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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:50:53 -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/20/t1258732381iojhbr6lbi48pr1.htm/, Retrieved Fri, 29 Mar 2024 15:12:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58286, Retrieved Fri, 29 Mar 2024 15:12:34 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7 Multiple Regr...] [2009-11-20 15:50:53] [2694a35f9be9144abd040893a0238ab5] [Current]
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Dataseries X:
103,9	91,1	110,3	114,1	96,8
101,6	79,8	103,9	110,3	114,1
94,6	71,9	101,6	103,9	110,3
95,9	82,9	94,6	101,6	103,9
104,7	90,1	95,9	94,6	101,6
102,8	100,7	104,7	95,9	94,6
98,1	90,7	102,8	104,7	95,9
113,9	108,8	98,1	102,8	104,7
80,9	44,1	113,9	98,1	102,8
95,7	93,6	80,9	113,9	98,1
113,2	107,4	95,7	80,9	113,9
105,9	96,5	113,2	95,7	80,9
108,8	93,6	105,9	113,2	95,7
102,3	76,5	108,8	105,9	113,2
99	76,7	102,3	108,8	105,9
100,7	84	99	102,3	108,8
115,5	103,3	100,7	99	102,3
100,7	88,5	115,5	100,7	99
109,9	99	100,7	115,5	100,7
114,6	105,9	109,9	100,7	115,5
85,4	44,7	114,6	109,9	100,7
100,5	94	85,4	114,6	109,9
114,8	107,1	100,5	85,4	114,6
116,5	104,8	114,8	100,5	85,4
112,9	102,5	116,5	114,8	100,5
102	77,7	112,9	116,5	114,8
106	85,2	102	112,9	116,5
105,3	91,3	106	102	112,9
118,8	106,5	105,3	106	102
106,1	92,4	118,8	105,3	106
109,3	97,5	106,1	118,8	105,3
117,2	107	109,3	106,1	118,8
92,5	51,1	117,2	109,3	106,1
104,2	98,6	92,5	117,2	109,3
112,5	102,2	104,2	92,5	117,2
122,4	114,3	112,5	104,2	92,5
113,3	99,4	122,4	112,5	104,2
100	72,5	113,3	122,4	112,5
110,7	92,3	100	113,3	122,4
112,8	99,4	110,7	100	113,3
109,8	85,9	112,8	110,7	100
117,3	109,4	109,8	112,8	110,7
109,1	97,6	117,3	109,8	112,8
115,9	104,7	109,1	117,3	109,8
96	56,9	115,9	109,1	117,3
99,8	86,7	96	115,9	109,1
116,8	108,5	99,8	96	115,9
115,7	103,4	116,8	99,8	96
99,4	86,2	115,7	116,8	99,8
94,3	71	99,4	115,7	116,8
91	75,9	94,3	99,4	115,7
93,2	87,1	91	94,3	99,4
103,1	102	93,2	91	94,3
94,1	88,5	103,1	93,2	91
91,8	87,8	94,1	103,1	93,2
102,7	100,8	91,8	94,1	103,1
82,6	50,6	102,7	91,8	94,1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58286&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]3 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=58286&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58286&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Totind[t] = -24.0516684867312 + 0.670388000769807Bouw[t] + 0.253998840317328`Yt-1`[t] + 0.269978457642583`Yt-2`[t] + 0.160638800436104`Yt-3`[t] -6.37661479692486M1[t] -1.88110503246155M2[t] -1.19156849898619M3[t] -2.50634377705874M4[t] + 1.38913552902864M5[t] -6.26009234136988M6[t] -6.86826776767659M7[t] -4.52699001781147M8[t] + 6.45481886381635M9[t] -7.94932174721686M10[t] + 0.517101623635753M11[t] -0.0448299433002486t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totind[t] =  -24.0516684867312 +  0.670388000769807Bouw[t] +  0.253998840317328`Yt-1`[t] +  0.269978457642583`Yt-2`[t] +  0.160638800436104`Yt-3`[t] -6.37661479692486M1[t] -1.88110503246155M2[t] -1.19156849898619M3[t] -2.50634377705874M4[t] +  1.38913552902864M5[t] -6.26009234136988M6[t] -6.86826776767659M7[t] -4.52699001781147M8[t] +  6.45481886381635M9[t] -7.94932174721686M10[t] +  0.517101623635753M11[t] -0.0448299433002486t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58286&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totind[t] =  -24.0516684867312 +  0.670388000769807Bouw[t] +  0.253998840317328`Yt-1`[t] +  0.269978457642583`Yt-2`[t] +  0.160638800436104`Yt-3`[t] -6.37661479692486M1[t] -1.88110503246155M2[t] -1.19156849898619M3[t] -2.50634377705874M4[t] +  1.38913552902864M5[t] -6.26009234136988M6[t] -6.86826776767659M7[t] -4.52699001781147M8[t] +  6.45481886381635M9[t] -7.94932174721686M10[t] +  0.517101623635753M11[t] -0.0448299433002486t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58286&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58286&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
