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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 05:01:44 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258718601pt3rpvsrip8wqld.htm/, Retrieved Fri, 29 Mar 2024 13:15:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58057, Retrieved Fri, 29 Mar 2024 13:15:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [link5] [2009-11-20 12:01:44] [454b2df2fae01897bad5ff38ed3cc924] [Current]
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Dataseries X:
1,6	0,55	1,6	1,6
1,6	0,56	1,6	1,6
1,61	0,56	1,6	1,6
1,61	0,56	1,61	1,6
1,62	0,56	1,61	1,61
1,63	0,56	1,62	1,61
1,63	0,55	1,63	1,62
1,63	0,56	1,63	1,63
1,63	0,55	1,63	1,63
1,63	0,55	1,63	1,63
1,64	0,56	1,63	1,63
1,64	0,55	1,64	1,63
1,64	0,55	1,64	1,64
1,65	0,55	1,64	1,64
1,65	0,55	1,65	1,64
1,65	0,53	1,65	1,65
1,65	0,53	1,65	1,65
1,65	0,53	1,65	1,65
1,66	0,53	1,65	1,65
1,67	0,54	1,66	1,65
1,68	0,54	1,67	1,66
1,68	0,54	1,68	1,67
1,68	0,55	1,68	1,68
1,68	0,55	1,68	1,68
1,69	0,54	1,68	1,68
1,7	0,55	1,69	1,68
1,7	0,56	1,7	1,69
1,71	0,58	1,7	1,7
1,73	0,59	1,71	1,7
1,73	0,6	1,73	1,71
1,73	0,6	1,73	1,73
1,74	0,6	1,73	1,73
1,74	0,59	1,74	1,73
1,74	0,6	1,74	1,74
1,75	0,6	1,74	1,74
1,78	0,62	1,75	1,74
1,82	0,65	1,78	1,75
1,83	0,68	1,82	1,78
1,84	0,73	1,83	1,82
1,85	0,78	1,84	1,83
1,86	0,78	1,85	1,84
1,86	0,82	1,86	1,85
1,87	0,82	1,86	1,86
1,87	0,81	1,87	1,86
1,87	0,83	1,87	1,87
1,87	0,85	1,87	1,87
1,87	0,86	1,87	1,87
1,87	0,85	1,87	1,87
1,87	0,85	1,87	1,87
1,88	0,82	1,87	1,87
1,88	0,8	1,88	1,87
1,87	0,81	1,88	1,88
1,87	0,8	1,87	1,88
1,87	0,8	1,87	1,87
1,87	0,8	1,87	1,87
1,87	0,8	1,87	1,87
1,87	0,79	1,87	1,87




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58057&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58057&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58057&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.190434608909453 + 0.0267422785774102X[t] + 1.41632685691026Y1[t] -0.543206943585124Y2[t] + 0.00256413973686938M1[t] -0.00102366937728268M2[t] -0.00371296611758367M3[t] -0.00393033843172351M4[t] + 0.00283302253654549M5[t] -0.00625541430959138M6[t] -0.00126574100982694M7[t] -0.00247493219970349M8[t] -0.00449074038810014M9[t] -0.00542218980057909M10[t] + 0.000158447376802211M11[t] + 0.000576813092250926t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.190434608909453 +  0.0267422785774102X[t] +  1.41632685691026Y1[t] -0.543206943585124Y2[t] +  0.00256413973686938M1[t] -0.00102366937728268M2[t] -0.00371296611758367M3[t] -0.00393033843172351M4[t] +  0.00283302253654549M5[t] -0.00625541430959138M6[t] -0.00126574100982694M7[t] -0.00247493219970349M8[t] -0.00449074038810014M9[t] -0.00542218980057909M10[t] +  0.000158447376802211M11[t] +  0.000576813092250926t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58057&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.190434608909453 +  0.0267422785774102X[t] +  1.41632685691026Y1[t] -0.543206943585124Y2[t] +  0.00256413973686938M1[t] -0.00102366937728268M2[t] -0.00371296611758367M3[t] -0.00393033843172351M4[t] +  0.00283302253654549M5[t] -0.00625541430959138M6[t] -0.00126574100982694M7[t] -0.00247493219970349M8[t] -0.00449074038810014M9[t] -0.00542218980057909M10[t] +  0.000158447376802211M11[t] +  0.000576813092250926t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58057&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58057&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] = + 0.190434608909453 + 0.0267422785774102X[t] + 1.41632685691026Y1[t] -0.543206943585124Y2[t] + 0.00256413973686938M1[t] -0.00102366937728268M2[t] -0.00371296611758367M3[t] -0.00393033843172351M4[t] + 0.00283302253654549M5[t] -0.00625541430959138M6[t] -0.00126574100982694M7[t] -0.00247493219970349M8[t] -0.00449074038810014M9[t] -0.00542218980057909M10[t] + 0.000158447376802211M11[t] + 0.000576813092250926t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1904346089094530.1845361.0320.3081380.154069
