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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 17 Nov 2009 11:12:21 -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/17/t1258481672hz64j3ycfxaoqkm.htm/, Retrieved Thu, 02 May 2024 03:25:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57392, Retrieved Thu, 02 May 2024 03:25:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact204

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2009-11-17 18:12:21] [6e025b5370bdd3143fbe248190b38274] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
16643	16196,7	18252,1	17570,4	15836,8	89,1
17729	16643	16196,7	18252,1	17570,4	82,6
16446,1	17729	16643	16196,7	18252,1	102,7
15993,8	16446,1	17729	16643	16196,7	91,8
16373,5	15993,8	16446,1	17729	16643	94,1
17842,2	16373,5	15993,8	16446,1	17729	103,1
22321,5	17842,2	16373,5	15993,8	16446,1	93,2
22786,7	22321,5	17842,2	16373,5	15993,8	91
18274,1	22786,7	22321,5	17842,2	16373,5	94,3
22392,9	18274,1	22786,7	22321,5	17842,2	99,4
23899,3	22392,9	18274,1	22786,7	22321,5	115,7
21343,5	23899,3	22392,9	18274,1	22786,7	116,8
22952,3	21343,5	23899,3	22392,9	18274,1	99,8
21374,4	22952,3	21343,5	23899,3	22392,9	96
21164,1	21374,4	22952,3	21343,5	23899,3	115,9
20906,5	21164,1	21374,4	22952,3	21343,5	109,1
17877,4	20906,5	21164,1	21374,4	22952,3	117,3
20664,3	17877,4	20906,5	21164,1	21374,4	109,8
22160	20664,3	17877,4	20906,5	21164,1	112,8
19813,6	22160	20664,3	17877,4	20906,5	110,7
17735,4	19813,6	22160	20664,3	17877,4	100
19640,2	17735,4	19813,6	22160	20664,3	113,3
20844,4	19640,2	17735,4	19813,6	22160	122,4
19823,1	20844,4	19640,2	17735,4	19813,6	112,5
18594,6	19823,1	20844,4	19640,2	17735,4	104,2
21350,6	18594,6	19823,1	20844,4	19640,2	92,5
18574,1	21350,6	18594,6	19823,1	20844,4	117,2
18924,2	18574,1	21350,6	18594,6	19823,1	109,3
17343,4	18924,2	18574,1	21350,6	18594,6	106,1
19961,2	17343,4	18924,2	18574,1	21350,6	118,8
19932,1	19961,2	17343,4	18924,2	18574,1	105,3
19464,6	19932,1	19961,2	17343,4	18924,2	106
16165,4	19464,6	19932,1	19961,2	17343,4	102
17574,9	16165,4	19464,6	19932,1	19961,2	112,9
19795,4	17574,9	16165,4	19464,6	19932,1	116,5
19439,5	19795,4	17574,9	16165,4	19464,6	114,8
17170	19439,5	19795,4	17574,9	16165,4	100,5
21072,4	17170	19439,5	19795,4	17574,9	85,4
17751,8	21072,4	17170	19439,5	19795,4	114,6
17515,5	17751,8	21072,4	17170	19439,5	109,9
18040,3	17515,5	17751,8	21072,4	17170	100,7
19090,1	18040,3	17515,5	17751,8	21072,4	115,5
17746,5	19090,1	18040,3	17515,5	17751,8	100,7
19202,1	17746,5	19090,1	18040,3	17515,5	99
15141,6	19202,1	17746,5	19090,1	18040,3	102,3
16258,1	15141,6	19202,1	17746,5	19090,1	108,8
18586,5	16258,1	15141,6	19202,1	17746,5	105,9
17209,4	18586,5	16258,1	15141,6	19202,1	113,2
17838,7	17209,4	18586,5	16258,1	15141,6	95,7
19123,5	17838,7	17209,4	18586,5	16258,1	80,9
16583,6	19123,5	17838,7	17209,4	18586,5	113,9
15991,2	16583,6	19123,5	17838,7	17209,4	98,1
16704,4	15991,2	16583,6	19123,5	17838,7	102,8
17420,4	16704,4	15991,2	16583,6	19123,5	104,7
17872	17420,4	16704,4	15991,2	16583,6	95,9
17823,2	17872	17420,4	16704,4	15991,2	94,6

Tables (Output of Computation)





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=57392&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=57392&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57392&T=0

As an alternative you can also use a QR Code:  
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
uitvoer[t] = + 6470.94617062108 + 0.322411155710347uitvoer1[t] + 0.383629152407573uitvoer2[t] + 0.40960378798841uitvoer3[t] -0.37518770882754uitvoer4[t] + 3.91110031572952indprod[t] -2876.51299467513M1[t] -583.286887128338M2[t] -1903.66235026434M3[t] -2486.12446424482M4[t] -2880.63111648108M5[t] + 519.876668044034M6[t] + 598.501113670949M7[t] -393.806494729233M8[t] -5232.10425325911M9[t] -1624.00755051358M10[t] + 1361.02153157619M11[t] -23.705109352381t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