Totind[t] = -24.0516684867312 + 0.670388000769807Bouw[t] + 0.253998840317328`Yt-1`[t] + 0.269978457642583`Yt-2`[t] + 0.160638800436104`Yt-3`[t] -6.37661479692486M1[t] -1.88110503246155M2[t] -1.19156849898619M3[t] -2.50634377705874M4[t] + 1.38913552902864M5[t] -6.26009234136988M6[t] -6.86826776767659M7[t] -4.52699001781147M8[t] + 6.45481886381635M9[t] -7.94932174721686M10[t] + 0.517101623635753M11[t] -0.0448299433002486t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-24.05166848673126.175792-3.89450.0003650.000182
Bouw0.6703880007698070.04823913.897300
`Yt-1`0.2539988403173280.0566694.48216.1e-053e-05
`Yt-2`0.2699784576425830.0513465.25815e-063e-06
`Yt-3`0.1606388004361040.0665532.41370.0204620.010231
M1-6.376614796924861.715957-3.71610.0006190.000309
M2-1.881105032461553.210205-0.5860.5611830.280592
M3-1.191568498986193.243081-0.36740.7152440.357622
M4-2.506343777058742.561296-0.97850.3336860.166843
M51.389135529028641.8912410.73450.4669220.233461
M6-6.260092341369881.661245-3.76830.0005310.000265
M7-6.868267767676592.043213-3.36150.0017160.000858
M8-4.526990017811472.173725-2.08260.0437270.021863
M96.454818863816353.5339821.82650.0752450.037622
M10-7.949321747216863.093976-2.56930.0140250.007012
M110.5171016236357532.6637180.19410.8470580.423529
t-0.04482994330024860.014545-3.08210.0037130.001856

\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) & -24.0516684867312 & 6.175792 & -3.8945 & 0.000365 & 0.000182 \tabularnewline
Bouw & 0.670388000769807 & 0.048239 & 13.8973 & 0 & 0 \tabularnewline
`Yt-1` & 0.253998840317328 & 0.056669 & 4.4821 & 6.1e-05 & 3e-05 \tabularnewline
`Yt-2` & 0.269978457642583 & 0.051346 & 5.2581 & 5e-06 & 3e-06 \tabularnewline
`Yt-3` & 0.160638800436104 & 0.066553 & 2.4137 & 0.020462 & 0.010231 \tabularnewline
M1 & -6.37661479692486 & 1.715957 & -3.7161 & 0.000619 & 0.000309 \tabularnewline
M2 & -1.88110503246155 & 3.210205 & -0.586 & 0.561183 & 0.280592 \tabularnewline
M3 & -1.19156849898619 & 3.243081 & -0.3674 & 0.715244 & 0.357622 \tabularnewline
M4 & -2.50634377705874 & 2.561296 & -0.9785 & 0.333686 & 0.166843 \tabularnewline
M5 & 1.38913552902864 & 1.891241 & 0.7345 & 0.466922 & 0.233461 \tabularnewline
M6 & -6.26009234136988 & 1.661245 & -3.7683 & 0.000531 & 0.000265 \tabularnewline
M7 & -6.86826776767659 & 2.043213 & -3.3615 & 0.001716 & 0.000858 \tabularnewline
M8 & -4.52699001781147 & 2.173725 & -2.0826 & 0.043727 & 0.021863 \tabularnewline
M9 & 6.45481886381635 & 3.533982 & 1.8265 & 0.075245 & 0.037622 \tabularnewline
M10 & -7.94932174721686 & 3.093976 & -2.5693 & 0.014025 & 0.007012 \tabularnewline
M11 & 0.517101623635753 & 2.663718 & 0.1941 & 0.847058 & 0.423529 \tabularnewline
t & -0.0448299433002486 & 0.014545 & -3.0821 & 0.003713 & 0.001856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58286&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]-24.0516684867312[/C][C]6.175792[/C][C]-3.8945[/C][C]0.000365[/C][C]0.000182[/C][/ROW]
[ROW][C]Bouw[/C][C]0.670388000769807[/C][C]0.048239[/C][C]13.8973[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]0.253998840317328[/C][C]0.056669[/C][C]4.4821[/C][C]6.1e-05[/C][C]3e-05[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]0.269978457642583[/C][C]0.051346[/C][C]5.2581[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]0.160638800436104[/C][C]0.066553[/C][C]2.4137[/C][C]0.020462[/C][C]0.010231[/C][/ROW]
[ROW][C]M1[/C][C]-6.37661479692486[/C][C]1.715957[/C][C]-3.7161[/C][C]0.000619[/C][C]0.000309[/C][/ROW]
[ROW][C]M2[/C][C]-1.88110503246155[/C][C]3.210205[/C][C]-0.586[/C][C]0.561183[/C][C]0.280592[/C][/ROW]
