Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 24 Nov 2008 02:53:26 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/24/t1227520492c7f5nhuakpssr7r.htm/, Retrieved Mon, 13 May 2024 23:31:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25363, Retrieved Mon, 13 May 2024 23:31:46 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

164

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

-       [Multiple Regression] [] [2008-11-24 09:53:26] [cae3b9b084628ae4df84563390017721] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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	'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25363&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25363&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25363&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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
koers[t] = + 1.09422371794872 + 0.130969230769231dummy[t] + 0.00186591880341965M1[t] -0.0163521367521368M2[t] -0.0240201923076923M3[t] -0.0278632478632479M4[t] -0.0298313034188034M5[t] -0.0140993589743589M6[t] + 0.00080758547008551M7[t] + 0.0113645299145300M8[t] + 0.0110214743589744M9[t] -0.0152138888888889M10[t] -0.0166069444444444M11[t] -0.00683194444444446t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
koers[t] =  +  1.09422371794872 +  0.130969230769231dummy[t] +  0.00186591880341965M1[t] -0.0163521367521368M2[t] -0.0240201923076923M3[t] -0.0278632478632479M4[t] -0.0298313034188034M5[t] -0.0140993589743589M6[t] +  0.00080758547008551M7[t] +  0.0113645299145300M8[t] +  0.0110214743589744M9[t] -0.0152138888888889M10[t] -0.0166069444444444M11[t] -0.00683194444444446t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25363&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]koers[t] =  +  1.09422371794872 +  0.130969230769231dummy[t] +  0.00186591880341965M1[t] -0.0163521367521368M2[t] -0.0240201923076923M3[t] -0.0278632478632479M4[t] -0.0298313034188034M5[t] -0.0140993589743589M6[t] +  0.00080758547008551M7[t] +  0.0113645299145300M8[t] +  0.0110214743589744M9[t] -0.0152138888888889M10[t] -0.0166069444444444M11[t] -0.00683194444444446t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25363&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25363&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
koers[t] = + 1.09422371794872 + 0.130969230769231dummy[t] + 0.00186591880341965M1[t] -0.0163521367521368M2[t] -0.0240201923076923M3[t] -0.0278632478632479M4[t] -0.0298313034188034M5[t] -0.0140993589743589M6[t] + 0.00080758547008551M7[t] + 0.0113645299145300M8[t] + 0.0110214743589744M9[t] -0.0152138888888889M10[t] -0.0166069444444444M11[t] -0.00683194444444446t + 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)	1.09422371794872	0.038054	28.7547	0	0
dummy	0.130969230769231	0.033295	3.9336	0.000392	0.000196
M1	0.00186591880341965	0.044125	0.0423	0.966517	0.483259
M2	-0.0163521367521368	0.043998	-0.3717	0.712452	0.356226
M3	-0.0240201923076923	0.043898	-0.5472	0.587827	0.293913
M4	-0.0278632478632479	0.043827	-0.6358	0.529189	0.264594
M5	-0.0298313034188034	0.043784	-0.6813	0.500276	0.250138
M6	-0.0140993589743589	0.043769	-0.3221	0.749328	0.374664
M7	0.00080758547008551	0.043784	0.0184	0.985392	0.492696
M8	0.0113645299145300	0.043827	0.2593	0.796962	0.398481
