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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 07:44:25 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258642864wjsswx725la5ftd.htm/, Retrieved Thu, 28 Mar 2024 14:55:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57748, Retrieved Thu, 28 Mar 2024 14:55:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-19 14:44:25] [82bf023f1e4d9556a54030fcde33aa09] [Current]
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Dataseries X:
286602	0
283042	0
276687	0
277915	0
277128	0
277103	0
275037	0
270150	0
267140	0
264993	0
287259	0
291186	0
292300	0
288186	0
281477	0
282656	0
280190	0
280408	0
276836	0
275216	0
274352	0
271311	0
289802	0
290726	0
292300	0
278506	0
269826	0
265861	0
269034	0
264176	0
255198	0
253353	0
246057	0
235372	0
258556	0
260993	0
254663	0
250643	0
243422	0
247105	0
248541	0
245039	0
237080	0
237085	0
225554	0
226839	0
247934	0
248333	0
246969	1
245098	1
246263	1
255765	1
264319	1
268347	1
273046	1
273963	1
267430	1
271993	1
292710	1
295881	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57748&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57748&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57748&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 266232.75 + 582.583333333325X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  266232.75 +  582.583333333325X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57748&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  266232.75 +  582.583333333325X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57748&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57748&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
Y[t] = + 266232.75 + 582.583333333325X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)266232.752616.953032101.733900
X582.5833333333255851.6848730.09960.9210380.460519

\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) & 266232.75 & 2616.953032 & 101.7339 & 0 & 0 \tabularnewline
X & 582.583333333325 & 5851.684873 & 0.0996 & 0.921038 & 0.460519 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57748&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]266232.75[/C][C]2616.953032[/C][C]101.7339[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]582.583333333325[/C][C]5851.684873[/C][C]0.0996[/C][C]0.921038[/C][C]0.460519[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57748&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57748&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)266232.752616.953032101.733900
X582.5833333333255851.6848730.09960.9210380.460519







Multiple Linear Regression - Regression Statistics
Multiple R0.0130715179340899
R-squared0.000170864581101233
Adjusted R-squared-0.0170675687881900
F-TEST (value)0.00991183928613908
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.921038382478863
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation18130.7824467722
Sum Squared Residuals19066065783.6667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0130715179340899 \tabularnewline
R-squared & 0.000170864581101233 \tabularnewline
Adjusted R-squared & -0.0170675687881900 \tabularnewline
F-TEST (value) & 0.00991183928613908 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.921038382478863 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 18130.7824467722 \tabularnewline
Sum Squared Residuals & 19066065783.6667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57748&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0130715179340899[/C][/ROW]
[ROW][C]R-squared[/C][C]0.000170864581101233[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0170675687881900[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.00991183928613908[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.921038382478863[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]18130.7824467722[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]19066065783.6667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57748&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57748&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 R0.0130715179340899