[ROW][C]M3[/C][C]-1.19156849898619[/C][C]3.243081[/C][C]-0.3674[/C][C]0.715244[/C][C]0.357622[/C][/ROW]
[ROW][C]M4[/C][C]-2.50634377705874[/C][C]2.561296[/C][C]-0.9785[/C][C]0.333686[/C][C]0.166843[/C][/ROW]
[ROW][C]M5[/C][C]1.38913552902864[/C][C]1.891241[/C][C]0.7345[/C][C]0.466922[/C][C]0.233461[/C][/ROW]
[ROW][C]M6[/C][C]-6.26009234136988[/C][C]1.661245[/C][C]-3.7683[/C][C]0.000531[/C][C]0.000265[/C][/ROW]
[ROW][C]M7[/C][C]-6.86826776767659[/C][C]2.043213[/C][C]-3.3615[/C][C]0.001716[/C][C]0.000858[/C][/ROW]
[ROW][C]M8[/C][C]-4.52699001781147[/C][C]2.173725[/C][C]-2.0826[/C][C]0.043727[/C][C]0.021863[/C][/ROW]
[ROW][C]M9[/C][C]6.45481886381635[/C][C]3.533982[/C][C]1.8265[/C][C]0.075245[/C][C]0.037622[/C][/ROW]
[ROW][C]M10[/C][C]-7.94932174721686[/C][C]3.093976[/C][C]-2.5693[/C][C]0.014025[/C][C]0.007012[/C][/ROW]
[ROW][C]M11[/C][C]0.517101623635753[/C][C]2.663718[/C][C]0.1941[/C][C]0.847058[/C][C]0.423529[/C][/ROW]
[ROW][C]t[/C][C]-0.0448299433002486[/C][C]0.014545[/C][C]-3.0821[/C][C]0.003713[/C][C]0.001856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58286&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58286&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)-24.05166848673126.175792-3.89450.0003650.000182
Bouw0.6703880007698070.04823913.897300
`Yt-1`0.2539988403173280.0566694.48216.1e-053e-05
`Yt-2`0.2699784576425830.0513465.25815e-063e-06
`Yt-3`0.1606388004361040.0665532.41370.0204620.010231
M1-6.376614796924861.715957-3.71610.0006190.000309
M2-1.881105032461553.210205-0.5860.5611830.280592
M3-1.191568498986193.243081-0.36740.7152440.357622
M4-2.506343777058742.561296-0.97850.3336860.166843
M51.389135529028641.8912410.73450.4669220.233461
M6-6.260092341369881.661245-3.76830.0005310.000265
M7-6.868267767676592.043213-3.36150.0017160.000858
M8-4.526990017811472.173725-2.08260.0437270.021863
M96.454818863816353.5339821.82650.0752450.037622
M10-7.949321747216863.093976-2.56930.0140250.007012
M110.5171016236357532.6637180.19410.8470580.423529
t-0.04482994330024860.014545-3.08210.0037130.001856







Multiple Linear Regression - Regression Statistics
Multiple R0.987973486143355
R-squared0.976091609322253
Adjusted R-squared0.966528253051154
F-TEST (value)102.065800086533
F-TEST (DF numerator)16
F-TEST (DF denominator)40
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.74653764901953
Sum Squared Residuals122.015750377706

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.987973486143355 \tabularnewline
R-squared & 0.976091609322253 \tabularnewline
Adjusted R-squared & 0.966528253051154 \tabularnewline
F-TEST (value) & 102.065800086533 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 40 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.74653764901953 \tabularnewline
Sum Squared Residuals & 122.015750377706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58286&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.987973486143355[/C][/ROW]
[ROW][C]R-squared[/C][C]0.976091609322253[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.966528253051154[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]102.065800086533[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]40[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.74653764901953[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]122.015750377706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58286&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58286&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 R0.987973486143355
R-squared0.976091609322253
Adjusted R-squared0.966528253051154
F-TEST (value)102.065800086533
F-TEST (DF numerator)16
F-TEST (DF denominator)40
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.74653764901953
Sum Squared Residuals122.015750377706







