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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 29 Dec 2010 18:27:20 +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/29/t1293647249sd7lpowqwpz9op4.htm/, Retrieved Fri, 03 May 2024 07:05:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117021, Retrieved Fri, 03 May 2024 07:05:03 +0000

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101

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-     [Multiple Regression] [] [2010-12-24 12:48:52] [055a14fb8042f7ec27c73c5dfc3bfa50]
-    D    [Multiple Regression] [Paper: Multiple R...] [2010-12-29 18:27:20] [4a884731c0d5b018eba30cab82c9416a] [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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117021&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117021&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117021&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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
Car_accidents[t] = + 32285.75 -171.258333333331M1[t] -179.166666666667M2[t] -141.925M3[t] -119.933333333333M4[t] -90.4416666666666M5[t] -130.45M6[t] -166.708333333333M7[t] -75.2166666666665M8[t] -150.225M9[t] -157.733333333333M10[t] -105.491666666666M11[t] -45.4916666666667t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Car_accidents[t] =  +  32285.75 -171.258333333331M1[t] -179.166666666667M2[t] -141.925M3[t] -119.933333333333M4[t] -90.4416666666666M5[t] -130.45M6[t] -166.708333333333M7[t] -75.2166666666665M8[t] -150.225M9[t] -157.733333333333M10[t] -105.491666666666M11[t] -45.4916666666667t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117021&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Car_accidents[t] =  +  32285.75 -171.258333333331M1[t] -179.166666666667M2[t] -141.925M3[t] -119.933333333333M4[t] -90.4416666666666M5[t] -130.45M6[t] -166.708333333333M7[t] -75.2166666666665M8[t] -150.225M9[t] -157.733333333333M10[t] -105.491666666666M11[t] -45.4916666666667t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117021&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117021&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
Car_accidents[t] = + 32285.75 -171.258333333331M1[t] -179.166666666667M2[t] -141.925M3[t] -119.933333333333M4[t] -90.4416666666666M5[t] -130.45M6[t] -166.708333333333M7[t] -75.2166666666665M8[t] -150.225M9[t] -157.733333333333M10[t] -105.491666666666M11[t] -45.4916666666667t + 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)	32285.75	444.027884	72.7111	0	0
M1	-171.258333333331	512.775453	-0.334	0.740278	0.370139
M2	-179.166666666667	512.269285	-0.3498	0.728509	0.364254
M3	-141.925	543.343628	-0.2612	0.795382	0.397691
M4	-119.933333333333	542.441019	-0.2211	0.826231	0.413115
M5	-90.4416666666666	541.643351	-0.167	0.868298	0.434149
M6	-130.45	540.951086	-0.2411	0.810771	0.405385
M7	-166.708333333333	540.364632	-0.3085	0.759424	0.379712
M8	-75.2166666666665	539.884331	-0.1393	0.889953	0.444976
M9	-150.225	539.510468	-0.2784	0.78222	0.39111
M10	-157.733333333333	539.243265	-0.2925	0.771532	0.385766
M11	-105.491666666666	539.082879	-0.1957	0.845926	0.422963
t	-45.4916666666667	7.592717	-5.9915	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) & 32285.75 & 444.027884 & 72.7111 & 0 & 0 \tabularnewline
M1 & -171.258333333331 & 512.775453 & -0.334 & 0.740278 & 0.370139 \tabularnewline
M2 & -179.166666666667 & 512.269285 & -0.3498 & 0.728509 & 0.364254 \tabularnewline
