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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 computationThu, 19 Nov 2009 02:58:07 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t12586247617rng5o4w12ni3kr.htm/, Retrieved Fri, 29 Mar 2024 01:10:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57676, Retrieved Fri, 29 Mar 2024 01:10:24 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-19 09:25:14] [69400782d28359bd00f6a8e8fb9347a1]
-    D        [Multiple Regression] [] [2009-11-19 09:58:07] [a1151e037da67acc5ce4bbcb8804d7f1] [Current]
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Dataseries X:
3499	1	4164	3186
4145	1	3499	3902
3796	1	4145	4164
3711	1	3796	3499
3949	1	3711	4145
3740	1	3949	3796
3243	1	3740	3711
4407	1	3243	3949
4814	1	4407	3740
3908	1	4814	3243
5250	1	3908	4407
3937	1	5250	4814
4004	1	3937	3908
5560	1	4004	5250
3922	1	5560	3937
3759	1	3922	4004
4138	1	3759	5560
4634	1	4138	3922
3996	1	4634	3759
4308	1	3996	4138
4143	0	4308	4634
4429	0	4143	3996
5219	0	4429	4308
4929	0	5219	4143
5755	0	4929	4429
5592	0	5755	5219
4163	0	5592	4929
4962	0	4163	5755
5208	0	4962	5592
4755	0	5208	4163
4491	0	4755	4962
5732	0	4491	5208
5731	0	5732	4755
5040	0	5731	4491
6102	0	5040	5732
4904	0	6102	5731
5369	0	4904	5040
5578	0	5369	6102
4619	0	5578	4904
4731	0	4619	5369
5011	0	4731	5578
5299	0	5011	4619
4146	0	5299	4731
4625	0	4146	5011
4736	0	4625	5299
4219	0	4736	4146
5116	0	4219	4625
4205	0	5116	4736
4121	0	4205	4219
5103	1	4121	5116
4300	1	5103	4205
4578	1	4300	4121
3809	1	4578	5103
5526	1	3809	4300
4247	1	5526	4578
3830	1	4247	3809
4394	1	3830	5526




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=57676&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=57676&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57676&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 873.552019317555 -166.509677507681X[t] + 0.336497568874567Y1[t] + 0.386622532227597Y3[t] + 679.099448674866M1[t] + 947.051054819987M2[t] -37.8228504198242M3[t] + 453.097384497474M4[t] + 216.144363232371M5[t] + 960.49461647157M6[t] -0.897572909612111M7[t] + 785.141475967574M8[t] + 607.148401348361M9[t] + 435.889731644429M10[t] + 1304.84169438637M11[t] -1.33397701611762t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  873.552019317555 -166.509677507681X[t] +  0.336497568874567Y1[t] +  0.386622532227597Y3[t] +  679.099448674866M1[t] +  947.051054819987M2[t] -37.8228504198242M3[t] +  453.097384497474M4[t] +  216.144363232371M5[t] +  960.49461647157M6[t] -0.897572909612111M7[t] +  785.141475967574M8[t] +  607.148401348361M9[t] +  435.889731644429M10[t] +  1304.84169438637M11[t] -1.33397701611762t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57676&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  873.552019317555 -166.509677507681X[t] +  0.336497568874567Y1[t] +  0.386622532227597Y3[t] +  679.099448674866M1[t] +  947.051054819987M2[t] -37.8228504198242M3[t] +  453.097384497474M4[t] +  216.144363232371M5[t] +  960.49461647157M6[t] -0.897572909612111M7[t] +  785.141475967574M8[t] +  607.148401348361M9[t] +  435.889731644429M10[t] +  1304.84169438637M11[t] -1.33397701611762t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57676&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57676&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 873.552019317555 -166.509677507681X[t] + 0.336497568874567Y1[t] + 0.386622532227597Y3[t] + 679.099448674866M1[t] + 947.051054819987M2[t] -37.8228504198242M3[t] + 453.097384497474M4[t] + 216.144363232371M5[t] + 960.49461647157M6[t] -0.897572909612111M7[t] + 785.141475967574M8[t] + 607.148401348361M9[t] + 435.889731644429M10[t] + 1304.84169438637M11[t] -1.33397701611762t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)873.552019317555990.0459260.88230.382740.19137