R-squared0.000170864581101233
Adjusted R-squared-0.0170675687881900
F-TEST (value)0.00991183928613908
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.921038382478863
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation18130.7824467722
Sum Squared Residuals19066065783.6667







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1286602266232.75000000020369.2500000004
2283042266232.7516809.25
3276687266232.7510454.25
4277915266232.7511682.25
5277128266232.7510895.25
6277103266232.7510870.25
7275037266232.758804.25
8270150266232.753917.24999999999
9267140266232.75907.249999999992
10264993266232.75-1239.75000000001
11287259266232.7521026.25
12291186266232.7524953.25
13292300266232.7526067.25
14288186266232.7521953.25
15281477266232.7515244.25
16282656266232.7516423.25
17280190266232.7513957.25
18280408266232.7514175.25
19276836266232.7510603.25
20275216266232.758983.25
21274352266232.758119.25
22271311266232.755078.24999999999
23289802266232.7523569.25
24290726266232.7524493.25
25292300266232.7526067.25
26278506266232.7512273.25
27269826266232.753593.24999999999
28265861266232.75-371.750000000008
29269034266232.752801.24999999999
30264176266232.75-2056.75000000001
31255198266232.75-11034.75
32253353266232.75-12879.75
33246057266232.75-20175.75
34235372266232.75-30860.75
35258556266232.75-7676.75
36260993266232.75-5239.75000000001
37254663266232.75-11569.75
38250643266232.75-15589.75
39243422266232.75-22810.75
40247105266232.75-19127.75
41248541266232.75-17691.75
42245039266232.75-21193.75
43237080266232.75-29152.75
44237085266232.75-29147.75
45225554266232.75-40678.75
46226839266232.75-39393.75
47247934266232.75-18298.75
48248333266232.75-17899.75
49246969266815.333333333-19846.3333333333
50245098266815.333333333-21717.3333333333
51246263266815.333333333-20552.3333333333
52255765266815.333333333-11050.3333333333
53264319266815.333333333-2496.33333333333
54268347266815.3333333331531.66666666667
55273046266815.3333333336230.66666666667
56273963266815.3333333337147.66666666667
57267430266815.333333333614.666666666668
58271993266815.3333333335177.66666666667
59292710266815.33333333325894.6666666667
60295881266815.33333333329065.6666666667

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 286602 & 266232.750000000 & 20369.2500000004 \tabularnewline
2 & 283042 & 266232.75 & 16809.25 \tabularnewline
3 & 276687 & 266232.75 & 10454.25 \tabularnewline
4 & 277915 & 266232.75 & 11682.25 \tabularnewline
5 & 277128 & 266232.75 & 10895.25 \tabularnewline
6 & 277103 & 266232.75 & 10870.25 \tabularnewline
7 & 275037 & 266232.75 & 8804.25 \tabularnewline
8 & 270150 & 266232.75 & 3917.24999999999 \tabularnewline
9 & 267140 & 266232.75 & 907.249999999992 \tabularnewline
10 & 264993 & 266232.75 & -1239.75000000001 \tabularnewline
11 & 287259 & 266232.75 & 21026.25 \tabularnewline
12 & 291186 & 266232.75 & 24953.25 \tabularnewline
13 & 292300 & 266232.75 & 26067.25 \tabularnewline
14 & 288186 & 266232.75 & 21953.25 \tabularnewline
15 & 281477 & 266232.75 & 15244.25 \tabularnewline
16 & 282656 & 266232.75 & 16423.25 \tabularnewline
17 & 280190 & 266232.75 & 13957.25 \tabularnewline
18 & 280408 & 266232.75 & 14175.25 \tabularnewline
19 & 276836 & 266232.75 & 10603.25 \tabularnewline
20 & 275216 & 266232.75 & 8983.25 \tabularnewline
21 & 274352 & 266232.75 & 8119.25 \tabularnewline
22 & 271311 & 266232.75 & 5078.24999999999 \tabularnewline
23 & 289802 & 266232.75 & 23569.25 \tabularnewline
24 & 290726 & 266232.75 & 24493.25 \tabularnewline
25 & 292300 & 266232.75 & 26067.25 \tabularnewline
26 & 278506 & 266232.75 & 12273.25 \tabularnewline
27 & 269826 & 266232.75 & 3593.24999999999 \tabularnewline
28 & 265861 & 266232.75 & -371.750000000008 \tabularnewline
29 & 269034 & 266232.75 & 2801.24999999999 \tabularnewline
30 & 264176 & 266232.75 & -2056.75000000001 \tabularnewline
31 & 255198 & 266232.75 & -11034.75 \tabularnewline