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1103.9104.969683629408-1.06968362940815
2101.6101.972519572344-0.372519572344110
394.694.39867405313820.201325946861819
495.996.986306182643-1.08630618264294
5104.7103.7346291988840.965370801115925
6102.8104.608374380020-1.80837438002040
798.199.353532073934-1.25353207393411
8113.9113.4908705192580.40912948074217
980.983.492815013044-2.59281501304397
1095.797.3567460350472-1.65674603504723
11113.2112.4176806546050.78231934539542
12105.9107.688100343550-1.78810034354973
13108.8104.5704161219754.22958387802476
14102.399.13439403373583.16560596626418
159997.87246004598221.12753995401782
16100.799.27948360376971.4205163962303
17115.5114.5653382968980.934661703101635
18100.7100.6375762450560.062423754943834
19109.9107.5332291806872.36677081931268
20114.6115.173916596827-0.573916596827391
2185.486.3832920013917-0.983292001391662
22100.5100.3144594626760.185540537324012
23114.8114.2251495879910.574850412009274
24116.5115.1395307734911.36046922650927
25112.9113.894329490909-0.994329490908626
26102103.561089292067-1.56108929206679
27106105.7662820417850.233717958215348
28105.3105.990974116503-0.690974116502855
29118.8119.182672808586-0.38267280858572
30106.1105.9186988097110.181301190288773
31109.3108.9911489898700.308851010130198
32117.2117.20897648659-0.00897648658987273
3392.591.501675319310.99832468069009
34104.2105.269237422476-1.06923742247619
35112.5113.676592704186-1.17659270418602
36122.4122.525515904845-0.125515904844945
37113.3112.7501736358270.549826364172864
38100100.862115563676-0.862115563675945
39110.7110.5358401526430.164159847357327
40112.8111.6012507575161.19874924248398
41109.8107.6873331255532.11266687444738
42117.3117.2711867347080.0288132652919417
43109.1110.140000366385-1.04000036638540
44115.9116.656334518825-0.756334518824865
459696.2669267851151-0.26692678511512
4699.897.25955707980062.54044292019941
47116.8116.980577053219-0.180577053218678
48115.7115.1468529781150.553147021885413
4999.4102.115397121881-2.71539712188085
5094.394.6698815381773-0.369881538177339
519192.7267437064523-1.72674370645231
5293.294.0419853395685-0.841985339568484
53103.1106.730026570079-3.63002657007922
5494.192.56416383050411.53583616949585
5591.892.1820893891234-0.382089389123366
56102.7101.76990187850.930098121499961
5782.679.75529088113932.84470911886066

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.9 & 104.969683629408 & -1.06968362940815 \tabularnewline
2 & 101.6 & 101.972519572344 & -0.372519572344110 \tabularnewline
3 & 94.6 & 94.3986740531382 & 0.201325946861819 \tabularnewline
4 & 95.9 & 96.986306182643 & -1.08630618264294 \tabularnewline
5 & 104.7 & 103.734629198884 & 0.965370801115925 \tabularnewline
6 & 102.8 & 104.608374380020 & -1.80837438002040 \tabularnewline
7 & 98.1 & 99.353532073934 & -1.25353207393411 \tabularnewline
8 & 113.9 & 113.490870519258 & 0.40912948074217 \tabularnewline
9 & 80.9 & 83.492815013044 & -2.59281501304397 \tabularnewline
10 & 95.7 & 97.3567460350472 & -1.65674603504723 \tabularnewline
11 & 113.2 & 112.417680654605 & 0.78231934539542 \tabularnewline
12 & 105.9 & 107.688100343550 & -1.78810034354973 \tabularnewline
13 & 108.8 & 104.570416121975 & 4.22958387802476 \tabularnewline
14 & 102.3 & 99.1343940337358 & 3.16560596626418 \tabularnewline
15 & 99 & 97.8724600459822 & 1.12753995401782 \tabularnewline
16 & 100.7 & 99.2794836037697 & 1.4205163962303 \tabularnewline
17 & 115.5 & 114.565338296898 & 0.934661703101635 \tabularnewline
18 & 100.7 & 100.637576245056 & 0.062423754943834 \tabularnewline
19 & 109.9 & 107.533229180687 & 2.36677081931268 \tabularnewline
20 & 114.6 & 115.173916596827 & -0.573916596827391 \tabularnewline
21 & 85.4 & 86.3832920013917 & -0.983292001391662 \tabularnewline
22 & 100.5 & 100.314459462676 & 0.185540537324012 \tabularnewline
