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part b exercise again

*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: Linear Regression with One Explanatory Variable- Free Statistics Software (Calculator)

Date of computation: Tue, 12 Jan 2010 07:45:20 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/12/t1263307929gms9gb8k7m1fk8c.htm/, Retrieved Tue, 12 Jan 2010 15:52:13 +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/Jan/12/t1263307929gms9gb8k7m1fk8c.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 «

111 52 102 55 108 45 109 60 118 34 79 45 88 68 102 26 105 70 92 54 131 55 104 40 83 55 84 50 85 55 110 70 121 55 120 60 100 65 94 66 89 55 93 55 128 60 84 35 127 55 106 45 129 35 82 65 106 35 109 60 91 60 111 60 105 65 118 45 103 50 101 60 101 48 95 40 108 55 95 54 98 40 82 40 100 34 100 60 107 30 95 30 97 60 93 46 81 35 89 60 111 54 95 45 106 60 83 70 81 35 115 60 112 50 92 50 85 70 95 45 115 50 91 55 107 30 102 60 86 40 96 40 114 50 105 50 82 50 120 50 88 50 90 50 85 50 106 50 109 50 75 50 91 50 96 50 108 50 86 50 98 50 99 50 95 50 88 50 111 50 103 50 107 50 118 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	'Gwilym Jenkins' @ 72.249.127.135

Linear Regression Model
Y ~ X
coefficients:
	Estimate	Std. Error	t value	Pr(>\|t\|)
(Intercept)	48.868	8.483	5.761	0
X	0.019	0.084	0.224	0.824
- - -
Residual Std. Err.	9.992 on 86 df
Multiple R-sq.	0.001
Adjusted R-sq.	-0.011

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
IQ	1	4.993	4.993	0.05	0.824
Residuals	86	8585.507	99.831

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263307929gms9gb8k7m1fk8c/3zj9n1263307517.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263307929gms9gb8k7m1fk8c/3zj9n1263307517.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263307929gms9gb8k7m1fk8c/4rs5q1263307517.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263307929gms9gb8k7m1fk8c/4rs5q1263307517.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263307929gms9gb8k7m1fk8c/5utx81263307517.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263307929gms9gb8k7m1fk8c/5utx81263307517.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 2 ; par2 = 1 ; 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,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'coefficients:',1,TRUE)

a<-table.element(a, ' ',nc-1,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, ' ',,TRUE)

a<-table.element(a, 'Df',,FALSE)

a<-table.element(a, 'Sum Sq',,FALSE)

a<-table.element(a, 'Mean Sq',,FALSE)

a<-table.element(a, 'F value',,FALSE)

a<-table.element(a, 'Pr(>F)',,FALSE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, V2,,TRUE)

a<-table.element(a, anova.xdf$Df[1],,FALSE)

a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)

a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)

a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)

a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residuals',,TRUE)

a<-table.element(a, anova.xdf$Df[2],,FALSE)

a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)

a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3),,FALSE)

a<-table.element(a, ' ',,FALSE)

a<-table.element(a, ' ',,FALSE)

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()

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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