M9	0.0110214743589744	0.043898	0.2511	0.803271	0.401635
M10	-0.0152138888888889	0.043549	-0.3494	0.728981	0.36449
M11	-0.0166069444444444	0.043506	-0.3817	0.705047	0.352523
t	-0.00683194444444446	0.001118	-6.1082	1e-06	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) & 1.09422371794872 & 0.038054 & 28.7547 & 0 & 0 \tabularnewline
dummy & 0.130969230769231 & 0.033295 & 3.9336 & 0.000392 & 0.000196 \tabularnewline
M1 & 0.00186591880341965 & 0.044125 & 0.0423 & 0.966517 & 0.483259 \tabularnewline
M2 & -0.0163521367521368 & 0.043998 & -0.3717 & 0.712452 & 0.356226 \tabularnewline
M3 & -0.0240201923076923 & 0.043898 & -0.5472 & 0.587827 & 0.293913 \tabularnewline
M4 & -0.0278632478632479 & 0.043827 & -0.6358 & 0.529189 & 0.264594 \tabularnewline
M5 & -0.0298313034188034 & 0.043784 & -0.6813 & 0.500276 & 0.250138 \tabularnewline
M6 & -0.0140993589743589 & 0.043769 & -0.3221 & 0.749328 & 0.374664 \tabularnewline
M7 & 0.00080758547008551 & 0.043784 & 0.0184 & 0.985392 & 0.492696 \tabularnewline
M8 & 0.0113645299145300 & 0.043827 & 0.2593 & 0.796962 & 0.398481 \tabularnewline
M9 & 0.0110214743589744 & 0.043898 & 0.2511 & 0.803271 & 0.401635 \tabularnewline
M10 & -0.0152138888888889 & 0.043549 & -0.3494 & 0.728981 & 0.36449 \tabularnewline
M11 & -0.0166069444444444 & 0.043506 & -0.3817 & 0.705047 & 0.352523 \tabularnewline
t & -0.00683194444444446 & 0.001118 & -6.1082 & 1e-06 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25363&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]1.09422371794872[/C][C]0.038054[/C][C]28.7547[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]dummy[/C][C]0.130969230769231[/C][C]0.033295[/C][C]3.9336[/C][C]0.000392[/C][C]0.000196[/C][/ROW]
[ROW][C]M1[/C][C]0.00186591880341965[/C][C]0.044125[/C][C]0.0423[/C][C]0.966517[/C][C]0.483259[/C][/ROW]
[ROW][C]M2[/C][C]-0.0163521367521368[/C][C]0.043998[/C][C]-0.3717[/C][C]0.712452[/C][C]0.356226[/C][/ROW]
[ROW][C]M3[/C][C]-0.0240201923076923[/C][C]0.043898[/C][C]-0.5472[/C][C]0.587827[/C][C]0.293913[/C][/ROW]
[ROW][C]M4[/C][C]-0.0278632478632479[/C][C]0.043827[/C][C]-0.6358[/C][C]0.529189[/C][C]0.264594[/C][/ROW]
[ROW][C]M5[/C][C]-0.0298313034188034[/C][C]0.043784[/C][C]-0.6813[/C][C]0.500276[/C][C]0.250138[/C][/ROW]
[ROW][C]M6[/C][C]-0.0140993589743589[/C][C]0.043769[/C][C]-0.3221[/C][C]0.749328[/C][C]0.374664[/C][/ROW]
[ROW][C]M7[/C][C]0.00080758547008551[/C][C]0.043784[/C][C]0.0184[/C][C]0.985392[/C][C]0.492696[/C][/ROW]
[ROW][C]M8[/C][C]0.0113645299145300[/C][C]0.043827[/C][C]0.2593[/C][C]0.796962[/C][C]0.398481[/C][/ROW]
[ROW][C]M9[/C][C]0.0110214743589744[/C][C]0.043898[/C][C]0.2511[/C][C]0.803271[/C][C]0.401635[/C][/ROW]
[ROW][C]M10[/C][C]-0.0152138888888889[/C][C]0.043549[/C][C]-0.3494[/C][C]0.728981[/C][C]0.36449[/C][/ROW]
[ROW][C]M11[/C][C]-0.0166069444444444[/C][C]0.043506[/C][C]-0.3817[/C][C]0.705047[/C][C]0.352523[/C][/ROW]
[ROW][C]t[/C][C]-0.00683194444444446[/C][C]0.001118[/C][C]-6.1082[/C][C]1e-06[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25363&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25363&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)	1.09422371794872	0.038054	28.7547	0	0
dummy	0.130969230769231	0.033295	3.9336	0.000392	0.000196
M1	0.00186591880341965	0.044125	0.0423	0.966517	0.483259
M2	-0.0163521367521368	0.043998	-0.3717	0.712452	0.356226