23 & 114.8 & 114.225149587991 & 0.574850412009274 \tabularnewline
24 & 116.5 & 115.139530773491 & 1.36046922650927 \tabularnewline
25 & 112.9 & 113.894329490909 & -0.994329490908626 \tabularnewline
26 & 102 & 103.561089292067 & -1.56108929206679 \tabularnewline
27 & 106 & 105.766282041785 & 0.233717958215348 \tabularnewline
28 & 105.3 & 105.990974116503 & -0.690974116502855 \tabularnewline
29 & 118.8 & 119.182672808586 & -0.38267280858572 \tabularnewline
30 & 106.1 & 105.918698809711 & 0.181301190288773 \tabularnewline
31 & 109.3 & 108.991148989870 & 0.308851010130198 \tabularnewline
32 & 117.2 & 117.20897648659 & -0.00897648658987273 \tabularnewline
33 & 92.5 & 91.50167531931 & 0.99832468069009 \tabularnewline
34 & 104.2 & 105.269237422476 & -1.06923742247619 \tabularnewline
35 & 112.5 & 113.676592704186 & -1.17659270418602 \tabularnewline
36 & 122.4 & 122.525515904845 & -0.125515904844945 \tabularnewline
37 & 113.3 & 112.750173635827 & 0.549826364172864 \tabularnewline
38 & 100 & 100.862115563676 & -0.862115563675945 \tabularnewline
39 & 110.7 & 110.535840152643 & 0.164159847357327 \tabularnewline
40 & 112.8 & 111.601250757516 & 1.19874924248398 \tabularnewline
41 & 109.8 & 107.687333125553 & 2.11266687444738 \tabularnewline
42 & 117.3 & 117.271186734708 & 0.0288132652919417 \tabularnewline
43 & 109.1 & 110.140000366385 & -1.04000036638540 \tabularnewline
44 & 115.9 & 116.656334518825 & -0.756334518824865 \tabularnewline
45 & 96 & 96.2669267851151 & -0.26692678511512 \tabularnewline
46 & 99.8 & 97.2595570798006 & 2.54044292019941 \tabularnewline
47 & 116.8 & 116.980577053219 & -0.180577053218678 \tabularnewline
48 & 115.7 & 115.146852978115 & 0.553147021885413 \tabularnewline
49 & 99.4 & 102.115397121881 & -2.71539712188085 \tabularnewline
50 & 94.3 & 94.6698815381773 & -0.369881538177339 \tabularnewline
51 & 91 & 92.7267437064523 & -1.72674370645231 \tabularnewline
52 & 93.2 & 94.0419853395685 & -0.841985339568484 \tabularnewline
53 & 103.1 & 106.730026570079 & -3.63002657007922 \tabularnewline
54 & 94.1 & 92.5641638305041 & 1.53583616949585 \tabularnewline
55 & 91.8 & 92.1820893891234 & -0.382089389123366 \tabularnewline
56 & 102.7 & 101.7699018785 & 0.930098121499961 \tabularnewline
57 & 82.6 & 79.7552908811393 & 2.84470911886066 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58286&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]103.9[/C][C]104.969683629408[/C][C]-1.06968362940815[/C][/ROW]
[ROW][C]2[/C][C]101.6[/C][C]101.972519572344[/C][C]-0.372519572344110[/C][/ROW]
[ROW][C]3[/C][C]94.6[/C][C]94.3986740531382[/C][C]0.201325946861819[/C][/ROW]
[ROW][C]4[/C][C]95.9[/C][C]96.986306182643[/C][C]-1.08630618264294[/C][/ROW]
[ROW][C]5[/C][C]104.7[/C][C]103.734629198884[/C][C]0.965370801115925[/C][/ROW]
[ROW][C]6[/C][C]102.8[/C][C]104.608374380020[/C][C]-1.80837438002040[/C][/ROW]
[ROW][C]7[/C][C]98.1[/C][C]99.353532073934[/C][C]-1.25353207393411[/C][/ROW]
[ROW][C]8[/C][C]113.9[/C][C]113.490870519258[/C][C]0.40912948074217[/C][/ROW]
[ROW][C]9[/C][C]80.9[/C][C]83.492815013044[/C][C]-2.59281501304397[/C][/ROW]
[ROW][C]10[/C][C]95.7[/C][C]97.3567460350472[/C][C]-1.65674603504723[/C][/ROW]
[ROW][C]11[/C][C]113.2[/C][C]112.417680654605[/C][C]0.78231934539542[/C][/ROW]
[ROW][C]12[/C][C]105.9[/C][C]107.688100343550[/C][C]-1.78810034354973[/C][/ROW]
[ROW][C]13[/C][C]108.8[/C][C]104.570416121975[/C][C]4.22958387802476[/C][/ROW]
[ROW][C]14[/C][C]102.3[/C][C]99.1343940337358[/C][C]3.16560596626418[/C][/ROW]
[ROW][C]15[/C][C]99[/C][C]97.8724600459822[/C][C]1.12753995401782[/C][/ROW]
[ROW][C]16[/C][C]100.7[/C][C]99.2794836037697[/C][C]1.4205163962303[/C][/ROW]
[ROW][C]17[/C][C]115.5[/C][C]114.565338296898[/C][C]0.934661703101635[/C][/ROW]
[ROW][C]18[/C][C]100.7[/C][C]100.637576245056[/C][C]0.062423754943834[/C][/ROW]
