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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 29 Nov 2015 22:21:27 +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/2015/Nov/29/t1448835712tksio0uxzvo7haq.htm/, Retrieved Wed, 15 May 2024 23:01:25 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284549, Retrieved Wed, 15 May 2024 23:01:25 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

102

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

-     [Multiple Regression] [] [2015-11-25 14:21:14] [32b17a345b130fdf5cc88718ed94a974]
- R PD    [Multiple Regression] [MLR X5] [2015-11-29 22:21:27] [83aba8bbc702c7812e095afce40a5d1d] [Current]

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Dataseries X:

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Histogram

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6.8	225	9.2
6.3	180	11.7
6.4	190	15.8
6.2	180	8.6
6.9	205	23.2
6.4	225	27.4
6.3	185	9.3
6.8	235	16
6.9	235	4.7
6.7	210	12.5
6.9	245	20.1
6.9	245	9.1
6.3	185	8.1
6.1	185	8.6
6.2	180	20.3
6.8	220	25
6.5	194	19.2
7.6	225	3.3
6.3	210	11.2
7.1	240	10.5
6.8	225	10.1
7.3	263	7.2
6.4	210	13.6
6.8	235	9
7.2	230	24.6
6.4	190	12.6
6.6	220	5.6
6.8	210	8.7
6.1	180	7.7
6.5	235	24.1
6.4	185	11.7
6	175	7.7
6	192	9.6
7.3	263	7.2
6.1	180	12.3
6.7	240	8.9
6.4	210	13.6
5.8	160	11.2
6.9	230	2.8
7	245	3.2
7.3	228	9.4
5.9	155	11.9
6.2	200	15.4
6.8	235	7.4
7	235	18.9
5.9	105	7.9
6.1	180	12.2
5.7	185	11
7.1	245	2.8
5.8	180	11.8
7.4	240	17.1
6.8	225	11.6
6.8	215	5.8
7	230	8.3

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	'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284549&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284549&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284549&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	'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
X5 [t] = + 22.2874 -2.56738X1[t] + 0.0305597X2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X5
[t] =  +  22.2874 -2.56738X1[t] +  0.0305597X2[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284549&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X5
[t] =  +  22.2874 -2.56738X1[t] +  0.0305597X2[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284549&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284549&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
X5 [t] = + 22.2874 -2.56738X1[t] + 0.0305597X2[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)	+22.29	14	+1.5920e+00	0.1175	0.05874
X1	-2.567	3.245	-7.9110e-01	0.4326	0.2163
X2	+0.03056	0.04921	+6.2100e-01	0.5373	0.2687

\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) & +22.29 &  14 & +1.5920e+00 &  0.1175 &  0.05874 \tabularnewline
X1 & -2.567 &  3.245 & -7.9110e-01 &  0.4326 &  0.2163 \tabularnewline
X2 & +0.03056 &  0.04921 & +6.2100e-01 &  0.5373 &  0.2687 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284549&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]+22.29[/C][C] 14[/C][C]+1.5920e+00[/C][C] 0.1175[/C][C] 0.05874[/C][/ROW]
[ROW][C]X1[/C][C]-2.567[/C][C] 3.245[/C][C]-7.9110e-01[/C][C] 0.4326[/C][C] 0.2163[/C][/ROW]
[ROW][C]X2[/C][C]+0.03056[/C][C] 0.04921[/C][C]+6.2100e-01[/C][C] 0.5373[/C][C] 0.2687[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284549&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284549&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)	+22.29	14	+1.5920e+00	0.1175	0.05874
X1	-2.567	3.245	-7.9110e-01	0.4326	0.2163
X2	+0.03056	0.04921	+6.2100e-01	0.5373	0.2687

Multiple Linear Regression - Regression Statistics
Multiple R	0.1105
R-squared	0.01222
Adjusted R-squared	-0.02652
F-TEST (value)	0.3154
F-TEST (DF numerator)	2
F-TEST (DF denominator)	51
p-value	0.7309
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.977
Sum Squared Residuals	1822