X0.02674227857741020.0554130.48260.6319450.315973
Y11.416326856910260.13642210.381900
Y2-0.5432069435851240.190211-2.85580.0067080.003354
M10.002564139736869380.005090.50370.6171560.308578
M2-0.001023669377282680.005139-0.19920.8431080.421554
M3-0.003712966117583670.005151-0.72090.4750880.237544
M4-0.003930338431723510.00513-0.76610.4480050.224002
M50.002833022536545490.0051440.55070.5848070.292403
M6-0.006255414309591380.0051-1.22660.2269780.113489
M7-0.001265741009826940.005141-0.24620.8067340.403367
M8-0.002474932199703490.005092-0.48610.6294960.314748
M9-0.004490740388100140.005112-0.87850.3847980.192399
M10-0.005422189800579090.005442-0.99630.3249530.162477
M110.0001584473768022110.0054020.02930.9767440.488372
t0.0005768130922509260.0004821.19780.2378660.118933

\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) & 0.190434608909453 & 0.184536 & 1.032 & 0.308138 & 0.154069 \tabularnewline
X & 0.0267422785774102 & 0.055413 & 0.4826 & 0.631945 & 0.315973 \tabularnewline
Y1 & 1.41632685691026 & 0.136422 & 10.3819 & 0 & 0 \tabularnewline
Y2 & -0.543206943585124 & 0.190211 & -2.8558 & 0.006708 & 0.003354 \tabularnewline
M1 & 0.00256413973686938 & 0.00509 & 0.5037 & 0.617156 & 0.308578 \tabularnewline
M2 & -0.00102366937728268 & 0.005139 & -0.1992 & 0.843108 & 0.421554 \tabularnewline
M3 & -0.00371296611758367 & 0.005151 & -0.7209 & 0.475088 & 0.237544 \tabularnewline
M4 & -0.00393033843172351 & 0.00513 & -0.7661 & 0.448005 & 0.224002 \tabularnewline
M5 & 0.00283302253654549 & 0.005144 & 0.5507 & 0.584807 & 0.292403 \tabularnewline
M6 & -0.00625541430959138 & 0.0051 & -1.2266 & 0.226978 & 0.113489 \tabularnewline
M7 & -0.00126574100982694 & 0.005141 & -0.2462 & 0.806734 & 0.403367 \tabularnewline
M8 & -0.00247493219970349 & 0.005092 & -0.4861 & 0.629496 & 0.314748 \tabularnewline
M9 & -0.00449074038810014 & 0.005112 & -0.8785 & 0.384798 & 0.192399 \tabularnewline
M10 & -0.00542218980057909 & 0.005442 & -0.9963 & 0.324953 & 0.162477 \tabularnewline
M11 & 0.000158447376802211 & 0.005402 & 0.0293 & 0.976744 & 0.488372 \tabularnewline
t & 0.000576813092250926 & 0.000482 & 1.1978 & 0.237866 & 0.118933 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58057&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]0.190434608909453[/C][C]0.184536[/C][C]1.032[/C][C]0.308138[/C][C]0.154069[/C][/ROW]
[ROW][C]X[/C][C]0.0267422785774102[/C][C]0.055413[/C][C]0.4826[/C][C]0.631945[/C][C]0.315973[/C][/ROW]
[ROW][C]Y1[/C][C]1.41632685691026[/C][C]0.136422[/C][C]10.3819[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.543206943585124[/C][C]0.190211[/C][C]-2.8558[/C][C]0.006708[/C][C]0.003354[/C][/ROW]
[ROW][C]M1[/C][C]0.00256413973686938[/C][C]0.00509[/C][C]0.5037[/C][C]0.617156[/C][C]0.308578[/C][/ROW]
[ROW][C]M2[/C][C]-0.00102366937728268[/C][C]0.005139[/C][C]-0.1992[/C][C]0.843108[/C][C]0.421554[/C][/ROW]
[ROW][C]M3[/C][C]-0.00371296611758367[/C][C]0.005151[/C][C]-0.7209[/C][C]0.475088[/C][C]0.237544[/C][/ROW]
[ROW][C]M4[/C][C]-0.00393033843172351[/C][C]0.00513[/C][C]-0.7661[/C][C]0.448005[/C][C]0.224002[/C][/ROW]
[ROW][C]M5[/C][C]0.00283302253654549[/C][C]0.005144[/C][C]0.5507[/C][C]0.584807[/C][C]0.292403[/C][/ROW]
[ROW][C]M6[/C][C]-0.00625541430959138[/C][C]0.0051[/C][C]-1.2266[/C][C]0.226978[/C][C]0.113489[/C][/ROW]
[ROW][C]M7[/C][C]-0.00126574100982694[/C][C]0.005141[/C][C]-0.2462[/C][C]0.806734[/C][C]0.403367[/C][/ROW]
[ROW][C]M8[/C][C]-0.00247493219970349[/C][C]0.005092[/C][C]-0.4861[/C][C]0.629496[/C][C]0.314748[/C][/ROW]
[ROW][C]M9[/C][C]-0.00449074038810014[/C][C]0.005112[/C][C]-0.8785[/C][C]0.384798[/C][C]0.192399[/C][/ROW]
[ROW][C]M10[/C][C]-0.00542218980057909[/C][C]0.005442[/C][C]-0.9963[/C][C]0.324953[/C][C]0.162477[/C][/ROW]
[ROW][C]M11[/C][C]0.000158447376802211[/C][C]0.005402[/C][C]0.0293[/C][C]0.976744[/C][C]0.488372[/C][/ROW]