X-166.509677507681175.658568-0.94790.3487260.174363
Y10.3364975688745670.1378512.4410.0190510.009526
Y30.3866225322275970.1446512.67280.0107470.005373
M1679.099448674866322.0126882.10890.0411040.020552
M2947.051054819987317.2346692.98530.004760.00238
M3-37.8228504198242294.395804-0.12850.89840.4492
M4453.097384497474326.2728941.38870.172420.08621
M5216.144363232371329.5252120.65590.5155360.257768
M6960.49461647157318.8982823.01190.0044330.002216
M7-0.897572909612111302.394262-0.0030.9976460.498823
M8785.141475967574338.213862.32140.0253120.012656
M9607.148401348361310.3111051.95660.0572310.028616
M10435.889731644429336.8808681.29390.2029420.101471
M111304.84169438637336.3070553.87990.0003710.000186
t-1.333977016117624.302301-0.31010.7580850.379042

\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) & 873.552019317555 & 990.045926 & 0.8823 & 0.38274 & 0.19137 \tabularnewline
X & -166.509677507681 & 175.658568 & -0.9479 & 0.348726 & 0.174363 \tabularnewline
Y1 & 0.336497568874567 & 0.137851 & 2.441 & 0.019051 & 0.009526 \tabularnewline
Y3 & 0.386622532227597 & 0.144651 & 2.6728 & 0.010747 & 0.005373 \tabularnewline
M1 & 679.099448674866 & 322.012688 & 2.1089 & 0.041104 & 0.020552 \tabularnewline
M2 & 947.051054819987 & 317.234669 & 2.9853 & 0.00476 & 0.00238 \tabularnewline
M3 & -37.8228504198242 & 294.395804 & -0.1285 & 0.8984 & 0.4492 \tabularnewline
M4 & 453.097384497474 & 326.272894 & 1.3887 & 0.17242 & 0.08621 \tabularnewline
M5 & 216.144363232371 & 329.525212 & 0.6559 & 0.515536 & 0.257768 \tabularnewline
M6 & 960.49461647157 & 318.898282 & 3.0119 & 0.004433 & 0.002216 \tabularnewline
M7 & -0.897572909612111 & 302.394262 & -0.003 & 0.997646 & 0.498823 \tabularnewline
M8 & 785.141475967574 & 338.21386 & 2.3214 & 0.025312 & 0.012656 \tabularnewline
M9 & 607.148401348361 & 310.311105 & 1.9566 & 0.057231 & 0.028616 \tabularnewline
M10 & 435.889731644429 & 336.880868 & 1.2939 & 0.202942 & 0.101471 \tabularnewline
M11 & 1304.84169438637 & 336.307055 & 3.8799 & 0.000371 & 0.000186 \tabularnewline
t & -1.33397701611762 & 4.302301 & -0.3101 & 0.758085 & 0.379042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57676&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]873.552019317555[/C][C]990.045926[/C][C]0.8823[/C][C]0.38274[/C][C]0.19137[/C][/ROW]
[ROW][C]X[/C][C]-166.509677507681[/C][C]175.658568[/C][C]-0.9479[/C][C]0.348726[/C][C]0.174363[/C][/ROW]
[ROW][C]Y1[/C][C]0.336497568874567[/C][C]0.137851[/C][C]2.441[/C][C]0.019051[/C][C]0.009526[/C][/ROW]
[ROW][C]Y3[/C][C]0.386622532227597[/C][C]0.144651[/C][C]2.6728[/C][C]0.010747[/C][C]0.005373[/C][/ROW]
[ROW][C]M1[/C][C]679.099448674866[/C][C]322.012688[/C][C]2.1089[/C][C]0.041104[/C][C]0.020552[/C][/ROW]
[ROW][C]M2[/C][C]947.051054819987[/C][C]317.234669[/C][C]2.9853[/C][C]0.00476[/C][C]0.00238[/C][/ROW]
[ROW][C]M3[/C][C]-37.8228504198242[/C][C]294.395804[/C][C]-0.1285[/C][C]0.8984[/C][C]0.4492[/C][/ROW]
[ROW][C]M4[/C][C]453.097384497474[/C][C]326.272894[/C][C]1.3887[/C][C]0.17242[/C][C]0.08621[/C][/ROW]
[ROW][C]M5[/C][C]216.144363232371[/C][C]329.525212[/C][C]0.6559[/C][C]0.515536[/C][C]0.257768[/C][/ROW]
[ROW][C]M6[/C][C]960.49461647157[/C][C]318.898282[/C][C]3.0119[/C][C]0.004433[/C][C]0.002216[/C][/ROW]
[ROW][C]M7[/C][C]-0.897572909612111[/C][C]302.394262[/C][C]-0.003[/C][C]0.997646[/C][C]0.498823[/C][/ROW]
[ROW][C]M8[/C][C]785.141475967574[/C][C]338.21386[/C][C]2.3214[/C][C]0.025312[/C][C]0.012656[/C][/ROW]
[ROW][C]M9[/C][C]607.148401348361[/C][C]310.311105[/C][C]1.9566[/C][C]0.057231[/C][C]0.028616[/C][/ROW]
[ROW][C]M10[/C][C]435.889731644429[/C][C]336.880868[/C][C]1.2939[/C][C]0.202942[/C][C]0.101471[/C][/ROW]
