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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 21 Dec 2010 13:36:45 +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/2010/Dec/21/t12929384820r53yzxugxg0841.htm/, Retrieved Wed, 08 May 2024 18:17:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113530, Retrieved Wed, 08 May 2024 18:17:37 +0000

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Estimated Impact

131

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS7 - Multiple Re...] [2010-11-20 15:02:39] [8ef49741e164ec6343c90c7935194465]
- R  D    [Multiple Regression] [Paper; Multiple R...] [2010-12-17 12:34:51] [8ffb4cfa64b4677df0d2c448735a40bb]
-   PD        [Multiple Regression] [Paper; Multiple R...] [2010-12-21 13:36:45] [50e0b5177c9c80b42996aa89930b928a] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113530&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113530&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113530&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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
Coffee[t] = + 112.416041666667 -0.36454861111108M1[t] -0.051180555555562M2[t] + 0.0646874999999913M3[t] + 1.55305555555555M4[t] + 2.08142361111110M5[t] + 2.36729166666666M6[t] + 1.89065972222222M7[t] + 1.82152777777777M8[t] + 1.28739583333333M9[t] + 1.16076388888888M10[t] + 0.76413194444444M11[t] + 0.319131944444444t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Coffee[t] =  +  112.416041666667 -0.36454861111108M1[t] -0.051180555555562M2[t] +  0.0646874999999913M3[t] +  1.55305555555555M4[t] +  2.08142361111110M5[t] +  2.36729166666666M6[t] +  1.89065972222222M7[t] +  1.82152777777777M8[t] +  1.28739583333333M9[t] +  1.16076388888888M10[t] +  0.76413194444444M11[t] +  0.319131944444444t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113530&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Coffee[t] =  +  112.416041666667 -0.36454861111108M1[t] -0.051180555555562M2[t] +  0.0646874999999913M3[t] +  1.55305555555555M4[t] +  2.08142361111110M5[t] +  2.36729166666666M6[t] +  1.89065972222222M7[t] +  1.82152777777777M8[t] +  1.28739583333333M9[t] +  1.16076388888888M10[t] +  0.76413194444444M11[t] +  0.319131944444444t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113530&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113530&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
Coffee[t] = + 112.416041666667 -0.36454861111108M1[t] -0.051180555555562M2[t] + 0.0646874999999913M3[t] + 1.55305555555555M4[t] + 2.08142361111110M5[t] + 2.36729166666666M6[t] + 1.89065972222222M7[t] + 1.82152777777777M8[t] + 1.28739583333333M9[t] + 1.16076388888888M10[t] + 0.76413194444444M11[t] + 0.319131944444444t + 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)	112.416041666667	0.959103	117.2095	0	0
M1	-0.36454861111108	1.155434	-0.3155	0.75425	0.377125
M2	-0.051180555555562	1.152698	-0.0444	0.964837	0.482419
M3	0.0646874999999913	1.150218	0.0562	0.955471	0.477735
M4	1.55305555555555	1.147995	1.3528	0.184781	0.092391
M5	2.08142361111110	1.146029	1.8162	0.077913	0.038956
M6	2.36729166666666	1.144323	2.0687	0.046021	0.023011
M7	1.89065972222222	1.142877	1.6543	0.107009	0.053505