[ROW][C]t[/C][C]0.000576813092250926[/C][C]0.000482[/C][C]1.1978[/C][C]0.237866[/C][C]0.118933[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58057&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58057&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)0.1904346089094530.1845361.0320.3081380.154069
X0.02674227857741020.0554130.48260.6319450.315973
Y11.416326856910260.13642210.381900
Y2-0.5432069435851240.190211-2.85580.0067080.003354
M10.002564139736869380.005090.50370.6171560.308578
M2-0.001023669377282680.005139-0.19920.8431080.421554
M3-0.003712966117583670.005151-0.72090.4750880.237544
M4-0.003930338431723510.00513-0.76610.4480050.224002
M50.002833022536545490.0051440.55070.5848070.292403
M6-0.006255414309591380.0051-1.22660.2269780.113489
M7-0.001265741009826940.005141-0.24620.8067340.403367
M8-0.002474932199703490.005092-0.48610.6294960.314748
M9-0.004490740388100140.005112-0.87850.3847980.192399
M10-0.005422189800579090.005442-0.99630.3249530.162477
M110.0001584473768022110.0054020.02930.9767440.488372
t0.0005768130922509260.0004821.19780.2378660.118933







Multiple Linear Regression - Regression Statistics
Multiple R0.99797045554514
R-squared0.995945030140976
Adjusted R-squared0.994461504582796
F-TEST (value)671.336617458153
F-TEST (DF numerator)15
F-TEST (DF denominator)41
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0075530743384303
Sum Squared Residuals0.00233900621043602

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99797045554514 \tabularnewline
R-squared & 0.995945030140976 \tabularnewline
Adjusted R-squared & 0.994461504582796 \tabularnewline
F-TEST (value) & 671.336617458153 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 41 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0075530743384303 \tabularnewline
Sum Squared Residuals & 0.00233900621043602 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58057&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99797045554514[/C][/ROW]
[ROW][C]R-squared[/C][C]0.995945030140976[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.994461504582796[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]671.336617458153[/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]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0075530743384303[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.00233900621043602[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58057&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58057&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 R0.99797045554514
R-squared0.995945030140976
Adjusted R-squared0.994461504582796
F-TEST (value)671.336617458153
F-TEST (DF numerator)15
F-TEST (DF denominator)41
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0075530743384303
Sum Squared Residuals0.00233900621043602







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.61.60527567627637-0.00527567627636534
21.61.60253210304024-0.00253210304023638
31.611.600419619392190.00958038060781355
41.611.6149423287394-0.00494232873939999
51.621.616850433364070.00314956663593134
61.631.622502078179290.00749792182071448
71.631.63653234091878-0.00653234091877781
81.631.63073531617108-0.00073531617107516
91.631.629028898289160.000971101710844663
101.631.628674261968930.00132573803107269
111.641.635099135024330.00490086497566637
121.641.64941334652311-0.00941334652311083
131.641.64712222991638-0.0071222299163799
141.651.644111233894480.00588876610552125
151.651.65616201881553-0.00616201881553127
161.651.65055454458624-0.000554544586242875
171.651.65789471864676-0.0078947186467628
181.651.649383094892880.000616905107123137
191.661.654949581284890.00505041871510778
201.671.668747894542140.00125210545785672
211.681.676040098579250.0039599014207511
221.681.68441666139227-0.00441666139227222
231.681.68540946501183-0.00540946501182732
241.681.68582783072728-0.00582783072727603
251.691.688701360770620.00129863922937779