M3 & -141.925 & 543.343628 & -0.2612 & 0.795382 & 0.397691 \tabularnewline
M4 & -119.933333333333 & 542.441019 & -0.2211 & 0.826231 & 0.413115 \tabularnewline
M5 & -90.4416666666666 & 541.643351 & -0.167 & 0.868298 & 0.434149 \tabularnewline
M6 & -130.45 & 540.951086 & -0.2411 & 0.810771 & 0.405385 \tabularnewline
M7 & -166.708333333333 & 540.364632 & -0.3085 & 0.759424 & 0.379712 \tabularnewline
M8 & -75.2166666666665 & 539.884331 & -0.1393 & 0.889953 & 0.444976 \tabularnewline
M9 & -150.225 & 539.510468 & -0.2784 & 0.78222 & 0.39111 \tabularnewline
M10 & -157.733333333333 & 539.243265 & -0.2925 & 0.771532 & 0.385766 \tabularnewline
M11 & -105.491666666666 & 539.082879 & -0.1957 & 0.845926 & 0.422963 \tabularnewline
t & -45.4916666666667 & 7.592717 & -5.9915 & 1e-06 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117021&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]32285.75[/C][C]444.027884[/C][C]72.7111[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-171.258333333331[/C][C]512.775453[/C][C]-0.334[/C][C]0.740278[/C][C]0.370139[/C][/ROW]
[ROW][C]M2[/C][C]-179.166666666667[/C][C]512.269285[/C][C]-0.3498[/C][C]0.728509[/C][C]0.364254[/C][/ROW]
[ROW][C]M3[/C][C]-141.925[/C][C]543.343628[/C][C]-0.2612[/C][C]0.795382[/C][C]0.397691[/C][/ROW]
[ROW][C]M4[/C][C]-119.933333333333[/C][C]542.441019[/C][C]-0.2211[/C][C]0.826231[/C][C]0.413115[/C][/ROW]
[ROW][C]M5[/C][C]-90.4416666666666[/C][C]541.643351[/C][C]-0.167[/C][C]0.868298[/C][C]0.434149[/C][/ROW]
[ROW][C]M6[/C][C]-130.45[/C][C]540.951086[/C][C]-0.2411[/C][C]0.810771[/C][C]0.405385[/C][/ROW]
[ROW][C]M7[/C][C]-166.708333333333[/C][C]540.364632[/C][C]-0.3085[/C][C]0.759424[/C][C]0.379712[/C][/ROW]
[ROW][C]M8[/C][C]-75.2166666666665[/C][C]539.884331[/C][C]-0.1393[/C][C]0.889953[/C][C]0.444976[/C][/ROW]
[ROW][C]M9[/C][C]-150.225[/C][C]539.510468[/C][C]-0.2784[/C][C]0.78222[/C][C]0.39111[/C][/ROW]
[ROW][C]M10[/C][C]-157.733333333333[/C][C]539.243265[/C][C]-0.2925[/C][C]0.771532[/C][C]0.385766[/C][/ROW]
[ROW][C]M11[/C][C]-105.491666666666[/C][C]539.082879[/C][C]-0.1957[/C][C]0.845926[/C][C]0.422963[/C][/ROW]
[ROW][C]t[/C][C]-45.4916666666667[/C][C]7.592717[/C][C]-5.9915[/C][C]1e-06[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117021&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117021&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)	32285.75	444.027884	72.7111	0	0
M1	-171.258333333331	512.775453	-0.334	0.740278	0.370139
M2	-179.166666666667	512.269285	-0.3498	0.728509	0.364254
M3	-141.925	543.343628	-0.2612	0.795382	0.397691
M4	-119.933333333333	542.441019	-0.2211	0.826231	0.413115
M5	-90.4416666666666	541.643351	-0.167	0.868298	0.434149
M6	-130.45	540.951086	-0.2411	0.810771	0.405385
M7	-166.708333333333	540.364632	-0.3085	0.759424	0.379712
M8	-75.2166666666665	539.884331	-0.1393	0.889953	0.444976
M9	-150.225	539.510468	-0.2784	0.78222	0.39111
M10	-157.733333333333	539.243265	-0.2925	0.771532	0.385766
M11	-105.491666666666	539.082879	-0.1957	0.845926	0.422963
t	-45.4916666666667	7.592717	-5.9915	1e-06	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.706886313650132
R-squared	0.499688260425872
Adjusted R-squared	0.337424993536966
F-TEST (value)	3.07949094090399
F-TEST (DF numerator)	12
F-TEST (DF denominator)	37
p-value	0.00423174719345321
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	762.302697557015
Sum Squared Residuals	21500899.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.706886313650132 \tabularnewline
