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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, 23 Oct 2009 10:51:23 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Oct/23/t1256316724jqvk8igbf0b8kfz.htm/, Retrieved Thu, 02 May 2024 05:51:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=50012, Retrieved Thu, 02 May 2024 05:51:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
-   PD  [Bivariate Data Series] [WS 4 1 PLOTS] [2009-10-23 11:13:05] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMPD    [Bivariate Explorative Data Analysis] [ws 4 part 2] [2009-10-23 11:56:36] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMP       [Pearson Correlation] [ws 4 2.1 r] [2009-10-23 12:05:33] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMP         [Bivariate Kernel Density Estimation] [ws 4 p2.1] [2009-10-23 16:26:45] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMPD          [Linear Regression Graphical Model Validation] [ws 2.2] [2009-10-23 16:47:44] [6e4e01d7eb22a9f33d58ebb35753a195]
-    D              [Linear Regression Graphical Model Validation] [ws 2.2] [2009-10-23 16:51:23] [2e4ef2c1b76db9b31c0a03b96e94ad77] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100,30
98,50
95,10
93,10
92,20
89,00
86,40
84,50
82,70
80,80
81,80
81,80
82,90
83,80
86,20
86,10
86,20
88,80
89,60
87,80
88,30
88,60
91,00
91,50
95,40
98,70
99,90
98,60
100,30
100,20
100,40
101,40
103,00
109,10
111,40
114,10
121,80
127,60
129,90
128,00
123,50
124,00
127,40
127,60
128,40
131,40
135,10
134,00
144,50
147,30
150,90
148,70
141,40
138,90
139,80
145,60
147,90
148,50
151,10
157,50
Dataseries Y:
Download CSV
Histogram 
103,63
103,64
103,66
103,77
103,88
103,91
103,91
103,92
104,05
104,23
104,30
104,31
104,31
104,34
104,55
104,65
104,73
104,75
104,75
104,76
104,94
105,29
105,38
105,43
105,43
105,42
105,52
105,69
105,72
105,74
105,74
105,74
105,95
106,17
106,34
106,37
106,37
106,36
106,44
106,29
106,23
106,23
106,23
106,23
106,34
106,44
106,44
106,48
106,50
106,57
106,40
106,37
106,25
106,21
106,21
106,24
106,19
106,08
106,13
106,09

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=50012&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=50012&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=50012&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term101.8459731993190.360874317885388282.2200643041180
slope0.03240178759467470.0031838651555094110.17687182467701.62092561595273e-14

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 101.845973199319 & 0.360874317885388 & 282.220064304118 & 0 \tabularnewline
slope & 0.0324017875946747 & 0.00318386515550941 & 10.1768718246770 & 1.62092561595273e-14 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=50012&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]101.845973199319[/C][C]0.360874317885388[/C][C]282.220064304118[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]0.0324017875946747[/C][C]0.00318386515550941[/C][C]10.1768718246770[/C][C]1.62092561595273e-14[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=50012&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=50012&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 term101.8459731993190.360874317885388282.2200643041180
slope0.03240178759467470.0031838651555094110.17687182467701.62092561595273e-14


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')