M8	1.82152777777777	1.141693	1.5955	0.1196	0.0598
M9	1.28739583333333	1.140771	1.1285	0.266772	0.133386
M10	1.16076388888888	1.140112	1.0181	0.31561	0.157805
M11	0.76413194444444	1.139716	0.6705	0.506966	0.253483
t	0.319131944444444	0.017338	18.4063	0	0

\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) & 112.416041666667 & 0.959103 & 117.2095 & 0 & 0 \tabularnewline
M1 & -0.36454861111108 & 1.155434 & -0.3155 & 0.75425 & 0.377125 \tabularnewline
M2 & -0.051180555555562 & 1.152698 & -0.0444 & 0.964837 & 0.482419 \tabularnewline
M3 & 0.0646874999999913 & 1.150218 & 0.0562 & 0.955471 & 0.477735 \tabularnewline
M4 & 1.55305555555555 & 1.147995 & 1.3528 & 0.184781 & 0.092391 \tabularnewline
M5 & 2.08142361111110 & 1.146029 & 1.8162 & 0.077913 & 0.038956 \tabularnewline
M6 & 2.36729166666666 & 1.144323 & 2.0687 & 0.046021 & 0.023011 \tabularnewline
M7 & 1.89065972222222 & 1.142877 & 1.6543 & 0.107009 & 0.053505 \tabularnewline
M8 & 1.82152777777777 & 1.141693 & 1.5955 & 0.1196 & 0.0598 \tabularnewline
M9 & 1.28739583333333 & 1.140771 & 1.1285 & 0.266772 & 0.133386 \tabularnewline
M10 & 1.16076388888888 & 1.140112 & 1.0181 & 0.31561 & 0.157805 \tabularnewline
M11 & 0.76413194444444 & 1.139716 & 0.6705 & 0.506966 & 0.253483 \tabularnewline
t & 0.319131944444444 & 0.017338 & 18.4063 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113530&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]112.416041666667[/C][C]0.959103[/C][C]117.2095[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.36454861111108[/C][C]1.155434[/C][C]-0.3155[/C][C]0.75425[/C][C]0.377125[/C][/ROW]
[ROW][C]M2[/C][C]-0.051180555555562[/C][C]1.152698[/C][C]-0.0444[/C][C]0.964837[/C][C]0.482419[/C][/ROW]
[ROW][C]M3[/C][C]0.0646874999999913[/C][C]1.150218[/C][C]0.0562[/C][C]0.955471[/C][C]0.477735[/C][/ROW]
[ROW][C]M4[/C][C]1.55305555555555[/C][C]1.147995[/C][C]1.3528[/C][C]0.184781[/C][C]0.092391[/C][/ROW]
[ROW][C]M5[/C][C]2.08142361111110[/C][C]1.146029[/C][C]1.8162[/C][C]0.077913[/C][C]0.038956[/C][/ROW]
[ROW][C]M6[/C][C]2.36729166666666[/C][C]1.144323[/C][C]2.0687[/C][C]0.046021[/C][C]0.023011[/C][/ROW]
[ROW][C]M7[/C][C]1.89065972222222[/C][C]1.142877[/C][C]1.6543[/C][C]0.107009[/C][C]0.053505[/C][/ROW]
[ROW][C]M8[/C][C]1.82152777777777[/C][C]1.141693[/C][C]1.5955[/C][C]0.1196[/C][C]0.0598[/C][/ROW]
[ROW][C]M9[/C][C]1.28739583333333[/C][C]1.140771[/C][C]1.1285[/C][C]0.266772[/C][C]0.133386[/C][/ROW]
[ROW][C]M10[/C][C]1.16076388888888[/C][C]1.140112[/C][C]1.0181[/C][C]0.31561[/C][C]0.157805[/C][/ROW]
[ROW][C]M11[/C][C]0.76413194444444[/C][C]1.139716[/C][C]0.6705[/C][C]0.506966[/C][C]0.253483[/C][/ROW]
[ROW][C]t[/C][C]0.319131944444444[/C][C]0.017338[/C][C]18.4063[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113530&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113530&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)	112.416041666667	0.959103	117.2095	0	0
M1	-0.36454861111108	1.155434	-0.3155	0.75425	0.377125
M2	-0.051180555555562	1.152698	-0.0444	0.964837	0.482419
M3	0.0646874999999913	1.150218	0.0562	0.955471	0.477735
M4	1.55305555555555	1.147995	1.3528	0.184781	0.092391
M5	2.08142361111110	1.146029	1.8162	0.077913	0.038956