[ROW][C]19[/C][C]109.9[/C][C]107.533229180687[/C][C]2.36677081931268[/C][/ROW]
[ROW][C]20[/C][C]114.6[/C][C]115.173916596827[/C][C]-0.573916596827391[/C][/ROW]
[ROW][C]21[/C][C]85.4[/C][C]86.3832920013917[/C][C]-0.983292001391662[/C][/ROW]
[ROW][C]22[/C][C]100.5[/C][C]100.314459462676[/C][C]0.185540537324012[/C][/ROW]
[ROW][C]23[/C][C]114.8[/C][C]114.225149587991[/C][C]0.574850412009274[/C][/ROW]
[ROW][C]24[/C][C]116.5[/C][C]115.139530773491[/C][C]1.36046922650927[/C][/ROW]
[ROW][C]25[/C][C]112.9[/C][C]113.894329490909[/C][C]-0.994329490908626[/C][/ROW]
[ROW][C]26[/C][C]102[/C][C]103.561089292067[/C][C]-1.56108929206679[/C][/ROW]
[ROW][C]27[/C][C]106[/C][C]105.766282041785[/C][C]0.233717958215348[/C][/ROW]
[ROW][C]28[/C][C]105.3[/C][C]105.990974116503[/C][C]-0.690974116502855[/C][/ROW]
[ROW][C]29[/C][C]118.8[/C][C]119.182672808586[/C][C]-0.38267280858572[/C][/ROW]
[ROW][C]30[/C][C]106.1[/C][C]105.918698809711[/C][C]0.181301190288773[/C][/ROW]
[ROW][C]31[/C][C]109.3[/C][C]108.991148989870[/C][C]0.308851010130198[/C][/ROW]
[ROW][C]32[/C][C]117.2[/C][C]117.20897648659[/C][C]-0.00897648658987273[/C][/ROW]
[ROW][C]33[/C][C]92.5[/C][C]91.50167531931[/C][C]0.99832468069009[/C][/ROW]
[ROW][C]34[/C][C]104.2[/C][C]105.269237422476[/C][C]-1.06923742247619[/C][/ROW]
[ROW][C]35[/C][C]112.5[/C][C]113.676592704186[/C][C]-1.17659270418602[/C][/ROW]
[ROW][C]36[/C][C]122.4[/C][C]122.525515904845[/C][C]-0.125515904844945[/C][/ROW]
[ROW][C]37[/C][C]113.3[/C][C]112.750173635827[/C][C]0.549826364172864[/C][/ROW]
[ROW][C]38[/C][C]100[/C][C]100.862115563676[/C][C]-0.862115563675945[/C][/ROW]
[ROW][C]39[/C][C]110.7[/C][C]110.535840152643[/C][C]0.164159847357327[/C][/ROW]
[ROW][C]40[/C][C]112.8[/C][C]111.601250757516[/C][C]1.19874924248398[/C][/ROW]
[ROW][C]41[/C][C]109.8[/C][C]107.687333125553[/C][C]2.11266687444738[/C][/ROW]
[ROW][C]42[/C][C]117.3[/C][C]117.271186734708[/C][C]0.0288132652919417[/C][/ROW]
[ROW][C]43[/C][C]109.1[/C][C]110.140000366385[/C][C]-1.04000036638540[/C][/ROW]
[ROW][C]44[/C][C]115.9[/C][C]116.656334518825[/C][C]-0.756334518824865[/C][/ROW]
[ROW][C]45[/C][C]96[/C][C]96.2669267851151[/C][C]-0.26692678511512[/C][/ROW]
[ROW][C]46[/C][C]99.8[/C][C]97.2595570798006[/C][C]2.54044292019941[/C][/ROW]
[ROW][C]47[/C][C]116.8[/C][C]116.980577053219[/C][C]-0.180577053218678[/C][/ROW]
[ROW][C]48[/C][C]115.7[/C][C]115.146852978115[/C][C]0.553147021885413[/C][/ROW]
[ROW][C]49[/C][C]99.4[/C][C]102.115397121881[/C][C]-2.71539712188085[/C][/ROW]
[ROW][C]50[/C][C]94.3[/C][C]94.6698815381773[/C][C]-0.369881538177339[/C][/ROW]
[ROW][C]51[/C][C]91[/C][C]92.7267437064523[/C][C]-1.72674370645231[/C][/ROW]
[ROW][C]52[/C][C]93.2[/C][C]94.0419853395685[/C][C]-0.841985339568484[/C][/ROW]
[ROW][C]53[/C][C]103.1[/C][C]106.730026570079[/C][C]-3.63002657007922[/C][/ROW]
[ROW][C]54[/C][C]94.1[/C][C]92.5641638305041[/C][C]1.53583616949585[/C][/ROW]
[ROW][C]55[/C][C]91.8[/C][C]92.1820893891234[/C][C]-0.382089389123366[/C][/ROW]
[ROW][C]56[/C][C]102.7[/C][C]101.7699018785[/C][C]0.930098121499961[/C][/ROW]
[ROW][C]57[/C][C]82.6[/C][C]79.7552908811393[/C][C]2.84470911886066[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58286&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58286&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
1103.9104.969683629408-1.06968362940815
2101.6101.972519572344-0.372519572344110
394.694.39867405313820.201325946861819
495.996.986306182643-1.08630618264294
5104.7103.7346291988840.965370801115925
6102.8104.608374380020-1.80837438002040
798.199.353532073934-1.25353207393411
8113.9113.4908705192580.40912948074217
980.983.492815013044-2.59281501304397
1095.797.3567460350472-1.65674603504723
11113.2112.4176806546050.78231934539542
12105.9107.688100343550-1.78810034354973