uitvoer[t] =  +  6470.94617062108 +  0.322411155710347uitvoer1[t] +  0.383629152407573uitvoer2[t] +  0.40960378798841uitvoer3[t] -0.37518770882754uitvoer4[t] +  3.91110031572952indprod[t] -2876.51299467513M1[t] -583.286887128338M2[t] -1903.66235026434M3[t] -2486.12446424482M4[t] -2880.63111648108M5[t] +  519.876668044034M6[t] +  598.501113670949M7[t] -393.806494729233M8[t] -5232.10425325911M9[t] -1624.00755051358M10[t] +  1361.02153157619M11[t] -23.705109352381t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57392&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]uitvoer[t] =  +  6470.94617062108 +  0.322411155710347uitvoer1[t] +  0.383629152407573uitvoer2[t] +  0.40960378798841uitvoer3[t] -0.37518770882754uitvoer4[t] +  3.91110031572952indprod[t] -2876.51299467513M1[t] -583.286887128338M2[t] -1903.66235026434M3[t] -2486.12446424482M4[t] -2880.63111648108M5[t] +  519.876668044034M6[t] +  598.501113670949M7[t] -393.806494729233M8[t] -5232.10425325911M9[t] -1624.00755051358M10[t] +  1361.02153157619M11[t] -23.705109352381t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57392&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57392&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
uitvoer[t] = + 6470.94617062108 + 0.322411155710347uitvoer1[t] + 0.383629152407573uitvoer2[t] + 0.40960378798841uitvoer3[t] -0.37518770882754uitvoer4[t] + 3.91110031572952indprod[t] -2876.51299467513M1[t] -583.286887128338M2[t] -1903.66235026434M3[t] -2486.12446424482M4[t] -2880.63111648108M5[t] + 519.876668044034M6[t] + 598.501113670949M7[t] -393.806494729233M8[t] -5232.10425325911M9[t] -1624.00755051358M10[t] + 1361.02153157619M11[t] -23.705109352381t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6470.946170621083486.2938461.85610.0712050.035602
uitvoer10.3224111557103470.1436022.24520.0306550.015328
uitvoer20.3836291524075730.1427912.68670.0106460.005323
uitvoer30.409603787988410.1410792.90340.0061170.003059
uitvoer4-0.375187708827540.262028-1.43190.1603580.080179
indprod3.9111003157295260.8226990.06430.9490660.474533
M1-2876.512994675131019.942416-2.82030.0075840.003792
M2-583.2868871283381496.609949-0.38970.6989060.349453
M3-1903.66235026434777.901234-2.44720.0191330.009566
M4-2486.12446424482937.017076-2.65320.0115730.005786
M5-2880.631116481081034.573273-2.78440.0083140.004157
M6519.876668044034911.7697310.57020.5719090.285954
M7598.501113670949868.6644260.6890.4950150.247508
M8-393.806494729233843.58781-0.46680.6432920.321646
M9-5232.104253259111003.400177-5.21447e-063e-06
M10-1624.007550513581174.388028-1.38290.1747820.087391
M111361.021531576191029.800461.32160.1941910.097096
t-23.70510935238111.878224-1.99570.0531740.026587

\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) & 6470.94617062108 & 3486.293846 & 1.8561 & 0.071205 & 0.035602 \tabularnewline
uitvoer1 & 0.322411155710347 & 0.143602 & 2.2452 & 0.030655 & 0.015328 \tabularnewline
uitvoer2 & 0.383629152407573 & 0.142791 & 2.6867 & 0.010646 & 0.005323 \tabularnewline
uitvoer3 & 0.40960378798841 & 0.141079 & 2.9034 & 0.006117 & 0.003059 \tabularnewline
uitvoer4 & -0.37518770882754 & 0.262028 & -1.4319 & 0.160358 & 0.080179 \tabularnewline
indprod & 3.91110031572952 & 60.822699 & 0.0643 & 0.949066 & 0.474533 \tabularnewline
M1 & -2876.51299467513 & 1019.942416 & -2.8203 & 0.007584 & 0.003792 \tabularnewline
M2 & -583.286887128338 & 1496.609949 & -0.3897 & 0.698906 & 0.349453 \tabularnewline
M3 & -1903.66235026434 & 777.901234 & -2.4472 & 0.019133 & 0.009566 \tabularnewline
M4 & -2486.12446424482 & 937.017076 & -2.6532 & 0.011573 & 0.005786 \tabularnewline
M5 & -2880.63111648108 & 1034.573273 & -2.7844 & 0.008314 & 0.004157 \tabularnewline
M6 & 519.876668044034 & 911.769731 & 0.5702 & 0.571909 & 0.285954 \tabularnewline
M7 & 598.501113670949 & 868.664426 & 0.689 & 0.495015 & 0.247508 \tabularnewline
M8 & -393.806494729233 & 843.58781 & -0.4668 & 0.643292 & 0.321646 \tabularnewline
M9 & -5232.10425325911 & 1003.400177 & -5.2144 & 7e-06 & 3e-06 \tabularnewline
M10 & -1624.00755051358 & 1174.388028 & -1.3829 & 0.174782 & 0.087391 \tabularnewline
