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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 computationTue, 16 Nov 2010 01:25:44 +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/16/t1289870660i2z24su0bfiodrb.htm/, Retrieved Sun, 05 May 2024 06:12:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=95218, Retrieved Sun, 05 May 2024 06:12:49 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

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] [WS - Minitut] [2010-11-16 00:58:47] [19f9551d4d95750ef21e9f3cf8fe2131]
-    D      [Linear Regression Graphical Model Validation] [WS Minitut] [2010-11-16 01:25:44] [fca744d17b21beb005bf086e7071b2bb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2,5
3
2,75
2,5
3
3
3,5
3,5
2,75
2,75
3,25
2,75
2,5
3,5
3,5
3
2,75
2,5
3
2,5
3,5
3
3,25
3,25
3
3,5
2,75
3
3,25
2,75
2,75
3,5
3
3,25
2,75
3,25
3,25
3,25
3
3,5
3,5
2,666666667
3,25
2,75
3,25
3,25
2,5
2,5
3,25
3
4
3,25
3
2,75
3
3
3,5
3,25
3,25
3
3,25
3
3,25
3,5
3,25
3,25
3
2,5
3,25
2,75
2,75
3,25
2,75
3,75
3,25
3,25
3
2,75
3
3,25
3
3,25
3,75
3,25
2,75
2,75
3,5
3,75
3
2,5
3
2,75
2,75
2,75
3,5
3,5
3,25
3,25
3,25
3
3
4
3,25
3,75
2,75
3,5
3,5
2,5
3
3
3
3,5
2,5
2,5
3,25
3,25
3,25
2,75
2,75
3,25
3,25
3,25
3,25
3,25
3,25
3,25
3,5
3,25
3,5
2,75
3,25
2,75
2,75
4
2
2,75
3,5
3
3,25
3,25
3
3,5
2,75
3,75
2,75
3,5
3,25
3,75
3,5
3,25
Dataseries Y:
Download CSV
Histogram 
6
6
13
8
7
9
5
8
9
11
8
11
12
8
7
9
12
20
7
8
8
16
10
6
8
9
9
11
12
8
7
8
9
4
8
8
8
6
8
4
7
14
10
9
6
8
11
8
8
10
8
10
7
8
7
9
5
7
7
7
9
5
8
8
8
9
6
8
6
4
6
4
12
6
11
8
10
10
4
8
9
9
7
7
11
8
8
7
5
7
9
8
6
8
10
10
8
11
8
8
6
20
6
12
9
5
10
5
6
10
6
10
5
13
7
9
11
8
5
4
9
7
5
5
4
7
9
8
8
11
10
9
12
10
10
7
10
6
6
11
8
9
9
13
11
4
9
5
4
9

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=95218&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=95218&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95218&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term10.63833643397661.891347056257095.62474052489849.0051307077843e-08
slope-0.7571106387327470.604904945315117-1.251619191736550.212683270645588

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 10.6383364339766 & 1.89134705625709 & 5.6247405248984 & 9.0051307077843e-08 \tabularnewline
slope & -0.757110638732747 & 0.604904945315117 & -1.25161919173655 & 0.212683270645588 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=95218&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]10.6383364339766[/C][C]1.89134705625709[/C][C]5.6247405248984[/C][C]9.0051307077843e-08[/C][/ROW]
[ROW][C]slope[/C][C]-0.757110638732747[/C][C]0.604904945315117[/C][C]-1.25161919173655[/C][C]0.212683270645588[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=95218&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95218&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 term10.63833643397661.891347056257095.62474052489849.0051307077843e-08
slope-0.7571106387327470.604904945315117-1.251619191736550.212683270645588


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 7
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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')