	50
0.71	2	0.3	65	0.4	0.5	50
0.71	2	0.3	65	0.4	0.5	50
0.71	2	0.3	65	0.4	0.5	50
0.71	2	0.3	65	0.4	0.5	50
0.63	2	0.3	65	0.7	0.5	50
0.63	2	0.3	65	0.7	0.5	50
0.63	2	0.3	65	0.7	0.5	50
0.71	2	0.8	65	0.4	0.5	50
0.71	2	0.8	65	0.4	0.5	50
0.63	2	0.8	65	0.7	0.5	55
0.71	3	0.4	58	0.3	0.8	55
0.49	1	0.2	100	0.3	0.8	65
0.48	1	0.2	100	0.3	0.8	85
0.47	1	0.2	78	0.3	0.8	85
0.48	1	0.2	78	0.3	0.4	85
0.49	2	0.3	100	0.5	0.4	55
0.49	2	0.3	110	0.3	0.4	55
0.59	2	0.3	55	0.3	0.4	55
0.59	2	0.3	55	0.3	0.4	55
0.59	2	0.5	55	0.3	0.4	55
0.59	2	0.6	55	0.3	0.4	55
0.59	2	0.5	55	0.3	0.4	55
0.59	2	0.5	55	0.3	0.4	55
0.59	2	0.5	55	0.3	0.4	55
0.59	2	0.5	55	0.3	0.4	56
0.51	2	0.5	55	0.3	0.4	71
0.50	3	0.5	54	0.5	0.4	71
0.56	3	0.5	54	0.5	0.4	71
0.56	3	0.5	54	0.5	0.4	71
0.41	4	0.1	100	0.1	0.1	45
0.41	4	0.1	88	0.1	0.1	45
0.41	4	0.1	88	0.1	0.1	45
0.41	4	0.1	88	0.1	0.1	45
0.56	2	0.2	55	0.5	0.7	45
0.56	2	0.2	55	0.5	0.7	45
0.28	1	0.3	75	0.5	0.7	45
0.28	1	0.3	75	0.5	0.7	45
0.28	1	0.3	75	0.5	0.7	45
0.28	1	0.3	75	0.5	0.7	45

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=0

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

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
a[t] = + 0.427584 + 0.0356097b[t] + 0.217596c[t] -0.00124135d[t] + 0.0377799e[t] + 0.0651121f[t] + 0.000261043g[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  0.427584 +  0.0356097b[t] +  0.217596c[t] -0.00124135d[t] +  0.0377799e[t] +  0.0651121f[t] +  0.000261043g[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  0.427584 +  0.0356097b[t] +  0.217596c[t] -0.00124135d[t] +  0.0377799e[t] +  0.0651121f[t] +  0.000261043g[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
a[t] = + 0.427584 + 0.0356097b[t] + 0.217596c[t] -0.00124135d[t] + 0.0377799e[t] + 0.0651121f[t] + 0.000261043g[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)	+0.4276	0.2126	+2.0110e+00	0.05073	0.02536
b	+0.03561	0.0304	+1.1710e+00	0.248	0.124
c	+0.2176	0.0916	+2.3750e+00	0.02217	0.01108
d	-0.001241	0.001044	-1.1890e+00	0.2412	0.1206
e	+0.03778	0.1319	+2.8640e-01	0.776	0.388
f	+0.06511	0.1187	+5.4860e-01	0.5862	0.2931
g	+0.000261	0.001621	+1.6100e-01	0.8728	0.4364