261.71.70012105610360-0.000121056103597770
271.71.70700719437457-0.00700719437457315
281.711.702469411288380.00753058871161881
291.731.724240276703780.0057597232962222
301.731.73889054343802-0.00889054343801988
311.731.73359289095833-0.00359289095833275
321.741.732960512860710.00703948713929288
331.741.74541736354789-0.00541736354788989
341.741.739898080577580.000101919422415291
351.751.746055530847220.00394446915278307
361.781.761172010703320.0188279892966836
371.821.802172968161220.0178270318387845
381.831.84032110646549-0.0103211064654933
391.841.831980727572010.00801927242798865
401.851.842408481412240.00759151858775569
411.861.858479854606020.00152014539398443
421.861.859769121128480.000230878871522624
431.871.859903538084640.0100964619153585
441.871.87316700577034-0.00316700577034434
451.871.866830786809900.00316921319010442
461.871.867010996061220.00298900393878422
471.871.87343586911662-0.00343586911662212
481.871.87358681204630-0.00358681204629672
491.871.87672776487542-0.00672776487541703
501.881.872914500496190.00708549950380622
511.881.8844304398457-0.00443043984569778
521.871.87962523397373-0.00962523397373163
531.871.87253471667938-0.00253471667937518
541.871.869455162361340.000544837638659635
551.871.87502164875336-0.00502164875335573
561.871.87438927065573-0.00438927065573010
571.871.87268285277381-0.00268285277381029

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.6 & 1.60527567627637 & -0.00527567627636534 \tabularnewline
2 & 1.6 & 1.60253210304024 & -0.00253210304023638 \tabularnewline
3 & 1.61 & 1.60041961939219 & 0.00958038060781355 \tabularnewline
4 & 1.61 & 1.6149423287394 & -0.00494232873939999 \tabularnewline
5 & 1.62 & 1.61685043336407 & 0.00314956663593134 \tabularnewline
6 & 1.63 & 1.62250207817929 & 0.00749792182071448 \tabularnewline
7 & 1.63 & 1.63653234091878 & -0.00653234091877781 \tabularnewline
8 & 1.63 & 1.63073531617108 & -0.00073531617107516 \tabularnewline
9 & 1.63 & 1.62902889828916 & 0.000971101710844663 \tabularnewline
10 & 1.63 & 1.62867426196893 & 0.00132573803107269 \tabularnewline
11 & 1.64 & 1.63509913502433 & 0.00490086497566637 \tabularnewline
12 & 1.64 & 1.64941334652311 & -0.00941334652311083 \tabularnewline
13 & 1.64 & 1.64712222991638 & -0.0071222299163799 \tabularnewline
14 & 1.65 & 1.64411123389448 & 0.00588876610552125 \tabularnewline
15 & 1.65 & 1.65616201881553 & -0.00616201881553127 \tabularnewline
16 & 1.65 & 1.65055454458624 & -0.000554544586242875 \tabularnewline
17 & 1.65 & 1.65789471864676 & -0.0078947186467628 \tabularnewline
18 & 1.65 & 1.64938309489288 & 0.000616905107123137 \tabularnewline
19 & 1.66 & 1.65494958128489 & 0.00505041871510778 \tabularnewline
20 & 1.67 & 1.66874789454214 & 0.00125210545785672 \tabularnewline
21 & 1.68 & 1.67604009857925 & 0.0039599014207511 \tabularnewline
22 & 1.68 & 1.68441666139227 & -0.00441666139227222 \tabularnewline
23 & 1.68 & 1.68540946501183 & -0.00540946501182732 \tabularnewline
24 & 1.68 & 1.68582783072728 & -0.00582783072727603 \tabularnewline
25 & 1.69 & 1.68870136077062 & 0.00129863922937779 \tabularnewline
26 & 1.7 & 1.70012105610360 & -0.000121056103597770 \tabularnewline
27 & 1.7 & 1.70700719437457 & -0.00700719437457315 \tabularnewline
28 & 1.71 & 1.70246941128838 & 0.00753058871161881 \tabularnewline
29 & 1.73 & 1.72424027670378 & 0.0057597232962222 \tabularnewline
30 & 1.73 & 1.73889054343802 & -0.00889054343801988 \tabularnewline
31 & 1.73 & 1.73359289095833 & -0.00359289095833275 \tabularnewline
32 & 1.74 & 1.73296051286071 & 0.00703948713929288 \tabularnewline
33 & 1.74 & 1.74541736354789 & -0.00541736354788989 \tabularnewline
34 & 1.74 & 1.73989808057758 & 0.000101919422415291 \tabularnewline
35 & 1.75 & 1.74605553084722 & 0.00394446915278307 \tabularnewline
36 & 1.78 & 1.76117201070332 & 0.0188279892966836 \tabularnewline
37 & 1.82 & 1.80217296816122 & 0.0178270318387845 \tabularnewline