32 & 253353 & 266232.75 & -12879.75 \tabularnewline
33 & 246057 & 266232.75 & -20175.75 \tabularnewline
34 & 235372 & 266232.75 & -30860.75 \tabularnewline
35 & 258556 & 266232.75 & -7676.75 \tabularnewline
36 & 260993 & 266232.75 & -5239.75000000001 \tabularnewline
37 & 254663 & 266232.75 & -11569.75 \tabularnewline
38 & 250643 & 266232.75 & -15589.75 \tabularnewline
39 & 243422 & 266232.75 & -22810.75 \tabularnewline
40 & 247105 & 266232.75 & -19127.75 \tabularnewline
41 & 248541 & 266232.75 & -17691.75 \tabularnewline
42 & 245039 & 266232.75 & -21193.75 \tabularnewline
43 & 237080 & 266232.75 & -29152.75 \tabularnewline
44 & 237085 & 266232.75 & -29147.75 \tabularnewline
45 & 225554 & 266232.75 & -40678.75 \tabularnewline
46 & 226839 & 266232.75 & -39393.75 \tabularnewline
47 & 247934 & 266232.75 & -18298.75 \tabularnewline
48 & 248333 & 266232.75 & -17899.75 \tabularnewline
49 & 246969 & 266815.333333333 & -19846.3333333333 \tabularnewline
50 & 245098 & 266815.333333333 & -21717.3333333333 \tabularnewline
51 & 246263 & 266815.333333333 & -20552.3333333333 \tabularnewline
52 & 255765 & 266815.333333333 & -11050.3333333333 \tabularnewline
53 & 264319 & 266815.333333333 & -2496.33333333333 \tabularnewline
54 & 268347 & 266815.333333333 & 1531.66666666667 \tabularnewline
55 & 273046 & 266815.333333333 & 6230.66666666667 \tabularnewline
56 & 273963 & 266815.333333333 & 7147.66666666667 \tabularnewline
57 & 267430 & 266815.333333333 & 614.666666666668 \tabularnewline
58 & 271993 & 266815.333333333 & 5177.66666666667 \tabularnewline
59 & 292710 & 266815.333333333 & 25894.6666666667 \tabularnewline
60 & 295881 & 266815.333333333 & 29065.6666666667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57748&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]286602[/C][C]266232.750000000[/C][C]20369.2500000004[/C][/ROW]
[ROW][C]2[/C][C]283042[/C][C]266232.75[/C][C]16809.25[/C][/ROW]
[ROW][C]3[/C][C]276687[/C][C]266232.75[/C][C]10454.25[/C][/ROW]
[ROW][C]4[/C][C]277915[/C][C]266232.75[/C][C]11682.25[/C][/ROW]
[ROW][C]5[/C][C]277128[/C][C]266232.75[/C][C]10895.25[/C][/ROW]
[ROW][C]6[/C][C]277103[/C][C]266232.75[/C][C]10870.25[/C][/ROW]
[ROW][C]7[/C][C]275037[/C][C]266232.75[/C][C]8804.25[/C][/ROW]
[ROW][C]8[/C][C]270150[/C][C]266232.75[/C][C]3917.24999999999[/C][/ROW]
[ROW][C]9[/C][C]267140[/C][C]266232.75[/C][C]907.249999999992[/C][/ROW]
[ROW][C]10[/C][C]264993[/C][C]266232.75[/C][C]-1239.75000000001[/C][/ROW]
[ROW][C]11[/C][C]287259[/C][C]266232.75[/C][C]21026.25[/C][/ROW]
[ROW][C]12[/C][C]291186[/C][C]266232.75[/C][C]24953.25[/C][/ROW]
[ROW][C]13[/C][C]292300[/C][C]266232.75[/C][C]26067.25[/C][/ROW]
[ROW][C]14[/C][C]288186[/C][C]266232.75[/C][C]21953.25[/C][/ROW]
[ROW][C]15[/C][C]281477[/C][C]266232.75[/C][C]15244.25[/C][/ROW]
[ROW][C]16[/C][C]282656[/C][C]266232.75[/C][C]16423.25[/C][/ROW]
[ROW][C]17[/C][C]280190[/C][C]266232.75[/C][C]13957.25[/C][/ROW]
[ROW][C]18[/C][C]280408[/C][C]266232.75[/C][C]14175.25[/C][/ROW]
[ROW][C]19[/C][C]276836[/C][C]266232.75[/C][C]10603.25[/C][/ROW]
[ROW][C]20[/C][C]275216[/C][C]266232.75[/C][C]8983.25[/C][/ROW]
[ROW][C]21[/C][C]274352[/C][C]266232.75[/C][C]8119.25[/C][/ROW]
[ROW][C]22[/C][C]271311[/C][C]266232.75[/C][C]5078.24999999999[/C][/ROW]
[ROW][C]23[/C][C]289802[/C][C]266232.75[/C][C]23569.25[/C][/ROW]
[ROW][C]24[/C][C]290726[/C][C]266232.75[/C][C]24493.25[/C][/ROW]
[ROW][C]25[/C][C]292300[/C][C]266232.75[/C][C]26067.25[/C][/ROW]
[ROW][C]26[/C][C]278506[/C][C]266232.75[/C][C]12273.25[/C][/ROW]
[ROW][C]27[/C][C]269826[/C][C]266232.75[/C][C]3593.24999999999[/C][/ROW]
[ROW][C]28[/C][C]265861[/C][C]266232.75[/C][C]-371.750000000008[/C][/ROW]
[ROW][C]29[/C][C]269034[/C][C]266232.75[/C][C]2801.24999999999[/C][/ROW]
[ROW][C]30[/C][C]264176[/C][C]266232.75[/C][C]-2056.75000000001[/C][/ROW]
[ROW][C]31[/C][C]255198[/C][C]266232.75[/C][C]-11034.75[/C][/ROW]
[ROW][C]32[/C][C]253353[/C][C]266232.75[/C][C]-12879.75[/C][/ROW]
[ROW][C]33[/C][C]246057[/C][C]266232.75[/C][C]-20175.75[/C][/ROW]
[ROW][C]34[/C][C]235372[/C][C]266232.75[/C][C]-30860.75[/C][/ROW]