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1105 \tabularnewline
R-squared &  0.01222 \tabularnewline
Adjusted R-squared & -0.02652 \tabularnewline
F-TEST (value) &  0.3154 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value &  0.7309 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  5.977 \tabularnewline
Sum Squared Residuals &  1822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284549&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1105[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01222[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.02652[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.3154[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C] 0.7309[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 5.977[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284549&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284549&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	0.1105
R-squared	0.01222
Adjusted R-squared	-0.02652
F-TEST (value)	0.3154
F-TEST (DF numerator)	2
F-TEST (DF denominator)	51
p-value	0.7309
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.977
Sum Squared Residuals	1822

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	9.2	11.71	-2.505
2	11.7	11.61	0.08629
3	15.8	11.66	4.137
4	8.6	11.87	-3.27
5	23.2	10.84	12.36
6	27.4	12.73	14.67
7	9.3	11.77	-2.467
8	16	12.01	3.989
9	4.7	11.75	-7.054
10	12.5	11.5	0.9964
11	20.1	12.06	8.04
12	9.1	12.06	-2.96
13	8.1	11.77	-3.667
14	8.6	12.28	-3.68
15	20.3	11.87	8.43
16	25	11.55	13.45
17	19.2	11.53	7.672
18	3.3	9.651	-6.351
19	11.2	12.53	-1.331
20	10.5	11.39	-0.8934
21	10.1	11.71	-1.605
22	7.2	11.58	-4.383
23	13.6	12.27	1.326
24	9	12.01	-3.011
25	24.6	10.83	13.77
26	12.6	11.66	0.9374
27	5.6	12.07	-6.466
28	8.7	11.25	-2.547
29	7.7	12.13	-4.427
30	24.1	12.78	11.32
31	11.7	11.51	0.1902
32	7.7	12.23	-4.531
33	9.6	12.75	-3.151
34	7.2	11.58	-4.383
35	12.3	12.13	0.1728
36	8.9	12.42	-3.52
37	13.6	12.27	1.326
38	11.2	12.29	-1.086
39	2.8	11.6	-8.801
40	3.2	11.8	-8.603
41	9.4	10.51	-1.113
42	11.9	11.88	0.02333
43	15.4	12.48	2.918
44	7.4	12.01	-4.611
45	18.9	11.5	7.403
46	7.9	10.35	-2.449
47	12.2	12.13	0.07281
48	11	13.31	-2.307
49	2.8	11.55	-8.746
50	11.8	12.9	-1.097
51	17.1	10.62	6.477
52	11.6	11.71	-0.1052
53	5.8	11.4	-5.6
54	8.3	11.34	-3.045