M6	2.36729166666666	1.144323	2.0687	0.046021	0.023011
M7	1.89065972222222	1.142877	1.6543	0.107009	0.053505
M8	1.82152777777777	1.141693	1.5955	0.1196	0.0598
M9	1.28739583333333	1.140771	1.1285	0.266772	0.133386
M10	1.16076388888888	1.140112	1.0181	0.31561	0.157805
M11	0.76413194444444	1.139716	0.6705	0.506966	0.253483
t	0.319131944444444	0.017338	18.4063	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.957362443624075
R-squared	0.91654284846186
Adjusted R-squared	0.887928967934497
F-TEST (value)	32.0314068406556
F-TEST (DF numerator)	12
F-TEST (DF denominator)	35
p-value	2.66453525910038e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.61161555663282
Sum Squared Residuals	90.9056645833323

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.957362443624075 \tabularnewline
R-squared & 0.91654284846186 \tabularnewline
Adjusted R-squared & 0.887928967934497 \tabularnewline
F-TEST (value) & 32.0314068406556 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 35 \tabularnewline
p-value & 2.66453525910038e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.61161555663282 \tabularnewline
Sum Squared Residuals & 90.9056645833323 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113530&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.957362443624075[/C][/ROW]
[ROW][C]R-squared[/C][C]0.91654284846186[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.887928967934497[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]32.0314068406556[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]35[/C][/ROW]
[ROW][C]p-value[/C][C]2.66453525910038e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.61161555663282[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]90.9056645833323[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113530&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113530&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	0.957362443624075
R-squared	0.91654284846186
Adjusted R-squared	0.887928967934497
F-TEST (value)	32.0314068406556
F-TEST (DF numerator)	12
F-TEST (DF denominator)	35
p-value	2.66453525910038e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.61161555663282
Sum Squared Residuals	90.9056645833323

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	108.35	112.370625000000	-4.02062499999990
2	109.87	113.003125	-3.13312500000000
3	111.3	113.438125	-2.13812500000001
4	115.5	115.245625	0.254374999999991
5	116.22	116.093125	0.126874999999994
6	116.63	116.698125	-0.0681250000000055
7	116.84	116.540625	0.299375000000002
8	116.63	116.790625	-0.160625000000012
9	117.03	116.575625	0.454374999999992
10	117	116.768125	0.231874999999998
11	117.14	116.690625	0.449374999999993
12	116.64	116.245625	0.394374999999992
13	117.24	116.200208333333	1.03979166666662
14	117.52	116.832708333333	0.687291666666665
15	117.83	117.267708333333	0.562291666666668
16	119.79	119.075208333333	0.71479166666667
17	120.86	119.922708333333	0.937291666666668
18	120.75	120.527708333333	0.222291666666666
19	120.63	120.370208333333	0.259791666666661
20	120.89	120.620208333333	0.269791666666667
21	120.23	120.405208333333	-0.175208333333333
22	121.19	120.597708333333	0.592291666666664
23	120.79	120.520208333333	0.26979166666667
