| Linier Regression | *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: Fri, 28 Jan 2011 13:36:26 +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/28/t1296222083s8em36zesogocm7.htm/, Retrieved Fri, 28 Jan 2011 14:41:23 +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/28/t1296222083s8em36zesogocm7.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
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
91 45
96 20
108 50
86 55
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!
Linear Regression Model | Y ~ X | coefficients: | | | Estimate | Std. Error | t value | Pr(>|t|) | (Intercept) | -16.299 | 12.63 | -1.29 | 0.2 | X | 0.668 | 0.125 | 5.361 | 0 | - - - | | Residual Std. Err. | 14.424 on 83 df | Multiple R-sq. | 0.257 | Adjusted R-sq. | 0.248 |
ANOVA Statistics | | Df | Sum Sq | Mean Sq | F value | Pr(>F) | IQ | 1 | 5980.821 | 5980.821 | 28.745 | 0 | Residuals | 83 | 17269.226 | 208.063 | | |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2011/Jan/28/t1296222083s8em36zesogocm7/3sbc81296221782.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/28/t1296222083s8em36zesogocm7/3sbc81296221782.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/28/t1296222083s8em36zesogocm7/4u6gg1296221782.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/28/t1296222083s8em36zesogocm7/4u6gg1296221782.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/28/t1296222083s8em36zesogocm7/500h71296221782.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/28/t1296222083s8em36zesogocm7/500h71296221782.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/28/t1296222083s8em36zesogocm7/6pstz1296221782.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/28/t1296222083s8em36zesogocm7/6pstz1296221782.ps (open in new window) |
| | Parameters (Session): | par1 = 1 ; par2 = 2 ; 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$call['formula'],nc+1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'coefficients:',1,TRUE)
a<-table.element(a, ' ',nc,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
for(i in 1 : nc){
a<-table.element(a, dimnames(sumlmxdf$'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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