Effect of IQ on average grade

*The author of this computation has been verified*

R Software Module: Ian.Holliday/rwasp_Simple Regression Y ~ X.wasp (opens new window with default values)

Title produced by software: Simple Linear Regression

Date of computation: Mon, 31 Jan 2011 19:00:19 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8.htm/, Retrieved Mon, 31 Jan 2011 20:52:21 +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/2011/Jan/31/t1296503541av8xf32j8ei9jh8.htm/}, year = {2011}, } @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 = {2011}, 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 «

111 52 102 55 108 80 109 45 118 60 79 34 88 45 102 68 105 26 92 70 131 85 104 54 83 55 84 40 85 55 110 50 121 71 120 55 100 70 94 55 89 60 93 65 128 66 84 55 127 90 106 55 129 60 82 35 106 55 109 26 91 14 111 45 105 35 118 65 103 35 101 60 101 60 95 60 108 65 95 45 98 20 82 50 100 60 100 48 107 40 95 55 97 54 93 40 81 40 89 34 111 60 95 30 106 75 83 24 81 30 115 80 112 60 92 46 85 35 95 60 115 75 91 54 107 78 102 20 86 45 96 60 114 70 105 35 82 20 120 60 88 20 90 50 85 50 106 75 109 70 75 20 91 45 96 20 108 50 86 55 98 15 99 26 95 25 88 30 111 60 103 40 107 40 118 50

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 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Linear Regression Model Y ~ X coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 81.297 3.525 23.063 0 X 0.38 0.067 5.674 0 - - - Residual Std. Err. 10.939 on 86 df Multiple R-sq. 0.272 Adjusted R-sq. 0.264 ANOVA Statistics Df Sum Sq Mean Sq F value Pr(>F) Grade 1 3852.596 3852.596 32.193 0 Residuals 86 10291.847 119.673

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8/3gqnq1296500416.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8/3gqnq1296500416.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8/4yijy1296500416.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8/4yijy1296500416.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8/5u2mi1296500416.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8/5u2mi1296500416.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8/6g1jl1296500416.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/31/t1296503541av8xf32j8ei9jh8/6g1jl1296500416.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = TRUE ;

Parameters (R input):

par1 = 1 ; par2 = 2 ; par3 = TRUE ;

R code (references can be found in the software module):

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) }# end cols a<-table.row.end(a) } #end rows 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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