[ROW][C]M11[/C][C]1304.84169438637[/C][C]336.307055[/C][C]3.8799[/C][C]0.000371[/C][C]0.000186[/C][/ROW]
[ROW][C]t[/C][C]-1.33397701611762[/C][C]4.302301[/C][C]-0.3101[/C][C]0.758085[/C][C]0.379042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57676&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57676&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)873.552019317555990.0459260.88230.382740.19137
X-166.509677507681175.658568-0.94790.3487260.174363
Y10.3364975688745670.1378512.4410.0190510.009526
Y30.3866225322275970.1446512.67280.0107470.005373
M1679.099448674866322.0126882.10890.0411040.020552
M2947.051054819987317.2346692.98530.004760.00238
M3-37.8228504198242294.395804-0.12850.89840.4492
M4453.097384497474326.2728941.38870.172420.08621
M5216.144363232371329.5252120.65590.5155360.257768
M6960.49461647157318.8982823.01190.0044330.002216
M7-0.897572909612111302.394262-0.0030.9976460.498823
M8785.141475967574338.213862.32140.0253120.012656
M9607.148401348361310.3111051.95660.0572310.028616
M10435.889731644429336.8808681.29390.2029420.101471
M111304.84169438637336.3070553.87990.0003710.000186
t-1.333977016117624.302301-0.31010.7580850.379042







Multiple Linear Regression - Regression Statistics
Multiple R0.833103487567017
R-squared0.694061420996326
Adjusted R-squared0.582132672580348
F-TEST (value)6.20092184375079
F-TEST (DF numerator)15
F-TEST (DF denominator)41
p-value1.60352825517851e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation432.709464877265
Sum Squared Residuals7676736.72076912

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.833103487567017 \tabularnewline
R-squared & 0.694061420996326 \tabularnewline
Adjusted R-squared & 0.582132672580348 \tabularnewline
F-TEST (value) & 6.20092184375079 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 1.60352825517851e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 432.709464877265 \tabularnewline
Sum Squared Residuals & 7676736.72076912 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57676&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.833103487567017[/C][/ROW]
[ROW][C]R-squared[/C][C]0.694061420996326[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.582132672580348[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.20092184375079[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]41[/C][/ROW]
[ROW][C]p-value[/C][C]1.60352825517851e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]432.709464877265[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7676736.72076912[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57676&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57676&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 R0.833103487567017
R-squared0.694061420996326
Adjusted R-squared0.582132672580348
F-TEST (value)6.20092184375079
F-TEST (DF numerator)15
F-TEST (DF denominator)41
p-value1.60352825517851e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation432.709464877265
Sum Squared Residuals7676736.72076912







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
134994017.76307793944-518.763077939445
241454337.43155684182-192.431556841816
337963669.89620752249126.103792477511
437113784.9408299551-73.9408299550955
539493767.80969413856181.190305861437
637404455.98112800636-715.981128006359
732433390.06405447493-147.064054474930
844074099.54599727551307.454002724493
948144231.09800657461582.901993425395
1039084003.30847186939-95.3084718693893
1152505016.08828770778233.911712292218
1239374318.84772435159-381.847724351590
1340044204.51187387983-200.511873879830
1455605012.52227837286547.477721627136
1539224042.26922847093-120.269228470927