38 & 1.83 & 1.84032110646549 & -0.0103211064654933 \tabularnewline
39 & 1.84 & 1.83198072757201 & 0.00801927242798865 \tabularnewline
40 & 1.85 & 1.84240848141224 & 0.00759151858775569 \tabularnewline
41 & 1.86 & 1.85847985460602 & 0.00152014539398443 \tabularnewline
42 & 1.86 & 1.85976912112848 & 0.000230878871522624 \tabularnewline
43 & 1.87 & 1.85990353808464 & 0.0100964619153585 \tabularnewline
44 & 1.87 & 1.87316700577034 & -0.00316700577034434 \tabularnewline
45 & 1.87 & 1.86683078680990 & 0.00316921319010442 \tabularnewline
46 & 1.87 & 1.86701099606122 & 0.00298900393878422 \tabularnewline
47 & 1.87 & 1.87343586911662 & -0.00343586911662212 \tabularnewline
48 & 1.87 & 1.87358681204630 & -0.00358681204629672 \tabularnewline
49 & 1.87 & 1.87672776487542 & -0.00672776487541703 \tabularnewline
50 & 1.88 & 1.87291450049619 & 0.00708549950380622 \tabularnewline
51 & 1.88 & 1.8844304398457 & -0.00443043984569778 \tabularnewline
52 & 1.87 & 1.87962523397373 & -0.00962523397373163 \tabularnewline
53 & 1.87 & 1.87253471667938 & -0.00253471667937518 \tabularnewline
54 & 1.87 & 1.86945516236134 & 0.000544837638659635 \tabularnewline
55 & 1.87 & 1.87502164875336 & -0.00502164875335573 \tabularnewline
56 & 1.87 & 1.87438927065573 & -0.00438927065573010 \tabularnewline
57 & 1.87 & 1.87268285277381 & -0.00268285277381029 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58057&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]1.6[/C][C]1.60527567627637[/C][C]-0.00527567627636534[/C][/ROW]
[ROW][C]2[/C][C]1.6[/C][C]1.60253210304024[/C][C]-0.00253210304023638[/C][/ROW]
[ROW][C]3[/C][C]1.61[/C][C]1.60041961939219[/C][C]0.00958038060781355[/C][/ROW]
[ROW][C]4[/C][C]1.61[/C][C]1.6149423287394[/C][C]-0.00494232873939999[/C][/ROW]
[ROW][C]5[/C][C]1.62[/C][C]1.61685043336407[/C][C]0.00314956663593134[/C][/ROW]
[ROW][C]6[/C][C]1.63[/C][C]1.62250207817929[/C][C]0.00749792182071448[/C][/ROW]
[ROW][C]7[/C][C]1.63[/C][C]1.63653234091878[/C][C]-0.00653234091877781[/C][/ROW]
[ROW][C]8[/C][C]1.63[/C][C]1.63073531617108[/C][C]-0.00073531617107516[/C][/ROW]
[ROW][C]9[/C][C]1.63[/C][C]1.62902889828916[/C][C]0.000971101710844663[/C][/ROW]
[ROW][C]10[/C][C]1.63[/C][C]1.62867426196893[/C][C]0.00132573803107269[/C][/ROW]
[ROW][C]11[/C][C]1.64[/C][C]1.63509913502433[/C][C]0.00490086497566637[/C][/ROW]
[ROW][C]12[/C][C]1.64[/C][C]1.64941334652311[/C][C]-0.00941334652311083[/C][/ROW]
[ROW][C]13[/C][C]1.64[/C][C]1.64712222991638[/C][C]-0.0071222299163799[/C][/ROW]
[ROW][C]14[/C][C]1.65[/C][C]1.64411123389448[/C][C]0.00588876610552125[/C][/ROW]
[ROW][C]15[/C][C]1.65[/C][C]1.65616201881553[/C][C]-0.00616201881553127[/C][/ROW]
[ROW][C]16[/C][C]1.65[/C][C]1.65055454458624[/C][C]-0.000554544586242875[/C][/ROW]
[ROW][C]17[/C][C]1.65[/C][C]1.65789471864676[/C][C]-0.0078947186467628[/C][/ROW]
[ROW][C]18[/C][C]1.65[/C][C]1.64938309489288[/C][C]0.000616905107123137[/C][/ROW]
[ROW][C]19[/C][C]1.66[/C][C]1.65494958128489[/C][C]0.00505041871510778[/C][/ROW]
[ROW][C]20[/C][C]1.67[/C][C]1.66874789454214[/C][C]0.00125210545785672[/C][/ROW]
[ROW][C]21[/C][C]1.68[/C][C]1.67604009857925[/C][C]0.0039599014207511[/C][/ROW]
[ROW][C]22[/C][C]1.68[/C][C]1.68441666139227[/C][C]-0.00441666139227222[/C][/ROW]
[ROW][C]23[/C][C]1.68[/C][C]1.68540946501183[/C][C]-0.00540946501182732[/C][/ROW]
[ROW][C]24[/C][C]1.68[/C][C]1.68582783072728[/C][C]-0.00582783072727603[/C][/ROW]
[ROW][C]25[/C][C]1.69[/C][C]1.68870136077062[/C][C]0.00129863922937779[/C][/ROW]
[ROW][C]26[/C][C]1.7[/C][C]1.70012105610360[/C][C]-0.000121056103597770[/C][/ROW]
[ROW][C]27[/C][C]1.7[/C][C]1.70700719437457[/C][C]-0.00700719437457315[/C][/ROW]
[ROW][C]28[/C][C]1.71[/C][C]1.70246941128838[/C][C]0.00753058871161881[/C][/ROW]
[ROW][C]29[/C][C]1.73[/C][C]1.72424027670378[/C][C]0.0057597232962222[/C][/ROW]
[ROW][C]30[/C][C]1.73[/C][C]1.73889054343802[/C][C]-0.00889054343801988[/C][/ROW]