M11 & 1361.02153157619 & 1029.80046 & 1.3216 & 0.194191 & 0.097096 \tabularnewline
t & -23.705109352381 & 11.878224 & -1.9957 & 0.053174 & 0.026587 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57392&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]6470.94617062108[/C][C]3486.293846[/C][C]1.8561[/C][C]0.071205[/C][C]0.035602[/C][/ROW]
[ROW][C]uitvoer1[/C][C]0.322411155710347[/C][C]0.143602[/C][C]2.2452[/C][C]0.030655[/C][C]0.015328[/C][/ROW]
[ROW][C]uitvoer2[/C][C]0.383629152407573[/C][C]0.142791[/C][C]2.6867[/C][C]0.010646[/C][C]0.005323[/C][/ROW]
[ROW][C]uitvoer3[/C][C]0.40960378798841[/C][C]0.141079[/C][C]2.9034[/C][C]0.006117[/C][C]0.003059[/C][/ROW]
[ROW][C]uitvoer4[/C][C]-0.37518770882754[/C][C]0.262028[/C][C]-1.4319[/C][C]0.160358[/C][C]0.080179[/C][/ROW]
[ROW][C]indprod[/C][C]3.91110031572952[/C][C]60.822699[/C][C]0.0643[/C][C]0.949066[/C][C]0.474533[/C][/ROW]
[ROW][C]M1[/C][C]-2876.51299467513[/C][C]1019.942416[/C][C]-2.8203[/C][C]0.007584[/C][C]0.003792[/C][/ROW]
[ROW][C]M2[/C][C]-583.286887128338[/C][C]1496.609949[/C][C]-0.3897[/C][C]0.698906[/C][C]0.349453[/C][/ROW]
[ROW][C]M3[/C][C]-1903.66235026434[/C][C]777.901234[/C][C]-2.4472[/C][C]0.019133[/C][C]0.009566[/C][/ROW]
[ROW][C]M4[/C][C]-2486.12446424482[/C][C]937.017076[/C][C]-2.6532[/C][C]0.011573[/C][C]0.005786[/C][/ROW]
[ROW][C]M5[/C][C]-2880.63111648108[/C][C]1034.573273[/C][C]-2.7844[/C][C]0.008314[/C][C]0.004157[/C][/ROW]
[ROW][C]M6[/C][C]519.876668044034[/C][C]911.769731[/C][C]0.5702[/C][C]0.571909[/C][C]0.285954[/C][/ROW]
[ROW][C]M7[/C][C]598.501113670949[/C][C]868.664426[/C][C]0.689[/C][C]0.495015[/C][C]0.247508[/C][/ROW]
[ROW][C]M8[/C][C]-393.806494729233[/C][C]843.58781[/C][C]-0.4668[/C][C]0.643292[/C][C]0.321646[/C][/ROW]
[ROW][C]M9[/C][C]-5232.10425325911[/C][C]1003.400177[/C][C]-5.2144[/C][C]7e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]M10[/C][C]-1624.00755051358[/C][C]1174.388028[/C][C]-1.3829[/C][C]0.174782[/C][C]0.087391[/C][/ROW]
[ROW][C]M11[/C][C]1361.02153157619[/C][C]1029.80046[/C][C]1.3216[/C][C]0.194191[/C][C]0.097096[/C][/ROW]
[ROW][C]t[/C][C]-23.705109352381[/C][C]11.878224[/C][C]-1.9957[/C][C]0.053174[/C][C]0.026587[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57392&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57392&T=2

As an alternative you can also use a QR Code:  
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)6470.946170621083486.2938461.85610.0712050.035602
uitvoer10.3224111557103470.1436022.24520.0306550.015328
uitvoer20.3836291524075730.1427912.68670.0106460.005323
uitvoer30.409603787988410.1410792.90340.0061170.003059
uitvoer4-0.375187708827540.262028-1.43190.1603580.080179
indprod3.9111003157295260.8226990.06430.9490660.474533
M1-2876.512994675131019.942416-2.82030.0075840.003792
M2-583.2868871283381496.609949-0.38970.6989060.349453
M3-1903.66235026434777.901234-2.44720.0191330.009566
M4-2486.12446424482937.017076-2.65320.0115730.005786
M5-2880.631116481081034.573273-2.78440.0083140.004157
M6519.876668044034911.7697310.57020.5719090.285954
M7598.501113670949868.6644260.6890.4950150.247508
M8-393.806494729233843.58781-0.46680.6432920.321646
M9-5232.104253259111003.400177-5.21447e-063e-06
M10-1624.007550513581174.388028-1.38290.1747820.087391
M111361.021531576191029.800461.32160.1941910.097096
t-23.70510935238111.878224-1.99570.0531740.026587






Multiple Linear Regression - Regression Statistics
Multiple R0.906633974931966
R-squared0.821985164500938
Adjusted R-squared0.742346948619778
F-TEST (value)10.3214914523895
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value1.75131598112443e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1050.47401315171
Sum Squared Residuals41932834.7876684

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.906633974931966 \tabularnewline
R-squared & 0.821985164500938 \tabularnewline
Adjusted R-squared & 0.742346948619778 \tabularnewline