[ROW][C]35[/C][C]258556[/C][C]266232.75[/C][C]-7676.75[/C][/ROW]
[ROW][C]36[/C][C]260993[/C][C]266232.75[/C][C]-5239.75000000001[/C][/ROW]
[ROW][C]37[/C][C]254663[/C][C]266232.75[/C][C]-11569.75[/C][/ROW]
[ROW][C]38[/C][C]250643[/C][C]266232.75[/C][C]-15589.75[/C][/ROW]
[ROW][C]39[/C][C]243422[/C][C]266232.75[/C][C]-22810.75[/C][/ROW]
[ROW][C]40[/C][C]247105[/C][C]266232.75[/C][C]-19127.75[/C][/ROW]
[ROW][C]41[/C][C]248541[/C][C]266232.75[/C][C]-17691.75[/C][/ROW]
[ROW][C]42[/C][C]245039[/C][C]266232.75[/C][C]-21193.75[/C][/ROW]
[ROW][C]43[/C][C]237080[/C][C]266232.75[/C][C]-29152.75[/C][/ROW]
[ROW][C]44[/C][C]237085[/C][C]266232.75[/C][C]-29147.75[/C][/ROW]
[ROW][C]45[/C][C]225554[/C][C]266232.75[/C][C]-40678.75[/C][/ROW]
[ROW][C]46[/C][C]226839[/C][C]266232.75[/C][C]-39393.75[/C][/ROW]
[ROW][C]47[/C][C]247934[/C][C]266232.75[/C][C]-18298.75[/C][/ROW]
[ROW][C]48[/C][C]248333[/C][C]266232.75[/C][C]-17899.75[/C][/ROW]
[ROW][C]49[/C][C]246969[/C][C]266815.333333333[/C][C]-19846.3333333333[/C][/ROW]
[ROW][C]50[/C][C]245098[/C][C]266815.333333333[/C][C]-21717.3333333333[/C][/ROW]
[ROW][C]51[/C][C]246263[/C][C]266815.333333333[/C][C]-20552.3333333333[/C][/ROW]
[ROW][C]52[/C][C]255765[/C][C]266815.333333333[/C][C]-11050.3333333333[/C][/ROW]
[ROW][C]53[/C][C]264319[/C][C]266815.333333333[/C][C]-2496.33333333333[/C][/ROW]
[ROW][C]54[/C][C]268347[/C][C]266815.333333333[/C][C]1531.66666666667[/C][/ROW]
[ROW][C]55[/C][C]273046[/C][C]266815.333333333[/C][C]6230.66666666667[/C][/ROW]
[ROW][C]56[/C][C]273963[/C][C]266815.333333333[/C][C]7147.66666666667[/C][/ROW]
[ROW][C]57[/C][C]267430[/C][C]266815.333333333[/C][C]614.666666666668[/C][/ROW]
[ROW][C]58[/C][C]271993[/C][C]266815.333333333[/C][C]5177.66666666667[/C][/ROW]
[ROW][C]59[/C][C]292710[/C][C]266815.333333333[/C][C]25894.6666666667[/C][/ROW]
[ROW][C]60[/C][C]295881[/C][C]266815.333333333[/C][C]29065.6666666667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57748&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57748&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 IndexActualsInterpolationForecastResidualsPrediction Error
1286602266232.75000000020369.2500000004
2283042266232.7516809.25
3276687266232.7510454.25
4277915266232.7511682.25
5277128266232.7510895.25
6277103266232.7510870.25
7275037266232.758804.25
8270150266232.753917.24999999999
9267140266232.75907.249999999992
10264993266232.75-1239.75000000001
11287259266232.7521026.25
12291186266232.7524953.25
13292300266232.7526067.25
14288186266232.7521953.25
15281477266232.7515244.25
16282656266232.7516423.25
17280190266232.7513957.25
18280408266232.7514175.25
19276836266232.7510603.25
20275216266232.758983.25
21274352266232.758119.25
22271311266232.755078.24999999999
23289802266232.7523569.25
24290726266232.7524493.25
25292300266232.7526067.25
26278506266232.7512273.25
27269826266232.753593.24999999999
28265861266232.75-371.750000000008
29269034266232.752801.24999999999
30264176266232.75-2056.75000000001
31255198266232.75-11034.75
32253353266232.75-12879.75
33246057266232.75-20175.75
34235372266232.75-30860.75
35258556266232.75-7676.75
36260993266232.75-5239.75000000001
37254663266232.75-11569.75
38250643266232.75-15589.75
39243422266232.75-22810.75
40247105266232.75-19127.75
41248541266232.75-17691.75
42245039266232.75-21193.75
43237080266232.75-29152.75
44237085266232.75-29147.75
45225554266232.75-40678.75
46226839266232.75-39393.75
47247934266232.75-18298.75
48248333266232.75-17899.75
49246969266815.333333333-19846.3333333333
50245098266815.333333333-21717.3333333333
51246263266815.333333333-20552.3333333333
52255765266815.333333333-11050.3333333333
53264319266815.333333333-2496.33333333333
54268347266815.3333333331531.66666666667
55273046266815.3333333336230.66666666667
56273963266815.3333333337147.66666666667
57267430266815.333333333614.666666666668
58271993266815.3333333335177.66666666667
59292710266815.33333333325894.6666666667
60295881266815.33333333329065.6666666667