\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) & +0.4276 &  0.2126 & +2.0110e+00 &  0.05073 &  0.02536 \tabularnewline
b & +0.03561 &  0.0304 & +1.1710e+00 &  0.248 &  0.124 \tabularnewline
c & +0.2176 &  0.0916 & +2.3750e+00 &  0.02217 &  0.01108 \tabularnewline
d & -0.001241 &  0.001044 & -1.1890e+00 &  0.2412 &  0.1206 \tabularnewline
e & +0.03778 &  0.1319 & +2.8640e-01 &  0.776 &  0.388 \tabularnewline
f & +0.06511 &  0.1187 & +5.4860e-01 &  0.5862 &  0.2931 \tabularnewline
g & +0.000261 &  0.001621 & +1.6100e-01 &  0.8728 &  0.4364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]+0.4276[/C][C] 0.2126[/C][C]+2.0110e+00[/C][C] 0.05073[/C][C] 0.02536[/C][/ROW]
[ROW][C]b[/C][C]+0.03561[/C][C] 0.0304[/C][C]+1.1710e+00[/C][C] 0.248[/C][C] 0.124[/C][/ROW]
[ROW][C]c[/C][C]+0.2176[/C][C] 0.0916[/C][C]+2.3750e+00[/C][C] 0.02217[/C][C] 0.01108[/C][/ROW]
[ROW][C]d[/C][C]-0.001241[/C][C] 0.001044[/C][C]-1.1890e+00[/C][C] 0.2412[/C][C] 0.1206[/C][/ROW]
[ROW][C]e[/C][C]+0.03778[/C][C] 0.1319[/C][C]+2.8640e-01[/C][C] 0.776[/C][C] 0.388[/C][/ROW]
[ROW][C]f[/C][C]+0.06511[/C][C] 0.1187[/C][C]+5.4860e-01[/C][C] 0.5862[/C][C] 0.2931[/C][/ROW]
[ROW][C]g[/C][C]+0.000261[/C][C] 0.001621[/C][C]+1.6100e-01[/C][C] 0.8728[/C][C] 0.4364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	+0.4276	0.2126	+2.0110e+00	0.05073	0.02536
b	+0.03561	0.0304	+1.1710e+00	0.248	0.124
c	+0.2176	0.0916	+2.3750e+00	0.02217	0.01108
d	-0.001241	0.001044	-1.1890e+00	0.2412	0.1206
e	+0.03778	0.1319	+2.8640e-01	0.776	0.388
f	+0.06511	0.1187	+5.4860e-01	0.5862	0.2931
g	+0.000261	0.001621	+1.6100e-01	0.8728	0.4364

Multiple Linear Regression - Regression Statistics
Multiple R	0.4519
R-squared	0.2042
Adjusted R-squared	0.09054
F-TEST (value)	1.796
F-TEST (DF numerator)	6
F-TEST (DF denominator)	42
p-value	0.1232
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1146
Sum Squared Residuals	0.552

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.4519 \tabularnewline
R-squared &  0.2042 \tabularnewline
Adjusted R-squared &  0.09054 \tabularnewline
F-TEST (value) &  1.796 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value &  0.1232 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.1146 \tabularnewline
Sum Squared Residuals &  0.552 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.4519[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2042[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.09054[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.796[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1232[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.1146[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.552[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.4519
R-squared	0.2042
Adjusted R-squared	0.09054
F-TEST (value)	1.796
F-TEST (DF numerator)	6
F-TEST (DF denominator)	42
p-value	0.1232
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1146
Sum Squared Residuals	0.552

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.56	0.5057	0.0543
2	0.56	0.5492	0.01078
3	0.6	0.5713	0.02869
4	0.6	0.6039	-0.003862
5	0.55	0.6152	-0.0652
6	0.55	0.5826	-0.03264
7	0.55	0.5826	-0.03264
8	0.7	0.5007	0.1993
9	0.7	0.5007	0.1993
10	0.71	0.5441	0.1659
11	0.71	0.5441	0.1659
12	0.71	0.5441	0.1659
13	0.71	0.5441	0.1659
14	0.71	0.5441	0.1659
15	0.63	0.5554	0.07455
16	0.63	0.5554	0.07455
17	0.63	0.5554	0.07455
18	0.71	0.6529	0.05709
19	0.71	0.6529	0.05709
20	0.63	0.6656	-0.03555
21	0.71	0.6272	0.08277
22	0.49	0.463	0.02703
23	0.48	0.4682	0.01181
24	0.47	0.4955	-0.0255
25	0.48	0.4695	0.01055
26	0.49	0.4992	-0.009239
27	0.49	0.4793	0.01073
28	0.59	0.5475	0.04246
29	0.59	0.5475	0.04246
30	0.59	0.5911	-0.001063
31	0.59	0.6128	-0.02282
32	0.59	0.5911	-0.001063
33	0.59	0.5911	-0.001063
34	0.59	0.5911	-0.001063
35	0.59	0.5913	-0.001324
36	0.51	0.5952	-0.08524
37	0.5	0.6396	-0.1396
38	0.56	0.6396	-0.07965
39	0.56	0.6396	-0.07965
40	0.41	0.4897	-0.07968
41	0.41	0.5046	-0.09458
42	0.41	0.5046	-0.09458
43	0.41	0.5046	-0.09458
44	0.56	0.5503	0.009737
45	0.56	0.5503	0.009737
46	0.28	0.5116	-0.2316
47	0.28	0.5116	-0.2316
48	0.28	0.5116	-0.2316
49	0.28	0.5116	-0.2316

