Free Statistics

of Irreproducible Research!

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 computationMon, 20 Dec 2010 18:15:58 +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/Dec/20/t1292869150ehlhfo2s53mjdgg.htm/, Retrieved Fri, 03 May 2024 17:17:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113050, Retrieved Fri, 03 May 2024 17:17:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Linear Regression Graphical Model Validation] [] [2010-12-15 17:33:32] [7f2363d2c77d3bf71367965cc53be730]
-    D  [Linear Regression Graphical Model Validation] [] [2010-12-20 18:07:09] [7f2363d2c77d3bf71367965cc53be730]
-    D      [Linear Regression Graphical Model Validation] [] [2010-12-20 18:15:58] [4dba6678eac10ee5c3460d144a14bd5c] [Current]
-    D        [Linear Regression Graphical Model Validation] [] [2010-12-20 18:28:05] [7f2363d2c77d3bf71367965cc53be730]
-    D          [Linear Regression Graphical Model Validation] [] [2010-12-20 18:38:13] [7f2363d2c77d3bf71367965cc53be730]
- RMPD          [Multiple Regression] [] [2010-12-20 18:49:41] [7f2363d2c77d3bf71367965cc53be730]
-   P             [Multiple Regression] [] [2010-12-22 13:52:50] [7f2363d2c77d3bf71367965cc53be730]
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Dataseries X:
29.58
29.61
27.24
27.41
28.64
27.60
26.45
27.47
25.88
22.21
19.67
19.33
19.67
20.74
24.42
26.27
27.02
25.52
26.94
28.38
29.67
28.85
26.27
29.42
32.94
35.87
33.55
28.25
28.14
30.72
30.76
31.59
28.29
30.33
31.09
32.15
34.27
34.74
36.76
36.69
40.28
38.02
40.69
44.94
45.95
53.13
48.46
43.33
46.84
47.97
54.31
53.04
49.83
56.26
58.70
64.97
65.57
62.37
58.30
59.43
65.51
61.63
62.90
69.69
70.94
70.96
74.41
73.05
63.87
58.88
59.37
62.03
54.57
59.26
60.56
63.97
63.46
67.48
74.18
72.39
79.93
86.20
94.62
91.73
92.95
95.35
105.56
112.57
125.39
133.93
133.44
116.61
103.90
76.65
57.44
41.02
41.74
39.16
47.98
49.79
59.16
69.68
64.09
71.06
69.46
75.82
78.08
74.30
Dataseries Y:
373,1
336,99
373,1
361,06
373,1
361,06
373,1
373,1
361,06
373,1
361,06
373,1
381,3
344,4
381,3
369
381,3
369
381,3
381,3
369
381,3
369
381,3
400,16
361,43
400,16
387,25
400,16
387,25
400,16
400,16
387,25
400,16
387,25
400,16
408,87
382,5
408,87
395,69
408,87
395,69
408,87
408,87
395,69
408,87
395,69
408,87
430,2
388,57
430,2
416,32
430,2
416,32
430,2
430,2
416,32
430,2
416,32
430,2
403,84
364,76
403,84
390,81
403,84
390,81
403,84
403,84
390,81
403,84
390,81
403,84
447,5
404,19
447,5
433,07
447,5
433,07
447,5
447,5
433,07
447,5
433,07
447,5
478,16
447,31
478,16
462,74
478,16
462,74
478,16
478,16
462,74
478,16
462,74
478,16
478,5
432,2
478,5
463,07
478,5
463,07
478,5
478,5
463,07
478,5
463,07
478,5




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=113050&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=113050&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113050&T=0

As an alternative you can also use a 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 term357.8492923929455.7043695022312362.73248818348370
slope1.064575054304770.095990157056744711.09046059457370

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 357.849292392945 & 5.70436950223123 & 62.7324881834837 & 0 \tabularnewline
slope & 1.06457505430477 & 0.0959901570567447 & 11.0904605945737 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113050&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]357.849292392945[/C][C]5.70436950223123[/C][C]62.7324881834837[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]1.06457505430477[/C][C]0.0959901570567447[/C][C]11.0904605945737[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113050&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113050&T=1

As an alternative you can also use a 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 term357.8492923929455.7043695022312362.73248818348370
slope1.064575054304770.095990157056744711.09046059457370



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')