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w11

*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: Thu, 17 Dec 2009 14:10:07 +0100
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055541msactslsnakfo30.htm/, Retrieved Thu, 17 Dec 2009 14:12:25 +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/2009/Dec/17/t1261055541msactslsnakfo30.htm/},
    year = {2009},
}
@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 = {2009},
    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 45 27 52 102 50 18 55 108 49 19 80 109 55 20 45 118 39 29 60 79 68 46 34 88 69 27 45 102 56 23 68 105 58 29 26 92 48 38 70 131 34 20 85 104 50 37 54 83 76 32 55 84 49 26 40 85 51 40 55 110 53 30 50 121 36 26 71 120 62 23 55 100 46 27 70 94 50 38 55 89 47 25 60 93 50 33 65 128 44 45 66 84 50 34 55 127 29 20 90 106 49 24 55 129 26 26 60 82 79 26 35 106 53 39 55 109 53 27 26 91 72 18 14 111 35 34 45 105 42 25 35 118 37 26 65 103 46 28 35 101 48 21 60 101 46 39 60 95 49 25 60 108 65 29 65 95 52 37 45 98 75 34 20 82 58 30 50 100 43 28 60 100 60 25 48 107 43 27 40 95 51 33 55 97 70 30 54 93 69 26 40 81 65 18 40 89 63 21 34 111 44 39 60 95 61 36 30 106 40 32 75 83 62 23 24 81 59 27 30 115 47 45 80 112 50 24 60 92 50 29 46 85 65 21 35 95 54 28 60 115 44 37 75 91 66 22 54 107 34 31 78 102 74 32 20 86 57 20 45 96 60 33 60 114 36 32 70 105 50 18 35 82 60 44 20 120 45 24 60 88 55 21 20 90 44 29 50 85 57 30 50 106 33 37 75 109 30 33 70 75 64 25 etc...
 
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)-22.15412.768-1.7350.086
X0.7170.1265.6740
- - -
Residual Std. Err. 15.039 on 86 df
Multiple R-sq. 0.272
Adjusted R-sq. 0.264


ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
IQ17281.1077281.10732.1930
Residuals8619450.79226.172
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055541msactslsnakfo30/394621261055403.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055541msactslsnakfo30/394621261055403.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055541msactslsnakfo30/4u3zt1261055403.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055541msactslsnakfo30/4u3zt1261055403.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055541msactslsnakfo30/5znaq1261055403.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/17/t1261055541msactslsnakfo30/5znaq1261055403.ps (open in new window)


 
Parameters (Session):
par1 = 4 ; par2 = 1 ; par3 = TRUE ;
 
Parameters (R input):
par1 = 4 ; 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()
 




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