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  9.2 &  11.71 & -2.505 \tabularnewline
2 &  11.7 &  11.61 &  0.08629 \tabularnewline
3 &  15.8 &  11.66 &  4.137 \tabularnewline
4 &  8.6 &  11.87 & -3.27 \tabularnewline
5 &  23.2 &  10.84 &  12.36 \tabularnewline
6 &  27.4 &  12.73 &  14.67 \tabularnewline
7 &  9.3 &  11.77 & -2.467 \tabularnewline
8 &  16 &  12.01 &  3.989 \tabularnewline
9 &  4.7 &  11.75 & -7.054 \tabularnewline
10 &  12.5 &  11.5 &  0.9964 \tabularnewline
11 &  20.1 &  12.06 &  8.04 \tabularnewline
12 &  9.1 &  12.06 & -2.96 \tabularnewline
13 &  8.1 &  11.77 & -3.667 \tabularnewline
14 &  8.6 &  12.28 & -3.68 \tabularnewline
15 &  20.3 &  11.87 &  8.43 \tabularnewline
16 &  25 &  11.55 &  13.45 \tabularnewline
17 &  19.2 &  11.53 &  7.672 \tabularnewline
18 &  3.3 &  9.651 & -6.351 \tabularnewline
19 &  11.2 &  12.53 & -1.331 \tabularnewline
20 &  10.5 &  11.39 & -0.8934 \tabularnewline
21 &  10.1 &  11.71 & -1.605 \tabularnewline
22 &  7.2 &  11.58 & -4.383 \tabularnewline
23 &  13.6 &  12.27 &  1.326 \tabularnewline
24 &  9 &  12.01 & -3.011 \tabularnewline
25 &  24.6 &  10.83 &  13.77 \tabularnewline
26 &  12.6 &  11.66 &  0.9374 \tabularnewline
27 &  5.6 &  12.07 & -6.466 \tabularnewline
28 &  8.7 &  11.25 & -2.547 \tabularnewline
29 &  7.7 &  12.13 & -4.427 \tabularnewline
30 &  24.1 &  12.78 &  11.32 \tabularnewline
31 &  11.7 &  11.51 &  0.1902 \tabularnewline
32 &  7.7 &  12.23 & -4.531 \tabularnewline
33 &  9.6 &  12.75 & -3.151 \tabularnewline
34 &  7.2 &  11.58 & -4.383 \tabularnewline
35 &  12.3 &  12.13 &  0.1728 \tabularnewline
36 &  8.9 &  12.42 & -3.52 \tabularnewline
37 &  13.6 &  12.27 &  1.326 \tabularnewline
38 &  11.2 &  12.29 & -1.086 \tabularnewline
39 &  2.8 &  11.6 & -8.801 \tabularnewline
40 &  3.2 &  11.8 & -8.603 \tabularnewline
41 &  9.4 &  10.51 & -1.113 \tabularnewline
42 &  11.9 &  11.88 &  0.02333 \tabularnewline
43 &  15.4 &  12.48 &  2.918 \tabularnewline
44 &  7.4 &  12.01 & -4.611 \tabularnewline
45 &  18.9 &  11.5 &  7.403 \tabularnewline
46 &  7.9 &  10.35 & -2.449 \tabularnewline
47 &  12.2 &  12.13 &  0.07281 \tabularnewline
48 &  11 &  13.31 & -2.307 \tabularnewline
49 &  2.8 &  11.55 & -8.746 \tabularnewline
50 &  11.8 &  12.9 & -1.097 \tabularnewline
51 &  17.1 &  10.62 &  6.477 \tabularnewline
