| Linear Regression | *Unverified author* | 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 20:46:10 +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/t1296507540c39txqbxnu0m35m.htm/, Retrieved Mon, 31 Jan 2011 21:59:04 +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/t1296507540c39txqbxnu0m35m.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 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!
Linear Regression Model | Y ~ X | coefficients: | | | Estimate | Std. Error | t value | Pr(>|t|) | (Intercept) | 135.163 | 4.782 | 28.264 | 0 | X | -0.667 | 0.089 | -7.505 | 0 | - - - | | Residual Std. Err. | 9.969 on 86 df | Multiple R-sq. | 0.396 | Adjusted R-sq. | 0.389 |
ANOVA Statistics | | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Add | 1 | 5597.714 | 5597.714 | 56.326 | 0 | Residuals | 86 | 8546.73 | 99.381 | | |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296507540c39txqbxnu0m35m/3lz711296506768.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296507540c39txqbxnu0m35m/3lz711296506768.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/31/t1296507540c39txqbxnu0m35m/4k2551296506768.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296507540c39txqbxnu0m35m/4k2551296506768.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/31/t1296507540c39txqbxnu0m35m/5c3qb1296506768.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296507540c39txqbxnu0m35m/5c3qb1296506768.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2011/Jan/31/t1296507540c39txqbxnu0m35m/6qca41296506768.png (open in new window) | http://www.freestatistics.org/blog/date/2011/Jan/31/t1296507540c39txqbxnu0m35m/6qca41296506768.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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