[ROW][C]31[/C][C]1.73[/C][C]1.73359289095833[/C][C]-0.00359289095833275[/C][/ROW]
[ROW][C]32[/C][C]1.74[/C][C]1.73296051286071[/C][C]0.00703948713929288[/C][/ROW]
[ROW][C]33[/C][C]1.74[/C][C]1.74541736354789[/C][C]-0.00541736354788989[/C][/ROW]
[ROW][C]34[/C][C]1.74[/C][C]1.73989808057758[/C][C]0.000101919422415291[/C][/ROW]
[ROW][C]35[/C][C]1.75[/C][C]1.74605553084722[/C][C]0.00394446915278307[/C][/ROW]
[ROW][C]36[/C][C]1.78[/C][C]1.76117201070332[/C][C]0.0188279892966836[/C][/ROW]
[ROW][C]37[/C][C]1.82[/C][C]1.80217296816122[/C][C]0.0178270318387845[/C][/ROW]
[ROW][C]38[/C][C]1.83[/C][C]1.84032110646549[/C][C]-0.0103211064654933[/C][/ROW]
[ROW][C]39[/C][C]1.84[/C][C]1.83198072757201[/C][C]0.00801927242798865[/C][/ROW]
[ROW][C]40[/C][C]1.85[/C][C]1.84240848141224[/C][C]0.00759151858775569[/C][/ROW]
[ROW][C]41[/C][C]1.86[/C][C]1.85847985460602[/C][C]0.00152014539398443[/C][/ROW]
[ROW][C]42[/C][C]1.86[/C][C]1.85976912112848[/C][C]0.000230878871522624[/C][/ROW]
[ROW][C]43[/C][C]1.87[/C][C]1.85990353808464[/C][C]0.0100964619153585[/C][/ROW]
[ROW][C]44[/C][C]1.87[/C][C]1.87316700577034[/C][C]-0.00316700577034434[/C][/ROW]
[ROW][C]45[/C][C]1.87[/C][C]1.86683078680990[/C][C]0.00316921319010442[/C][/ROW]
[ROW][C]46[/C][C]1.87[/C][C]1.86701099606122[/C][C]0.00298900393878422[/C][/ROW]
[ROW][C]47[/C][C]1.87[/C][C]1.87343586911662[/C][C]-0.00343586911662212[/C][/ROW]
[ROW][C]48[/C][C]1.87[/C][C]1.87358681204630[/C][C]-0.00358681204629672[/C][/ROW]
[ROW][C]49[/C][C]1.87[/C][C]1.87672776487542[/C][C]-0.00672776487541703[/C][/ROW]
[ROW][C]50[/C][C]1.88[/C][C]1.87291450049619[/C][C]0.00708549950380622[/C][/ROW]
[ROW][C]51[/C][C]1.88[/C][C]1.8844304398457[/C][C]-0.00443043984569778[/C][/ROW]
[ROW][C]52[/C][C]1.87[/C][C]1.87962523397373[/C][C]-0.00962523397373163[/C][/ROW]
[ROW][C]53[/C][C]1.87[/C][C]1.87253471667938[/C][C]-0.00253471667937518[/C][/ROW]
[ROW][C]54[/C][C]1.87[/C][C]1.86945516236134[/C][C]0.000544837638659635[/C][/ROW]
[ROW][C]55[/C][C]1.87[/C][C]1.87502164875336[/C][C]-0.00502164875335573[/C][/ROW]
[ROW][C]56[/C][C]1.87[/C][C]1.87438927065573[/C][C]-0.00438927065573010[/C][/ROW]
[ROW][C]57[/C][C]1.87[/C][C]1.87268285277381[/C][C]-0.00268285277381029[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58057&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58057&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
11.61.60527567627637-0.00527567627636534
21.61.60253210304024-0.00253210304023638
31.611.600419619392190.00958038060781355
41.611.6149423287394-0.00494232873939999
51.621.616850433364070.00314956663593134
61.631.622502078179290.00749792182071448
71.631.63653234091878-0.00653234091877781
81.631.63073531617108-0.00073531617107516
91.631.629028898289160.000971101710844663
101.631.628674261968930.00132573803107269
111.641.635099135024330.00490086497566637
121.641.64941334652311-0.00941334652311083
131.641.64712222991638-0.0071222299163799
141.651.644111233894480.00588876610552125
151.651.65616201881553-0.00616201881553127
161.651.65055454458624-0.000554544586242875
171.651.65789471864676-0.0078947186467628
181.651.649383094892880.000616905107123137
191.661.654949581284890.00505041871510778
201.671.668747894542140.00125210545785672
211.681.676040098579250.0039599014207511
221.681.68441666139227-0.00441666139227222
231.681.68540946501183-0.00540946501182732
241.681.68582783072728-0.00582783072727603
251.691.688701360770620.00129863922937779
261.71.70012105610360-0.000121056103597770
271.71.70700719437457-0.00700719437457315
281.711.702469411288380.00753058871161881
291.731.724240276703780.0057597232962222
301.731.73889054343802-0.00889054343801988
311.731.73359289095833-0.00359289095833275
321.741.732960512860710.00703948713929288
331.741.74541736354789-0.00541736354788989
341.741.739898080577580.000101919422415291
351.751.746055530847220.00394446915278307
361.781.761172010703320.0188279892966836