24	120.09	120.075208333333	0.0147916666666638
25	120.86	120.029791666667	0.830208333333303
26	121.1	120.662291666667	0.437708333333334
27	121.47	121.097291666667	0.372708333333339
28	122.01	122.904791666667	-0.894791666666662
29	123.94	123.752291666667	0.187708333333333
30	125.78	124.357291666667	1.42270833333334
31	125.31	124.199791666667	1.11020833333334
32	125.79	124.449791666667	1.34020833333334
33	126.12	124.234791666667	1.88520833333334
34	125.57	124.427291666667	1.14270833333333
35	125.44	124.349791666667	1.09020833333333
36	126.12	123.904791666667	2.21520833333334
37	126.01	123.859375	2.15062499999998
38	126.5	124.491875	2.00812500000001
39	126.13	124.926875	1.20312500000000
40	126.66	126.734375	-0.0743749999999993
41	126.33	127.581875	-1.25187500000000
42	126.61	128.186875	-1.57687500000000
43	126.36	128.029375	-1.66937500000000
44	126.83	128.279375	-1.44937500000000
45	125.9	128.064375	-2.16437499999999
46	126.29	128.256875	-1.96687499999999
47	126.37	128.179375	-1.80937499999999
48	125.11	127.734375	-2.62437500000000

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 108.35 & 112.370625000000 & -4.02062499999990 \tabularnewline
2 & 109.87 & 113.003125 & -3.13312500000000 \tabularnewline
3 & 111.3 & 113.438125 & -2.13812500000001 \tabularnewline
4 & 115.5 & 115.245625 & 0.254374999999991 \tabularnewline
5 & 116.22 & 116.093125 & 0.126874999999994 \tabularnewline
6 & 116.63 & 116.698125 & -0.0681250000000055 \tabularnewline
7 & 116.84 & 116.540625 & 0.299375000000002 \tabularnewline
8 & 116.63 & 116.790625 & -0.160625000000012 \tabularnewline
9 & 117.03 & 116.575625 & 0.454374999999992 \tabularnewline
10 & 117 & 116.768125 & 0.231874999999998 \tabularnewline
11 & 117.14 & 116.690625 & 0.449374999999993 \tabularnewline
12 & 116.64 & 116.245625 & 0.394374999999992 \tabularnewline
13 & 117.24 & 116.200208333333 & 1.03979166666662 \tabularnewline
14 & 117.52 & 116.832708333333 & 0.687291666666665 \tabularnewline
15 & 117.83 & 117.267708333333 & 0.562291666666668 \tabularnewline
16 & 119.79 & 119.075208333333 & 0.71479166666667 \tabularnewline
17 & 120.86 & 119.922708333333 & 0.937291666666668 \tabularnewline
18 & 120.75 & 120.527708333333 & 0.222291666666666 \tabularnewline
19 & 120.63 & 120.370208333333 & 0.259791666666661 \tabularnewline
20 & 120.89 & 120.620208333333 & 0.269791666666667 \tabularnewline
21 & 120.23 & 120.405208333333 & -0.175208333333333 \tabularnewline
22 & 121.19 & 120.597708333333 & 0.592291666666664 \tabularnewline
23 & 120.79 & 120.520208333333 & 0.26979166666667 \tabularnewline
24 & 120.09 & 120.075208333333 & 0.0147916666666638 \tabularnewline
25 & 120.86 & 120.029791666667 & 0.830208333333303 \tabularnewline
26 & 121.1 & 120.662291666667 & 0.437708333333334 \tabularnewline
27 & 121.47 & 121.097291666667 & 0.372708333333339 \tabularnewline
28 & 122.01 & 122.904791666667 & -0.894791666666662 \tabularnewline
29 & 123.94 & 123.752291666667 & 0.187708333333333 \tabularnewline
30 & 125.78 & 124.357291666667 & 1.42270833333334 \tabularnewline
31 & 125.31 & 124.199791666667 & 1.11020833333334 \tabularnewline
32 & 125.79 & 124.449791666667 & 1.34020833333334 \tabularnewline