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.56 &  0.5057 &  0.0543 \tabularnewline
2 &  0.56 &  0.5492 &  0.01078 \tabularnewline
3 &  0.6 &  0.5713 &  0.02869 \tabularnewline
4 &  0.6 &  0.6039 & -0.003862 \tabularnewline
5 &  0.55 &  0.6152 & -0.0652 \tabularnewline
6 &  0.55 &  0.5826 & -0.03264 \tabularnewline
7 &  0.55 &  0.5826 & -0.03264 \tabularnewline
8 &  0.7 &  0.5007 &  0.1993 \tabularnewline
9 &  0.7 &  0.5007 &  0.1993 \tabularnewline
10 &  0.71 &  0.5441 &  0.1659 \tabularnewline
11 &  0.71 &  0.5441 &  0.1659 \tabularnewline
12 &  0.71 &  0.5441 &  0.1659 \tabularnewline
13 &  0.71 &  0.5441 &  0.1659 \tabularnewline
14 &  0.71 &  0.5441 &  0.1659 \tabularnewline
15 &  0.63 &  0.5554 &  0.07455 \tabularnewline
16 &  0.63 &  0.5554 &  0.07455 \tabularnewline
17 &  0.63 &  0.5554 &  0.07455 \tabularnewline
18 &  0.71 &  0.6529 &  0.05709 \tabularnewline
19 &  0.71 &  0.6529 &  0.05709 \tabularnewline
20 &  0.63 &  0.6656 & -0.03555 \tabularnewline
21 &  0.71 &  0.6272 &  0.08277 \tabularnewline
22 &  0.49 &  0.463 &  0.02703 \tabularnewline
23 &  0.48 &  0.4682 &  0.01181 \tabularnewline
24 &  0.47 &  0.4955 & -0.0255 \tabularnewline
25 &  0.48 &  0.4695 &  0.01055 \tabularnewline
26 &  0.49 &  0.4992 & -0.009239 \tabularnewline
27 &  0.49 &  0.4793 &  0.01073 \tabularnewline
28 &  0.59 &  0.5475 &  0.04246 \tabularnewline
29 &  0.59 &  0.5475 &  0.04246 \tabularnewline
30 &  0.59 &  0.5911 & -0.001063 \tabularnewline
31 &  0.59 &  0.6128 & -0.02282 \tabularnewline
32 &  0.59 &  0.5911 & -0.001063 \tabularnewline
33 &  0.59 &  0.5911 & -0.001063 \tabularnewline
34 &  0.59 &  0.5911 & -0.001063 \tabularnewline
35 &  0.59 &  0.5913 & -0.001324 \tabularnewline
36 &  0.51 &  0.5952 & -0.08524 \tabularnewline
37 &  0.5 &  0.6396 & -0.1396 \tabularnewline
38 &  0.56 &  0.6396 & -0.07965 \tabularnewline
39 &  0.56 &  0.6396 & -0.07965 \tabularnewline
40 &  0.41 &  0.4897 & -0.07968 \tabularnewline
41 &  0.41 &  0.5046 & -0.09458 \tabularnewline
42 &  0.41 &  0.5046 & -0.09458 \tabularnewline
43 &  0.41 &  0.5046 & -0.09458 \tabularnewline
44 &  0.56 &  0.5503 &  0.009737 \tabularnewline
45 &  0.56 &  0.5503 &  0.009737 \tabularnewline
46 &  0.28 &  0.5116 & -0.2316 \tabularnewline
47 &  0.28 &  0.5116 & -0.2316 \tabularnewline
48 &  0.28 &  0.5116 & -0.2316 \tabularnewline
49 &  0.28 &  0.5116 & -0.2316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 0.56[/C][C] 0.5057[/C][C] 0.0543[/C][/ROW]
[ROW][C]2[/C][C] 0.56[/C][C] 0.5492[/C][C] 0.01078[/C][/ROW]
[ROW][C]3[/C][C] 0.6[/C][C] 0.5713[/C][C] 0.02869[/C][/ROW]
[ROW][C]4[/C][C] 0.6[/C][C] 0.6039[/C][C]-0.003862[/C][/ROW]
[ROW][C]5[/C][C] 0.55[/C][C] 0.6152[/C][C]-0.0652[/C][/ROW]
[ROW][C]6[/C][C] 0.55[/C][C] 0.5826[/C][C]-0.03264[/C][/ROW]
[ROW][C]7[/C][C] 0.55[/C][C] 0.5826[/C][C]-0.03264[/C][/ROW]
[ROW][C]8[/C][C] 0.7[/C][C] 0.5007[/C][C] 0.1993[/C][/ROW]
[ROW][C]9[/C][C] 0.7[/C][C] 0.5007[/C][C] 0.1993[/C][/ROW]
[ROW][C]10[/C][C] 0.71[/C][C] 0.5441[/C][C] 0.1659[/C][/ROW]
[ROW][C]11[/C][C] 0.71[/C][C] 0.5441[/C][C] 0.1659[/C][/ROW]
[ROW][C]12[/C][C] 0.71[/C][C] 0.5441[/C][C] 0.1659[/C][/ROW]
[ROW][C]13[/C][C] 0.71[/C][C] 0.5441[/C][C] 0.1659[/C][/ROW]
[ROW][C]14[/C][C] 0.71[/C][C] 0.5441[/C][C] 0.1659[/C][/ROW]
[ROW][C]15[/C][C] 0.63[/C][C] 0.5554[/C][C] 0.07455[/C][/ROW]