F-TEST (value) & 10.3214914523895 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 1.75131598112443e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1050.47401315171 \tabularnewline
Sum Squared Residuals & 41932834.7876684 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57392&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.906633974931966[/C][/ROW]
[ROW][C]R-squared[/C][C]0.821985164500938[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.742346948619778[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.3214914523895[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/C][/ROW]
[ROW][C]p-value[/C][C]1.75131598112443e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1050.47401315171[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]41932834.7876684[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57392&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57392&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.906633974931966
R-squared0.821985164500938
Adjusted R-squared0.742346948619778
F-TEST (value)10.3214914523895
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value1.75131598112443e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1050.47401315171
Sum Squared Residuals41932834.7876684






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11664317398.3712123886-755.371212388642
21772918626.6522877141-897.652287714118
316446.116784.8719504537-338.771950453735
415993.817093.0407088366-1099.24070883659
516373.516323.223511928350.2764880716641
617842.218751.1955887349-908.995588734874
722321.519682.64880379232638.85119620768
822786.720990.86805026141795.83194973859
918274.118469.2738558135-195.173855813523
1022392.922080.8638208399312.036179160147
1123899.323212.69026173686.609738269987
1221343.521875.1223732240-531.622373224047
1322952.322042.4442240732909.855775926784
1421374.422907.0227317583-1532.62273175825
1521164.120137.17494743221026.92505256775
1620906.520449.1560566598457.343943340185
1717877.418649.3691901692-771.969190169195
1820664.321427.2691206966-762.969120696569
192216021203.7863815903956.213618409697
2019813.620586.8443232136-773.24432321362
2117735.417778.2871550047-42.8871550046998
2219640.219411.5478355535228.652164446524
2320844.420703.0709017983141.329098201703
2419823.120427.7105387518-604.610538751777
2518594.619187.6464059516-593.046405951647
2621350.620402.1123060290948.487693971049
2718574.118701.7832551014-127.683255101415
2818924.218106.8066629616817.393337038418
2917343.418313.5953243079-970.19532430786
3019961.219193.4277419228767.77225807718
3119932.120618.2251429621-686.125142962074
3219464.619820.9775410593-356.377541059292
3316165.416446.7969745951-281.396974595102
3417574.917836.6881933604-261.788193360386
3519795.419820.2895430274-24.8895430273701
3619439.518509.5887296809929.911270319125
371717018105.7001218756-935.700121875617
3821072.419828.61590771191243.78409228813
3917751.818007.3881015012-255.588101501237
4017515.517013.24813612502.25186388
4118040.317658.9158594778381.384140522226
4219090.118347.8897718033742.210228196713
4317746.520033.7795646843-2287.27956468431
4419202.119284.2771553123-82.1771553123166
4515141.614622.1420145867519.457985413325
4616258.116537.0001502463-278.900150246286
4718586.519389.5492934443-803.04929344432
4817209.417003.0783583433206.321641656701
4917838.716464.43803571091374.26196428912
5019123.518885.4967667868238.003233213188
5116583.616888.4817455114-304.881745511361
5215991.216668.9484354220-677.748435422016
5316704.415393.89611411681310.50388588316
5417420.417258.4177768425161.98222315755
551787218493.660106971-621.660106970993
5617823.218407.2329301534-584.03293015336

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16643 & 17398.3712123886 & -755.371212388642 \tabularnewline
2 & 17729 & 18626.6522877141 & -897.652287714118 \tabularnewline
3 & 16446.1 & 16784.8719504537 & -338.771950453735 \tabularnewline
4 & 15993.8 & 17093.0407088366 & -1099.24070883659 \tabularnewline
5 & 16373.5 & 16323.2235119283 & 50.2764880716641 \tabularnewline
