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Data X:
4 12.9 4 12.2 5 12.8 4 7.4 4 6.7 9 12.6 8 14.8 11 13.3 4 11.1 4 8.2 6 11.4 4 6.4 8 10.6 4 12.0 4 6.3 11 11.3 4 11.9 4 9.3 6 9.6 6 10.0 4 6.4 8 13.8 5 10.8 4 13.8 9 11.7 4 10.9 7 16.1 10 13.4 4 9.9 4 11.5 7 8.3 12 11.7 7 9.0 5 9.7 8 10.8 5 10.3 4 10.4 9 12.7 7 9.3 4 11.8 4 5.9 4 11.4 4 13.0 4 10.8 7 12.3 4 11.3 7 11.8 4 7.9 4 12.7 4 12.3 4 11.6 8 6.7 4 10.9 4 12.1 4 13.3 4 10.1 7 5.7 12 14.3 4 8.0 4 13.3 4 9.3 5 12.5 15 7.6 5 15.9 10 9.2 9 9.1 8 11.1 4 13.0 5 14.5 4 12.2 9 12.3 4 11.4 10 8.8 4 14.6 4 12.6 7 13.0 5 12.6 4 13.2 4 9.9 4 7.7 4 10.5 4 13.4 4 10.9 6 4.3 10 10.3 7 11.8 4 11.2 4 11.4 7 8.6 4 13.2 8 12.6 11 5.6 6 9.9 14 8.8 5 7.7 4 9.0 8 7.3 9 11.4 4 13.6 4 7.9 5 10.7 4 10.3 5 8.3 4 9.6 4 14.2 7 8.5 10 13.5 4 4.9 5 6.4 4 9.6 4 11.6 4 11.1 6 4.35 4 12.7 8 18.1 5 17.85 4 16.6 17 12.6 4 17.1 4 19.1 8 16.1 4 13.35 7 18.4 4 14.7 4 10.6 5 12.6 7 16.2 4 13.6 4 18.9 7 14.1 11 14.5 7 16.15 4 14.75 4 14.8 4 12.45 4 12.65 4 17.35 4 8.6 6 18.4 8 16.1 23 11.6 4 17.75 8 15.25 6 17.65 4 16.35 7 17.65 4 13.6 4 14.35 4 14.75 10 18.25 6 9.9 5 16 5 18.25 4 16.85 4 14.6 5 13.85 5 18.95 5 15.6 5 14.85 4 11.75 6 18.45 4 15.9 4 17.1 4 16.1 9 19.9 18 10.95 6 18.45 5 15.1 4 15 11 11.35 4 15.95 10 18.1 6 14.6 8 15.4 8 15.4 6 17.6 8 13.35 4 19.1 4 15.35 9 7.6 9 13.4 5 13.9 4 19.1 4 15.25 15 12.9 10 16.1 9 17.35 7 13.15 9 12.15 6 12.6 4 10.35 7 15.4 4 9.6 7 18.2 4 13.6 15 14.85 4 14.75 9 14.1 4 14.9 4 16.25 28 19.25 4 13.6 4 13.6 4 15.65 5 12.75 4 14.6 4 9.85 12 12.65 4 19.2 6 16.6 6 11.2 5 15.25 4 11.9 4 13.2 4 16.35 10 12.4 7 15.85 4 18.15 7 11.15 4 15.65 4 17.75 12 7.65 5 12.35 8 15.6 6 19.3 17 15.2 4 17.1 5 15.6 4 18.4 5 19.05 5 18.55 6 19.1 4 13.1 4 12.85 4 9.5 6 4.5 8 11.85 10 13.6 4 11.7 5 12.4 4 13.35 4 11.4 4 14.9 16 19.9 7 11.2 4 14.6 4 17.6 14 14.05 5 16.1 5 13.35 5 11.85 5 11.95 7 14.75 19 15.15 16 13.2 4 16.85 4 7.85 7 7.7 9 12.6 5 7.85 14 10.95 4 12.35 16 9.95 10 14.9 5 16.65 6 13.4 4 13.95 4 15.7 4 16.85 5 10.95 4 15.35 4 12.2 5 15.1 4 17.75 4 15.2 5 14.6 8 16.65 15 8.1
Names of X columns:
Motivation TotScores
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
TRUE
TRUE
FALSE
Chart options
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Label y-axis:
Label x-axis:
R Code
par3 <- 'TRUE' par2 <- '1' par1 <- '2' cat1 <- as.numeric(par1) cat2<- as.numeric(par2) intercept<-as.logical(par3) x <- t(x) xdf<-data.frame(t(y)) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) xdf <- data.frame(xdf[[cat1]], xdf[[cat2]]) names(xdf)<-c('Y', 'X') if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) ) sumlmxdf<-summary(lmxdf) (aov.xdf<-aov(lmxdf) ) (anova.xdf<-anova(lmxdf) ) load(file='createtable') a<-table.start() nc <- ncol(sumlmxdf$'coefficients') nr <- nrow(sumlmxdf$'coefficients') a<-table.row.start(a) a<-table.element(a,'Linear Regression Model', nc+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, lmxdf$call['formula'],nc+1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'coefficients:',1,TRUE) a<-table.element(a, ' ',nc,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) for(i in 1 : nc){ a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE) }#end header a<-table.row.end(a) for(i in 1: nr){ a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE) for(j in 1 : nc){ a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE) } a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a, '- - - ',1,TRUE) a<-table.element(a, ' ',nc,FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Std. Err. ',1,TRUE) a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ANOVA Statistics', 5+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) a<-table.element(a, 'Df',1,TRUE) a<-table.element(a, 'Sum Sq',1,TRUE) a<-table.element(a, 'Mean Sq',1,TRUE) a<-table.element(a, 'F value',1,TRUE) a<-table.element(a, 'Pr(>F)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, V2,1,TRUE) a<-table.element(a, anova.xdf$Df[1]) a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'F value'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residuals',1,TRUE) a<-table.element(a, anova.xdf$Df[2]) a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3)) a<-table.element(a, ' ') a<-table.element(a, ' ') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') bitmap(file='regressionplot.png') plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution') if(intercept == TRUE) abline(coef(lmxdf), col='red') if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red') dev.off() library(car) bitmap(file='residualsQQplot.png') qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit') dev.off() bitmap(file='residualsplot.png') plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit') dev.off() bitmap(file='cooksDistanceLmplot.png') plot.lm(lmxdf, which=4) dev.off()
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