33 & 126.12 & 124.234791666667 & 1.88520833333334 \tabularnewline
34 & 125.57 & 124.427291666667 & 1.14270833333333 \tabularnewline
35 & 125.44 & 124.349791666667 & 1.09020833333333 \tabularnewline
36 & 126.12 & 123.904791666667 & 2.21520833333334 \tabularnewline
37 & 126.01 & 123.859375 & 2.15062499999998 \tabularnewline
38 & 126.5 & 124.491875 & 2.00812500000001 \tabularnewline
39 & 126.13 & 124.926875 & 1.20312500000000 \tabularnewline
40 & 126.66 & 126.734375 & -0.0743749999999993 \tabularnewline
41 & 126.33 & 127.581875 & -1.25187500000000 \tabularnewline
42 & 126.61 & 128.186875 & -1.57687500000000 \tabularnewline
43 & 126.36 & 128.029375 & -1.66937500000000 \tabularnewline
44 & 126.83 & 128.279375 & -1.44937500000000 \tabularnewline
45 & 125.9 & 128.064375 & -2.16437499999999 \tabularnewline
46 & 126.29 & 128.256875 & -1.96687499999999 \tabularnewline
47 & 126.37 & 128.179375 & -1.80937499999999 \tabularnewline
48 & 125.11 & 127.734375 & -2.62437500000000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113530&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]108.35[/C][C]112.370625000000[/C][C]-4.02062499999990[/C][/ROW]
[ROW][C]2[/C][C]109.87[/C][C]113.003125[/C][C]-3.13312500000000[/C][/ROW]
[ROW][C]3[/C][C]111.3[/C][C]113.438125[/C][C]-2.13812500000001[/C][/ROW]
[ROW][C]4[/C][C]115.5[/C][C]115.245625[/C][C]0.254374999999991[/C][/ROW]
[ROW][C]5[/C][C]116.22[/C][C]116.093125[/C][C]0.126874999999994[/C][/ROW]
[ROW][C]6[/C][C]116.63[/C][C]116.698125[/C][C]-0.0681250000000055[/C][/ROW]
[ROW][C]7[/C][C]116.84[/C][C]116.540625[/C][C]0.299375000000002[/C][/ROW]
[ROW][C]8[/C][C]116.63[/C][C]116.790625[/C][C]-0.160625000000012[/C][/ROW]
[ROW][C]9[/C][C]117.03[/C][C]116.575625[/C][C]0.454374999999992[/C][/ROW]
[ROW][C]10[/C][C]117[/C][C]116.768125[/C][C]0.231874999999998[/C][/ROW]
[ROW][C]11[/C][C]117.14[/C][C]116.690625[/C][C]0.449374999999993[/C][/ROW]
[ROW][C]12[/C][C]116.64[/C][C]116.245625[/C][C]0.394374999999992[/C][/ROW]
[ROW][C]13[/C][C]117.24[/C][C]116.200208333333[/C][C]1.03979166666662[/C][/ROW]
[ROW][C]14[/C][C]117.52[/C][C]116.832708333333[/C][C]0.687291666666665[/C][/ROW]
[ROW][C]15[/C][C]117.83[/C][C]117.267708333333[/C][C]0.562291666666668[/C][/ROW]
[ROW][C]16[/C][C]119.79[/C][C]119.075208333333[/C][C]0.71479166666667[/C][/ROW]
[ROW][C]17[/C][C]120.86[/C][C]119.922708333333[/C][C]0.937291666666668[/C][/ROW]
[ROW][C]18[/C][C]120.75[/C][C]120.527708333333[/C][C]0.222291666666666[/C][/ROW]
[ROW][C]19[/C][C]120.63[/C][C]120.370208333333[/C][C]0.259791666666661[/C][/ROW]
[ROW][C]20[/C][C]120.89[/C][C]120.620208333333[/C][C]0.269791666666667[/C][/ROW]
[ROW][C]21[/C][C]120.23[/C][C]120.405208333333[/C][C]-0.175208333333333[/C][/ROW]
[ROW][C]22[/C][C]121.19[/C][C]120.597708333333[/C][C]0.592291666666664[/C][/ROW]
[ROW][C]23[/C][C]120.79[/C][C]120.520208333333[/C][C]0.26979166666667[/C][/ROW]
[ROW][C]24[/C][C]120.09[/C][C]120.075208333333[/C][C]0.0147916666666638[/C][/ROW]
[ROW][C]25[/C][C]120.86[/C][C]120.029791666667[/C][C]0.830208333333303[/C][/ROW]
[ROW][C]26[/C][C]121.1[/C][C]120.662291666667[/C][C]0.437708333333334[/C][/ROW]
[ROW][C]27[/C][C]121.47[/C][C]121.097291666667[/C][C]0.372708333333339[/C][/ROW]