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.02686367979896920.05372735959793850.97313632020103
60.00728563439899680.01457126879799360.992714365601003
70.002496690778613820.004993381557227650.997503309221386
80.002278274856927290.004556549713854570.997721725143073
90.002614476222891750.00522895244578350.997385523777108
100.00290729662880410.00581459325760820.997092703371196
110.003130538482752740.006261076965505480.996869461517247
120.005144039821025750.01028807964205150.994855960178974
130.007622691074696710.01524538214939340.992377308925303
140.006251462138379430.01250292427675890.99374853786162
150.003363272717997830.006726545435995670.996636727282002
160.001900090212562200.003800180425124400.998099909787438
170.0009973107564961630.001994621512992330.999002689243504
180.000532104835381690.001064209670763380.999467895164618
190.0002836311188510840.0005672622377021680.999716368881149
200.0001594414004778320.0003188828009556640.999840558599522
219.35829471347002e-050.0001871658942694000.999906417052865
226.73070376131386e-050.0001346140752262770.999932692962387
230.0001420156837680270.0002840313675360550.999857984316232
240.0004108982401116050.000821796480223210.999589101759888
250.002019165926977140.004038331853954270.997980834073023
260.002528665039218710.005057330078437410.997471334960781
270.003656276852216320.007312553704432630.996343723147784
280.006513632446135180.01302726489227040.993486367553865
290.01010521781422870.02021043562845740.989894782185771
300.01850293511047960.03700587022095930.98149706488952
310.04858647535598960.09717295071197910.95141352464401
320.09730850378415530.1946170075683110.902691496215845
330.2047669374585450.4095338749170910.795233062541455
340.4558064262541250.911612852508250.544193573745875
350.4695232410014750.939046482002950.530476758998525
360.4976248598077460.9952497196154920.502375140192254
370.5177702934666040.9644594130667910.482229706533396
380.5370183524664590.9259632950670820.462981647533541
390.5700744997015580.8598510005968840.429925500298442
400.5737775834998740.8524448330002510.426222416500126
410.571019596446010.8579608071079790.428980403553989
420.5662299233309030.8675401533381950.433770076669097
430.5766034046094440.8467931907811120.423396595390556
440.5700888623278380.8598222753443240.429911137672162
450.6460031402864860.7079937194270290.353996859713514
460.7150046050652130.5699907898695740.284995394934787
470.6433402730268580.7133194539462830.356659726973142
480.5612680463299860.8774639073400280.438731953670014
490.5709062080875780.8581875838248450.429093791912423
500.6441131659724420.7117736680551160.355886834027558
510.7662048420250120.4675903159499770.233795157974988
520.802774411534670.3944511769306620.197225588465331
530.7661497092489020.4677005815021950.233850290751098
540.6898630526186230.6202738947627530.310136947381377
550.5529317241605430.8941365516789140.447068275839457

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0268636797989692 & 0.0537273595979385 & 0.97313632020103 \tabularnewline
6 & 0.0072856343989968 & 0.0145712687979936 & 0.992714365601003 \tabularnewline
7 & 0.00249669077861382 & 0.00499338155722765 & 0.997503309221386 \tabularnewline
8 & 0.00227827485692729 & 0.00455654971385457 & 0.997721725143073 \tabularnewline
9 & 0.00261447622289175 & 0.0052289524457835 & 0.997385523777108 \tabularnewline
10 & 0.0029072966288041 & 0.0058145932576082 & 0.997092703371196 \tabularnewline
11 & 0.00313053848275274 & 0.00626107696550548 & 0.996869461517247 \tabularnewline
12 & 0.00514403982102575 & 0.0102880796420515 & 0.994855960178974 \tabularnewline
13 & 0.00762269107469671 & 0.0152453821493934 & 0.992377308925303 \tabularnewline
14 & 0.00625146213837943 & 0.0125029242767589 & 0.99374853786162 \tabularnewline
15 & 0.00336327271799783 & 0.00672654543599567 & 0.996636727282002 \tabularnewline
16 & 0.00190009021256220 & 0.00380018042512440 & 0.998099909787438 \tabularnewline
17 & 0.000997310756496163 & 0.00199462151299233 & 0.999002689243504 \tabularnewline