M3	-0.0240201923076923	0.043898	-0.5472	0.587827	0.293913
M4	-0.0278632478632479	0.043827	-0.6358	0.529189	0.264594
M5	-0.0298313034188034	0.043784	-0.6813	0.500276	0.250138
M6	-0.0140993589743589	0.043769	-0.3221	0.749328	0.374664
M7	0.00080758547008551	0.043784	0.0184	0.985392	0.492696
M8	0.0113645299145300	0.043827	0.2593	0.796962	0.398481
M9	0.0110214743589744	0.043898	0.2511	0.803271	0.401635
M10	-0.0152138888888889	0.043549	-0.3494	0.728981	0.36449
M11	-0.0166069444444444	0.043506	-0.3817	0.705047	0.352523
t	-0.00683194444444446	0.001118	-6.1082	1e-06	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.74703080051469
R-squared	0.55805501691762
Adjusted R-squared	0.389076052797886
F-TEST (value)	3.3025117642584
F-TEST (DF numerator)	13
F-TEST (DF denominator)	34
p-value	0.00256445494977298
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0615063815681336
Sum Squared Residuals	0.128623189102565

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.74703080051469 \tabularnewline
R-squared & 0.55805501691762 \tabularnewline
Adjusted R-squared & 0.389076052797886 \tabularnewline
F-TEST (value) & 3.3025117642584 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 0.00256445494977298 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0615063815681336 \tabularnewline
Sum Squared Residuals & 0.128623189102565 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25363&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.74703080051469[/C][/ROW]
[ROW][C]R-squared[/C][C]0.55805501691762[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.389076052797886[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.3025117642584[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]0.00256445494977298[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0615063815681336[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.128623189102565[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25363&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25363&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	0.74703080051469
R-squared	0.55805501691762
Adjusted R-squared	0.389076052797886
F-TEST (value)	3.3025117642584
F-TEST (DF numerator)	13
F-TEST (DF denominator)	34
p-value	0.00256445494977298
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0615063815681336
Sum Squared Residuals	0.128623189102565

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.1608	1.08925769230769	0.0715423076923101
2	1.1208	1.06420769230769	0.0565923076923076
3	1.0883	1.04970769230769	0.0385923076923075
4	1.0704	1.03903269230769	0.0313673076923075
5	1.0628	1.03023269230769	0.0325673076923075
6	1.0378	1.03913269230769	-0.00133269230769239
7	1.0353	1.04720769230769	-0.0119076923076924
8	1.0604	1.05093269230769	0.00946730769230745
9	1.0501	1.04375769230769	0.0063423076923075
10	1.0706	1.01069038461538	0.0599096153846153
11	1.0338	1.00246538461538	0.0313346153846153
12	1.011	1.01224038461538	-0.00124038461538479
13	1.0137	1.00727435897436	0.00642564102564014
14	0.9834	0.98222435897436	0.00117564102564106
15	0.9643	0.96772435897436	-0.00342435897435896
16	0.947	0.95704935897436	-0.0100493589743590
17	0.906	0.94824935897436	-0.0422493589743590
18	0.9492	0.95714935897436	-0.00794935897435898
19	0.9397	0.96522435897436	-0.025524358974359
20	0.9041	0.96894935897436	-0.064849358974359