6 & 17842.2 & 18751.1955887349 & -908.995588734874 \tabularnewline
7 & 22321.5 & 19682.6488037923 & 2638.85119620768 \tabularnewline
8 & 22786.7 & 20990.8680502614 & 1795.83194973859 \tabularnewline
9 & 18274.1 & 18469.2738558135 & -195.173855813523 \tabularnewline
10 & 22392.9 & 22080.8638208399 & 312.036179160147 \tabularnewline
11 & 23899.3 & 23212.69026173 & 686.609738269987 \tabularnewline
12 & 21343.5 & 21875.1223732240 & -531.622373224047 \tabularnewline
13 & 22952.3 & 22042.4442240732 & 909.855775926784 \tabularnewline
14 & 21374.4 & 22907.0227317583 & -1532.62273175825 \tabularnewline
15 & 21164.1 & 20137.1749474322 & 1026.92505256775 \tabularnewline
16 & 20906.5 & 20449.1560566598 & 457.343943340185 \tabularnewline
17 & 17877.4 & 18649.3691901692 & -771.969190169195 \tabularnewline
18 & 20664.3 & 21427.2691206966 & -762.969120696569 \tabularnewline
19 & 22160 & 21203.7863815903 & 956.213618409697 \tabularnewline
20 & 19813.6 & 20586.8443232136 & -773.24432321362 \tabularnewline
21 & 17735.4 & 17778.2871550047 & -42.8871550046998 \tabularnewline
22 & 19640.2 & 19411.5478355535 & 228.652164446524 \tabularnewline
23 & 20844.4 & 20703.0709017983 & 141.329098201703 \tabularnewline
24 & 19823.1 & 20427.7105387518 & -604.610538751777 \tabularnewline
25 & 18594.6 & 19187.6464059516 & -593.046405951647 \tabularnewline
26 & 21350.6 & 20402.1123060290 & 948.487693971049 \tabularnewline
27 & 18574.1 & 18701.7832551014 & -127.683255101415 \tabularnewline
28 & 18924.2 & 18106.8066629616 & 817.393337038418 \tabularnewline
29 & 17343.4 & 18313.5953243079 & -970.19532430786 \tabularnewline
30 & 19961.2 & 19193.4277419228 & 767.77225807718 \tabularnewline
31 & 19932.1 & 20618.2251429621 & -686.125142962074 \tabularnewline
32 & 19464.6 & 19820.9775410593 & -356.377541059292 \tabularnewline
33 & 16165.4 & 16446.7969745951 & -281.396974595102 \tabularnewline
34 & 17574.9 & 17836.6881933604 & -261.788193360386 \tabularnewline
35 & 19795.4 & 19820.2895430274 & -24.8895430273701 \tabularnewline
36 & 19439.5 & 18509.5887296809 & 929.911270319125 \tabularnewline
37 & 17170 & 18105.7001218756 & -935.700121875617 \tabularnewline
38 & 21072.4 & 19828.6159077119 & 1243.78409228813 \tabularnewline
39 & 17751.8 & 18007.3881015012 & -255.588101501237 \tabularnewline
40 & 17515.5 & 17013.24813612 & 502.25186388 \tabularnewline
41 & 18040.3 & 17658.9158594778 & 381.384140522226 \tabularnewline
42 & 19090.1 & 18347.8897718033 & 742.210228196713 \tabularnewline
43 & 17746.5 & 20033.7795646843 & -2287.27956468431 \tabularnewline
44 & 19202.1 & 19284.2771553123 & -82.1771553123166 \tabularnewline
45 & 15141.6 & 14622.1420145867 & 519.457985413325 \tabularnewline
46 & 16258.1 & 16537.0001502463 & -278.900150246286 \tabularnewline
47 & 18586.5 & 19389.5492934443 & -803.04929344432 \tabularnewline
48 & 17209.4 & 17003.0783583433 & 206.321641656701 \tabularnewline
49 & 17838.7 & 16464.4380357109 & 1374.26196428912 \tabularnewline
50 & 19123.5 & 18885.4967667868 & 238.003233213188 \tabularnewline
51 & 16583.6 & 16888.4817455114 & -304.881745511361 \tabularnewline
52 & 15991.2 & 16668.9484354220 & -677.748435422016 \tabularnewline
53 & 16704.4 & 15393.8961141168 & 1310.50388588316 \tabularnewline
54 & 17420.4 & 17258.4177768425 & 161.98222315755 \tabularnewline
55 & 17872 & 18493.660106971 & -621.660106970993 \tabularnewline
56 & 17823.2 & 18407.2329301534 & -584.03293015336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57392&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]16643[/C][C]17398.3712123886[/C][C]-755.371212388642[/C][/ROW]
[ROW][C]2[/C][C]17729[/C][C]18626.6522877141[/C][C]-897.652287714118[/C][/ROW]
[ROW][C]3[/C][C]16446.1[/C][C]16784.8719504537[/C][C]-338.771950453735[/C][/ROW]
[ROW][C]4[/C][C]15993.8[/C][C]17093.0407088366[/C][C]-1099.24070883659[/C][/ROW]
[ROW][C]5[/C][C]16373.5[/C][C]16323.2235119283[/C][C]50.2764880716641[/C][/ROW]
[ROW][C]6[/C][C]17842.2[/C][C]18751.1955887349[/C][C]-908.995588734874[/C][/ROW]