1637594006.57617821482-247.576178214815
1741384315.02473635318-177.024736353180
1846344552.2858833909281.7141166090809
1939963693.44303840231302.556961597694
2043084409.99260103566-101.992601035661
2141434693.92724438177-550.927244381765
2244294219.14732323621209.852676763795
2352195303.62984371517-84.6298437151687
2449294199.49453390603729.505466093969
2557554890.24975480825864.750245191752
2655925740.24617628745-148.246176287446
2741634587.06865595896-424.068655958959
2849624915.1500995583846.8499004416228
2952084882.70518605484325.294813945162
3047555156.01626566783-401.016265667827
3144914349.7681038202141.231896179802
3257325140.74696042637591.25303957363
3357315203.87338466528527.126615334724
3450404928.87589186827111.124108131734
3561026043.7726199962258.2273800037847
3649045094.57074420629-190.570744206286
3753695102.05595858403266.944041415966
3855785935.73808646542-357.738086465419
3946194556.6844024956162.3155975043864
4047314903.34896933192-172.348969331916
4150114783.55380800021227.446191999785
4252995250.0183951019148.9816048980898
4341464427.50525214998-281.505252149977
4446254932.4829361224-307.482936122397
4547365025.68550925953-289.685509259532
4642194444.66831302614-225.66831302614
4751165323.50924858083-207.509248580835
4842054362.08699753609-157.086997536093
4941214533.41933478844-412.419334788443
5051034952.06190203246150.938097967544
5143003944.08150555201355.918494447989
5245784130.9839229398447.016077060204
5338094365.90657545320-556.906575453204
5455264539.69832783298986.301672167016
5542474262.21955115259-15.2195511525879
5638304319.23150514006-489.231505140064
5743944663.41585511882-269.415855118822

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3499 & 4017.76307793944 & -518.763077939445 \tabularnewline
2 & 4145 & 4337.43155684182 & -192.431556841816 \tabularnewline
3 & 3796 & 3669.89620752249 & 126.103792477511 \tabularnewline
4 & 3711 & 3784.9408299551 & -73.9408299550955 \tabularnewline
5 & 3949 & 3767.80969413856 & 181.190305861437 \tabularnewline
6 & 3740 & 4455.98112800636 & -715.981128006359 \tabularnewline
7 & 3243 & 3390.06405447493 & -147.064054474930 \tabularnewline
8 & 4407 & 4099.54599727551 & 307.454002724493 \tabularnewline
9 & 4814 & 4231.09800657461 & 582.901993425395 \tabularnewline
10 & 3908 & 4003.30847186939 & -95.3084718693893 \tabularnewline
11 & 5250 & 5016.08828770778 & 233.911712292218 \tabularnewline
12 & 3937 & 4318.84772435159 & -381.847724351590 \tabularnewline
13 & 4004 & 4204.51187387983 & -200.511873879830 \tabularnewline
14 & 5560 & 5012.52227837286 & 547.477721627136 \tabularnewline
15 & 3922 & 4042.26922847093 & -120.269228470927 \tabularnewline
16 & 3759 & 4006.57617821482 & -247.576178214815 \tabularnewline
17 & 4138 & 4315.02473635318 & -177.024736353180 \tabularnewline
18 & 4634 & 4552.28588339092 & 81.7141166090809 \tabularnewline
19 & 3996 & 3693.44303840231 & 302.556961597694 \tabularnewline
20 & 4308 & 4409.99260103566 & -101.992601035661 \tabularnewline
21 & 4143 & 4693.92724438177 & -550.927244381765 \tabularnewline
22 & 4429 & 4219.14732323621 & 209.852676763795 \tabularnewline
23 & 5219 & 5303.62984371517 & -84.6298437151687 \tabularnewline
24 & 4929 & 4199.49453390603 & 729.505466093969 \tabularnewline
25 & 5755 & 4890.24975480825 & 864.750245191752 \tabularnewline
26 & 5592 & 5740.24617628745 & -148.246176287446 \tabularnewline
27 & 4163 & 4587.06865595896 & -424.068655958959 \tabularnewline
28 & 4962 & 4915.15009955838 & 46.8499004416228 \tabularnewline
29 & 5208 & 4882.70518605484 & 325.294813945162 \tabularnewline
30 & 4755 & 5156.01626566783 & -401.016265667827 \tabularnewline
31 & 4491 & 4349.7681038202 & 141.231896179802 \tabularnewline