[ROW][C]16[/C][C] 0.63[/C][C] 0.5554[/C][C] 0.07455[/C][/ROW]
[ROW][C]17[/C][C] 0.63[/C][C] 0.5554[/C][C] 0.07455[/C][/ROW]
[ROW][C]18[/C][C] 0.71[/C][C] 0.6529[/C][C] 0.05709[/C][/ROW]
[ROW][C]19[/C][C] 0.71[/C][C] 0.6529[/C][C] 0.05709[/C][/ROW]
[ROW][C]20[/C][C] 0.63[/C][C] 0.6656[/C][C]-0.03555[/C][/ROW]
[ROW][C]21[/C][C] 0.71[/C][C] 0.6272[/C][C] 0.08277[/C][/ROW]
[ROW][C]22[/C][C] 0.49[/C][C] 0.463[/C][C] 0.02703[/C][/ROW]
[ROW][C]23[/C][C] 0.48[/C][C] 0.4682[/C][C] 0.01181[/C][/ROW]
[ROW][C]24[/C][C] 0.47[/C][C] 0.4955[/C][C]-0.0255[/C][/ROW]
[ROW][C]25[/C][C] 0.48[/C][C] 0.4695[/C][C] 0.01055[/C][/ROW]
[ROW][C]26[/C][C] 0.49[/C][C] 0.4992[/C][C]-0.009239[/C][/ROW]
[ROW][C]27[/C][C] 0.49[/C][C] 0.4793[/C][C] 0.01073[/C][/ROW]
[ROW][C]28[/C][C] 0.59[/C][C] 0.5475[/C][C] 0.04246[/C][/ROW]
[ROW][C]29[/C][C] 0.59[/C][C] 0.5475[/C][C] 0.04246[/C][/ROW]
[ROW][C]30[/C][C] 0.59[/C][C] 0.5911[/C][C]-0.001063[/C][/ROW]
[ROW][C]31[/C][C] 0.59[/C][C] 0.6128[/C][C]-0.02282[/C][/ROW]
[ROW][C]32[/C][C] 0.59[/C][C] 0.5911[/C][C]-0.001063[/C][/ROW]
[ROW][C]33[/C][C] 0.59[/C][C] 0.5911[/C][C]-0.001063[/C][/ROW]
[ROW][C]34[/C][C] 0.59[/C][C] 0.5911[/C][C]-0.001063[/C][/ROW]
[ROW][C]35[/C][C] 0.59[/C][C] 0.5913[/C][C]-0.001324[/C][/ROW]
[ROW][C]36[/C][C] 0.51[/C][C] 0.5952[/C][C]-0.08524[/C][/ROW]
[ROW][C]37[/C][C] 0.5[/C][C] 0.6396[/C][C]-0.1396[/C][/ROW]
[ROW][C]38[/C][C] 0.56[/C][C] 0.6396[/C][C]-0.07965[/C][/ROW]
[ROW][C]39[/C][C] 0.56[/C][C] 0.6396[/C][C]-0.07965[/C][/ROW]
[ROW][C]40[/C][C] 0.41[/C][C] 0.4897[/C][C]-0.07968[/C][/ROW]
[ROW][C]41[/C][C] 0.41[/C][C] 0.5046[/C][C]-0.09458[/C][/ROW]
[ROW][C]42[/C][C] 0.41[/C][C] 0.5046[/C][C]-0.09458[/C][/ROW]
[ROW][C]43[/C][C] 0.41[/C][C] 0.5046[/C][C]-0.09458[/C][/ROW]
[ROW][C]44[/C][C] 0.56[/C][C] 0.5503[/C][C] 0.009737[/C][/ROW]
[ROW][C]45[/C][C] 0.56[/C][C] 0.5503[/C][C] 0.009737[/C][/ROW]
[ROW][C]46[/C][C] 0.28[/C][C] 0.5116[/C][C]-0.2316[/C][/ROW]
[ROW][C]47[/C][C] 0.28[/C][C] 0.5116[/C][C]-0.2316[/C][/ROW]
[ROW][C]48[/C][C] 0.28[/C][C] 0.5116[/C][C]-0.2316[/C][/ROW]
[ROW][C]49[/C][C] 0.28[/C][C] 0.5116[/C][C]-0.2316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.56	0.5057	0.0543
2	0.56	0.5492	0.01078
3	0.6	0.5713	0.02869
4	0.6	0.6039	-0.003862
5	0.55	0.6152	-0.0652
6	0.55	0.5826	-0.03264
7	0.55	0.5826	-0.03264
8	0.7	0.5007	0.1993
9	0.7	0.5007	0.1993
10	0.71	0.5441	0.1659
11	0.71	0.5441	0.1659
12	0.71	0.5441	0.1659
13	0.71	0.5441	0.1659
14	0.71	0.5441	0.1659
15	0.63	0.5554	0.07455
16	0.63	0.5554	0.07455
17	0.63	0.5554	0.07455
18	0.71	0.6529	0.05709
19	0.71	0.6529	0.05709
20	0.63	0.6656	-0.03555
21	0.71	0.6272	0.08277
22	0.49	0.463	0.02703
23	0.48	0.4682	0.01181
24	0.47	0.4955	-0.0255
25	0.48	0.4695	0.01055
26	0.49	0.4992	-0.009239
27	0.49	0.4793	0.01073
28	0.59	0.5475	0.04246
29	0.59	0.5475	0.04246
30	0.59	0.5911	-0.001063
31	0.59	0.6128	-0.02282
32	0.59	0.5911	-0.001063
33	0.59	0.5911	-0.001063
34	0.59	0.5911	-0.001063
35	0.59	0.5913	-0.001324
36	0.51	0.5952	-0.08524
37	0.5	0.6396	-0.1396
38	0.56	0.6396	-0.07965
39	0.56	0.6396	-0.07965
40	0.41	0.4897	-0.07968
41	0.41	0.5046	-0.09458
42	0.41	0.5046	-0.09458
43	0.41	0.5046	-0.09458
44	0.56	0.5503	0.009737
45	0.56	0.5503	0.009737
46	0.28	0.5116	-0.2316
47	0.28	0.5116	-0.2316
48	0.28	0.5116	-0.2316
49	0.28	0.5116	-0.2316

