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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_linear_regression.wasp
Title produced by softwareLinear Regression Graphical Model Validation
Date of computationWed, 17 Nov 2010 03:21:42 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/17/t1289964024kxp9umwh14ify2z.htm/, Retrieved Mon, 12 May 2025 20:39:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=96507, Retrieved Mon, 12 May 2025 20:39:10 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact239

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Linear Regression Graphical Model Validation] [Colombia Coffee -...] [2008-02-26 10:22:06] [74be16979710d4c4e7c6647856088456]
-  M D  [Linear Regression Graphical Model Validation] [workshop 6 tutorial] [2010-11-12 10:13:29] [87d60b8864dc39f7ed759c345edfb471]
-    D    [Linear Regression Graphical Model Validation] [workshop 6 mini-t...] [2010-11-12 14:05:27] [87d60b8864dc39f7ed759c345edfb471]
-    D      [Linear Regression Graphical Model Validation] [W6-mini tutorial] [2010-11-12 20:07:18] [48146708a479232c43a8f6e52fbf83b4]
-    D        [Linear Regression Graphical Model Validation] [tutorial 1] [2010-11-17 02:46:03] [c61422349906618fd7554832db11aa8e]
-    D            [Linear Regression Graphical Model Validation] [tutorial 2] [2010-11-17 03:21:42] [05a3d060fb1d372fa2d84e37f098db3c] [Current]
-    D              [Linear Regression Graphical Model Validation] [Simple Linear Reg...] [2010-12-09 19:40:21] [1251ac2db27b84d4a3ba43449388906b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
24
25
30
19
22
22
25
23
17
21
19
19
15
16
23
27
22
14
22
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29
32
25
29
28
17
28
29
26
25
14
25
26
20
18
32
25
25
23
21
20
15
30
24
26
24
22
14
24
24
24
24
19
31
22
27
19
25
20
21
27
23
25
20
21
22
23
25
25
17
19
25
19
20
26
23
27
17
17
19
17
22
21
32
21
21
18
18
23
19
20
21
20
17
18
19
22
15
14
18
24
35
29
21
25
20
22
13
26
17
25
20
19
21
22
24
21
26
24
16
23
18
16
26
19
21
21
22
23
29
21
21
23
27
25
21
10
20
26
24
29
19
24
19
24
22
17
Dataseries Y:
Download CSV
Histogram 
26
23
25
23
19
29
25
21
22
25
24
18
22
15
22
28
20
12
24
20
21
20
21
23
28
24
24
24
23
23
29
24
18
25
21
26
22
22
22
23
30
23
17
23
23
25
24
24
23
21
24
24
28
16
20
29
27
22
28
16
25
24
28
24
23
30
24
21
25
25
22
23
26
23
25
21
25
24
29
22
27
26
22
24
27
24
24
29
22
21
24
24
23
20
27
26
25
21
21
19
21
21
16
22
29
15
17
15
21
21
19
24
20
17
23
24
14
19
24
13
22
16
19
25
25
23
24
26
26
25
18
21
26
23
23
22
20
13
24
15
14
22
10
24
22
24
19
20
13
20
22
24
29
12
20
21
24
22
20

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96507&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96507&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96507&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term15.11118149788021.559083760353799.692347442866780
slope0.3261352291499710.06916930269491474.715028436652855.29774482638246e-06

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 15.1111814978802 & 1.55908376035379 & 9.69234744286678 & 0 \tabularnewline
slope & 0.326135229149971 & 0.0691693026949147 & 4.71502843665285 & 5.29774482638246e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96507&T=1

[TABLE]
[ROW][C]Simple Linear Regression[/C][/ROW]
[ROW][C]Statistics[/C][C]Estimate[/C][C]S.D.[/C][C]T-STAT (H0: coeff=0)[/C][C]P-value (two-sided)[/C][/ROW]
[ROW][C]constant term[/C][C]15.1111814978802[/C][C]1.55908376035379[/C][C]9.69234744286678[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]0.326135229149971[/C][C]0.0691693026949147[/C][C]4.71502843665285[/C][C]5.29774482638246e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96507&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96507&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term15.11118149788021.559083760353799.692347442866780
slope0.3261352291499710.06916930269491474.715028436652855.29774482638246e-06


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 8
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Figure 9
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Input Parameters & R Code

Parameters (Session):
par1 = 0 ;
Parameters (R input):
par1 = 0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
library(lattice)
z <- as.data.frame(cbind(x,y))
m <- lm(y~x)
summary(m)
bitmap(file='test1.png')
plot(z,main='Scatterplot, lowess, and regression line')
lines(lowess(z),col='red')
abline(m)
grid()
dev.off()
bitmap(file='test2.png')
m2 <- lm(m$fitted.values ~ x)
summary(m2)
z2 <- as.data.frame(cbind(x,m$fitted.values))
names(z2) <- list('x','Fitted')
plot(z2,main='Scatterplot, lowess, and regression line')
lines(lowess(z2),col='red')
abline(m2)
grid()
dev.off()
bitmap(file='test3.png')
m3 <- lm(m$residuals ~ x)
summary(m3)
z3 <- as.data.frame(cbind(x,m$residuals))
names(z3) <- list('x','Residuals')
plot(z3,main='Scatterplot, lowess, and regression line')
lines(lowess(z3),col='red')
abline(m3)
grid()
dev.off()
bitmap(file='test4.png')
m4 <- lm(m$fitted.values ~ m$residuals)
summary(m4)
z4 <- as.data.frame(cbind(m$residuals,m$fitted.values))
names(z4) <- list('Residuals','Fitted')
plot(z4,main='Scatterplot, lowess, and regression line')
lines(lowess(z4),col='red')
abline(m4)
grid()
dev.off()
bitmap(file='test5.png')
myr <- as.ts(m$residuals)
z5 <- as.data.frame(cbind(lag(myr,1),myr))
names(z5) <- list('Lagged Residuals','Residuals')
plot(z5,main='Lag plot')
m5 <- lm(z5)
summary(m5)
abline(m5)
grid()
dev.off()
bitmap(file='test6.png')
hist(m$residuals,main='Residual Histogram',xlab='Residuals')
dev.off()
bitmap(file='test7.png')
if (par1 > 0)
{
densityplot(~m$residuals,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
} else {
densityplot(~m$residuals,col='black',main='Density Plot')
}
dev.off()
bitmap(file='test8.png')
acf(m$residuals,main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test9.png')
qqnorm(x)
qqline(x)
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Simple Linear Regression',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Statistics',1,TRUE)
a<-table.element(a,'Estimate',1,TRUE)
a<-table.element(a,'S.D.',1,TRUE)
a<-table.element(a,'T-STAT (H0: coeff=0)',1,TRUE)
a<-table.element(a,'P-value (two-sided)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'constant term',header=TRUE)
a<-table.element(a,m$coefficients[[1]])
sd <- sqrt(vcov(m)[1,1])
a<-table.element(a,sd)
tstat <- m$coefficients[[1]]/sd
a<-table.element(a,tstat)
pval <- 2*(1-pt(abs(tstat),length(x)-2))
a<-table.element(a,pval)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'slope',header=TRUE)
a<-table.element(a,m$coefficients[[2]])
sd <- sqrt(vcov(m)[2,2])
a<-table.element(a,sd)
tstat <- m$coefficients[[2]]/sd
a<-table.element(a,tstat)
pval <- 2*(1-pt(abs(tstat),length(x)-2))
a<-table.element(a,pval)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')