[ROW][C]28[/C][C]122.01[/C][C]122.904791666667[/C][C]-0.894791666666662[/C][/ROW]
[ROW][C]29[/C][C]123.94[/C][C]123.752291666667[/C][C]0.187708333333333[/C][/ROW]
[ROW][C]30[/C][C]125.78[/C][C]124.357291666667[/C][C]1.42270833333334[/C][/ROW]
[ROW][C]31[/C][C]125.31[/C][C]124.199791666667[/C][C]1.11020833333334[/C][/ROW]
[ROW][C]32[/C][C]125.79[/C][C]124.449791666667[/C][C]1.34020833333334[/C][/ROW]
[ROW][C]33[/C][C]126.12[/C][C]124.234791666667[/C][C]1.88520833333334[/C][/ROW]
[ROW][C]34[/C][C]125.57[/C][C]124.427291666667[/C][C]1.14270833333333[/C][/ROW]
[ROW][C]35[/C][C]125.44[/C][C]124.349791666667[/C][C]1.09020833333333[/C][/ROW]
[ROW][C]36[/C][C]126.12[/C][C]123.904791666667[/C][C]2.21520833333334[/C][/ROW]
[ROW][C]37[/C][C]126.01[/C][C]123.859375[/C][C]2.15062499999998[/C][/ROW]
[ROW][C]38[/C][C]126.5[/C][C]124.491875[/C][C]2.00812500000001[/C][/ROW]
[ROW][C]39[/C][C]126.13[/C][C]124.926875[/C][C]1.20312500000000[/C][/ROW]
[ROW][C]40[/C][C]126.66[/C][C]126.734375[/C][C]-0.0743749999999993[/C][/ROW]
[ROW][C]41[/C][C]126.33[/C][C]127.581875[/C][C]-1.25187500000000[/C][/ROW]
[ROW][C]42[/C][C]126.61[/C][C]128.186875[/C][C]-1.57687500000000[/C][/ROW]
[ROW][C]43[/C][C]126.36[/C][C]128.029375[/C][C]-1.66937500000000[/C][/ROW]
[ROW][C]44[/C][C]126.83[/C][C]128.279375[/C][C]-1.44937500000000[/C][/ROW]
[ROW][C]45[/C][C]125.9[/C][C]128.064375[/C][C]-2.16437499999999[/C][/ROW]
[ROW][C]46[/C][C]126.29[/C][C]128.256875[/C][C]-1.96687499999999[/C][/ROW]
[ROW][C]47[/C][C]126.37[/C][C]128.179375[/C][C]-1.80937499999999[/C][/ROW]
[ROW][C]48[/C][C]125.11[/C][C]127.734375[/C][C]-2.62437500000000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113530&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113530&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	108.35	112.370625000000	-4.02062499999990
2	109.87	113.003125	-3.13312500000000
3	111.3	113.438125	-2.13812500000001
4	115.5	115.245625	0.254374999999991
5	116.22	116.093125	0.126874999999994
6	116.63	116.698125	-0.0681250000000055
7	116.84	116.540625	0.299375000000002
8	116.63	116.790625	-0.160625000000012
9	117.03	116.575625	0.454374999999992
10	117	116.768125	0.231874999999998
11	117.14	116.690625	0.449374999999993
12	116.64	116.245625	0.394374999999992
13	117.24	116.200208333333	1.03979166666662
14	117.52	116.832708333333	0.687291666666665
15	117.83	117.267708333333	0.562291666666668
16	119.79	119.075208333333	0.71479166666667
17	120.86	119.922708333333	0.937291666666668
18	120.75	120.527708333333	0.222291666666666
19	120.63	120.370208333333	0.259791666666661
20	120.89	120.620208333333	0.269791666666667
21	120.23	120.405208333333	-0.175208333333333
22	121.19	120.597708333333	0.592291666666664
23	120.79	120.520208333333	0.26979166666667
24	120.09	120.075208333333	0.0147916666666638
25	120.86	120.029791666667	0.830208333333303
26	121.1	120.662291666667	0.437708333333334
27	121.47	121.097291666667	0.372708333333339
28	122.01	122.904791666667	-0.894791666666662
29	123.94	123.752291666667	0.187708333333333
30	125.78	124.357291666667	1.42270833333334
31	125.31	124.199791666667	1.11020833333334
32	125.79	124.449791666667	1.34020833333334
33	126.12	124.234791666667	1.88520833333334
34	125.57	124.427291666667	1.14270833333333