32 & 5732 & 5140.74696042637 & 591.25303957363 \tabularnewline
33 & 5731 & 5203.87338466528 & 527.126615334724 \tabularnewline
34 & 5040 & 4928.87589186827 & 111.124108131734 \tabularnewline
35 & 6102 & 6043.77261999622 & 58.2273800037847 \tabularnewline
36 & 4904 & 5094.57074420629 & -190.570744206286 \tabularnewline
37 & 5369 & 5102.05595858403 & 266.944041415966 \tabularnewline
38 & 5578 & 5935.73808646542 & -357.738086465419 \tabularnewline
39 & 4619 & 4556.68440249561 & 62.3155975043864 \tabularnewline
40 & 4731 & 4903.34896933192 & -172.348969331916 \tabularnewline
41 & 5011 & 4783.55380800021 & 227.446191999785 \tabularnewline
42 & 5299 & 5250.01839510191 & 48.9816048980898 \tabularnewline
43 & 4146 & 4427.50525214998 & -281.505252149977 \tabularnewline
44 & 4625 & 4932.4829361224 & -307.482936122397 \tabularnewline
45 & 4736 & 5025.68550925953 & -289.685509259532 \tabularnewline
46 & 4219 & 4444.66831302614 & -225.66831302614 \tabularnewline
47 & 5116 & 5323.50924858083 & -207.509248580835 \tabularnewline
48 & 4205 & 4362.08699753609 & -157.086997536093 \tabularnewline
49 & 4121 & 4533.41933478844 & -412.419334788443 \tabularnewline
50 & 5103 & 4952.06190203246 & 150.938097967544 \tabularnewline
51 & 4300 & 3944.08150555201 & 355.918494447989 \tabularnewline
52 & 4578 & 4130.9839229398 & 447.016077060204 \tabularnewline
53 & 3809 & 4365.90657545320 & -556.906575453204 \tabularnewline
54 & 5526 & 4539.69832783298 & 986.301672167016 \tabularnewline
55 & 4247 & 4262.21955115259 & -15.2195511525879 \tabularnewline
56 & 3830 & 4319.23150514006 & -489.231505140064 \tabularnewline
57 & 4394 & 4663.41585511882 & -269.415855118822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57676&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]3499[/C][C]4017.76307793944[/C][C]-518.763077939445[/C][/ROW]
[ROW][C]2[/C][C]4145[/C][C]4337.43155684182[/C][C]-192.431556841816[/C][/ROW]
[ROW][C]3[/C][C]3796[/C][C]3669.89620752249[/C][C]126.103792477511[/C][/ROW]
[ROW][C]4[/C][C]3711[/C][C]3784.9408299551[/C][C]-73.9408299550955[/C][/ROW]
[ROW][C]5[/C][C]3949[/C][C]3767.80969413856[/C][C]181.190305861437[/C][/ROW]
[ROW][C]6[/C][C]3740[/C][C]4455.98112800636[/C][C]-715.981128006359[/C][/ROW]
[ROW][C]7[/C][C]3243[/C][C]3390.06405447493[/C][C]-147.064054474930[/C][/ROW]
[ROW][C]8[/C][C]4407[/C][C]4099.54599727551[/C][C]307.454002724493[/C][/ROW]
[ROW][C]9[/C][C]4814[/C][C]4231.09800657461[/C][C]582.901993425395[/C][/ROW]
[ROW][C]10[/C][C]3908[/C][C]4003.30847186939[/C][C]-95.3084718693893[/C][/ROW]
[ROW][C]11[/C][C]5250[/C][C]5016.08828770778[/C][C]233.911712292218[/C][/ROW]
[ROW][C]12[/C][C]3937[/C][C]4318.84772435159[/C][C]-381.847724351590[/C][/ROW]
[ROW][C]13[/C][C]4004[/C][C]4204.51187387983[/C][C]-200.511873879830[/C][/ROW]
[ROW][C]14[/C][C]5560[/C][C]5012.52227837286[/C][C]547.477721627136[/C][/ROW]
[ROW][C]15[/C][C]3922[/C][C]4042.26922847093[/C][C]-120.269228470927[/C][/ROW]
[ROW][C]16[/C][C]3759[/C][C]4006.57617821482[/C][C]-247.576178214815[/C][/ROW]
[ROW][C]17[/C][C]4138[/C][C]4315.02473635318[/C][C]-177.024736353180[/C][/ROW]
[ROW][C]18[/C][C]4634[/C][C]4552.28588339092[/C][C]81.7141166090809[/C][/ROW]
[ROW][C]19[/C][C]3996[/C][C]3693.44303840231[/C][C]302.556961597694[/C][/ROW]
[ROW][C]20[/C][C]4308[/C][C]4409.99260103566[/C][C]-101.992601035661[/C][/ROW]
[ROW][C]21[/C][C]4143[/C][C]4693.92724438177[/C][C]-550.927244381765[/C][/ROW]
[ROW][C]22[/C][C]4429[/C][C]4219.14732323621[/C][C]209.852676763795[/C][/ROW]
[ROW][C]23[/C][C]5219[/C][C]5303.62984371517[/C][C]-84.6298437151687[/C][/ROW]
[ROW][C]24[/C][C]4929[/C][C]4199.49453390603[/C][C]729.505466093969[/C][/ROW]
[ROW][C]25[/C][C]5755[/C][C]4890.24975480825[/C][C]864.750245191752[/C][/ROW]