13108.8104.5704161219754.22958387802476
14102.399.13439403373583.16560596626418
159997.87246004598221.12753995401782
16100.799.27948360376971.4205163962303
17115.5114.5653382968980.934661703101635
18100.7100.6375762450560.062423754943834
19109.9107.5332291806872.36677081931268
20114.6115.173916596827-0.573916596827391
2185.486.3832920013917-0.983292001391662
22100.5100.3144594626760.185540537324012
23114.8114.2251495879910.574850412009274
24116.5115.1395307734911.36046922650927
25112.9113.894329490909-0.994329490908626
26102103.561089292067-1.56108929206679
27106105.7662820417850.233717958215348
28105.3105.990974116503-0.690974116502855
29118.8119.182672808586-0.38267280858572
30106.1105.9186988097110.181301190288773
31109.3108.9911489898700.308851010130198
32117.2117.20897648659-0.00897648658987273
3392.591.501675319310.99832468069009
34104.2105.269237422476-1.06923742247619
35112.5113.676592704186-1.17659270418602
36122.4122.525515904845-0.125515904844945
37113.3112.7501736358270.549826364172864
38100100.862115563676-0.862115563675945
39110.7110.5358401526430.164159847357327
40112.8111.6012507575161.19874924248398
41109.8107.6873331255532.11266687444738
42117.3117.2711867347080.0288132652919417
43109.1110.140000366385-1.04000036638540
44115.9116.656334518825-0.756334518824865
459696.2669267851151-0.26692678511512
4699.897.25955707980062.54044292019941
47116.8116.980577053219-0.180577053218678
48115.7115.1468529781150.553147021885413
4999.4102.115397121881-2.71539712188085
5094.394.6698815381773-0.369881538177339
519192.7267437064523-1.72674370645231
5293.294.0419853395685-0.841985339568484
53103.1106.730026570079-3.63002657007922
5494.192.56416383050411.53583616949585
5591.892.1820893891234-0.382089389123366
56102.7101.76990187850.930098121499961
5782.679.75529088113932.84470911886066







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.2257320035344640.4514640070689280.774267996465536
210.161564782426140.323129564852280.83843521757386
220.09003874231194810.1800774846238960.909961257688052
230.09443936950901820.1888787390180360.905560630490982
240.0753684320008610.1507368640017220.924631567999139
250.1556560935670070.3113121871340150.844343906432993
260.4461536737891210.8923073475782430.553846326210879
270.5181015844853970.9637968310292060.481898415514603
280.3994471806297790.7988943612595580.600552819370221
290.3034143851241310.6068287702482630.696585614875868
300.2259747044290220.4519494088580430.774025295570978
310.1972377408533240.3944754817066480.802762259146676
320.1465918681171600.2931837362343200.85340813188284
330.1005067422596010.2010134845192020.8994932577404
340.08281197431711220.1656239486342240.917188025682888
350.1147846553964280.2295693107928560.885215344603572
360.1113911434746540.2227822869493080.888608856525346
370.05580666141428120.1116133228285620.94419333858572

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.225732003534464 & 0.451464007068928 & 0.774267996465536 \tabularnewline
21 & 0.16156478242614 & 0.32312956485228 & 0.83843521757386 \tabularnewline
22 & 0.0900387423119481 & 0.180077484623896 & 0.909961257688052 \tabularnewline
23 & 0.0944393695090182 & 0.188878739018036 & 0.905560630490982 \tabularnewline
24 & 0.075368432000861 & 0.150736864001722 & 0.924631567999139 \tabularnewline
25 & 0.155656093567007 & 0.311312187134015 & 0.844343906432993 \tabularnewline
26 & 0.446153673789121 & 0.892307347578243 & 0.553846326210879 \tabularnewline
27 & 0.518101584485397 & 0.963796831029206 & 0.481898415514603 \tabularnewline
28 & 0.399447180629779 & 0.798894361259558 & 0.600552819370221 \tabularnewline
29 & 0.303414385124131 & 0.606828770248263 & 0.696585614875868 \tabularnewline
30 & 0.225974704429022 & 0.451949408858043 & 0.774025295570978 \tabularnewline