R-squared & 0.499688260425872 \tabularnewline
Adjusted R-squared & 0.337424993536966 \tabularnewline
F-TEST (value) & 3.07949094090399 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 37 \tabularnewline
p-value & 0.00423174719345321 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 762.302697557015 \tabularnewline
Sum Squared Residuals & 21500899.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117021&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.706886313650132[/C][/ROW]
[ROW][C]R-squared[/C][C]0.499688260425872[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.337424993536966[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.07949094090399[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]37[/C][/ROW]
[ROW][C]p-value[/C][C]0.00423174719345321[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]762.302697557015[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]21500899.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117021&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117021&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	0.706886313650132
R-squared	0.499688260425872
Adjusted R-squared	0.337424993536966
F-TEST (value)	3.07949094090399
F-TEST (DF numerator)	12
F-TEST (DF denominator)	37
p-value	0.00423174719345321
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	762.302697557015
Sum Squared Residuals	21500899.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	31245	32069	-823.999999999991
2	30951	32015.6	-1064.6
3	30872	32007.35	-1135.35000000000
4	30752	31983.85	-1231.85
5	30967	31967.85	-1000.85
6	30781	31882.35	-1101.35
7	30681	31800.6	-1119.6
8	31356	31846.6	-490.6
9	31434	31726.1	-292.100000000001
10	31594	31673.1	-79.1000000000007
11	31949	31679.85	269.149999999999
12	32396	31739.85	656.15
13	32441	31523.1	917.899999999997
14	32447	31469.7	977.3
15	32288	31461.45	826.55
16	32418	31437.95	980.05
17	32346	31421.95	924.05
18	32091	31336.45	754.55
19	31855	31254.7	600.3
20	31683	31300.7	382.299999999999
21	31615	31180.2	434.8
22	31840	31127.2	712.8
23	31536	31133.95	402.050000000000
24	31383	31193.95	189.05
25	31638	30977.2	660.799999999998
26	31626	30923.8	702.2
27	31720	30915.55	804.45
28	31472	30892.05	579.95
29	31372	30876.05	495.95
30	31419	30790.55	628.45
31	31341	30708.8	632.2
32	31171	30754.8	416.2
33	31036	30634.3	401.7
34	30532	30581.3	-49.2999999999999
35	30666	30588.05	77.9500000000001
36	30571	30648.05	-77.0499999999998
37	30173	30431.3	-258.300000000002
38	30032	30377.9	-345.9
39	29874	30369.65	-495.649999999999
40	30018	30346.15	-328.15
41	29911	30330.15	-419.150000000000
42	29963	30244.65	-281.649999999999
43	30050	30162.9	-112.900000000000
44	29901	30208.9	-307.900000000000
45	29544	30088.4	-544.4
46	29451	30035.4	-584.4
47	29293	30042.15	-749.15
48	29334	30102.15	-768.149999999999
49	29389	29885.4	-496.400000000002
50	29563	29832	-268.999999999999

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 31245 & 32069 & -823.999999999991 \tabularnewline
2 & 30951 & 32015.6 & -1064.6 \tabularnewline
3 & 30872 & 32007.35 & -1135.35000000000 \tabularnewline
4 & 30752 & 31983.85 & -1231.85 \tabularnewline
5 & 30967 & 31967.85 & -1000.85 \tabularnewline
6 & 30781 & 31882.35 & -1101.35 \tabularnewline
7 & 30681 & 31800.6 & -1119.6 \tabularnewline
8 & 31356 & 31846.6 & -490.6 \tabularnewline
9 & 31434 & 31726.1 & -292.100000000001 \tabularnewline
10 & 31594 & 31673.1 & -79.1000000000007 \tabularnewline
