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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 computationSat, 13 Nov 2010 11:31:05 +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/13/t12896523062524dwm3hz276x2.htm/, Retrieved Sat, 27 Apr 2024 06:02:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94361, Retrieved Sat, 27 Apr 2024 06:02:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

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] [ws6vb] [2010-11-13 11:31:05] [0cadca125c925bcc9e6efbdd1941e458] [Current]
-    D      [Linear Regression Graphical Model Validation] [tt] [2010-11-13 12:51:28] [4a7069087cf9e0eda253aeed7d8c30d6]
-    D        [Linear Regression Graphical Model Validation] [W6tutorial] [2010-11-13 14:33:02] [c7506ced21a6c0dca45d37c8a93c80e0]
-    D          [Linear Regression Graphical Model Validation] [Workshop 6, Mini ...] [2010-11-14 15:39:04] [3635fb7041b1998c5a1332cf9de22bce]
-    D            [Linear Regression Graphical Model Validation] [Workshop 6, Simpl...] [2010-11-16 19:47:28] [3635fb7041b1998c5a1332cf9de22bce]
- R  D              [Linear Regression Graphical Model Validation] [Mini Toturial - H...] [2010-11-17 00:22:02] [b20a509e36241371274681d9edf773da]
- R  D              [Linear Regression Graphical Model Validation] [] [2011-11-15 15:55:49] [46896e8a404bb9354f2d070359621409]
- R  D              [Linear Regression Graphical Model Validation] [Mini Tutorial - k...] [2011-11-15 16:56:43] [74be16979710d4c4e7c6647856088456]
- RMPD              [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Chi Squared Test ...] [2011-11-15 17:42:51] [d0cddc92c01af61bef0226b9e5ade9b3]
- R  D            [Linear Regression Graphical Model Validation] [Mini Toturial - H...] [2010-11-17 00:19:55] [b20a509e36241371274681d9edf773da]
-    D            [Linear Regression Graphical Model Validation] [] [2011-11-15 08:53:32] [ea9976aa04c7322b215e949114660791]
- R  D            [Linear Regression Graphical Model Validation] [] [2011-11-15 15:35:27] [46896e8a404bb9354f2d070359621409]
- R  D            [Linear Regression Graphical Model Validation] [Mini Tutorial - t...] [2011-11-15 16:42:35] [74be16979710d4c4e7c6647856088456]
- R  D            [Linear Regression Graphical Model Validation] [Workshop 6: Mini-...] [2011-11-15 17:30:14] [21b3d52ef28595defb5676e0f3570994]
- R  D            [Linear Regression Graphical Model Validation] [Workshop 6: Mini-...] [2011-11-15 17:32:29] [21b3d52ef28595defb5676e0f3570994]
- R  D            [Linear Regression Graphical Model Validation] [] [2011-12-19 16:17:39] [c505444e07acba7694d29053ca5d114e]
- R  D            [Linear Regression Graphical Model Validation] [] [2011-12-19 16:19:11] [c505444e07acba7694d29053ca5d114e]
-               [Linear Regression Graphical Model Validation] [Tutorial1] [2010-11-16 20:35:31] [fc9068db680cd880760a7c0fccd81a61]
F    D          [Linear Regression Graphical Model Validation] [] [2010-11-17 08:28:03] [f9eaed74daea918f73b9f505c5b1f19e]
-    D          [Linear Regression Graphical Model Validation] [Spaargeld NL vs L...] [2010-12-21 14:30:35] [fc9068db680cd880760a7c0fccd81a61]
-    D          [Linear Regression Graphical Model Validation] [Spaargeld NL vs L...] [2010-12-21 14:42:24] [fc9068db680cd880760a7c0fccd81a61]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Dataseries Y:
Download CSV
Histogram 
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94361&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]5 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=94361&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94361&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term2.677664974619290.22005873789473112.16795570235530
slope0.104272419627750.06651974498825461.567540880473320.119557427379179

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 2.67766497461929 & 0.220058737894731 & 12.1679557023553 & 0 \tabularnewline
slope & 0.10427241962775 & 0.0665197449882546 & 1.56754088047332 & 0.119557427379179 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94361&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]2.67766497461929[/C][C]0.220058737894731[/C][C]12.1679557023553[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]0.10427241962775[/C][C]0.0665197449882546[/C][C]1.56754088047332[/C][C]0.119557427379179[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94361&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94361&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 term2.677664974619290.22005873789473112.16795570235530
slope0.104272419627750.06651974498825461.567540880473320.119557427379179


Figures (Output of Computation)

Figure 1
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Figure 2
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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 ; par2 = ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')