21	0.8721	0.96177435897436	-0.089674358974359
22	0.8552	0.928707051282051	-0.0735070512820513
23	0.8564	0.920482051282051	-0.0640820512820513
24	0.8973	0.930257051282051	-0.0329570512820512
25	0.9383	0.925291025641026	0.0130089743589736
26	0.9217	0.900241025641025	0.0214589743589744
27	0.9095	0.885741025641025	0.0237589743589745
28	0.892	0.875066025641026	0.0169339743589745
29	0.8742	0.866266025641026	0.00793397435897452
30	0.8532	0.875166025641026	-0.0219660256410256
31	0.8607	0.883241025641025	-0.0225410256410254
32	0.9005	0.886966025641025	0.0135339743589745
33	0.9111	0.879791025641025	0.0313089743589746
34	0.9059	0.977692948717949	-0.0717929487179487
35	0.8883	0.969467948717949	-0.0811679487179488
36	0.8924	0.979242948717949	-0.0868429487179488
37	0.8833	0.974276923076924	-0.0909769230769239
38	0.87	0.949226923076923	-0.079226923076923
39	0.8758	0.934726923076923	-0.058926923076923
40	0.8858	0.924051923076923	-0.038251923076923
41	0.917	0.915251923076923	0.00174807692307704
42	0.9554	0.924151923076923	0.031248076923077
43	0.9922	0.932226923076923	0.059973076923077
44	0.9778	0.935951923076923	0.041848076923077
45	0.9808	0.928776923076923	0.052023076923077
46	0.9811	0.895709615384615	0.0853903846153847
47	1.0014	0.887484615384615	0.113915384615385
48	1.0183	0.897259615384615	0.121040384615385

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.1608 & 1.08925769230769 & 0.0715423076923101 \tabularnewline
2 & 1.1208 & 1.06420769230769 & 0.0565923076923076 \tabularnewline
3 & 1.0883 & 1.04970769230769 & 0.0385923076923075 \tabularnewline
4 & 1.0704 & 1.03903269230769 & 0.0313673076923075 \tabularnewline
5 & 1.0628 & 1.03023269230769 & 0.0325673076923075 \tabularnewline
6 & 1.0378 & 1.03913269230769 & -0.00133269230769239 \tabularnewline
7 & 1.0353 & 1.04720769230769 & -0.0119076923076924 \tabularnewline
8 & 1.0604 & 1.05093269230769 & 0.00946730769230745 \tabularnewline
9 & 1.0501 & 1.04375769230769 & 0.0063423076923075 \tabularnewline
10 & 1.0706 & 1.01069038461538 & 0.0599096153846153 \tabularnewline
11 & 1.0338 & 1.00246538461538 & 0.0313346153846153 \tabularnewline
12 & 1.011 & 1.01224038461538 & -0.00124038461538479 \tabularnewline
13 & 1.0137 & 1.00727435897436 & 0.00642564102564014 \tabularnewline
14 & 0.9834 & 0.98222435897436 & 0.00117564102564106 \tabularnewline
15 & 0.9643 & 0.96772435897436 & -0.00342435897435896 \tabularnewline
16 & 0.947 & 0.95704935897436 & -0.0100493589743590 \tabularnewline
17 & 0.906 & 0.94824935897436 & -0.0422493589743590 \tabularnewline
18 & 0.9492 & 0.95714935897436 & -0.00794935897435898 \tabularnewline
19 & 0.9397 & 0.96522435897436 & -0.025524358974359 \tabularnewline
20 & 0.9041 & 0.96894935897436 & -0.064849358974359 \tabularnewline
21 & 0.8721 & 0.96177435897436 & -0.089674358974359 \tabularnewline
22 & 0.8552 & 0.928707051282051 & -0.0735070512820513 \tabularnewline
23 & 0.8564 & 0.920482051282051 & -0.0640820512820513 \tabularnewline
24 & 0.8973 & 0.930257051282051 & -0.0329570512820512 \tabularnewline
25 & 0.9383 & 0.925291025641026 & 0.0130089743589736 \tabularnewline
26 & 0.9217 & 0.900241025641025 & 0.0214589743589744 \tabularnewline
27 & 0.9095 & 0.885741025641025 & 0.0237589743589745 \tabularnewline
28 & 0.892 & 0.875066025641026 & 0.0169339743589745 \tabularnewline
29 & 0.8742 & 0.866266025641026 & 0.00793397435897452 \tabularnewline