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	9.045e-50	1.809e-49	1
11	2.092e-64	4.183e-64	1
12	4.095e-81	8.19e-81	1
13	2.146e-100	4.292e-100	1
14	8.133e-116	1.627e-115	1
15	2.062e-08	4.125e-08	1
16	8.417e-09	1.683e-08	1
17	4.38e-09	8.76e-09	1
18	4.151e-10	8.302e-10	1
19	3.928e-11	7.857e-11	1
20	4.575e-12	9.15e-12	1
21	1.629e-07	3.257e-07	1
22	5.138e-06	1.028e-05	1
23	2.59e-06	5.179e-06	1
24	7.339e-07	1.468e-06	1
25	2.666e-07	5.332e-07	1
26	0.004949	0.009898	0.9951
27	0.9615	0.07694	0.03847
28	0.9762	0.04754	0.02377
29	0.9933	0.01337	0.006686
30	0.9917	0.01659	0.008296
31	1	3.341e-05	1.67e-05
32	0.9999	0.0001119	5.596e-05
33	0.9998	0.0003835	0.0001918
34	0.9994	0.001288	0.0006441
35	0.9978	0.004339	0.00217
36	0.9934	0.01323	0.006615
37	1	3.952e-70	1.976e-70
38	1	1.85e-62	9.248e-63
39	1	3.075e-44	1.538e-44