18 & 0.00053210483538169 & 0.00106420967076338 & 0.999467895164618 \tabularnewline
19 & 0.000283631118851084 & 0.000567262237702168 & 0.999716368881149 \tabularnewline
20 & 0.000159441400477832 & 0.000318882800955664 & 0.999840558599522 \tabularnewline
21 & 9.35829471347002e-05 & 0.000187165894269400 & 0.999906417052865 \tabularnewline
22 & 6.73070376131386e-05 & 0.000134614075226277 & 0.999932692962387 \tabularnewline
23 & 0.000142015683768027 & 0.000284031367536055 & 0.999857984316232 \tabularnewline
24 & 0.000410898240111605 & 0.00082179648022321 & 0.999589101759888 \tabularnewline
25 & 0.00201916592697714 & 0.00403833185395427 & 0.997980834073023 \tabularnewline
26 & 0.00252866503921871 & 0.00505733007843741 & 0.997471334960781 \tabularnewline
27 & 0.00365627685221632 & 0.00731255370443263 & 0.996343723147784 \tabularnewline
28 & 0.00651363244613518 & 0.0130272648922704 & 0.993486367553865 \tabularnewline
29 & 0.0101052178142287 & 0.0202104356284574 & 0.989894782185771 \tabularnewline
30 & 0.0185029351104796 & 0.0370058702209593 & 0.98149706488952 \tabularnewline
31 & 0.0485864753559896 & 0.0971729507119791 & 0.95141352464401 \tabularnewline
32 & 0.0973085037841553 & 0.194617007568311 & 0.902691496215845 \tabularnewline
33 & 0.204766937458545 & 0.409533874917091 & 0.795233062541455 \tabularnewline
34 & 0.455806426254125 & 0.91161285250825 & 0.544193573745875 \tabularnewline
35 & 0.469523241001475 & 0.93904648200295 & 0.530476758998525 \tabularnewline
36 & 0.497624859807746 & 0.995249719615492 & 0.502375140192254 \tabularnewline
37 & 0.517770293466604 & 0.964459413066791 & 0.482229706533396 \tabularnewline
38 & 0.537018352466459 & 0.925963295067082 & 0.462981647533541 \tabularnewline
39 & 0.570074499701558 & 0.859851000596884 & 0.429925500298442 \tabularnewline
40 & 0.573777583499874 & 0.852444833000251 & 0.426222416500126 \tabularnewline
41 & 0.57101959644601 & 0.857960807107979 & 0.428980403553989 \tabularnewline
42 & 0.566229923330903 & 0.867540153338195 & 0.433770076669097 \tabularnewline
43 & 0.576603404609444 & 0.846793190781112 & 0.423396595390556 \tabularnewline
44 & 0.570088862327838 & 0.859822275344324 & 0.429911137672162 \tabularnewline
45 & 0.646003140286486 & 0.707993719427029 & 0.353996859713514 \tabularnewline
46 & 0.715004605065213 & 0.569990789869574 & 0.284995394934787 \tabularnewline
47 & 0.643340273026858 & 0.713319453946283 & 0.356659726973142 \tabularnewline
48 & 0.561268046329986 & 0.877463907340028 & 0.438731953670014 \tabularnewline
49 & 0.570906208087578 & 0.858187583824845 & 0.429093791912423 \tabularnewline
50 & 0.644113165972442 & 0.711773668055116 & 0.355886834027558 \tabularnewline
51 & 0.766204842025012 & 0.467590315949977 & 0.233795157974988 \tabularnewline
52 & 0.80277441153467 & 0.394451176930662 & 0.197225588465331 \tabularnewline
53 & 0.766149709248902 & 0.467700581502195 & 0.233850290751098 \tabularnewline
54 & 0.689863052618623 & 0.620273894762753 & 0.310136947381377 \tabularnewline
55 & 0.552931724160543 & 0.894136551678914 & 0.447068275839457 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57748&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]5[/C][C]0.0268636797989692[/C][C]0.0537273595979385[/C][C]0.97313632020103[/C][/ROW]
[ROW][C]6[/C][C]0.0072856343989968[/C][C]0.0145712687979936[/C][C]0.992714365601003[/C][/ROW]
[ROW][C]7[/C][C]0.00249669077861382[/C][C]0.00499338155722765[/C][C]0.997503309221386[/C][/ROW]
[ROW][C]8[/C][C]0.00227827485692729[/C][C]0.00455654971385457[/C][C]0.997721725143073[/C][/ROW]
[ROW][C]9[/C][C]0.00261447622289175[/C][C]0.0052289524457835[/C][C]0.997385523777108[/C][/ROW]
[ROW][C]10[/C][C]0.0029072966288041[/C][C]0.0058145932576082[/C][C]0.997092703371196[/C][/ROW]
[ROW][C]11[/C][C]0.00313053848275274[/C][C]0.00626107696550548[/C][C]0.996869461517247[/C][/ROW]
[ROW][C]12[/C][C]0.00514403982102575[/C][C]0.0102880796420515[/C][C]0.994855960178974[/C][/ROW]
[ROW][C]13[/C][C]0.00762269107469671[/C][C]0.0152453821493934[/C][C]0.992377308925303[/C][/ROW]