30 & 0.8532 & 0.875166025641026 & -0.0219660256410256 \tabularnewline
31 & 0.8607 & 0.883241025641025 & -0.0225410256410254 \tabularnewline
32 & 0.9005 & 0.886966025641025 & 0.0135339743589745 \tabularnewline
33 & 0.9111 & 0.879791025641025 & 0.0313089743589746 \tabularnewline
34 & 0.9059 & 0.977692948717949 & -0.0717929487179487 \tabularnewline
35 & 0.8883 & 0.969467948717949 & -0.0811679487179488 \tabularnewline
36 & 0.8924 & 0.979242948717949 & -0.0868429487179488 \tabularnewline
37 & 0.8833 & 0.974276923076924 & -0.0909769230769239 \tabularnewline
38 & 0.87 & 0.949226923076923 & -0.079226923076923 \tabularnewline
39 & 0.8758 & 0.934726923076923 & -0.058926923076923 \tabularnewline
40 & 0.8858 & 0.924051923076923 & -0.038251923076923 \tabularnewline
41 & 0.917 & 0.915251923076923 & 0.00174807692307704 \tabularnewline
42 & 0.9554 & 0.924151923076923 & 0.031248076923077 \tabularnewline
43 & 0.9922 & 0.932226923076923 & 0.059973076923077 \tabularnewline
44 & 0.9778 & 0.935951923076923 & 0.041848076923077 \tabularnewline
45 & 0.9808 & 0.928776923076923 & 0.052023076923077 \tabularnewline
46 & 0.9811 & 0.895709615384615 & 0.0853903846153847 \tabularnewline
47 & 1.0014 & 0.887484615384615 & 0.113915384615385 \tabularnewline
48 & 1.0183 & 0.897259615384615 & 0.121040384615385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25363&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]1.1608[/C][C]1.08925769230769[/C][C]0.0715423076923101[/C][/ROW]
[ROW][C]2[/C][C]1.1208[/C][C]1.06420769230769[/C][C]0.0565923076923076[/C][/ROW]
[ROW][C]3[/C][C]1.0883[/C][C]1.04970769230769[/C][C]0.0385923076923075[/C][/ROW]
[ROW][C]4[/C][C]1.0704[/C][C]1.03903269230769[/C][C]0.0313673076923075[/C][/ROW]
[ROW][C]5[/C][C]1.0628[/C][C]1.03023269230769[/C][C]0.0325673076923075[/C][/ROW]
[ROW][C]6[/C][C]1.0378[/C][C]1.03913269230769[/C][C]-0.00133269230769239[/C][/ROW]
[ROW][C]7[/C][C]1.0353[/C][C]1.04720769230769[/C][C]-0.0119076923076924[/C][/ROW]
[ROW][C]8[/C][C]1.0604[/C][C]1.05093269230769[/C][C]0.00946730769230745[/C][/ROW]
[ROW][C]9[/C][C]1.0501[/C][C]1.04375769230769[/C][C]0.0063423076923075[/C][/ROW]
[ROW][C]10[/C][C]1.0706[/C][C]1.01069038461538[/C][C]0.0599096153846153[/C][/ROW]
[ROW][C]11[/C][C]1.0338[/C][C]1.00246538461538[/C][C]0.0313346153846153[/C][/ROW]
[ROW][C]12[/C][C]1.011[/C][C]1.01224038461538[/C][C]-0.00124038461538479[/C][/ROW]
[ROW][C]13[/C][C]1.0137[/C][C]1.00727435897436[/C][C]0.00642564102564014[/C][/ROW]
[ROW][C]14[/C][C]0.9834[/C][C]0.98222435897436[/C][C]0.00117564102564106[/C][/ROW]
[ROW][C]15[/C][C]0.9643[/C][C]0.96772435897436[/C][C]-0.00342435897435896[/C][/ROW]
[ROW][C]16[/C][C]0.947[/C][C]0.95704935897436[/C][C]-0.0100493589743590[/C][/ROW]
[ROW][C]17[/C][C]0.906[/C][C]0.94824935897436[/C][C]-0.0422493589743590[/C][/ROW]
[ROW][C]18[/C][C]0.9492[/C][C]0.95714935897436[/C][C]-0.00794935897435898[/C][/ROW]
[ROW][C]19[/C][C]0.9397[/C][C]0.96522435897436[/C][C]-0.025524358974359[/C][/ROW]
[ROW][C]20[/C][C]0.9041[/C][C]0.96894935897436[/C][C]-0.064849358974359[/C][/ROW]
[ROW][C]21[/C][C]0.8721[/C][C]0.96177435897436[/C][C]-0.089674358974359[/C][/ROW]
[ROW][C]22[/C][C]0.8552[/C][C]0.928707051282051[/C][C]-0.0735070512820513[/C][/ROW]
[ROW][C]23[/C][C]0.8564[/C][C]0.920482051282051[/C][C]-0.0640820512820513[/C][/ROW]
