| Linear Regression (Comp 14) | *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 09:28:16 +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/t1296467795xsxczdm1a73irev.htm/, Retrieved Mon, 31 Jan 2011 10:56:40 +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/t1296467795xsxczdm1a73irev.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 « | 52 111
55 102
80 108
45 109
60 118
34 79
45 88
68 102
26 105
70 92
85 131
54 104
55 83
40 84
55 85
50 110
71 121
55 120
70 100
55 94
60 89
65 93
66 128
55 84
90 127
55 106
60 129
35 82
55 106
26 109
14 91
45 111
35 105
65 118
35 103
60 101
60 101
60 95
65 108
45 95
20 98
50 82
60 100
48 100
40 107
55 95
54 97
40 93
40 81
34 89
60 111
30 95
75 106
24 83
30 81
80 115
60 112
46 92
35 85
60 95
75 115
54 91
78 107
20 102
45 86
60 96
70 114
35 105
20 82
60 120
20 88
50 90
50 85
75 106
70 109
20 75
45 91
20 96
50 108
55 86
15 98
26 99
25 95
30 88
60 111
40 103
40 107
50 118 | | 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!
Linear Regression Model | Y ~ X | coefficients: | | | Estimate | Std. Error | t value | Pr(>|t|) | (Intercept) | -22.154 | 12.768 | -1.735 | 0.086 | X | 0.717 | 0.126 | 5.674 | 0 | - - - | | Residual Std. Err. | 15.039 on 86 df | Multiple R-sq. | 0.272 | Adjusted R-sq. | 0.264 |
ANOVA Statistics | | Df | Sum Sq | Mean Sq | F value | Pr(>F) | IQ | 1 | 7281.107 | 7281.107 | 32.193 | 0 | Residuals | 86 | 19450.79 | 226.172 | | |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296467795xsxczdm1a73irev/3ilu21296466094.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296467795xsxczdm1a73irev/3ilu21296466094.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/31/t1296467795xsxczdm1a73irev/412oc1296466094.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296467795xsxczdm1a73irev/412oc1296466094.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/31/t1296467795xsxczdm1a73irev/5bh3l1296466094.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296467795xsxczdm1a73irev/5bh3l1296466094.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/31/t1296467795xsxczdm1a73irev/6x2qd1296466094.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296467795xsxczdm1a73irev/6x2qd1296466094.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)
}
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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