[ROW][C]7[/C][C]22321.5[/C][C]19682.6488037923[/C][C]2638.85119620768[/C][/ROW]
[ROW][C]8[/C][C]22786.7[/C][C]20990.8680502614[/C][C]1795.83194973859[/C][/ROW]
[ROW][C]9[/C][C]18274.1[/C][C]18469.2738558135[/C][C]-195.173855813523[/C][/ROW]
[ROW][C]10[/C][C]22392.9[/C][C]22080.8638208399[/C][C]312.036179160147[/C][/ROW]
[ROW][C]11[/C][C]23899.3[/C][C]23212.69026173[/C][C]686.609738269987[/C][/ROW]
[ROW][C]12[/C][C]21343.5[/C][C]21875.1223732240[/C][C]-531.622373224047[/C][/ROW]
[ROW][C]13[/C][C]22952.3[/C][C]22042.4442240732[/C][C]909.855775926784[/C][/ROW]
[ROW][C]14[/C][C]21374.4[/C][C]22907.0227317583[/C][C]-1532.62273175825[/C][/ROW]
[ROW][C]15[/C][C]21164.1[/C][C]20137.1749474322[/C][C]1026.92505256775[/C][/ROW]
[ROW][C]16[/C][C]20906.5[/C][C]20449.1560566598[/C][C]457.343943340185[/C][/ROW]
[ROW][C]17[/C][C]17877.4[/C][C]18649.3691901692[/C][C]-771.969190169195[/C][/ROW]
[ROW][C]18[/C][C]20664.3[/C][C]21427.2691206966[/C][C]-762.969120696569[/C][/ROW]
[ROW][C]19[/C][C]22160[/C][C]21203.7863815903[/C][C]956.213618409697[/C][/ROW]
[ROW][C]20[/C][C]19813.6[/C][C]20586.8443232136[/C][C]-773.24432321362[/C][/ROW]
[ROW][C]21[/C][C]17735.4[/C][C]17778.2871550047[/C][C]-42.8871550046998[/C][/ROW]
[ROW][C]22[/C][C]19640.2[/C][C]19411.5478355535[/C][C]228.652164446524[/C][/ROW]
[ROW][C]23[/C][C]20844.4[/C][C]20703.0709017983[/C][C]141.329098201703[/C][/ROW]
[ROW][C]24[/C][C]19823.1[/C][C]20427.7105387518[/C][C]-604.610538751777[/C][/ROW]
[ROW][C]25[/C][C]18594.6[/C][C]19187.6464059516[/C][C]-593.046405951647[/C][/ROW]
[ROW][C]26[/C][C]21350.6[/C][C]20402.1123060290[/C][C]948.487693971049[/C][/ROW]
[ROW][C]27[/C][C]18574.1[/C][C]18701.7832551014[/C][C]-127.683255101415[/C][/ROW]
[ROW][C]28[/C][C]18924.2[/C][C]18106.8066629616[/C][C]817.393337038418[/C][/ROW]
[ROW][C]29[/C][C]17343.4[/C][C]18313.5953243079[/C][C]-970.19532430786[/C][/ROW]
[ROW][C]30[/C][C]19961.2[/C][C]19193.4277419228[/C][C]767.77225807718[/C][/ROW]
[ROW][C]31[/C][C]19932.1[/C][C]20618.2251429621[/C][C]-686.125142962074[/C][/ROW]
[ROW][C]32[/C][C]19464.6[/C][C]19820.9775410593[/C][C]-356.377541059292[/C][/ROW]
[ROW][C]33[/C][C]16165.4[/C][C]16446.7969745951[/C][C]-281.396974595102[/C][/ROW]
[ROW][C]34[/C][C]17574.9[/C][C]17836.6881933604[/C][C]-261.788193360386[/C][/ROW]
[ROW][C]35[/C][C]19795.4[/C][C]19820.2895430274[/C][C]-24.8895430273701[/C][/ROW]
[ROW][C]36[/C][C]19439.5[/C][C]18509.5887296809[/C][C]929.911270319125[/C][/ROW]
[ROW][C]37[/C][C]17170[/C][C]18105.7001218756[/C][C]-935.700121875617[/C][/ROW]
[ROW][C]38[/C][C]21072.4[/C][C]19828.6159077119[/C][C]1243.78409228813[/C][/ROW]
[ROW][C]39[/C][C]17751.8[/C][C]18007.3881015012[/C][C]-255.588101501237[/C][/ROW]
[ROW][C]40[/C][C]17515.5[/C][C]17013.24813612[/C][C]502.25186388[/C][/ROW]
[ROW][C]41[/C][C]18040.3[/C][C]17658.9158594778[/C][C]381.384140522226[/C][/ROW]
[ROW][C]42[/C][C]19090.1[/C][C]18347.8897718033[/C][C]742.210228196713[/C][/ROW]
[ROW][C]43[/C][C]17746.5[/C][C]20033.7795646843[/C][C]-2287.27956468431[/C][/ROW]
[ROW][C]44[/C][C]19202.1[/C][C]19284.2771553123[/C][C]-82.1771553123166[/C][/ROW]
[ROW][C]45[/C][C]15141.6[/C][C]14622.1420145867[/C][C]519.457985413325[/C][/ROW]
[ROW][C]46[/C][C]16258.1[/C][C]16537.0001502463[/C][C]-278.900150246286[/C][/ROW]
[ROW][C]47[/C][C]18586.5[/C][C]19389.5492934443[/C][C]-803.04929344432[/C][/ROW]
[ROW][C]48[/C][C]17209.4[/C][C]17003.0783583433[/C][C]206.321641656701[/C][/ROW]
[ROW][C]49[/C][C]17838.7[/C][C]16464.4380357109[/C][C]1374.26196428912[/C][/ROW]
[ROW][C]50[/C][C]19123.5[/C][C]18885.4967667868[/C][C]238.003233213188[/C][/ROW]
[ROW][C]51[/C][C]16583.6[/C][C]16888.4817455114[/C][C]-304.881745511361[/C][/ROW]
[ROW][C]52[/C][C]15991.2[/C][C]16668.9484354220[/C][C]-677.748435422016[/C][/ROW]
[ROW][C]53[/C][C]16704.4[/C][C]15393.8961141168[/C][C]1310.50388588316[/C][/ROW]
[ROW][C]54[/C][C]17420.4[/C][C]17258.4177768425[/C][C]161.98222315755[/C][/ROW]
[ROW][C]55[/C][C]17872[/C][C]18493.660106971[/C][C]-621.660106970993[/C][/ROW]