[ROW][C]24[/C][C]0.8973[/C][C]0.930257051282051[/C][C]-0.0329570512820512[/C][/ROW]
[ROW][C]25[/C][C]0.9383[/C][C]0.925291025641026[/C][C]0.0130089743589736[/C][/ROW]
[ROW][C]26[/C][C]0.9217[/C][C]0.900241025641025[/C][C]0.0214589743589744[/C][/ROW]
[ROW][C]27[/C][C]0.9095[/C][C]0.885741025641025[/C][C]0.0237589743589745[/C][/ROW]
[ROW][C]28[/C][C]0.892[/C][C]0.875066025641026[/C][C]0.0169339743589745[/C][/ROW]
[ROW][C]29[/C][C]0.8742[/C][C]0.866266025641026[/C][C]0.00793397435897452[/C][/ROW]
[ROW][C]30[/C][C]0.8532[/C][C]0.875166025641026[/C][C]-0.0219660256410256[/C][/ROW]
[ROW][C]31[/C][C]0.8607[/C][C]0.883241025641025[/C][C]-0.0225410256410254[/C][/ROW]
[ROW][C]32[/C][C]0.9005[/C][C]0.886966025641025[/C][C]0.0135339743589745[/C][/ROW]
[ROW][C]33[/C][C]0.9111[/C][C]0.879791025641025[/C][C]0.0313089743589746[/C][/ROW]
[ROW][C]34[/C][C]0.9059[/C][C]0.977692948717949[/C][C]-0.0717929487179487[/C][/ROW]
[ROW][C]35[/C][C]0.8883[/C][C]0.969467948717949[/C][C]-0.0811679487179488[/C][/ROW]
[ROW][C]36[/C][C]0.8924[/C][C]0.979242948717949[/C][C]-0.0868429487179488[/C][/ROW]
[ROW][C]37[/C][C]0.8833[/C][C]0.974276923076924[/C][C]-0.0909769230769239[/C][/ROW]
[ROW][C]38[/C][C]0.87[/C][C]0.949226923076923[/C][C]-0.079226923076923[/C][/ROW]
[ROW][C]39[/C][C]0.8758[/C][C]0.934726923076923[/C][C]-0.058926923076923[/C][/ROW]
[ROW][C]40[/C][C]0.8858[/C][C]0.924051923076923[/C][C]-0.038251923076923[/C][/ROW]
[ROW][C]41[/C][C]0.917[/C][C]0.915251923076923[/C][C]0.00174807692307704[/C][/ROW]
[ROW][C]42[/C][C]0.9554[/C][C]0.924151923076923[/C][C]0.031248076923077[/C][/ROW]
[ROW][C]43[/C][C]0.9922[/C][C]0.932226923076923[/C][C]0.059973076923077[/C][/ROW]
[ROW][C]44[/C][C]0.9778[/C][C]0.935951923076923[/C][C]0.041848076923077[/C][/ROW]
[ROW][C]45[/C][C]0.9808[/C][C]0.928776923076923[/C][C]0.052023076923077[/C][/ROW]
[ROW][C]46[/C][C]0.9811[/C][C]0.895709615384615[/C][C]0.0853903846153847[/C][/ROW]
[ROW][C]47[/C][C]1.0014[/C][C]0.887484615384615[/C][C]0.113915384615385[/C][/ROW]
[ROW][C]48[/C][C]1.0183[/C][C]0.897259615384615[/C][C]0.121040384615385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25363&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25363&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	1.1608	1.08925769230769	0.0715423076923101
2	1.1208	1.06420769230769	0.0565923076923076
3	1.0883	1.04970769230769	0.0385923076923075
4	1.0704	1.03903269230769	0.0313673076923075
5	1.0628	1.03023269230769	0.0325673076923075
6	1.0378	1.03913269230769	-0.00133269230769239
7	1.0353	1.04720769230769	-0.0119076923076924
8	1.0604	1.05093269230769	0.00946730769230745
9	1.0501	1.04375769230769	0.0063423076923075
10	1.0706	1.01069038461538	0.0599096153846153
11	1.0338	1.00246538461538	0.0313346153846153
12	1.011	1.01224038461538	-0.00124038461538479
13	1.0137	1.00727435897436	0.00642564102564014
14	0.9834	0.98222435897436	0.00117564102564106
15	0.9643	0.96772435897436	-0.00342435897435896
16	0.947	0.95704935897436	-0.0100493589743590
17	0.906	0.94824935897436	-0.0422493589743590
18	0.9492	0.95714935897436	-0.00794935897435898
19	0.9397	0.96522435897436	-0.025524358974359
20	0.9041	0.96894935897436	-0.064849358974359
21	0.8721	0.96177435897436	-0.089674358974359
22	0.8552	0.928707051282051	-0.0735070512820513
23	0.8564	0.920482051282051	-0.0640820512820513
24	0.8973	0.930257051282051	-0.0329570512820512
