Home » date » 2009 » Nov » 19 »

# ws 7

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Thu, 19 Nov 2009 11:07:30 -0700

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363.htm/, Retrieved Mon, 20 May 2013 03:21:22 +0000

Original text written by user:

IsPrivate?
No (this computation is public)

User-defined keywords:

System-generated keywords (parent):
t1258035301gehccu34anmb0m8 (pk = 56008)
Estimated Impact
53

Dataseries X:
» Textfile « » CSV « » Correlation Matrix « » Notched Boxplots «

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 4.14999041251723 -0.0152438597880977X[t] + 1.34171092585125Y1[t] -0.632193949965465Y2[t] + 0.0835740790049867M1[t] + 0.0525149196902091M2[t] -0.176803720858912M3[t] -0.0232878832478820M4[t] -0.105116917657922M5[t] -0.129479933541223M6[t] + 0.0778523659487016M7[t] + 0.310812223645426M8[t] -0.447340912981457M9[t] -0.0193029508698561M10[t] + 0.00728265666300871M11[t] -0.00840207262544193t + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 4.14999041251723 0.990631 4.1892 0.00015 7.5e-05 X -0.0152438597880977 0.004999 -3.0491 0.004058 0.002029 Y1 1.34171092585125 0.116031 11.5634 0 0 Y2 -0.632193949965465 0.118245 -5.3465 4e-06 2e-06 M1 0.0835740790049867 0.126953 0.6583 0.514113 0.257056 M2 0.0525149196902091 0.13346 0.3935 0.696049 0.348025 M3 -0.176803720858912 0.134934 -1.3103 0.197569 0.098784 M4 -0.0232878832478820 0.123139 -0.1891 0.850957 0.425478 M5 -0.105116917657922 0.11934 -0.8808 0.38368 0.19184 M6 -0.129479933541223 0.117649 -1.1006 0.277666 0.138833 M7 0.0778523659487016 0.118305 0.6581 0.514266 0.257133 M8 0.310812223645426 0.161086 1.9295 0.060786 0.030393 M9 -0.447340912981457 0.161306 -2.7732 0.00839 0.004195 M10 -0.0193029508698561 0.13103 -0.1473 0.883622 0.441811 M11 0.00728265666300871 0.128388 0.0567 0.955048 0.477524 t -0.00840207262544193 0.002576 -3.2615 0.002269 0.001135

 Multiple Linear Regression - Regression Statistics Multiple R 0.974568997085734 R-squared 0.949784730080693 Adjusted R-squared 0.930954003860953 F-TEST (value) 50.4380297922358 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.174779636784900 Sum Squared Residuals 1.22191685738646