[ROW][C]56[/C][C]17823.2[/C][C]18407.2329301534[/C][C]-584.03293015336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57392&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57392&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11664317398.3712123886-755.371212388642
21772918626.6522877141-897.652287714118
316446.116784.8719504537-338.771950453735
415993.817093.0407088366-1099.24070883659
516373.516323.223511928350.2764880716641
617842.218751.1955887349-908.995588734874
722321.519682.64880379232638.85119620768
822786.720990.86805026141795.83194973859
918274.118469.2738558135-195.173855813523
1022392.922080.8638208399312.036179160147
1123899.323212.69026173686.609738269987
1221343.521875.1223732240-531.622373224047
1322952.322042.4442240732909.855775926784
1421374.422907.0227317583-1532.62273175825
1521164.120137.17494743221026.92505256775
1620906.520449.1560566598457.343943340185
1717877.418649.3691901692-771.969190169195
1820664.321427.2691206966-762.969120696569
192216021203.7863815903956.213618409697
2019813.620586.8443232136-773.24432321362
2117735.417778.2871550047-42.8871550046998
2219640.219411.5478355535228.652164446524
2320844.420703.0709017983141.329098201703
2419823.120427.7105387518-604.610538751777
2518594.619187.6464059516-593.046405951647
2621350.620402.1123060290948.487693971049
2718574.118701.7832551014-127.683255101415
2818924.218106.8066629616817.393337038418
2917343.418313.5953243079-970.19532430786
3019961.219193.4277419228767.77225807718
3119932.120618.2251429621-686.125142962074
3219464.619820.9775410593-356.377541059292
3316165.416446.7969745951-281.396974595102
3417574.917836.6881933604-261.788193360386
3519795.419820.2895430274-24.8895430273701
3619439.518509.5887296809929.911270319125
371717018105.7001218756-935.700121875617
3821072.419828.61590771191243.78409228813
3917751.818007.3881015012-255.588101501237
4017515.517013.24813612502.25186388
4118040.317658.9158594778381.384140522226
4219090.118347.8897718033742.210228196713
4317746.520033.7795646843-2287.27956468431
4419202.119284.2771553123-82.1771553123166
4515141.614622.1420145867519.457985413325
4616258.116537.0001502463-278.900150246286
4718586.519389.5492934443-803.04929344432
4817209.417003.0783583433206.321641656701
4917838.716464.43803571091374.26196428912
5019123.518885.4967667868238.003233213188
5116583.616888.4817455114-304.881745511361
5215991.216668.9484354220-677.748435422016
5316704.415393.89611411681310.50388588316
5417420.417258.4177768425161.98222315755
551787218493.660106971-621.660106970993
5617823.218407.2329301534-584.03293015336






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.877398664266250.2452026714674990.122601335733749
220.9676789006666520.06464219866669630.0323210993333481
230.9474269290885620.1051461418228760.0525730709114378
240.9402029817259080.1195940365481830.0597970182740916
250.9002187966738020.1995624066523950.0997812033261976
260.9235787468048650.1528425063902690.0764212531951347
270.8846692851420180.2306614297159640.115330714857982
280.8501594752605940.2996810494788110.149840524739406
290.9157553261716170.1684893476567660.0842446738283828
300.943265611524870.1134687769502620.0567343884751308
310.9669617782229360.06607644355412840.0330382217770642
320.9520026815575480.09599463688490370.0479973184424518
330.9556841176951250.08863176460974980.0443158823048749
340.9644753861880980.07104922762380330.0355246138119017
350.9887954131351930.02240917372961460.0112045868648073

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.87739866426625 & 0.245202671467499 & 0.122601335733749 \tabularnewline
22 & 0.967678900666652 & 0.0646421986666963 & 0.0323210993333481 \tabularnewline
23 & 0.947426929088562 & 0.105146141822876 & 0.0525730709114378 \tabularnewline
24 & 0.940202981725908 & 0.119594036548183 & 0.0597970182740916 \tabularnewline
25 & 0.900218796673802 & 0.199562406652395 & 0.0997812033261976 \tabularnewline
26 & 0.923578746804865 & 0.152842506390269 & 0.0764212531951347 \tabularnewline