52 &  11.6 &  11.71 & -0.1052 \tabularnewline
53 &  5.8 &  11.4 & -5.6 \tabularnewline
54 &  8.3 &  11.34 & -3.045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284549&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] 11.71[/C][C]-2.505[/C][/ROW]
[ROW][C]2[/C][C] 11.7[/C][C] 11.61[/C][C] 0.08629[/C][/ROW]
[ROW][C]3[/C][C] 15.8[/C][C] 11.66[/C][C] 4.137[/C][/ROW]
[ROW][C]4[/C][C] 8.6[/C][C] 11.87[/C][C]-3.27[/C][/ROW]
[ROW][C]5[/C][C] 23.2[/C][C] 10.84[/C][C] 12.36[/C][/ROW]
[ROW][C]6[/C][C] 27.4[/C][C] 12.73[/C][C] 14.67[/C][/ROW]
[ROW][C]7[/C][C] 9.3[/C][C] 11.77[/C][C]-2.467[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 12.01[/C][C] 3.989[/C][/ROW]
[ROW][C]9[/C][C] 4.7[/C][C] 11.75[/C][C]-7.054[/C][/ROW]
[ROW][C]10[/C][C] 12.5[/C][C] 11.5[/C][C] 0.9964[/C][/ROW]
[ROW][C]11[/C][C] 20.1[/C][C] 12.06[/C][C] 8.04[/C][/ROW]
[ROW][C]12[/C][C] 9.1[/C][C] 12.06[/C][C]-2.96[/C][/ROW]
[ROW][C]13[/C][C] 8.1[/C][C] 11.77[/C][C]-3.667[/C][/ROW]
[ROW][C]14[/C][C] 8.6[/C][C] 12.28[/C][C]-3.68[/C][/ROW]
[ROW][C]15[/C][C] 20.3[/C][C] 11.87[/C][C] 8.43[/C][/ROW]
[ROW][C]16[/C][C] 25[/C][C] 11.55[/C][C] 13.45[/C][/ROW]
[ROW][C]17[/C][C] 19.2[/C][C] 11.53[/C][C] 7.672[/C][/ROW]
[ROW][C]18[/C][C] 3.3[/C][C] 9.651[/C][C]-6.351[/C][/ROW]
[ROW][C]19[/C][C] 11.2[/C][C] 12.53[/C][C]-1.331[/C][/ROW]
[ROW][C]20[/C][C] 10.5[/C][C] 11.39[/C][C]-0.8934[/C][/ROW]
[ROW][C]21[/C][C] 10.1[/C][C] 11.71[/C][C]-1.605[/C][/ROW]
[ROW][C]22[/C][C] 7.2[/C][C] 11.58[/C][C]-4.383[/C][/ROW]
[ROW][C]23[/C][C] 13.6[/C][C] 12.27[/C][C] 1.326[/C][/ROW]
[ROW][C]24[/C][C] 9[/C][C] 12.01[/C][C]-3.011[/C][/ROW]
[ROW][C]25[/C][C] 24.6[/C][C] 10.83[/C][C] 13.77[/C][/ROW]
[ROW][C]26[/C][C] 12.6[/C][C] 11.66[/C][C] 0.9374[/C][/ROW]
[ROW][C]27[/C][C] 5.6[/C][C] 12.07[/C][C]-6.466[/C][/ROW]
[ROW][C]28[/C][C] 8.7[/C][C] 11.25[/C][C]-2.547[/C][/ROW]
[ROW][C]29[/C][C] 7.7[/C][C] 12.13[/C][C]-4.427[/C][/ROW]
[ROW][C]30[/C][C] 24.1[/C][C] 12.78[/C][C] 11.32[/C][/ROW]
[ROW][C]31[/C][C] 11.7[/C][C] 11.51[/C][C] 0.1902[/C][/ROW]
[ROW][C]32[/C][C] 7.7[/C][C] 12.23[/C][C]-4.531[/C][/ROW]
[ROW][C]33[/C][C] 9.6[/C][C] 12.75[/C][C]-3.151[/C][/ROW]
[ROW][C]34[/C][C] 7.2[/C][C] 11.58[/C][C]-4.383[/C][/ROW]
[ROW][C]35[/C][C] 12.3[/C][C] 12.13[/C][C] 0.1728[/C][/ROW]
[ROW][C]36[/C][C] 8.9[/C][C] 12.42[/C][C]-3.52[/C][/ROW]