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 8.3 8.22406007107107 0.0759399289289281 2 8.5 8.6668790122537 -0.166879012253704 3 8.6 8.6434274051257 -0.0434274051257002 4 8.5 8.67737136635616 -0.177371366356160 5 8.2 8.39889608761187 -0.198896087611866 6 8.1 8.01769080047143 0.0823091995285654 7 7.9 8.14405969752041 -0.244059697520411 8 8.6 8.54763995907805 0.0523600409219463 9 8.7 8.7232459236311 -0.0232459236311051 10 8.7 8.57842029628652 0.121579703713478 11 8.5 8.5791160155617 -0.0791160155616911 12 8.4 8.28746717120894 0.112532828791058 13 8.5 8.47533336732243 0.0246666326775733 14 8.7 8.64698209677317 0.0530179032268333 15 8.7 8.61133540181469 0.0886645981853136 16 8.6 8.43488897151953 0.165111028480468 17 8.5 8.32329133433085 0.176708665669152 18 8.3 8.13573331939899 0.164266680601011 19 8 8.07923601878905 -0.0792360187890467 20 8.2 8.42863282852502 -0.228632828525016 21 8.1 7.95391991774231 0.146080082257685 22 8.1 7.87514171195593 0.224858288044067 23 8 7.91233744847442 0.0876625515255819 24 7.9 7.86309110120229 0.0369088987977128 25 7.9 7.92981123512445 -0.0298112351244543 26 8 7.98405711775698 0.0159428822430233 27 8 7.88050749716754 0.119492502832462 28 7.9 7.77032923382655 0.129670766173451 29 8 7.6633047545743 0.336695245425706 30 7.7 7.7943936621134 -0.0943936621134074 31 7.2 7.44984753130667 -0.249847531306671 32 7.5 7.56668260325036 -0.0666826032503617 33 7.3 7.35867711896112 -0.0586771189611185 34 7 7.12519123299974 -0.125191232999737 35 7 6.81089799892892 0.189102001071084 36 7 7.07176145542226 -0.071761455422262 37 7.2 7.29327451576754 -0.0932745157675443 38 7.3 7.44441178407828 -0.144411784078277 39 7.1 7.17174056608907 -0.0717405660890719 40 6.8 6.95785380328929 -0.157853803289287 41 6.4 6.571731190767 -0.171731190766996 42 6.1 6.20718377669549 -0.107183776695492 43 6.5 6.20007602457482 0.299923975425178 44 7.7 7.50920706999654 0.190792930003459 45 7.9 7.96415703966546 -0.0641570396654613 46 7.5 7.72124675875781 -0.221246758757809 47 6.9 7.09764853703497 -0.197648537034974 48 6.6 6.67768027216651 -0.0776802721665084 49 6.9 6.8775208107145 0.0224791892854972 50 7.7 7.45766998913787 0.242330010862125 51 8 8.092989129803 -0.0929891298030035 52 8 7.95955662500847 0.0404433749915284 53 7.7 7.842776632716 -0.142776632715995 54 7.3 7.34499844132068 -0.0449984413206777 55 7.4 7.12678072780905 0.273219272190951 56 8.1 8.04783753915003 0.0521624608499723

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0579517813761658 0.115903562752332 0.942048218623834 20 0.633095328491959 0.733809343016082 0.366904671508041 21 0.489142159225657 0.978284318451313 0.510857840774343 22 0.378595869534654 0.757191739069307 0.621404130465346 23 0.26144365379909 0.52288730759818 0.73855634620091 24 0.166170751466174 0.332341502932348 0.833829248533826 25 0.123668340175387 0.247336680350774 0.876331659824613 26 0.0702743195039103 0.140548639007821 0.92972568049609 27 0.04308655788192 0.08617311576384 0.95691344211808 28 0.0300358848603494 0.0600717697206987 0.96996411513965 29 0.370231502233012 0.740463004466024 0.629768497766988 30 0.407682545058558 0.815365090117116 0.592317454941442 31 0.532341696441734 0.935316607116532 0.467658303558266 32 0.443387063382272 0.886774126764544 0.556612936617728 33 0.361829756297596 0.723659512595192 0.638170243702404 34 0.283028299628391 0.566056599256783 0.716971700371609 35 0.579298503490978 0.841402993018044 0.420701496509022 36 0.451588126053134 0.903176252106268 0.548411873946866 37 0.362616537448729 0.725233074897457 0.637383462551271

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 2 0.105263157894737 NOK

Charts produced by software:
 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/10ud4l1258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/10ud4l1258654045.ps (opens in new window) Click here to open pdf file.

 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/1ru1m1258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/1ru1m1258654045.ps (opens in new window) Click here to open pdf file.

 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/2il5v1258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/2il5v1258654045.ps (opens in new window) Click here to open pdf file.

 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/3acka1258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/3acka1258654045.ps (opens in new window) Click here to open pdf file.

 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/4z30b1258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/4z30b1258654045.ps (opens in new window) Click here to open pdf file.

 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/6uip91258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/6uip91258654045.ps (opens in new window) Click here to open pdf file.

 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/7qp1o1258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/7qp1o1258654045.ps (opens in new window) Click here to open pdf file.

 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/8uw9z1258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/8uw9z1258654045.ps (opens in new window) Click here to open pdf file.

 http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/9lzwf1258654045.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586541506cjqdoxspnvq363/9lzwf1258654045.ps (opens in new window) Click here to open pdf file.

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):