huwelijken vs bevolkingsgroei | ||||||||||||||||||||||||||||||
| *The author of this computation has been verified* | ||||||||||||||||||||||||||||||
| R Software Module: /rwasp_linear_regression.wasp (opens new window with default values) | ||||||||||||||||||||||||||||||
| Title produced by software: Linear Regression Graphical Model Validation | ||||||||||||||||||||||||||||||
| Date of computation: Sat, 18 Dec 2010 15:59:42 +0000 | ||||||||||||||||||||||||||||||
| Cite this page as follows: | ||||||||||||||||||||||||||||||
| Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t12926878818ved8ej2agg5eqb.htm/, Retrieved Sat, 18 Dec 2010 16:58:11 +0100 | ||||||||||||||||||||||||||||||
| BibTeX entries for LaTeX users: | ||||||||||||||||||||||||||||||
@Manual{KEY,
author = {{YOUR NAME}},
publisher = {Office for Research Development and Education},
title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t12926878818ved8ej2agg5eqb.htm/},
year = {2010},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2010},
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org},
}
| ||||||||||||||||||||||||||||||
| Original text written by user: | ||||||||||||||||||||||||||||||
| IsPrivate? | ||||||||||||||||||||||||||||||
| No (this computation is public) | ||||||||||||||||||||||||||||||
| User-defined keywords: | ||||||||||||||||||||||||||||||
| Dataseries X: | ||||||||||||||||||||||||||||||
| » Textbox « » Textfile « » CSV « | ||||||||||||||||||||||||||||||
| 5140 4749 3635 4305 5805 4260 3869 7325 9280 6222 3272 7598 1345 1900 1480 1472 3823 4454 3357 5393 8329 4152 4042 7747 1451 911 406 1387 2150 1577 2642 4273 8064 3243 1112 2280 505 744 1369 531 1041 2076 577 5080 6584 3761 294 5020 1141 3805 2127 2531 3682 3263 2798 5936 10568 5296 1870 4390 3707 5201 3748 5282 5349 6249 5517 8640 15767 8850 5582 6496 3255 6189 6452 5099 6833 7046 7739 10142 16054 7721 6182 6490 | ||||||||||||||||||||||||||||||
| Dataseries Y: | ||||||||||||||||||||||||||||||
| » Textbox « » Textfile « » CSV « | ||||||||||||||||||||||||||||||
| 3111 3995 5245 5588 10681 10516 7496 9935 10249 6271 3616 3724 2886 3318 4166 6401 9209 9820 7470 8207 9564 5309 3385 3706 2733 3045 3449 5542 10072 9418 7516 7840 10081 4956 3641 3970 2931 3170 3889 4850 8037 12370 6712 7297 10613 5184 3506 3810 2692 3073 3713 4555 7807 10869 9682 7704 9826 5456 3677 3431 2765 3483 3445 6081 8767 9407 6551 12480 9530 5960 3252 3717 2642 2989 3607 5366 8898 9435 7328 8594 11349 5797 3621 3851 | ||||||||||||||||||||||||||||||
| Output produced by software: | ||||||||||||||||||||||||||||||
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| Charts produced by software: | ||||||||||||||||||||||||||||||
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| Parameters (Session): | ||||||||||||||||||||||||||||||
| par1 = 0 ; | ||||||||||||||||||||||||||||||
| Parameters (R input): | ||||||||||||||||||||||||||||||
| par1 = 0 ; | ||||||||||||||||||||||||||||||
| R code (references can be found in the software module): | ||||||||||||||||||||||||||||||
| par1 <- as.numeric(par1)
library(lattice) z <- as.data.frame(cbind(x,y)) m <- lm(y~x) summary(m) bitmap(file='test1.png') plot(z,main='Scatterplot, lowess, and regression line') lines(lowess(z),col='red') abline(m) grid() dev.off() bitmap(file='test2.png') m2 <- lm(m$fitted.values ~ x) summary(m2) z2 <- as.data.frame(cbind(x,m$fitted.values)) names(z2) <- list('x','Fitted') plot(z2,main='Scatterplot, lowess, and regression line') lines(lowess(z2),col='red') abline(m2) grid() dev.off() bitmap(file='test3.png') m3 <- lm(m$residuals ~ x) summary(m3) z3 <- as.data.frame(cbind(x,m$residuals)) names(z3) <- list('x','Residuals') plot(z3,main='Scatterplot, lowess, and regression line') lines(lowess(z3),col='red') abline(m3) grid() dev.off() bitmap(file='test4.png') m4 <- lm(m$fitted.values ~ m$residuals) summary(m4) z4 <- as.data.frame(cbind(m$residuals,m$fitted.values)) names(z4) <- list('Residuals','Fitted') plot(z4,main='Scatterplot, lowess, and regression line') lines(lowess(z4),col='red') abline(m4) grid() dev.off() bitmap(file='test5.png') myr <- as.ts(m$residuals) z5 <- as.data.frame(cbind(lag(myr,1),myr)) names(z5) <- list('Lagged Residuals','Residuals') plot(z5,main='Lag plot') m5 <- lm(z5) summary(m5) abline(m5) grid() dev.off() bitmap(file='test6.png') hist(m$residuals,main='Residual Histogram',xlab='Residuals') dev.off() bitmap(file='test7.png') if (par1 > 0) { densityplot(~m$residuals,col='black',main=paste('Density Plot bw = ',par1),bw=par1) } else { densityplot(~m$residuals,col='black',main='Density Plot') } dev.off() bitmap(file='test8.png') acf(m$residuals,main='Residual Autocorrelation Function') dev.off() bitmap(file='test9.png') qqnorm(x) qqline(x) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Simple Linear Regression',5,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Statistics',1,TRUE) a<-table.element(a,'Estimate',1,TRUE) a<-table.element(a,'S.D.',1,TRUE) a<-table.element(a,'T-STAT (H0: coeff=0)',1,TRUE) a<-table.element(a,'P-value (two-sided)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'constant term',header=TRUE) a<-table.element(a,m$coefficients[[1]]) sd <- sqrt(vcov(m)[1,1]) a<-table.element(a,sd) tstat <- m$coefficients[[1]]/sd a<-table.element(a,tstat) pval <- 2*(1-pt(abs(tstat),length(x)-2)) a<-table.element(a,pval) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'slope',header=TRUE) a<-table.element(a,m$coefficients[[2]]) sd <- sqrt(vcov(m)[2,2]) a<-table.element(a,sd) tstat <- m$coefficients[[2]]/sd a<-table.element(a,tstat) pval <- 2*(1-pt(abs(tstat),length(x)-2)) a<-table.element(a,pval) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') | ||||||||||||||||||||||||||||||
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