25	0.9383	0.925291025641026	0.0130089743589736
26	0.9217	0.900241025641025	0.0214589743589744
27	0.9095	0.885741025641025	0.0237589743589745
28	0.892	0.875066025641026	0.0169339743589745
29	0.8742	0.866266025641026	0.00793397435897452
30	0.8532	0.875166025641026	-0.0219660256410256
31	0.8607	0.883241025641025	-0.0225410256410254
32	0.9005	0.886966025641025	0.0135339743589745
33	0.9111	0.879791025641025	0.0313089743589746
34	0.9059	0.977692948717949	-0.0717929487179487
35	0.8883	0.969467948717949	-0.0811679487179488
36	0.8924	0.979242948717949	-0.0868429487179488
37	0.8833	0.974276923076924	-0.0909769230769239
38	0.87	0.949226923076923	-0.079226923076923
39	0.8758	0.934726923076923	-0.058926923076923
40	0.8858	0.924051923076923	-0.038251923076923
41	0.917	0.915251923076923	0.00174807692307704
42	0.9554	0.924151923076923	0.031248076923077
43	0.9922	0.932226923076923	0.059973076923077
44	0.9778	0.935951923076923	0.041848076923077
45	0.9808	0.928776923076923	0.052023076923077
46	0.9811	0.895709615384615	0.0853903846153847
47	1.0014	0.887484615384615	0.113915384615385
48	1.0183	0.897259615384615	0.121040384615385

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0208919329713117	0.0417838659426233	0.979108067028688
18	0.0587698536065827	0.117539707213165	0.941230146393417
19	0.0606381440050899	0.121276288010180	0.93936185599491
20	0.0516800776201212	0.103360155240242	0.948319922379879
21	0.0642119749791326	0.128423949958265	0.935788025020867
22	0.145104429893832	0.290208859787665	0.854895570106168
23	0.100271375552046	0.200542751104092	0.899728624447954
24	0.0665400738142143	0.133080147628429	0.933459926185786
25	0.137564545795512	0.275129091591024	0.862435454204488
26	0.321508389704407	0.643016779408815	0.678491610295593
27	0.624218982832281	0.751562034335438	0.375781017167719
28	0.874037186275861	0.251925627448277	0.125962813724139
29	0.916899087038637	0.166201825922726	0.0831009129613629
30	0.833083005714168	0.333833988571664	0.166916994285832
31	0.901185349716756	0.197629300566488	0.0988146502832441

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0208919329713117 & 0.0417838659426233 & 0.979108067028688 \tabularnewline
18 & 0.0587698536065827 & 0.117539707213165 & 0.941230146393417 \tabularnewline
19 & 0.0606381440050899 & 0.121276288010180 & 0.93936185599491 \tabularnewline
20 & 0.0516800776201212 & 0.103360155240242 & 0.948319922379879 \tabularnewline
21 & 0.0642119749791326 & 0.128423949958265 & 0.935788025020867 \tabularnewline
22 & 0.145104429893832 & 0.290208859787665 & 0.854895570106168 \tabularnewline
23 & 0.100271375552046 & 0.200542751104092 & 0.899728624447954 \tabularnewline
24 & 0.0665400738142143 & 0.133080147628429 & 0.933459926185786 \tabularnewline
25 & 0.137564545795512 & 0.275129091591024 & 0.862435454204488 \tabularnewline
26 & 0.321508389704407 & 0.643016779408815 & 0.678491610295593 \tabularnewline
27 & 0.624218982832281 & 0.751562034335438 & 0.375781017167719 \tabularnewline
28 & 0.874037186275861 & 0.251925627448277 & 0.125962813724139 \tabularnewline
29 & 0.916899087038637 & 0.166201825922726 & 0.0831009129613629 \tabularnewline
30 & 0.833083005714168 & 0.333833988571664 & 0.166916994285832 \tabularnewline