[ROW][C]37[/C][C] 13.6[/C][C] 12.27[/C][C] 1.326[/C][/ROW]
[ROW][C]38[/C][C] 11.2[/C][C] 12.29[/C][C]-1.086[/C][/ROW]
[ROW][C]39[/C][C] 2.8[/C][C] 11.6[/C][C]-8.801[/C][/ROW]
[ROW][C]40[/C][C] 3.2[/C][C] 11.8[/C][C]-8.603[/C][/ROW]
[ROW][C]41[/C][C] 9.4[/C][C] 10.51[/C][C]-1.113[/C][/ROW]
[ROW][C]42[/C][C] 11.9[/C][C] 11.88[/C][C] 0.02333[/C][/ROW]
[ROW][C]43[/C][C] 15.4[/C][C] 12.48[/C][C] 2.918[/C][/ROW]
[ROW][C]44[/C][C] 7.4[/C][C] 12.01[/C][C]-4.611[/C][/ROW]
[ROW][C]45[/C][C] 18.9[/C][C] 11.5[/C][C] 7.403[/C][/ROW]
[ROW][C]46[/C][C] 7.9[/C][C] 10.35[/C][C]-2.449[/C][/ROW]
[ROW][C]47[/C][C] 12.2[/C][C] 12.13[/C][C] 0.07281[/C][/ROW]
[ROW][C]48[/C][C] 11[/C][C] 13.31[/C][C]-2.307[/C][/ROW]
[ROW][C]49[/C][C] 2.8[/C][C] 11.55[/C][C]-8.746[/C][/ROW]
[ROW][C]50[/C][C] 11.8[/C][C] 12.9[/C][C]-1.097[/C][/ROW]
[ROW][C]51[/C][C] 17.1[/C][C] 10.62[/C][C] 6.477[/C][/ROW]
[ROW][C]52[/C][C] 11.6[/C][C] 11.71[/C][C]-0.1052[/C][/ROW]
[ROW][C]53[/C][C] 5.8[/C][C] 11.4[/C][C]-5.6[/C][/ROW]
[ROW][C]54[/C][C] 8.3[/C][C] 11.34[/C][C]-3.045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284549&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284549&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	11.71	-2.505
2	11.7	11.61	0.08629
3	15.8	11.66	4.137
4	8.6	11.87	-3.27
5	23.2	10.84	12.36
6	27.4	12.73	14.67
7	9.3	11.77	-2.467
8	16	12.01	3.989
9	4.7	11.75	-7.054
10	12.5	11.5	0.9964
11	20.1	12.06	8.04
12	9.1	12.06	-2.96
13	8.1	11.77	-3.667
14	8.6	12.28	-3.68
15	20.3	11.87	8.43
16	25	11.55	13.45
17	19.2	11.53	7.672
18	3.3	9.651	-6.351
19	11.2	12.53	-1.331
20	10.5	11.39	-0.8934
21	10.1	11.71	-1.605
22	7.2	11.58	-4.383
23	13.6	12.27	1.326
24	9	12.01	-3.011
25	24.6	10.83	13.77
26	12.6	11.66	0.9374
27	5.6	12.07	-6.466
28	8.7	11.25	-2.547
29	7.7	12.13	-4.427
30	24.1	12.78	11.32
31	11.7	11.51	0.1902
32	7.7	12.23	-4.531
33	9.6	12.75	-3.151
34	7.2	11.58	-4.383
35	12.3	12.13	0.1728
36	8.9	12.42	-3.52
37	13.6	12.27	1.326
38	11.2	12.29	-1.086
39	2.8	11.6	-8.801
40	3.2	11.8	-8.603
41	9.4	10.51	-1.113
42	11.9	11.88	0.02333
43	15.4	12.48	2.918
44	7.4	12.01	-4.611
45	18.9	11.5	7.403
46	7.9	10.35	-2.449
47	12.2	12.13	0.07281
48	11	13.31	-2.307
49	2.8	11.55	-8.746
50	11.8	12.9	-1.097
51	17.1	10.62	6.477
52	11.6	11.71	-0.1052
53	5.8	11.4	-5.6
54	8.3	11.34	-3.045