371.821.802172968161220.0178270318387845
381.831.84032110646549-0.0103211064654933
391.841.831980727572010.00801927242798865
401.851.842408481412240.00759151858775569
411.861.858479854606020.00152014539398443
421.861.859769121128480.000230878871522624
431.871.859903538084640.0100964619153585
441.871.87316700577034-0.00316700577034434
451.871.866830786809900.00316921319010442
461.871.867010996061220.00298900393878422
471.871.87343586911662-0.00343586911662212
481.871.87358681204630-0.00358681204629672
491.871.87672776487542-0.00672776487541703
501.881.872914500496190.00708549950380622
511.881.8844304398457-0.00443043984569778
521.871.87962523397373-0.00962523397373163
531.871.87253471667938-0.00253471667937518
541.871.869455162361340.000544837638659635
551.871.87502164875336-0.00502164875335573
561.871.87438927065573-0.00438927065573010
571.871.87268285277381-0.00268285277381029







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.3354702598033810.6709405196067610.66452974019662
200.2739790083046370.5479580166092730.726020991695363
210.1924497022643320.3848994045286630.807550297735668
220.1120620413265750.2241240826531500.887937958673425
230.06569327179007660.1313865435801530.934306728209923
240.04394837478623940.08789674957247890.95605162521376
250.03298238037921050.06596476075842090.96701761962079
260.01577314488722660.03154628977445330.984226855112773
270.01308870011657560.02617740023315120.986911299883424
280.005755228574435370.01151045714887070.994244771425565
290.002713253792047090.005426507584094180.997286746207953
300.00411716996372490.00823433992744980.995882830036275
310.004298788386724320.008597576773448650.995701211613276
320.002665061788772940.005330123577545870.997334938211227
330.01221680005087410.02443360010174830.987783199949126
340.03184724241586550.0636944848317310.968152757584134
350.07116661251852040.1423332250370410.92883338748148
360.3186427997241390.6372855994482770.681357200275861
370.8242536550555780.3514926898888440.175746344944422
380.7131932739401030.5736134521197930.286806726059897

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.335470259803381 & 0.670940519606761 & 0.66452974019662 \tabularnewline
20 & 0.273979008304637 & 0.547958016609273 & 0.726020991695363 \tabularnewline
21 & 0.192449702264332 & 0.384899404528663 & 0.807550297735668 \tabularnewline
22 & 0.112062041326575 & 0.224124082653150 & 0.887937958673425 \tabularnewline
23 & 0.0656932717900766 & 0.131386543580153 & 0.934306728209923 \tabularnewline
24 & 0.0439483747862394 & 0.0878967495724789 & 0.95605162521376 \tabularnewline
25 & 0.0329823803792105 & 0.0659647607584209 & 0.96701761962079 \tabularnewline
26 & 0.0157731448872266 & 0.0315462897744533 & 0.984226855112773 \tabularnewline
27 & 0.0130887001165756 & 0.0261774002331512 & 0.986911299883424 \tabularnewline
28 & 0.00575522857443537 & 0.0115104571488707 & 0.994244771425565 \tabularnewline
29 & 0.00271325379204709 & 0.00542650758409418 & 0.997286746207953 \tabularnewline
30 & 0.0041171699637249 & 0.0082343399274498 & 0.995882830036275 \tabularnewline
31 & 0.00429878838672432 & 0.00859757677344865 & 0.995701211613276 \tabularnewline
32 & 0.00266506178877294 & 0.00533012357754587 & 0.997334938211227 \tabularnewline
33 & 0.0122168000508741 & 0.0244336001017483 & 0.987783199949126 \tabularnewline
34 & 0.0318472424158655 & 0.063694484831731 & 0.968152757584134 \tabularnewline
35 & 0.0711666125185204 & 0.142333225037041 & 0.92883338748148 \tabularnewline
36 & 0.318642799724139 & 0.637285599448277 & 0.681357200275861 \tabularnewline
37 & 0.824253655055578 & 0.351492689888844 & 0.175746344944422 \tabularnewline
38 & 0.713193273940103 & 0.573613452119793 & 0.286806726059897 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58057&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]0.335470259803381[/C][C]0.670940519606761[/C][C]0.66452974019662[/C][/ROW]