11 & 31949 & 31679.85 & 269.149999999999 \tabularnewline
12 & 32396 & 31739.85 & 656.15 \tabularnewline
13 & 32441 & 31523.1 & 917.899999999997 \tabularnewline
14 & 32447 & 31469.7 & 977.3 \tabularnewline
15 & 32288 & 31461.45 & 826.55 \tabularnewline
16 & 32418 & 31437.95 & 980.05 \tabularnewline
17 & 32346 & 31421.95 & 924.05 \tabularnewline
18 & 32091 & 31336.45 & 754.55 \tabularnewline
19 & 31855 & 31254.7 & 600.3 \tabularnewline
20 & 31683 & 31300.7 & 382.299999999999 \tabularnewline
21 & 31615 & 31180.2 & 434.8 \tabularnewline
22 & 31840 & 31127.2 & 712.8 \tabularnewline
23 & 31536 & 31133.95 & 402.050000000000 \tabularnewline
24 & 31383 & 31193.95 & 189.05 \tabularnewline
25 & 31638 & 30977.2 & 660.799999999998 \tabularnewline
26 & 31626 & 30923.8 & 702.2 \tabularnewline
27 & 31720 & 30915.55 & 804.45 \tabularnewline
28 & 31472 & 30892.05 & 579.95 \tabularnewline
29 & 31372 & 30876.05 & 495.95 \tabularnewline
30 & 31419 & 30790.55 & 628.45 \tabularnewline
31 & 31341 & 30708.8 & 632.2 \tabularnewline
32 & 31171 & 30754.8 & 416.2 \tabularnewline
33 & 31036 & 30634.3 & 401.7 \tabularnewline
34 & 30532 & 30581.3 & -49.2999999999999 \tabularnewline
35 & 30666 & 30588.05 & 77.9500000000001 \tabularnewline
36 & 30571 & 30648.05 & -77.0499999999998 \tabularnewline
37 & 30173 & 30431.3 & -258.300000000002 \tabularnewline
38 & 30032 & 30377.9 & -345.9 \tabularnewline
39 & 29874 & 30369.65 & -495.649999999999 \tabularnewline
40 & 30018 & 30346.15 & -328.15 \tabularnewline
41 & 29911 & 30330.15 & -419.150000000000 \tabularnewline
42 & 29963 & 30244.65 & -281.649999999999 \tabularnewline
43 & 30050 & 30162.9 & -112.900000000000 \tabularnewline
44 & 29901 & 30208.9 & -307.900000000000 \tabularnewline
45 & 29544 & 30088.4 & -544.4 \tabularnewline
46 & 29451 & 30035.4 & -584.4 \tabularnewline
47 & 29293 & 30042.15 & -749.15 \tabularnewline
48 & 29334 & 30102.15 & -768.149999999999 \tabularnewline
49 & 29389 & 29885.4 & -496.400000000002 \tabularnewline
50 & 29563 & 29832 & -268.999999999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117021&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]31245[/C][C]32069[/C][C]-823.999999999991[/C][/ROW]
[ROW][C]2[/C][C]30951[/C][C]32015.6[/C][C]-1064.6[/C][/ROW]
[ROW][C]3[/C][C]30872[/C][C]32007.35[/C][C]-1135.35000000000[/C][/ROW]
[ROW][C]4[/C][C]30752[/C][C]31983.85[/C][C]-1231.85[/C][/ROW]
[ROW][C]5[/C][C]30967[/C][C]31967.85[/C][C]-1000.85[/C][/ROW]
[ROW][C]6[/C][C]30781[/C][C]31882.35[/C][C]-1101.35[/C][/ROW]
[ROW][C]7[/C][C]30681[/C][C]31800.6[/C][C]-1119.6[/C][/ROW]
[ROW][C]8[/C][C]31356[/C][C]31846.6[/C][C]-490.6[/C][/ROW]
[ROW][C]9[/C][C]31434[/C][C]31726.1[/C][C]-292.100000000001[/C][/ROW]
[ROW][C]10[/C][C]31594[/C][C]31673.1[/C][C]-79.1000000000007[/C][/ROW]
[ROW][C]11[/C][C]31949[/C][C]31679.85[/C][C]269.149999999999[/C][/ROW]
[ROW][C]12[/C][C]32396[/C][C]31739.85[/C][C]656.15[/C][/ROW]
[ROW][C]13[/C][C]32441[/C][C]31523.1[/C][C]917.899999999997[/C][/ROW]
[ROW][C]14[/C][C]32447[/C][C]31469.7[/C][C]977.3[/C][/ROW]
[ROW][C]15[/C][C]32288[/C][C]31461.45[/C][C]826.55[/C][/ROW]
[ROW][C]16[/C][C]32418[/C][C]31437.95[/C][C]980.05[/C][/ROW]
[ROW][C]17[/C][C]32346[/C][C]31421.95[/C][C]924.05[/C][/ROW]
[ROW][C]18[/C][C]32091[/C][C]31336.45[/C][C]754.55[/C][/ROW]
[ROW][C]19[/C][C]31855[/C][C]31254.7[/C][C]600.3[/C][/ROW]
[ROW][C]20[/C][C]31683[/C][C]31300.7[/C][C]382.299999999999[/C][/ROW]
[ROW][C]21[/C][C]31615[/C][C]31180.2[/C][C]434.8[/C][/ROW]
[ROW][C]22[/C][C]31840[/C][C]31127.2[/C][C]712.8[/C][/ROW]
