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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 computationFri, 12 Nov 2010 17:50:33 +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/12/t12895842365x9hpc86nddanbd.htm/, Retrieved Tue, 30 Apr 2024 10:38:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94261, Retrieved Tue, 30 Apr 2024 10:38:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

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] [regressiemodel 1] [2010-11-12 17:50:33] [ecfb965f5669057f3ac5b58964283289] [Current]
-    D      [Linear Regression Graphical Model Validation] [lineaire regressie] [2010-12-12 11:35:01] [39c51da0be01189e8a44eb69e891b7a1]
-   PD      [Linear Regression Graphical Model Validation] [Regressie model 1] [2010-12-18 12:39:36] [39c51da0be01189e8a44eb69e891b7a1]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,892201
1,007095
0,966709
1,003381
0,950054
0,942173
0,938196
0,731490
0,815207
0,841577
0,896191
0,918043
0,819296
0,907075
0,892324
0,927952
0,897811
0,917274
0,909615
0,876498
0,918068
0,888221
0,928924
0,921158
0,832321
0,918037
0,902913
0,934450
0,886786
0,903352
0,883465
0,849764
0,906730
0,872937
0,909101
0,916899
0,861511
0,926093
0,890331
0,917292
0,887005
0,922855
0,879873
0,683812
0,839532
0,874177
0,909158
0,926634
0,854157
0,939830
0,917559
0,967147
0,918967
0,971321
0,974110
0,965109
0,989029
0,944283
0,980111
0,976797
Dataseries Y:
Download CSV
Histogram 
0,163174
0,201921
0,232752
0,228971
0,245583
0,254378
0,224034
0,230488
0,145256
0,151829
0,165472
0,196739
0,187848
0,204079
0,207498
0,240080
0,239256
0,228852
0,245555
0,222047
0,266542
0,267053
0,231662
0,206036
0,257087
0,216368
0,225624
0,231318
0,253926
0,227463
0,263592
0,260886
0,232007
0,233059
0,299560
0,270942
0,283427
0,301381
0,316548
0,292079
0,312963
0,361612
0,358814
0,358174
0,220870
0,288982
0,291079
0,327455
0,335642
0,284180
0,325792
0,326291
0,356246
0,335943
0,414564
0,374787
0,361182
0,393189
0,344907
0,352865

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94261&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94261&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94261&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term0.04095903001633130.1286289045531550.3184278849191730.751304205209159
slope0.2506509378245850.1417512263882271.768245285851030.082278901642709

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 0.0409590300163313 & 0.128628904553155 & 0.318427884919173 & 0.751304205209159 \tabularnewline
slope & 0.250650937824585 & 0.141751226388227 & 1.76824528585103 & 0.082278901642709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94261&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]0.0409590300163313[/C][C]0.128628904553155[/C][C]0.318427884919173[/C][C]0.751304205209159[/C][/ROW]
[ROW][C]slope[/C][C]0.250650937824585[/C][C]0.141751226388227[/C][C]1.76824528585103[/C][C]0.082278901642709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94261&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94261&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 term0.04095903001633130.1286289045531550.3184278849191730.751304205209159
slope0.2506509378245850.1417512263882271.768245285851030.082278901642709


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 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')