[ROW][C]26[/C][C]5592[/C][C]5740.24617628745[/C][C]-148.246176287446[/C][/ROW]
[ROW][C]27[/C][C]4163[/C][C]4587.06865595896[/C][C]-424.068655958959[/C][/ROW]
[ROW][C]28[/C][C]4962[/C][C]4915.15009955838[/C][C]46.8499004416228[/C][/ROW]
[ROW][C]29[/C][C]5208[/C][C]4882.70518605484[/C][C]325.294813945162[/C][/ROW]
[ROW][C]30[/C][C]4755[/C][C]5156.01626566783[/C][C]-401.016265667827[/C][/ROW]
[ROW][C]31[/C][C]4491[/C][C]4349.7681038202[/C][C]141.231896179802[/C][/ROW]
[ROW][C]32[/C][C]5732[/C][C]5140.74696042637[/C][C]591.25303957363[/C][/ROW]
[ROW][C]33[/C][C]5731[/C][C]5203.87338466528[/C][C]527.126615334724[/C][/ROW]
[ROW][C]34[/C][C]5040[/C][C]4928.87589186827[/C][C]111.124108131734[/C][/ROW]
[ROW][C]35[/C][C]6102[/C][C]6043.77261999622[/C][C]58.2273800037847[/C][/ROW]
[ROW][C]36[/C][C]4904[/C][C]5094.57074420629[/C][C]-190.570744206286[/C][/ROW]
[ROW][C]37[/C][C]5369[/C][C]5102.05595858403[/C][C]266.944041415966[/C][/ROW]
[ROW][C]38[/C][C]5578[/C][C]5935.73808646542[/C][C]-357.738086465419[/C][/ROW]
[ROW][C]39[/C][C]4619[/C][C]4556.68440249561[/C][C]62.3155975043864[/C][/ROW]
[ROW][C]40[/C][C]4731[/C][C]4903.34896933192[/C][C]-172.348969331916[/C][/ROW]
[ROW][C]41[/C][C]5011[/C][C]4783.55380800021[/C][C]227.446191999785[/C][/ROW]
[ROW][C]42[/C][C]5299[/C][C]5250.01839510191[/C][C]48.9816048980898[/C][/ROW]
[ROW][C]43[/C][C]4146[/C][C]4427.50525214998[/C][C]-281.505252149977[/C][/ROW]
[ROW][C]44[/C][C]4625[/C][C]4932.4829361224[/C][C]-307.482936122397[/C][/ROW]
[ROW][C]45[/C][C]4736[/C][C]5025.68550925953[/C][C]-289.685509259532[/C][/ROW]
[ROW][C]46[/C][C]4219[/C][C]4444.66831302614[/C][C]-225.66831302614[/C][/ROW]
[ROW][C]47[/C][C]5116[/C][C]5323.50924858083[/C][C]-207.509248580835[/C][/ROW]
[ROW][C]48[/C][C]4205[/C][C]4362.08699753609[/C][C]-157.086997536093[/C][/ROW]
[ROW][C]49[/C][C]4121[/C][C]4533.41933478844[/C][C]-412.419334788443[/C][/ROW]
[ROW][C]50[/C][C]5103[/C][C]4952.06190203246[/C][C]150.938097967544[/C][/ROW]
[ROW][C]51[/C][C]4300[/C][C]3944.08150555201[/C][C]355.918494447989[/C][/ROW]
[ROW][C]52[/C][C]4578[/C][C]4130.9839229398[/C][C]447.016077060204[/C][/ROW]
[ROW][C]53[/C][C]3809[/C][C]4365.90657545320[/C][C]-556.906575453204[/C][/ROW]
[ROW][C]54[/C][C]5526[/C][C]4539.69832783298[/C][C]986.301672167016[/C][/ROW]
[ROW][C]55[/C][C]4247[/C][C]4262.21955115259[/C][C]-15.2195511525879[/C][/ROW]
[ROW][C]56[/C][C]3830[/C][C]4319.23150514006[/C][C]-489.231505140064[/C][/ROW]
[ROW][C]57[/C][C]4394[/C][C]4663.41585511882[/C][C]-269.415855118822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57676&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57676&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
134994017.76307793944-518.763077939445
241454337.43155684182-192.431556841816
337963669.89620752249126.103792477511
437113784.9408299551-73.9408299550955
539493767.80969413856181.190305861437
637404455.98112800636-715.981128006359
732433390.06405447493-147.064054474930
844074099.54599727551307.454002724493
948144231.09800657461582.901993425395
1039084003.30847186939-95.3084718693893
1152505016.08828770778233.911712292218
1239374318.84772435159-381.847724351590
1340044204.51187387983-200.511873879830
1455605012.52227837286547.477721627136
1539224042.26922847093-120.269228470927
1637594006.57617821482-247.576178214815
1741384315.02473635318-177.024736353180
1846344552.2858833909281.7141166090809
1939963693.44303840231302.556961597694
2043084409.99260103566-101.992601035661
2141434693.92724438177-550.927244381765
2244294219.14732323621209.852676763795
2352195303.62984371517-84.6298437151687
2449294199.49453390603729.505466093969
2557554890.24975480825864.750245191752
2655925740.24617628745-148.246176287446