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.9261	0.1478	0.07392
7	0.8748	0.2503	0.1252
8	0.8364	0.3271	0.1636
9	0.9377	0.1245	0.06227
10	0.8955	0.209	0.1045
11	0.8777	0.2447	0.1223
12	0.8701	0.2597	0.1299
13	0.838	0.324	0.162
14	0.7954	0.4092	0.2046
15	0.8398	0.3204	0.1602
16	0.9467	0.1065	0.05325
17	0.9534	0.09312	0.04656
18	0.9589	0.08214	0.04107
19	0.9457	0.1085	0.05427
20	0.9216	0.1568	0.0784
21	0.8931	0.2139	0.1069
22	0.8755	0.2489	0.1245
23	0.8338	0.3324	0.1662
24	0.7955	0.4091	0.2045
25	0.9656	0.06876	0.03438
26	0.9495	0.101	0.05052
27	0.9525	0.09493	0.04747
28	0.9326	0.1348	0.06742
29	0.9188	0.1624	0.0812
30	0.9866	0.0268	0.0134
31	0.9778	0.04442	0.02221
32	0.9711	0.0578	0.0289
33	0.9569	0.08624	0.04312
34	0.9421	0.1158	0.05791
35	0.9125	0.1749	0.08746
36	0.8784	0.2433	0.1216
37	0.8401	0.3197	0.1599
38	0.7756	0.4488	0.2244
39	0.8235	0.353	0.1765
40	0.8715	0.2571	0.1285
41	0.8083	0.3833	0.1917
42	0.7281	0.5439	0.2719
43	0.6852	0.6296	0.3148
44	0.6186	0.7629	0.3814
45	0.7397	0.5206	0.2603
46	0.7422	0.5156	0.2578
47	0.6206	0.7587	0.3794
48	0.4988	0.9975	0.5012