[ROW][C]14[/C][C]0.00625146213837943[/C][C]0.0125029242767589[/C][C]0.99374853786162[/C][/ROW]
[ROW][C]15[/C][C]0.00336327271799783[/C][C]0.00672654543599567[/C][C]0.996636727282002[/C][/ROW]
[ROW][C]16[/C][C]0.00190009021256220[/C][C]0.00380018042512440[/C][C]0.998099909787438[/C][/ROW]
[ROW][C]17[/C][C]0.000997310756496163[/C][C]0.00199462151299233[/C][C]0.999002689243504[/C][/ROW]
[ROW][C]18[/C][C]0.00053210483538169[/C][C]0.00106420967076338[/C][C]0.999467895164618[/C][/ROW]
[ROW][C]19[/C][C]0.000283631118851084[/C][C]0.000567262237702168[/C][C]0.999716368881149[/C][/ROW]
[ROW][C]20[/C][C]0.000159441400477832[/C][C]0.000318882800955664[/C][C]0.999840558599522[/C][/ROW]
[ROW][C]21[/C][C]9.35829471347002e-05[/C][C]0.000187165894269400[/C][C]0.999906417052865[/C][/ROW]
[ROW][C]22[/C][C]6.73070376131386e-05[/C][C]0.000134614075226277[/C][C]0.999932692962387[/C][/ROW]
[ROW][C]23[/C][C]0.000142015683768027[/C][C]0.000284031367536055[/C][C]0.999857984316232[/C][/ROW]
[ROW][C]24[/C][C]0.000410898240111605[/C][C]0.00082179648022321[/C][C]0.999589101759888[/C][/ROW]
[ROW][C]25[/C][C]0.00201916592697714[/C][C]0.00403833185395427[/C][C]0.997980834073023[/C][/ROW]
[ROW][C]26[/C][C]0.00252866503921871[/C][C]0.00505733007843741[/C][C]0.997471334960781[/C][/ROW]
[ROW][C]27[/C][C]0.00365627685221632[/C][C]0.00731255370443263[/C][C]0.996343723147784[/C][/ROW]
[ROW][C]28[/C][C]0.00651363244613518[/C][C]0.0130272648922704[/C][C]0.993486367553865[/C][/ROW]
[ROW][C]29[/C][C]0.0101052178142287[/C][C]0.0202104356284574[/C][C]0.989894782185771[/C][/ROW]
[ROW][C]30[/C][C]0.0185029351104796[/C][C]0.0370058702209593[/C][C]0.98149706488952[/C][/ROW]
[ROW][C]31[/C][C]0.0485864753559896[/C][C]0.0971729507119791[/C][C]0.95141352464401[/C][/ROW]
[ROW][C]32[/C][C]0.0973085037841553[/C][C]0.194617007568311[/C][C]0.902691496215845[/C][/ROW]
[ROW][C]33[/C][C]0.204766937458545[/C][C]0.409533874917091[/C][C]0.795233062541455[/C][/ROW]
[ROW][C]34[/C][C]0.455806426254125[/C][C]0.91161285250825[/C][C]0.544193573745875[/C][/ROW]
[ROW][C]35[/C][C]0.469523241001475[/C][C]0.93904648200295[/C][C]0.530476758998525[/C][/ROW]
[ROW][C]36[/C][C]0.497624859807746[/C][C]0.995249719615492[/C][C]0.502375140192254[/C][/ROW]
[ROW][C]37[/C][C]0.517770293466604[/C][C]0.964459413066791[/C][C]0.482229706533396[/C][/ROW]
[ROW][C]38[/C][C]0.537018352466459[/C][C]0.925963295067082[/C][C]0.462981647533541[/C][/ROW]
[ROW][C]39[/C][C]0.570074499701558[/C][C]0.859851000596884[/C][C]0.429925500298442[/C][/ROW]
[ROW][C]40[/C][C]0.573777583499874[/C][C]0.852444833000251[/C][C]0.426222416500126[/C][/ROW]
[ROW][C]41[/C][C]0.57101959644601[/C][C]0.857960807107979[/C][C]0.428980403553989[/C][/ROW]
[ROW][C]42[/C][C]0.566229923330903[/C][C]0.867540153338195[/C][C]0.433770076669097[/C][/ROW]
[ROW][C]43[/C][C]0.576603404609444[/C][C]0.846793190781112[/C][C]0.423396595390556[/C][/ROW]
[ROW][C]44[/C][C]0.570088862327838[/C][C]0.859822275344324[/C][C]0.429911137672162[/C][/ROW]
[ROW][C]45[/C][C]0.646003140286486[/C][C]0.707993719427029[/C][C]0.353996859713514[/C][/ROW]
[ROW][C]46[/C][C]0.715004605065213[/C][C]0.569990789869574[/C][C]0.284995394934787[/C][/ROW]
[ROW][C]47[/C][C]0.643340273026858[/C][C]0.713319453946283[/C][C]0.356659726973142[/C][/ROW]
[ROW][C]48[/C][C]0.561268046329986[/C][C]0.877463907340028[/C][C]0.438731953670014[/C][/ROW]
[ROW][C]49[/C][C]0.570906208087578[/C][C]0.858187583824845[/C][C]0.429093791912423[/C][/ROW]
[ROW][C]50[/C][C]0.644113165972442[/C][C]0.711773668055116[/C][C]0.355886834027558[/C][/ROW]
[ROW][C]51[/C][C]0.766204842025012[/C][C]0.467590315949977[/C][C]0.233795157974988[/C][/ROW]
[ROW][C]52[/C][C]0.80277441153467[/C][C]0.394451176930662[/C][C]0.197225588465331[/C][/ROW]
[ROW][C]53[/C][C]0.766149709248902[/C][C]0.467700581502195[/C][C]0.233850290751098[/C][/ROW]
[ROW][C]54[/C][C]0.689863052618623[/C][C]0.620273894762753[/C][C]0.310136947381377[/C][/ROW]
[ROW][C]55[/C][C]0.552931724160543[/C][C]0.894136551678914[/C][C]0.447068275839457[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57748&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57748&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.02686367979896920.05372735959793850.97313632020103