[ROW][C]23[/C][C]31536[/C][C]31133.95[/C][C]402.050000000000[/C][/ROW]
[ROW][C]24[/C][C]31383[/C][C]31193.95[/C][C]189.05[/C][/ROW]
[ROW][C]25[/C][C]31638[/C][C]30977.2[/C][C]660.799999999998[/C][/ROW]
[ROW][C]26[/C][C]31626[/C][C]30923.8[/C][C]702.2[/C][/ROW]
[ROW][C]27[/C][C]31720[/C][C]30915.55[/C][C]804.45[/C][/ROW]
[ROW][C]28[/C][C]31472[/C][C]30892.05[/C][C]579.95[/C][/ROW]
[ROW][C]29[/C][C]31372[/C][C]30876.05[/C][C]495.95[/C][/ROW]
[ROW][C]30[/C][C]31419[/C][C]30790.55[/C][C]628.45[/C][/ROW]
[ROW][C]31[/C][C]31341[/C][C]30708.8[/C][C]632.2[/C][/ROW]
[ROW][C]32[/C][C]31171[/C][C]30754.8[/C][C]416.2[/C][/ROW]
[ROW][C]33[/C][C]31036[/C][C]30634.3[/C][C]401.7[/C][/ROW]
[ROW][C]34[/C][C]30532[/C][C]30581.3[/C][C]-49.2999999999999[/C][/ROW]
[ROW][C]35[/C][C]30666[/C][C]30588.05[/C][C]77.9500000000001[/C][/ROW]
[ROW][C]36[/C][C]30571[/C][C]30648.05[/C][C]-77.0499999999998[/C][/ROW]
[ROW][C]37[/C][C]30173[/C][C]30431.3[/C][C]-258.300000000002[/C][/ROW]
[ROW][C]38[/C][C]30032[/C][C]30377.9[/C][C]-345.9[/C][/ROW]
[ROW][C]39[/C][C]29874[/C][C]30369.65[/C][C]-495.649999999999[/C][/ROW]
[ROW][C]40[/C][C]30018[/C][C]30346.15[/C][C]-328.15[/C][/ROW]
[ROW][C]41[/C][C]29911[/C][C]30330.15[/C][C]-419.150000000000[/C][/ROW]
[ROW][C]42[/C][C]29963[/C][C]30244.65[/C][C]-281.649999999999[/C][/ROW]
[ROW][C]43[/C][C]30050[/C][C]30162.9[/C][C]-112.900000000000[/C][/ROW]
[ROW][C]44[/C][C]29901[/C][C]30208.9[/C][C]-307.900000000000[/C][/ROW]
[ROW][C]45[/C][C]29544[/C][C]30088.4[/C][C]-544.4[/C][/ROW]
[ROW][C]46[/C][C]29451[/C][C]30035.4[/C][C]-584.4[/C][/ROW]
[ROW][C]47[/C][C]29293[/C][C]30042.15[/C][C]-749.15[/C][/ROW]
[ROW][C]48[/C][C]29334[/C][C]30102.15[/C][C]-768.149999999999[/C][/ROW]
[ROW][C]49[/C][C]29389[/C][C]29885.4[/C][C]-496.400000000002[/C][/ROW]
[ROW][C]50[/C][C]29563[/C][C]29832[/C][C]-268.999999999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117021&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117021&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	31245	32069	-823.999999999991
2	30951	32015.6	-1064.6
3	30872	32007.35	-1135.35000000000
4	30752	31983.85	-1231.85
5	30967	31967.85	-1000.85
6	30781	31882.35	-1101.35
7	30681	31800.6	-1119.6
8	31356	31846.6	-490.6
9	31434	31726.1	-292.100000000001
10	31594	31673.1	-79.1000000000007
11	31949	31679.85	269.149999999999
12	32396	31739.85	656.15
13	32441	31523.1	917.899999999997
14	32447	31469.7	977.3
15	32288	31461.45	826.55
16	32418	31437.95	980.05
17	32346	31421.95	924.05
18	32091	31336.45	754.55
19	31855	31254.7	600.3
20	31683	31300.7	382.299999999999
21	31615	31180.2	434.8
22	31840	31127.2	712.8
23	31536	31133.95	402.050000000000
24	31383	31193.95	189.05
25	31638	30977.2	660.799999999998
26	31626	30923.8	702.2
27	31720	30915.55	804.45
28	31472	30892.05	579.95
29	31372	30876.05	495.95
30	31419	30790.55	628.45
31	31341	30708.8	632.2
32	31171	30754.8	416.2
33	31036	30634.3	401.7
34	30532	30581.3	-49.2999999999999
35	30666	30588.05	77.9500000000001
36	30571	30648.05	-77.0499999999998
37	30173	30431.3	-258.300000000002
38	30032	30377.9	-345.9
39	29874	30369.65	-495.649999999999
40	30018	30346.15	-328.15
41	29911	30330.15	-419.150000000000
42	29963	30244.65	-281.649999999999
43	30050	30162.9	-112.900000000000
44	29901	30208.9	-307.900000000000
45	29544	30088.4	-544.4
46	29451	30035.4	-584.4
47	29293	30042.15	-749.15
48	29334	30102.15	-768.149999999999
49	29389	29885.4	-496.400000000002
50	29563	29832	-268.999999999999