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.9261 &  0.1478 &  0.07392 \tabularnewline
7 &  0.8748 &  0.2503 &  0.1252 \tabularnewline
8 &  0.8364 &  0.3271 &  0.1636 \tabularnewline
9 &  0.9377 &  0.1245 &  0.06227 \tabularnewline
10 &  0.8955 &  0.209 &  0.1045 \tabularnewline
11 &  0.8777 &  0.2447 &  0.1223 \tabularnewline
12 &  0.8701 &  0.2597 &  0.1299 \tabularnewline
13 &  0.838 &  0.324 &  0.162 \tabularnewline
14 &  0.7954 &  0.4092 &  0.2046 \tabularnewline
15 &  0.8398 &  0.3204 &  0.1602 \tabularnewline
16 &  0.9467 &  0.1065 &  0.05325 \tabularnewline
17 &  0.9534 &  0.09312 &  0.04656 \tabularnewline
18 &  0.9589 &  0.08214 &  0.04107 \tabularnewline
19 &  0.9457 &  0.1085 &  0.05427 \tabularnewline
20 &  0.9216 &  0.1568 &  0.0784 \tabularnewline
21 &  0.8931 &  0.2139 &  0.1069 \tabularnewline
22 &  0.8755 &  0.2489 &  0.1245 \tabularnewline
23 &  0.8338 &  0.3324 &  0.1662 \tabularnewline
24 &  0.7955 &  0.4091 &  0.2045 \tabularnewline
25 &  0.9656 &  0.06876 &  0.03438 \tabularnewline
26 &  0.9495 &  0.101 &  0.05052 \tabularnewline
27 &  0.9525 &  0.09493 &  0.04747 \tabularnewline
28 &  0.9326 &  0.1348 &  0.06742 \tabularnewline
29 &  0.9188 &  0.1624 &  0.0812 \tabularnewline
30 &  0.9866 &  0.0268 &  0.0134 \tabularnewline
31 &  0.9778 &  0.04442 &  0.02221 \tabularnewline
32 &  0.9711 &  0.0578 &  0.0289 \tabularnewline
33 &  0.9569 &  0.08624 &  0.04312 \tabularnewline
34 &  0.9421 &  0.1158 &  0.05791 \tabularnewline
35 &  0.9125 &  0.1749 &  0.08746 \tabularnewline
36 &  0.8784 &  0.2433 &  0.1216 \tabularnewline
37 &  0.8401 &  0.3197 &  0.1599 \tabularnewline
38 &  0.7756 &  0.4488 &  0.2244 \tabularnewline
39 &  0.8235 &  0.353 &  0.1765 \tabularnewline
40 &  0.8715 &  0.2571 &  0.1285 \tabularnewline
41 &  0.8083 &  0.3833 &  0.1917 \tabularnewline
42 &  0.7281 &  0.5439 &  0.2719 \tabularnewline
43 &  0.6852 &  0.6296 &  0.3148 \tabularnewline
44 &  0.6186 &  0.7629 &  0.3814 \tabularnewline
45 &  0.7397 &  0.5206 &  0.2603 \tabularnewline
46 &  0.7422 &  0.5156 &  0.2578 \tabularnewline
47 &  0.6206 &  0.7587 &  0.3794 \tabularnewline
48 &  0.4988 &  0.9975 &  0.5012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284549&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]6[/C][C] 0.9261[/C][C] 0.1478[/C][C] 0.07392[/C][/ROW]
[ROW][C]7[/C][C] 0.8748[/C][C] 0.2503[/C][C] 0.1252[/C][/ROW]
[ROW][C]8[/C][C] 0.8364[/C][C] 0.3271[/C][C] 0.1636[/C][/ROW]
[ROW][C]9[/C][C] 0.9377[/C][C] 0.1245[/C][C] 0.06227[/C][/ROW]
[ROW][C]10[/C][C] 0.8955[/C][C] 0.209[/C][C] 0.1045[/C][/ROW]
[ROW][C]11[/C][C] 0.8777[/C][C] 0.2447[/C][C] 0.1223[/C][/ROW]
[ROW][C]12[/C][C] 0.8701[/C][C] 0.2597[/C][C] 0.1299[/C][/ROW]
[ROW][C]13[/C][C] 0.838[/C][C] 0.324[/C][C] 0.162[/C][/ROW]
[ROW][C]14[/C][C] 0.7954[/C][C] 0.4092[/C][C] 0.2046[/C][/ROW]
[ROW][C]15[/C][C] 0.8398[/C][C] 0.3204[/C][C] 0.1602[/C][/ROW]
[ROW][C]16[/C][C] 0.9467[/C][C] 0.1065[/C][C] 0.05325[/C][/ROW]
[ROW][C]17[/C][C] 0.9534[/C][C] 0.09312[/C][C] 0.04656[/C][/ROW]
[ROW][C]18[/C][C] 0.9589[/C][C] 0.08214[/C][C] 0.04107[/C][/ROW]
[ROW][C]19[/C][C] 0.9457[/C][C] 0.1085[/C][C] 0.05427[/C][/ROW]
[ROW][C]20[/C][C] 0.9216[/C][C] 0.1568[/C][C] 0.0784[/C][/ROW]
[ROW][C]21[/C][C] 0.8931[/C][C] 0.2139[/C][C] 0.1069[/C][/ROW]
[ROW][C]22[/C][C] 0.8755[/C][C] 0.2489[/C][C] 0.1245[/C][/ROW]
[ROW][C]23[/C][C] 0.8338[/C][C] 0.3324[/C][C] 0.1662[/C][/ROW]
[ROW][C]24[/C][C] 0.7955[/C][C] 0.4091[/C][C] 0.2045[/C][/ROW]
[ROW][C]25[/C][C] 0.9656[/C][C] 0.06876[/C][C] 0.03438[/C][/ROW]
[ROW][C]26[/C][C] 0.9495[/C][C] 0.101[/C][C] 0.05052[/C][/ROW]