2741634587.06865595896-424.068655958959
2849624915.1500995583846.8499004416228
2952084882.70518605484325.294813945162
3047555156.01626566783-401.016265667827
3144914349.7681038202141.231896179802
3257325140.74696042637591.25303957363
3357315203.87338466528527.126615334724
3450404928.87589186827111.124108131734
3561026043.7726199962258.2273800037847
3649045094.57074420629-190.570744206286
3753695102.05595858403266.944041415966
3855785935.73808646542-357.738086465419
3946194556.6844024956162.3155975043864
4047314903.34896933192-172.348969331916
4150114783.55380800021227.446191999785
4252995250.0183951019148.9816048980898
4341464427.50525214998-281.505252149977
4446254932.4829361224-307.482936122397
4547365025.68550925953-289.685509259532
4642194444.66831302614-225.66831302614
4751165323.50924858083-207.509248580835
4842054362.08699753609-157.086997536093
4941214533.41933478844-412.419334788443
5051034952.06190203246150.938097967544
5143003944.08150555201355.918494447989
5245784130.9839229398447.016077060204
5338094365.90657545320-556.906575453204
5455264539.69832783298986.301672167016
5542474262.21955115259-15.2195511525879
5638304319.23150514006-489.231505140064
5743944663.41585511882-269.415855118822







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.6216029541288690.7567940917422620.378397045871131
200.5804285455051220.8391429089897560.419571454494878
210.5006035440216450.998792911956710.499396455978355
220.5733219671800180.8533560656399640.426678032819982
230.4961340464059570.9922680928119130.503865953594043
240.5314902101435210.9370195797129580.468509789856479
250.7911492771177470.4177014457645070.208850722882253
260.715392938265480.5692141234690390.284607061734520
270.7494803540357190.5010392919285620.250519645964281
280.7065689482120760.5868621035758470.293431051787924
290.6141123236998290.7717753526003420.385887676300171
300.8724650515343450.255069896931310.127534948465655
310.9450837651795270.1098324696409450.0549162348204727
320.9123284586862170.1753430826275660.0876715413137831
330.873502925512730.2529941489745410.126497074487271
340.7899136814114120.4201726371771750.210086318588588
350.6765581855951680.6468836288096630.323441814404832
360.6062704432190570.7874591135618860.393729556780943
370.4490041353218660.8980082706437330.550995864678134
380.3395777609290350.6791555218580690.660422239070965

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.621602954128869 & 0.756794091742262 & 0.378397045871131 \tabularnewline
20 & 0.580428545505122 & 0.839142908989756 & 0.419571454494878 \tabularnewline
21 & 0.500603544021645 & 0.99879291195671 & 0.499396455978355 \tabularnewline
22 & 0.573321967180018 & 0.853356065639964 & 0.426678032819982 \tabularnewline
23 & 0.496134046405957 & 0.992268092811913 & 0.503865953594043 \tabularnewline
24 & 0.531490210143521 & 0.937019579712958 & 0.468509789856479 \tabularnewline
25 & 0.791149277117747 & 0.417701445764507 & 0.208850722882253 \tabularnewline
26 & 0.71539293826548 & 0.569214123469039 & 0.284607061734520 \tabularnewline
27 & 0.749480354035719 & 0.501039291928562 & 0.250519645964281 \tabularnewline
28 & 0.706568948212076 & 0.586862103575847 & 0.293431051787924 \tabularnewline
29 & 0.614112323699829 & 0.771775352600342 & 0.385887676300171 \tabularnewline
30 & 0.872465051534345 & 0.25506989693131 & 0.127534948465655 \tabularnewline
31 & 0.945083765179527 & 0.109832469640945 & 0.0549162348204727 \tabularnewline
32 & 0.912328458686217 & 0.175343082627566 & 0.0876715413137831 \tabularnewline
33 & 0.87350292551273 & 0.252994148974541 & 0.126497074487271 \tabularnewline
34 & 0.789913681411412 & 0.420172637177175 & 0.210086318588588 \tabularnewline
35 & 0.676558185595168 & 0.646883628809663 & 0.323441814404832 \tabularnewline