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.160674797109252	0.321349594218503	0.839325202890748
17	0.0680545332390232	0.136109066478046	0.931945466760977
18	0.0322812589708627	0.0645625179417254	0.967718741029137
19	0.0344471716158547	0.0688943432317094	0.965552828384145
20	0.727405528444938	0.545188943110124	0.272594471555062
21	0.94162591211212	0.116748175775761	0.0583740878878806
22	0.959283606582594	0.0814327868348119	0.0407163934174060
23	0.991029334104533	0.0179413317909344	0.00897066589546718
24	0.999097581321116	0.00180483735776754	0.000902418678883768
25	0.999042360336635	0.00191527932673007	0.000957639663365035
26	0.998205697349515	0.00358860530097028	0.00179430265048514
27	0.9987093565003	0.00258128699939963	0.00129064349969982
28	0.997522044980742	0.00495591003851545	0.00247795501925773
29	0.995662165266905	0.00867566946618905	0.00433783473309452
30	0.991678197107735	0.0166436057845302	0.00832180289226511
31	0.979642332016015	0.040715335967969	0.0203576679839845
32	0.956390915524756	0.0872181689504875	0.0436090844752437
33	0.944775908100806	0.110448183798388	0.0552240918991939
34	0.886075608533028	0.227848782933944	0.113924391466972

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.160674797109252 & 0.321349594218503 & 0.839325202890748 \tabularnewline
17 & 0.0680545332390232 & 0.136109066478046 & 0.931945466760977 \tabularnewline
18 & 0.0322812589708627 & 0.0645625179417254 & 0.967718741029137 \tabularnewline
19 & 0.0344471716158547 & 0.0688943432317094 & 0.965552828384145 \tabularnewline
20 & 0.727405528444938 & 0.545188943110124 & 0.272594471555062 \tabularnewline
21 & 0.94162591211212 & 0.116748175775761 & 0.0583740878878806 \tabularnewline
22 & 0.959283606582594 & 0.0814327868348119 & 0.0407163934174060 \tabularnewline
23 & 0.991029334104533 & 0.0179413317909344 & 0.00897066589546718 \tabularnewline
24 & 0.999097581321116 & 0.00180483735776754 & 0.000902418678883768 \tabularnewline
25 & 0.999042360336635 & 0.00191527932673007 & 0.000957639663365035 \tabularnewline
26 & 0.998205697349515 & 0.00358860530097028 & 0.00179430265048514 \tabularnewline
27 & 0.9987093565003 & 0.00258128699939963 & 0.00129064349969982 \tabularnewline
28 & 0.997522044980742 & 0.00495591003851545 & 0.00247795501925773 \tabularnewline
29 & 0.995662165266905 & 0.00867566946618905 & 0.00433783473309452 \tabularnewline
30 & 0.991678197107735 & 0.0166436057845302 & 0.00832180289226511 \tabularnewline
31 & 0.979642332016015 & 0.040715335967969 & 0.0203576679839845 \tabularnewline
32 & 0.956390915524756 & 0.0872181689504875 & 0.0436090844752437 \tabularnewline
33 & 0.944775908100806 & 0.110448183798388 & 0.0552240918991939 \tabularnewline
34 & 0.886075608533028 & 0.227848782933944 & 0.113924391466972 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117021&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]0.160674797109252[/C][C]0.321349594218503[/C][C]0.839325202890748[/C][/ROW]
[ROW][C]17[/C][C]0.0680545332390232[/C][C]0.136109066478046[/C][C]0.931945466760977[/C][/ROW]
[ROW][C]18[/C][C]0.0322812589708627[/C][C]0.0645625179417254[/C][C]0.967718741029137[/C][/ROW]
[ROW][C]19[/C][C]0.0344471716158547[/C][C]0.0688943432317094[/C][C]0.965552828384145[/C][/ROW]
[ROW][C]20[/C][C]0.727405528444938[/C][C]0.545188943110124[/C][C]0.272594471555062[/C][/ROW]
[ROW][C]21[/C][C]0.94162591211212[/C][C]0.116748175775761[/C][C]0.0583740878878806[/C][/ROW]
[ROW][C]22[/C][C]0.959283606582594[/C][C]0.0814327868348119[/C][C]0.0407163934174060[/C][/ROW]