31 & 0.197237740853324 & 0.394475481706648 & 0.802762259146676 \tabularnewline
32 & 0.146591868117160 & 0.293183736234320 & 0.85340813188284 \tabularnewline
33 & 0.100506742259601 & 0.201013484519202 & 0.8994932577404 \tabularnewline
34 & 0.0828119743171122 & 0.165623948634224 & 0.917188025682888 \tabularnewline
35 & 0.114784655396428 & 0.229569310792856 & 0.885215344603572 \tabularnewline
36 & 0.111391143474654 & 0.222782286949308 & 0.888608856525346 \tabularnewline
37 & 0.0558066614142812 & 0.111613322828562 & 0.94419333858572 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58286&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]20[/C][C]0.225732003534464[/C][C]0.451464007068928[/C][C]0.774267996465536[/C][/ROW]
[ROW][C]21[/C][C]0.16156478242614[/C][C]0.32312956485228[/C][C]0.83843521757386[/C][/ROW]
[ROW][C]22[/C][C]0.0900387423119481[/C][C]0.180077484623896[/C][C]0.909961257688052[/C][/ROW]
[ROW][C]23[/C][C]0.0944393695090182[/C][C]0.188878739018036[/C][C]0.905560630490982[/C][/ROW]
[ROW][C]24[/C][C]0.075368432000861[/C][C]0.150736864001722[/C][C]0.924631567999139[/C][/ROW]
[ROW][C]25[/C][C]0.155656093567007[/C][C]0.311312187134015[/C][C]0.844343906432993[/C][/ROW]
[ROW][C]26[/C][C]0.446153673789121[/C][C]0.892307347578243[/C][C]0.553846326210879[/C][/ROW]
[ROW][C]27[/C][C]0.518101584485397[/C][C]0.963796831029206[/C][C]0.481898415514603[/C][/ROW]
[ROW][C]28[/C][C]0.399447180629779[/C][C]0.798894361259558[/C][C]0.600552819370221[/C][/ROW]
[ROW][C]29[/C][C]0.303414385124131[/C][C]0.606828770248263[/C][C]0.696585614875868[/C][/ROW]
[ROW][C]30[/C][C]0.225974704429022[/C][C]0.451949408858043[/C][C]0.774025295570978[/C][/ROW]
[ROW][C]31[/C][C]0.197237740853324[/C][C]0.394475481706648[/C][C]0.802762259146676[/C][/ROW]
[ROW][C]32[/C][C]0.146591868117160[/C][C]0.293183736234320[/C][C]0.85340813188284[/C][/ROW]
[ROW][C]33[/C][C]0.100506742259601[/C][C]0.201013484519202[/C][C]0.8994932577404[/C][/ROW]
[ROW][C]34[/C][C]0.0828119743171122[/C][C]0.165623948634224[/C][C]0.917188025682888[/C][/ROW]
[ROW][C]35[/C][C]0.114784655396428[/C][C]0.229569310792856[/C][C]0.885215344603572[/C][/ROW]
[ROW][C]36[/C][C]0.111391143474654[/C][C]0.222782286949308[/C][C]0.888608856525346[/C][/ROW]
[ROW][C]37[/C][C]0.0558066614142812[/C][C]0.111613322828562[/C][C]0.94419333858572[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58286&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58286&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
200.2257320035344640.4514640070689280.774267996465536
210.161564782426140.323129564852280.83843521757386
220.09003874231194810.1800774846238960.909961257688052
230.09443936950901820.1888787390180360.905560630490982
240.0753684320008610.1507368640017220.924631567999139
250.1556560935670070.3113121871340150.844343906432993
260.4461536737891210.8923073475782430.553846326210879
270.5181015844853970.9637968310292060.481898415514603
280.3994471806297790.7988943612595580.600552819370221
290.3034143851241310.6068287702482630.696585614875868
300.2259747044290220.4519494088580430.774025295570978
310.1972377408533240.3944754817066480.802762259146676
320.1465918681171600.2931837362343200.85340813188284
330.1005067422596010.2010134845192020.8994932577404
340.08281197431711220.1656239486342240.917188025682888
350.1147846553964280.2295693107928560.885215344603572
360.1113911434746540.2227822869493080.888608856525346
370.05580666141428120.1116133228285620.94419333858572







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58286&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58286&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58286&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 level00OK
5% type I error level00OK
10% type I error level00OK



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')
}