35	125.44	124.349791666667	1.09020833333333
36	126.12	123.904791666667	2.21520833333334
37	126.01	123.859375	2.15062499999998
38	126.5	124.491875	2.00812500000001
39	126.13	124.926875	1.20312500000000
40	126.66	126.734375	-0.0743749999999993
41	126.33	127.581875	-1.25187500000000
42	126.61	128.186875	-1.57687500000000
43	126.36	128.029375	-1.66937500000000
44	126.83	128.279375	-1.44937500000000
45	125.9	128.064375	-2.16437499999999
46	126.29	128.256875	-1.96687499999999
47	126.37	128.179375	-1.80937499999999
48	125.11	127.734375	-2.62437500000000

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.590677848796468	0.818644302407065	0.409322151203532
17	0.548406721663743	0.903186556672515	0.451593278336257
18	0.524691686349612	0.950616627300775	0.475308313650388
19	0.491562194159341	0.983124388318683	0.508437805840659
20	0.408401032521556	0.816802065043112	0.591598967478444
21	0.408815643024056	0.817631286048112	0.591184356975944
22	0.313395371217085	0.62679074243417	0.686604628782915
23	0.258265440455749	0.516530880911498	0.741734559544251
24	0.240798107746769	0.481596215493538	0.759201892253231
25	0.250097083932165	0.50019416786433	0.749902916067835
26	0.342610699500183	0.685221399000365	0.657389300499817
27	0.487570798952624	0.975141597905248	0.512429201047376
28	0.936796483044144	0.126407033911712	0.0632035169558558
29	0.972971842636036	0.0540563147279274	0.0270281573639637
30	0.938768073148116	0.122463853703767	0.0612319268518837
31	0.889836421458116	0.220327157083769	0.110163578541884
32	0.824719501545731	0.350560996908538	0.175280498454269

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.590677848796468 & 0.818644302407065 & 0.409322151203532 \tabularnewline
17 & 0.548406721663743 & 0.903186556672515 & 0.451593278336257 \tabularnewline
18 & 0.524691686349612 & 0.950616627300775 & 0.475308313650388 \tabularnewline
19 & 0.491562194159341 & 0.983124388318683 & 0.508437805840659 \tabularnewline
20 & 0.408401032521556 & 0.816802065043112 & 0.591598967478444 \tabularnewline
21 & 0.408815643024056 & 0.817631286048112 & 0.591184356975944 \tabularnewline
22 & 0.313395371217085 & 0.62679074243417 & 0.686604628782915 \tabularnewline
23 & 0.258265440455749 & 0.516530880911498 & 0.741734559544251 \tabularnewline
24 & 0.240798107746769 & 0.481596215493538 & 0.759201892253231 \tabularnewline
25 & 0.250097083932165 & 0.50019416786433 & 0.749902916067835 \tabularnewline
26 & 0.342610699500183 & 0.685221399000365 & 0.657389300499817 \tabularnewline
27 & 0.487570798952624 & 0.975141597905248 & 0.512429201047376 \tabularnewline
28 & 0.936796483044144 & 0.126407033911712 & 0.0632035169558558 \tabularnewline
29 & 0.972971842636036 & 0.0540563147279274 & 0.0270281573639637 \tabularnewline
30 & 0.938768073148116 & 0.122463853703767 & 0.0612319268518837 \tabularnewline
31 & 0.889836421458116 & 0.220327157083769 & 0.110163578541884 \tabularnewline
32 & 0.824719501545731 & 0.350560996908538 & 0.175280498454269 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113530&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]16[/C][C]0.590677848796468[/C][C]0.818644302407065[/C][C]0.409322151203532[/C][/ROW]