31 & 0.901185349716756 & 0.197629300566488 & 0.0988146502832441 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25363&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]17[/C][C]0.0208919329713117[/C][C]0.0417838659426233[/C][C]0.979108067028688[/C][/ROW]
[ROW][C]18[/C][C]0.0587698536065827[/C][C]0.117539707213165[/C][C]0.941230146393417[/C][/ROW]
[ROW][C]19[/C][C]0.0606381440050899[/C][C]0.121276288010180[/C][C]0.93936185599491[/C][/ROW]
[ROW][C]20[/C][C]0.0516800776201212[/C][C]0.103360155240242[/C][C]0.948319922379879[/C][/ROW]
[ROW][C]21[/C][C]0.0642119749791326[/C][C]0.128423949958265[/C][C]0.935788025020867[/C][/ROW]
[ROW][C]22[/C][C]0.145104429893832[/C][C]0.290208859787665[/C][C]0.854895570106168[/C][/ROW]
[ROW][C]23[/C][C]0.100271375552046[/C][C]0.200542751104092[/C][C]0.899728624447954[/C][/ROW]
[ROW][C]24[/C][C]0.0665400738142143[/C][C]0.133080147628429[/C][C]0.933459926185786[/C][/ROW]
[ROW][C]25[/C][C]0.137564545795512[/C][C]0.275129091591024[/C][C]0.862435454204488[/C][/ROW]
[ROW][C]26[/C][C]0.321508389704407[/C][C]0.643016779408815[/C][C]0.678491610295593[/C][/ROW]
[ROW][C]27[/C][C]0.624218982832281[/C][C]0.751562034335438[/C][C]0.375781017167719[/C][/ROW]
[ROW][C]28[/C][C]0.874037186275861[/C][C]0.251925627448277[/C][C]0.125962813724139[/C][/ROW]
[ROW][C]29[/C][C]0.916899087038637[/C][C]0.166201825922726[/C][C]0.0831009129613629[/C][/ROW]
[ROW][C]30[/C][C]0.833083005714168[/C][C]0.333833988571664[/C][C]0.166916994285832[/C][/ROW]
[ROW][C]31[/C][C]0.901185349716756[/C][C]0.197629300566488[/C][C]0.0988146502832441[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25363&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25363&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
17	0.0208919329713117	0.0417838659426233	0.979108067028688
18	0.0587698536065827	0.117539707213165	0.941230146393417
19	0.0606381440050899	0.121276288010180	0.93936185599491
20	0.0516800776201212	0.103360155240242	0.948319922379879
21	0.0642119749791326	0.128423949958265	0.935788025020867
22	0.145104429893832	0.290208859787665	0.854895570106168
23	0.100271375552046	0.200542751104092	0.899728624447954
24	0.0665400738142143	0.133080147628429	0.933459926185786
25	0.137564545795512	0.275129091591024	0.862435454204488
26	0.321508389704407	0.643016779408815	0.678491610295593
27	0.624218982832281	0.751562034335438	0.375781017167719
28	0.874037186275861	0.251925627448277	0.125962813724139
29	0.916899087038637	0.166201825922726	0.0831009129613629
30	0.833083005714168	0.333833988571664	0.166916994285832
31	0.901185349716756	0.197629300566488	0.0988146502832441

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	1	0.0666666666666667	NOK
10% type I error level	1	0.0666666666666667	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 & 1 & 0.0666666666666667 & NOK \tabularnewline
10% type I error level & 1 & 0.0666666666666667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25363&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]1[/C][C]0.0666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0666666666666667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25363&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25363&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	1	0.0666666666666667	NOK
10% type I error level	1	0.0666666666666667	OK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

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