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  9.045e-50 &  1.809e-49 &  1 \tabularnewline
11 &  2.092e-64 &  4.183e-64 &  1 \tabularnewline
12 &  4.095e-81 &  8.19e-81 &  1 \tabularnewline
13 &  2.146e-100 &  4.292e-100 &  1 \tabularnewline
14 &  8.133e-116 &  1.627e-115 &  1 \tabularnewline
15 &  2.062e-08 &  4.125e-08 &  1 \tabularnewline
16 &  8.417e-09 &  1.683e-08 &  1 \tabularnewline
17 &  4.38e-09 &  8.76e-09 &  1 \tabularnewline
18 &  4.151e-10 &  8.302e-10 &  1 \tabularnewline
19 &  3.928e-11 &  7.857e-11 &  1 \tabularnewline
20 &  4.575e-12 &  9.15e-12 &  1 \tabularnewline
21 &  1.629e-07 &  3.257e-07 &  1 \tabularnewline
22 &  5.138e-06 &  1.028e-05 &  1 \tabularnewline
23 &  2.59e-06 &  5.179e-06 &  1 \tabularnewline
24 &  7.339e-07 &  1.468e-06 &  1 \tabularnewline
25 &  2.666e-07 &  5.332e-07 &  1 \tabularnewline
26 &  0.004949 &  0.009898 &  0.9951 \tabularnewline
27 &  0.9615 &  0.07694 &  0.03847 \tabularnewline
28 &  0.9762 &  0.04754 &  0.02377 \tabularnewline
29 &  0.9933 &  0.01337 &  0.006686 \tabularnewline
30 &  0.9917 &  0.01659 &  0.008296 \tabularnewline
31 &  1 &  3.341e-05 &  1.67e-05 \tabularnewline
32 &  0.9999 &  0.0001119 &  5.596e-05 \tabularnewline
33 &  0.9998 &  0.0003835 &  0.0001918 \tabularnewline
34 &  0.9994 &  0.001288 &  0.0006441 \tabularnewline
35 &  0.9978 &  0.004339 &  0.00217 \tabularnewline
36 &  0.9934 &  0.01323 &  0.006615 \tabularnewline
37 &  1 &  3.952e-70 &  1.976e-70 \tabularnewline
38 &  1 &  1.85e-62 &  9.248e-63 \tabularnewline
39 &  1 &  3.075e-44 &  1.538e-44 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]10[/C][C] 9.045e-50[/C][C] 1.809e-49[/C][C] 1[/C][/ROW]
[ROW][C]11[/C][C] 2.092e-64[/C][C] 4.183e-64[/C][C] 1[/C][/ROW]
[ROW][C]12[/C][C] 4.095e-81[/C][C] 8.19e-81[/C][C] 1[/C][/ROW]
[ROW][C]13[/C][C] 2.146e-100[/C][C] 4.292e-100[/C][C] 1[/C][/ROW]
[ROW][C]14[/C][C] 8.133e-116[/C][C] 1.627e-115[/C][C] 1[/C][/ROW]
[ROW][C]15[/C][C] 2.062e-08[/C][C] 4.125e-08[/C][C] 1[/C][/ROW]
[ROW][C]16[/C][C] 8.417e-09[/C][C] 1.683e-08[/C][C] 1[/C][/ROW]
[ROW][C]17[/C][C] 4.38e-09[/C][C] 8.76e-09[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 4.151e-10[/C][C] 8.302e-10[/C][C] 1[/C][/ROW]
[ROW][C]19[/C][C] 3.928e-11[/C][C] 7.857e-11[/C][C] 1[/C][/ROW]
[ROW][C]20[/C][C] 4.575e-12[/C][C] 9.15e-12[/C][C] 1[/C][/ROW]
[ROW][C]21[/C][C] 1.629e-07[/C][C] 3.257e-07[/C][C] 1[/C][/ROW]
[ROW][C]22[/C][C] 5.138e-06[/C][C] 1.028e-05[/C][C] 1[/C][/ROW]
[ROW][C]23[/C][C] 2.59e-06[/C][C] 5.179e-06[/C][C] 1[/C][/ROW]
[ROW][C]24[/C][C] 7.339e-07[/C][C] 1.468e-06[/C][C] 1[/C][/ROW]