[ROW][C]17[/C][C]0.548406721663743[/C][C]0.903186556672515[/C][C]0.451593278336257[/C][/ROW]
[ROW][C]18[/C][C]0.524691686349612[/C][C]0.950616627300775[/C][C]0.475308313650388[/C][/ROW]
[ROW][C]19[/C][C]0.491562194159341[/C][C]0.983124388318683[/C][C]0.508437805840659[/C][/ROW]
[ROW][C]20[/C][C]0.408401032521556[/C][C]0.816802065043112[/C][C]0.591598967478444[/C][/ROW]
[ROW][C]21[/C][C]0.408815643024056[/C][C]0.817631286048112[/C][C]0.591184356975944[/C][/ROW]
[ROW][C]22[/C][C]0.313395371217085[/C][C]0.62679074243417[/C][C]0.686604628782915[/C][/ROW]
[ROW][C]23[/C][C]0.258265440455749[/C][C]0.516530880911498[/C][C]0.741734559544251[/C][/ROW]
[ROW][C]24[/C][C]0.240798107746769[/C][C]0.481596215493538[/C][C]0.759201892253231[/C][/ROW]
[ROW][C]25[/C][C]0.250097083932165[/C][C]0.50019416786433[/C][C]0.749902916067835[/C][/ROW]
[ROW][C]26[/C][C]0.342610699500183[/C][C]0.685221399000365[/C][C]0.657389300499817[/C][/ROW]
[ROW][C]27[/C][C]0.487570798952624[/C][C]0.975141597905248[/C][C]0.512429201047376[/C][/ROW]
[ROW][C]28[/C][C]0.936796483044144[/C][C]0.126407033911712[/C][C]0.0632035169558558[/C][/ROW]
[ROW][C]29[/C][C]0.972971842636036[/C][C]0.0540563147279274[/C][C]0.0270281573639637[/C][/ROW]
[ROW][C]30[/C][C]0.938768073148116[/C][C]0.122463853703767[/C][C]0.0612319268518837[/C][/ROW]
[ROW][C]31[/C][C]0.889836421458116[/C][C]0.220327157083769[/C][C]0.110163578541884[/C][/ROW]
[ROW][C]32[/C][C]0.824719501545731[/C][C]0.350560996908538[/C][C]0.175280498454269[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113530&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113530&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
16	0.590677848796468	0.818644302407065	0.409322151203532
17	0.548406721663743	0.903186556672515	0.451593278336257
18	0.524691686349612	0.950616627300775	0.475308313650388
19	0.491562194159341	0.983124388318683	0.508437805840659
20	0.408401032521556	0.816802065043112	0.591598967478444
21	0.408815643024056	0.817631286048112	0.591184356975944
22	0.313395371217085	0.62679074243417	0.686604628782915
23	0.258265440455749	0.516530880911498	0.741734559544251
24	0.240798107746769	0.481596215493538	0.759201892253231
25	0.250097083932165	0.50019416786433	0.749902916067835
26	0.342610699500183	0.685221399000365	0.657389300499817
27	0.487570798952624	0.975141597905248	0.512429201047376
28	0.936796483044144	0.126407033911712	0.0632035169558558
29	0.972971842636036	0.0540563147279274	0.0270281573639637
30	0.938768073148116	0.122463853703767	0.0612319268518837
31	0.889836421458116	0.220327157083769	0.110163578541884
32	0.824719501545731	0.350560996908538	0.175280498454269

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	1	0.0588235294117647	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 & 1 & 0.0588235294117647 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113530&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]1[/C][C]0.0588235294117647[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113530&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113530&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0588235294117647	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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