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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 computationThu, 11 Nov 2010 10:31:04 +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/11/t1289471363awaddi1veu3ptq4.htm/, Retrieved Thu, 28 Mar 2024 08:11:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=93172, Retrieved Thu, 28 Mar 2024 08:11:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact185
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] [Hypothese 1] [2010-11-11 10:31:04] [606daa46683961cdd2a740c3e0051d62] [Current]
-           [Linear Regression Graphical Model Validation] [Invoer en Uitvoer...] [2010-11-11 15:31:40] [62f7c80c4d96454bbd2b2b026ea9aad9]
-    D        [Linear Regression Graphical Model Validation] [Invoer en Uitvoer...] [2010-11-11 15:38:29] [62f7c80c4d96454bbd2b2b026ea9aad9]
-    D          [Linear Regression Graphical Model Validation] [Simple lineair re...] [2010-11-30 09:48:39] [62f7c80c4d96454bbd2b2b026ea9aad9]
-    D        [Linear Regression Graphical Model Validation] [ws 6 mini tutoria...] [2010-11-16 16:25:48] [c1a9f1d6a1a56eda57b5ddd6daa7a288]
-    D        [Linear Regression Graphical Model Validation] [ws 6 mini tutoria...] [2010-11-16 16:51:39] [c1a9f1d6a1a56eda57b5ddd6daa7a288]
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Dataseries X:
17681,2
19858,9
16997,5
16969,9
18908,9
15692,1
15160
15806,8
16007,1
16059,1
16189,4
12522,5
14733,8
15686,3
13779,7
14423,8
15290,6
14308,3
13855,6
14384,5
15638,6
19711,6
20359,8
16141,4
20056,9
20605,5
19325,8
20547,7
19211,2
19009,5
18746,8
16471,5
18957,2
20515,2
18374,4
16192,9
18147,5
19301,4
18344,7
17183,6
19630
17167,2
17428,5
16016,5
18466,5
18406,6
18174,1
14851,9
16260,7
18329,6
18003,8
15903,8
19554,2
16554,2
16198,9
Dataseries Y:
17203,6
19542,1
17332,7
17962,5
18664,9
15878,6
16014,6
16867,9
16014
16793,9
16127,6
13557,7
14238,2
15465,9
13881
14346,9
15668,8
14521,1
15071,8
15816,8
17180,4
20432,3
21289,5
18203,6
20159,5
21053,2
19673,6
21473,3
20244,7
19049,6
20194,3
18021,9
19537,3
20286,6
17967,7
16409,9
17802,7
18509,9
18161,3
16721,3
19106,9
16772,1
17463,6
16162,3
17862,9
17664,9
17180,8
15672,7
15189,8
17699,4
17444,6
15930,7
19691,6
16698
16896,2




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=93172&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=93172&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93172&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 term1765.4500252572821.2742184577552.149647444884470.0361668110280349
slope0.9109532287495520.047324805106569919.24895890639580

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 1765.4500252572 & 821.274218457755 & 2.14964744488447 & 0.0361668110280349 \tabularnewline
slope & 0.910953228749552 & 0.0473248051065699 & 19.2489589063958 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=93172&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]1765.4500252572[/C][C]821.274218457755[/C][C]2.14964744488447[/C][C]0.0361668110280349[/C][/ROW]
[ROW][C]slope[/C][C]0.910953228749552[/C][C]0.0473248051065699[/C][C]19.2489589063958[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=93172&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=93172&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 term1765.4500252572821.2742184577552.149647444884470.0361668110280349
slope0.9109532287495520.047324805106569919.24895890639580



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')