60.00728563439899680.01457126879799360.992714365601003
70.002496690778613820.004993381557227650.997503309221386
80.002278274856927290.004556549713854570.997721725143073
90.002614476222891750.00522895244578350.997385523777108
100.00290729662880410.00581459325760820.997092703371196
110.003130538482752740.006261076965505480.996869461517247
120.005144039821025750.01028807964205150.994855960178974
130.007622691074696710.01524538214939340.992377308925303
140.006251462138379430.01250292427675890.99374853786162
150.003363272717997830.006726545435995670.996636727282002
160.001900090212562200.003800180425124400.998099909787438
170.0009973107564961630.001994621512992330.999002689243504
180.000532104835381690.001064209670763380.999467895164618
190.0002836311188510840.0005672622377021680.999716368881149
200.0001594414004778320.0003188828009556640.999840558599522
219.35829471347002e-050.0001871658942694000.999906417052865
226.73070376131386e-050.0001346140752262770.999932692962387
230.0001420156837680270.0002840313675360550.999857984316232
240.0004108982401116050.000821796480223210.999589101759888
250.002019165926977140.004038331853954270.997980834073023
260.002528665039218710.005057330078437410.997471334960781
270.003656276852216320.007312553704432630.996343723147784
280.006513632446135180.01302726489227040.993486367553865
290.01010521781422870.02021043562845740.989894782185771
300.01850293511047960.03700587022095930.98149706488952
310.04858647535598960.09717295071197910.95141352464401
320.09730850378415530.1946170075683110.902691496215845
330.2047669374585450.4095338749170910.795233062541455
340.4558064262541250.911612852508250.544193573745875
350.4695232410014750.939046482002950.530476758998525
360.4976248598077460.9952497196154920.502375140192254
370.5177702934666040.9644594130667910.482229706533396
380.5370183524664590.9259632950670820.462981647533541
390.5700744997015580.8598510005968840.429925500298442
400.5737775834998740.8524448330002510.426222416500126
410.571019596446010.8579608071079790.428980403553989
420.5662299233309030.8675401533381950.433770076669097
430.5766034046094440.8467931907811120.423396595390556
440.5700888623278380.8598222753443240.429911137672162
450.6460031402864860.7079937194270290.353996859713514
460.7150046050652130.5699907898695740.284995394934787
470.6433402730268580.7133194539462830.356659726973142
480.5612680463299860.8774639073400280.438731953670014
490.5709062080875780.8581875838248450.429093791912423
500.6441131659724420.7117736680551160.355886834027558
510.7662048420250120.4675903159499770.233795157974988
520.802774411534670.3944511769306620.197225588465331
530.7661497092489020.4677005815021950.233850290751098
540.6898630526186230.6202738947627530.310136947381377
550.5529317241605430.8941365516789140.447068275839457







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.352941176470588NOK
5% type I error level250.490196078431373NOK
10% type I error level270.529411764705882NOK

\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 & 18 & 0.352941176470588 & NOK \tabularnewline
5% type I error level & 25 & 0.490196078431373 & NOK \tabularnewline
10% type I error level & 27 & 0.529411764705882 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57748&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]18[/C][C]0.352941176470588[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.490196078431373[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.529411764705882[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57748&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57748&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 testsOK/NOK
1% type I error level180.352941176470588NOK
5% type I error level250.490196078431373NOK
10% type I error level270.529411764705882NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
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')
}