[ROW][C]27[/C][C] 0.9525[/C][C] 0.09493[/C][C] 0.04747[/C][/ROW]
[ROW][C]28[/C][C] 0.9326[/C][C] 0.1348[/C][C] 0.06742[/C][/ROW]
[ROW][C]29[/C][C] 0.9188[/C][C] 0.1624[/C][C] 0.0812[/C][/ROW]
[ROW][C]30[/C][C] 0.9866[/C][C] 0.0268[/C][C] 0.0134[/C][/ROW]
[ROW][C]31[/C][C] 0.9778[/C][C] 0.04442[/C][C] 0.02221[/C][/ROW]
[ROW][C]32[/C][C] 0.9711[/C][C] 0.0578[/C][C] 0.0289[/C][/ROW]
[ROW][C]33[/C][C] 0.9569[/C][C] 0.08624[/C][C] 0.04312[/C][/ROW]
[ROW][C]34[/C][C] 0.9421[/C][C] 0.1158[/C][C] 0.05791[/C][/ROW]
[ROW][C]35[/C][C] 0.9125[/C][C] 0.1749[/C][C] 0.08746[/C][/ROW]
[ROW][C]36[/C][C] 0.8784[/C][C] 0.2433[/C][C] 0.1216[/C][/ROW]
[ROW][C]37[/C][C] 0.8401[/C][C] 0.3197[/C][C] 0.1599[/C][/ROW]
[ROW][C]38[/C][C] 0.7756[/C][C] 0.4488[/C][C] 0.2244[/C][/ROW]
[ROW][C]39[/C][C] 0.8235[/C][C] 0.353[/C][C] 0.1765[/C][/ROW]
[ROW][C]40[/C][C] 0.8715[/C][C] 0.2571[/C][C] 0.1285[/C][/ROW]
[ROW][C]41[/C][C] 0.8083[/C][C] 0.3833[/C][C] 0.1917[/C][/ROW]
[ROW][C]42[/C][C] 0.7281[/C][C] 0.5439[/C][C] 0.2719[/C][/ROW]
[ROW][C]43[/C][C] 0.6852[/C][C] 0.6296[/C][C] 0.3148[/C][/ROW]
[ROW][C]44[/C][C] 0.6186[/C][C] 0.7629[/C][C] 0.3814[/C][/ROW]
[ROW][C]45[/C][C] 0.7397[/C][C] 0.5206[/C][C] 0.2603[/C][/ROW]
[ROW][C]46[/C][C] 0.7422[/C][C] 0.5156[/C][C] 0.2578[/C][/ROW]
[ROW][C]47[/C][C] 0.6206[/C][C] 0.7587[/C][C] 0.3794[/C][/ROW]
[ROW][C]48[/C][C] 0.4988[/C][C] 0.9975[/C][C] 0.5012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284549&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284549&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
6	0.9261	0.1478	0.07392
7	0.8748	0.2503	0.1252
8	0.8364	0.3271	0.1636
9	0.9377	0.1245	0.06227
10	0.8955	0.209	0.1045
11	0.8777	0.2447	0.1223
12	0.8701	0.2597	0.1299
13	0.838	0.324	0.162
14	0.7954	0.4092	0.2046
15	0.8398	0.3204	0.1602
16	0.9467	0.1065	0.05325
17	0.9534	0.09312	0.04656
18	0.9589	0.08214	0.04107
19	0.9457	0.1085	0.05427
20	0.9216	0.1568	0.0784
21	0.8931	0.2139	0.1069
22	0.8755	0.2489	0.1245
23	0.8338	0.3324	0.1662
24	0.7955	0.4091	0.2045
25	0.9656	0.06876	0.03438
26	0.9495	0.101	0.05052
27	0.9525	0.09493	0.04747
28	0.9326	0.1348	0.06742
29	0.9188	0.1624	0.0812
30	0.9866	0.0268	0.0134
31	0.9778	0.04442	0.02221
32	0.9711	0.0578	0.0289
33	0.9569	0.08624	0.04312
34	0.9421	0.1158	0.05791
35	0.9125	0.1749	0.08746
36	0.8784	0.2433	0.1216
37	0.8401	0.3197	0.1599
38	0.7756	0.4488	0.2244
39	0.8235	0.353	0.1765
40	0.8715	0.2571	0.1285
41	0.8083	0.3833	0.1917
42	0.7281	0.5439	0.2719
43	0.6852	0.6296	0.3148
44	0.6186	0.7629	0.3814
45	0.7397	0.5206	0.2603
46	0.7422	0.5156	0.2578
47	0.6206	0.7587	0.3794
48	0.4988	0.9975	0.5012

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	2	0.0465116	OK
10% type I error level	8	0.186047	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 & 2 & 0.0465116 & OK \tabularnewline
10% type I error level & 8 & 0.186047 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284549&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]2[/C][C]0.0465116[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.186047[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284549&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284549&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	2	0.0465116	OK
10% type I error level	8	0.186047	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 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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
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.'
}
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'){
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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