[ROW][C]25[/C][C] 2.666e-07[/C][C] 5.332e-07[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 0.004949[/C][C] 0.009898[/C][C] 0.9951[/C][/ROW]
[ROW][C]27[/C][C] 0.9615[/C][C] 0.07694[/C][C] 0.03847[/C][/ROW]
[ROW][C]28[/C][C] 0.9762[/C][C] 0.04754[/C][C] 0.02377[/C][/ROW]
[ROW][C]29[/C][C] 0.9933[/C][C] 0.01337[/C][C] 0.006686[/C][/ROW]
[ROW][C]30[/C][C] 0.9917[/C][C] 0.01659[/C][C] 0.008296[/C][/ROW]
[ROW][C]31[/C][C] 1[/C][C] 3.341e-05[/C][C] 1.67e-05[/C][/ROW]
[ROW][C]32[/C][C] 0.9999[/C][C] 0.0001119[/C][C] 5.596e-05[/C][/ROW]
[ROW][C]33[/C][C] 0.9998[/C][C] 0.0003835[/C][C] 0.0001918[/C][/ROW]
[ROW][C]34[/C][C] 0.9994[/C][C] 0.001288[/C][C] 0.0006441[/C][/ROW]
[ROW][C]35[/C][C] 0.9978[/C][C] 0.004339[/C][C] 0.00217[/C][/ROW]
[ROW][C]36[/C][C] 0.9934[/C][C] 0.01323[/C][C] 0.006615[/C][/ROW]
[ROW][C]37[/C][C] 1[/C][C] 3.952e-70[/C][C] 1.976e-70[/C][/ROW]
[ROW][C]38[/C][C] 1[/C][C] 1.85e-62[/C][C] 9.248e-63[/C][/ROW]
[ROW][C]39[/C][C] 1[/C][C] 3.075e-44[/C][C] 1.538e-44[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
10	9.045e-50	1.809e-49	1
11	2.092e-64	4.183e-64	1
12	4.095e-81	8.19e-81	1
13	2.146e-100	4.292e-100	1
14	8.133e-116	1.627e-115	1
15	2.062e-08	4.125e-08	1
16	8.417e-09	1.683e-08	1
17	4.38e-09	8.76e-09	1
18	4.151e-10	8.302e-10	1
19	3.928e-11	7.857e-11	1
20	4.575e-12	9.15e-12	1
21	1.629e-07	3.257e-07	1
22	5.138e-06	1.028e-05	1
23	2.59e-06	5.179e-06	1
24	7.339e-07	1.468e-06	1
25	2.666e-07	5.332e-07	1
26	0.004949	0.009898	0.9951
27	0.9615	0.07694	0.03847
28	0.9762	0.04754	0.02377
29	0.9933	0.01337	0.006686
30	0.9917	0.01659	0.008296
31	1	3.341e-05	1.67e-05
32	0.9999	0.0001119	5.596e-05
33	0.9998	0.0003835	0.0001918
34	0.9994	0.001288	0.0006441
35	0.9978	0.004339	0.00217
36	0.9934	0.01323	0.006615
37	1	3.952e-70	1.976e-70
38	1	1.85e-62	9.248e-63
39	1	3.075e-44	1.538e-44

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	25	0.8333	NOK
5% type I error level	29	0.966667	NOK
10% type I error level	30	1	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 & 25 &  0.8333 & NOK \tabularnewline
5% type I error level & 29 & 0.966667 & NOK \tabularnewline
10% type I error level & 30 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]25[/C][C] 0.8333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.966667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	25	0.8333	NOK
5% type I error level	29	0.966667	NOK
10% type I error level	30	1	NOK

Figure 1

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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 ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

par5 <- '0'
par4 <- '0'
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
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code