36 & 0.606270443219057 & 0.787459113561886 & 0.393729556780943 \tabularnewline
37 & 0.449004135321866 & 0.898008270643733 & 0.550995864678134 \tabularnewline
38 & 0.339577760929035 & 0.679155521858069 & 0.660422239070965 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57676&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]19[/C][C]0.621602954128869[/C][C]0.756794091742262[/C][C]0.378397045871131[/C][/ROW]
[ROW][C]20[/C][C]0.580428545505122[/C][C]0.839142908989756[/C][C]0.419571454494878[/C][/ROW]
[ROW][C]21[/C][C]0.500603544021645[/C][C]0.99879291195671[/C][C]0.499396455978355[/C][/ROW]
[ROW][C]22[/C][C]0.573321967180018[/C][C]0.853356065639964[/C][C]0.426678032819982[/C][/ROW]
[ROW][C]23[/C][C]0.496134046405957[/C][C]0.992268092811913[/C][C]0.503865953594043[/C][/ROW]
[ROW][C]24[/C][C]0.531490210143521[/C][C]0.937019579712958[/C][C]0.468509789856479[/C][/ROW]
[ROW][C]25[/C][C]0.791149277117747[/C][C]0.417701445764507[/C][C]0.208850722882253[/C][/ROW]
[ROW][C]26[/C][C]0.71539293826548[/C][C]0.569214123469039[/C][C]0.284607061734520[/C][/ROW]
[ROW][C]27[/C][C]0.749480354035719[/C][C]0.501039291928562[/C][C]0.250519645964281[/C][/ROW]
[ROW][C]28[/C][C]0.706568948212076[/C][C]0.586862103575847[/C][C]0.293431051787924[/C][/ROW]
[ROW][C]29[/C][C]0.614112323699829[/C][C]0.771775352600342[/C][C]0.385887676300171[/C][/ROW]
[ROW][C]30[/C][C]0.872465051534345[/C][C]0.25506989693131[/C][C]0.127534948465655[/C][/ROW]
[ROW][C]31[/C][C]0.945083765179527[/C][C]0.109832469640945[/C][C]0.0549162348204727[/C][/ROW]
[ROW][C]32[/C][C]0.912328458686217[/C][C]0.175343082627566[/C][C]0.0876715413137831[/C][/ROW]
[ROW][C]33[/C][C]0.87350292551273[/C][C]0.252994148974541[/C][C]0.126497074487271[/C][/ROW]
[ROW][C]34[/C][C]0.789913681411412[/C][C]0.420172637177175[/C][C]0.210086318588588[/C][/ROW]
[ROW][C]35[/C][C]0.676558185595168[/C][C]0.646883628809663[/C][C]0.323441814404832[/C][/ROW]
[ROW][C]36[/C][C]0.606270443219057[/C][C]0.787459113561886[/C][C]0.393729556780943[/C][/ROW]
[ROW][C]37[/C][C]0.449004135321866[/C][C]0.898008270643733[/C][C]0.550995864678134[/C][/ROW]
[ROW][C]38[/C][C]0.339577760929035[/C][C]0.679155521858069[/C][C]0.660422239070965[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57676&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57676&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
190.6216029541288690.7567940917422620.378397045871131
200.5804285455051220.8391429089897560.419571454494878
210.5006035440216450.998792911956710.499396455978355
220.5733219671800180.8533560656399640.426678032819982
230.4961340464059570.9922680928119130.503865953594043
240.5314902101435210.9370195797129580.468509789856479
250.7911492771177470.4177014457645070.208850722882253
260.715392938265480.5692141234690390.284607061734520
270.7494803540357190.5010392919285620.250519645964281
280.7065689482120760.5868621035758470.293431051787924
290.6141123236998290.7717753526003420.385887676300171
300.8724650515343450.255069896931310.127534948465655
310.9450837651795270.1098324696409450.0549162348204727
320.9123284586862170.1753430826275660.0876715413137831
330.873502925512730.2529941489745410.126497074487271
340.7899136814114120.4201726371771750.210086318588588
350.6765581855951680.6468836288096630.323441814404832
360.6062704432190570.7874591135618860.393729556780943
370.4490041353218660.8980082706437330.550995864678134
380.3395777609290350.6791555218580690.660422239070965







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=57676&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=57676&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57676&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')
}