[ROW][C]23[/C][C]0.991029334104533[/C][C]0.0179413317909344[/C][C]0.00897066589546718[/C][/ROW]
[ROW][C]24[/C][C]0.999097581321116[/C][C]0.00180483735776754[/C][C]0.000902418678883768[/C][/ROW]
[ROW][C]25[/C][C]0.999042360336635[/C][C]0.00191527932673007[/C][C]0.000957639663365035[/C][/ROW]
[ROW][C]26[/C][C]0.998205697349515[/C][C]0.00358860530097028[/C][C]0.00179430265048514[/C][/ROW]
[ROW][C]27[/C][C]0.9987093565003[/C][C]0.00258128699939963[/C][C]0.00129064349969982[/C][/ROW]
[ROW][C]28[/C][C]0.997522044980742[/C][C]0.00495591003851545[/C][C]0.00247795501925773[/C][/ROW]
[ROW][C]29[/C][C]0.995662165266905[/C][C]0.00867566946618905[/C][C]0.00433783473309452[/C][/ROW]
[ROW][C]30[/C][C]0.991678197107735[/C][C]0.0166436057845302[/C][C]0.00832180289226511[/C][/ROW]
[ROW][C]31[/C][C]0.979642332016015[/C][C]0.040715335967969[/C][C]0.0203576679839845[/C][/ROW]
[ROW][C]32[/C][C]0.956390915524756[/C][C]0.0872181689504875[/C][C]0.0436090844752437[/C][/ROW]
[ROW][C]33[/C][C]0.944775908100806[/C][C]0.110448183798388[/C][C]0.0552240918991939[/C][/ROW]
[ROW][C]34[/C][C]0.886075608533028[/C][C]0.227848782933944[/C][C]0.113924391466972[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117021&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117021&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	0.160674797109252	0.321349594218503	0.839325202890748
17	0.0680545332390232	0.136109066478046	0.931945466760977
18	0.0322812589708627	0.0645625179417254	0.967718741029137
19	0.0344471716158547	0.0688943432317094	0.965552828384145
20	0.727405528444938	0.545188943110124	0.272594471555062
21	0.94162591211212	0.116748175775761	0.0583740878878806
22	0.959283606582594	0.0814327868348119	0.0407163934174060
23	0.991029334104533	0.0179413317909344	0.00897066589546718
24	0.999097581321116	0.00180483735776754	0.000902418678883768
25	0.999042360336635	0.00191527932673007	0.000957639663365035
26	0.998205697349515	0.00358860530097028	0.00179430265048514
27	0.9987093565003	0.00258128699939963	0.00129064349969982
28	0.997522044980742	0.00495591003851545	0.00247795501925773
29	0.995662165266905	0.00867566946618905	0.00433783473309452
30	0.991678197107735	0.0166436057845302	0.00832180289226511
31	0.979642332016015	0.040715335967969	0.0203576679839845
32	0.956390915524756	0.0872181689504875	0.0436090844752437
33	0.944775908100806	0.110448183798388	0.0552240918991939
34	0.886075608533028	0.227848782933944	0.113924391466972

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	6	0.315789473684211	NOK
5% type I error level	9	0.473684210526316	NOK
10% type I error level	13	0.68421052631579	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 & 6 & 0.315789473684211 & NOK \tabularnewline
5% type I error level & 9 & 0.473684210526316 & NOK \tabularnewline
10% type I error level & 13 & 0.68421052631579 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117021&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]6[/C][C]0.315789473684211[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.473684210526316[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.68421052631579[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117021&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117021&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	6	0.315789473684211	NOK
5% type I error level	9	0.473684210526316	NOK
10% type I error level	13	0.68421052631579	NOK

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