27 & 0.884669285142018 & 0.230661429715964 & 0.115330714857982 \tabularnewline
28 & 0.850159475260594 & 0.299681049478811 & 0.149840524739406 \tabularnewline
29 & 0.915755326171617 & 0.168489347656766 & 0.0842446738283828 \tabularnewline
30 & 0.94326561152487 & 0.113468776950262 & 0.0567343884751308 \tabularnewline
31 & 0.966961778222936 & 0.0660764435541284 & 0.0330382217770642 \tabularnewline
32 & 0.952002681557548 & 0.0959946368849037 & 0.0479973184424518 \tabularnewline
33 & 0.955684117695125 & 0.0886317646097498 & 0.0443158823048749 \tabularnewline
34 & 0.964475386188098 & 0.0710492276238033 & 0.0355246138119017 \tabularnewline
35 & 0.988795413135193 & 0.0224091737296146 & 0.0112045868648073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57392&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]21[/C][C]0.87739866426625[/C][C]0.245202671467499[/C][C]0.122601335733749[/C][/ROW]
[ROW][C]22[/C][C]0.967678900666652[/C][C]0.0646421986666963[/C][C]0.0323210993333481[/C][/ROW]
[ROW][C]23[/C][C]0.947426929088562[/C][C]0.105146141822876[/C][C]0.0525730709114378[/C][/ROW]
[ROW][C]24[/C][C]0.940202981725908[/C][C]0.119594036548183[/C][C]0.0597970182740916[/C][/ROW]
[ROW][C]25[/C][C]0.900218796673802[/C][C]0.199562406652395[/C][C]0.0997812033261976[/C][/ROW]
[ROW][C]26[/C][C]0.923578746804865[/C][C]0.152842506390269[/C][C]0.0764212531951347[/C][/ROW]
[ROW][C]27[/C][C]0.884669285142018[/C][C]0.230661429715964[/C][C]0.115330714857982[/C][/ROW]
[ROW][C]28[/C][C]0.850159475260594[/C][C]0.299681049478811[/C][C]0.149840524739406[/C][/ROW]
[ROW][C]29[/C][C]0.915755326171617[/C][C]0.168489347656766[/C][C]0.0842446738283828[/C][/ROW]
[ROW][C]30[/C][C]0.94326561152487[/C][C]0.113468776950262[/C][C]0.0567343884751308[/C][/ROW]
[ROW][C]31[/C][C]0.966961778222936[/C][C]0.0660764435541284[/C][C]0.0330382217770642[/C][/ROW]
[ROW][C]32[/C][C]0.952002681557548[/C][C]0.0959946368849037[/C][C]0.0479973184424518[/C][/ROW]
[ROW][C]33[/C][C]0.955684117695125[/C][C]0.0886317646097498[/C][C]0.0443158823048749[/C][/ROW]
[ROW][C]34[/C][C]0.964475386188098[/C][C]0.0710492276238033[/C][C]0.0355246138119017[/C][/ROW]
[ROW][C]35[/C][C]0.988795413135193[/C][C]0.0224091737296146[/C][C]0.0112045868648073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57392&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57392&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.877398664266250.2452026714674990.122601335733749
220.9676789006666520.06464219866669630.0323210993333481
230.9474269290885620.1051461418228760.0525730709114378
240.9402029817259080.1195940365481830.0597970182740916
250.9002187966738020.1995624066523950.0997812033261976
260.9235787468048650.1528425063902690.0764212531951347
270.8846692851420180.2306614297159640.115330714857982
280.8501594752605940.2996810494788110.149840524739406
290.9157553261716170.1684893476567660.0842446738283828
300.943265611524870.1134687769502620.0567343884751308
310.9669617782229360.06607644355412840.0330382217770642
320.9520026815575480.09599463688490370.0479973184424518
330.9556841176951250.08863176460974980.0443158823048749
340.9644753861880980.07104922762380330.0355246138119017
350.9887954131351930.02240917372961460.0112045868648073






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0666666666666667NOK
10% type I error level60.4NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0666666666666667 & NOK \tabularnewline
10% type I error level & 6 & 0.4 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57392&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.4[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57392&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57392&T=6

As an alternative you can also use a QR Code:  
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 level10.0666666666666667NOK
10% type I error level60.4NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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')
}