[ROW][C]20[/C][C]0.273979008304637[/C][C]0.547958016609273[/C][C]0.726020991695363[/C][/ROW]
[ROW][C]21[/C][C]0.192449702264332[/C][C]0.384899404528663[/C][C]0.807550297735668[/C][/ROW]
[ROW][C]22[/C][C]0.112062041326575[/C][C]0.224124082653150[/C][C]0.887937958673425[/C][/ROW]
[ROW][C]23[/C][C]0.0656932717900766[/C][C]0.131386543580153[/C][C]0.934306728209923[/C][/ROW]
[ROW][C]24[/C][C]0.0439483747862394[/C][C]0.0878967495724789[/C][C]0.95605162521376[/C][/ROW]
[ROW][C]25[/C][C]0.0329823803792105[/C][C]0.0659647607584209[/C][C]0.96701761962079[/C][/ROW]
[ROW][C]26[/C][C]0.0157731448872266[/C][C]0.0315462897744533[/C][C]0.984226855112773[/C][/ROW]
[ROW][C]27[/C][C]0.0130887001165756[/C][C]0.0261774002331512[/C][C]0.986911299883424[/C][/ROW]
[ROW][C]28[/C][C]0.00575522857443537[/C][C]0.0115104571488707[/C][C]0.994244771425565[/C][/ROW]
[ROW][C]29[/C][C]0.00271325379204709[/C][C]0.00542650758409418[/C][C]0.997286746207953[/C][/ROW]
[ROW][C]30[/C][C]0.0041171699637249[/C][C]0.0082343399274498[/C][C]0.995882830036275[/C][/ROW]
[ROW][C]31[/C][C]0.00429878838672432[/C][C]0.00859757677344865[/C][C]0.995701211613276[/C][/ROW]
[ROW][C]32[/C][C]0.00266506178877294[/C][C]0.00533012357754587[/C][C]0.997334938211227[/C][/ROW]
[ROW][C]33[/C][C]0.0122168000508741[/C][C]0.0244336001017483[/C][C]0.987783199949126[/C][/ROW]
[ROW][C]34[/C][C]0.0318472424158655[/C][C]0.063694484831731[/C][C]0.968152757584134[/C][/ROW]
[ROW][C]35[/C][C]0.0711666125185204[/C][C]0.142333225037041[/C][C]0.92883338748148[/C][/ROW]
[ROW][C]36[/C][C]0.318642799724139[/C][C]0.637285599448277[/C][C]0.681357200275861[/C][/ROW]
[ROW][C]37[/C][C]0.824253655055578[/C][C]0.351492689888844[/C][C]0.175746344944422[/C][/ROW]
[ROW][C]38[/C][C]0.713193273940103[/C][C]0.573613452119793[/C][C]0.286806726059897[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58057&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58057&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
190.3354702598033810.6709405196067610.66452974019662
200.2739790083046370.5479580166092730.726020991695363
210.1924497022643320.3848994045286630.807550297735668
220.1120620413265750.2241240826531500.887937958673425
230.06569327179007660.1313865435801530.934306728209923
240.04394837478623940.08789674957247890.95605162521376
250.03298238037921050.06596476075842090.96701761962079
260.01577314488722660.03154628977445330.984226855112773
270.01308870011657560.02617740023315120.986911299883424
280.005755228574435370.01151045714887070.994244771425565
290.002713253792047090.005426507584094180.997286746207953
300.00411716996372490.00823433992744980.995882830036275
310.004298788386724320.008597576773448650.995701211613276
320.002665061788772940.005330123577545870.997334938211227
330.01221680005087410.02443360010174830.987783199949126
340.03184724241586550.0636944848317310.968152757584134
350.07116661251852040.1423332250370410.92883338748148
360.3186427997241390.6372855994482770.681357200275861
370.8242536550555780.3514926898888440.175746344944422
380.7131932739401030.5736134521197930.286806726059897







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.2NOK
5% type I error level80.4NOK
10% type I error level110.55NOK

\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 & 4 & 0.2 & NOK \tabularnewline
5% type I error level & 8 & 0.4 & NOK \tabularnewline
10% type I error level & 11 & 0.55 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58057&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]4[/C][C]0.2[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.4[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.55[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58057&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58057&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 level40.2NOK
5% type I error level80.4NOK
10% type I error level110.55NOK



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')
}