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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Simple Regression Y ~ X.wasp
Title produced by softwareSimple Linear Regression
Date of computationFri, 12 Dec 2014 16:43:06 +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/2014/Dec/12/t1418402789r62x2rljlne72mt.htm/, Retrieved Thu, 16 May 2024 03:37:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266844, Retrieved Thu, 16 May 2024 03:37:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Simple Linear Regression] [kjdhb] [2014-12-12 16:43:06] [cf34f1111566f5ca061ad80c95189d56] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7.5 12
6 8
6.5 11
1 13
1 11
5.5 10
8.5 7
6.5 10
4.5 15
2 12
5 12
0.5 10
5 10
5 14
2.5 6
5 12
5.5 14
3.5 11
3 8
4 12
0.5 15
6.5 13
4.5 11
7.5 12
5.5 7
4 11
7.5 7
7 12
4 12
5.5 13
2.5 9
5.5 11
3.5 12
2.5 15
4.5 12
4.5 6
4.5 5
6 13
2.5 11
5 6
0 12
5 10
6.5 6
5 12
6 11
4.5 6
5.5 12
1 12
7.5 8
6 10
5 11
1 7
5 12
6.5 13
7 14
4.5 12
0 6
8.5 14
3.5 10
7.5 12
3.5 11
6 10
1.5 7
9 12
3.5 7
3.5 12
4 12
6.5 10
7.5 10
6 12
5 12
5.5 12
3.5 8
7.5 10
6.5 5
6.5 10
6.5 12
7 11
3.5 9
1.5 12
4 11
7.5 10
4.5 12
0 10
3.5 9
5.5 11
5 12
4.5 7
2.5 11
7.5 12
7 6
0 9
4.5 15
3 10
1.5 11
3.5 12
2.5 12
5.5 12
8 11
1 9
5 11
4.5 12
3 12
3 14
8 8
2.5 10
7 9
0 10
1 9
3.5 10
5.5 12
5.5 11
0.5 9
7.5 11
9 12
9.5 12
8.5 7
7 12
8 12
10 12
7 10
8.5 15
9 10
9.5 15
4 10
6 15
8 9
5.5 15
9.5 12
7.5 13
7 12
7.5 12
8 8
7 9
7 15
6 12
10 12
2.5 15
9 11
8 12
6 6
8.5 14
6 12
9 12
8 12
9 11
5.5 12
7 12
5.5 12
9 12
2 8
8.5 8
9 12
8.5 12
9 11
7.5 10
10 11
9 12
7.5 13
6 12
10.5 12
8.5 10
8 10
10 11
10.5 8
6.5 12
9.5 9
8.5 12
7.5 9
5 11
8 15
10 8
7 8
7.5 11
7.5 11
9.5 11
6 13
10 7
7 12
3 8
6 8
7 4
10 11
7 10
3.5 7
8 12
10 11
5.5 9
6 10
6.5 8
6.5 8
8.5 11
4 12
9.5 10
8 10
8.5 12
5.5 8
7 11
9 8
8 10
10 14
8 9
6 9
8 10
5 13
9 12
4.5 13
8.5 8
9.5 3
8.5 8
7.5 12
7.5 11
5 9
7 12
8 12
5.5 12
8.5 10
9.5 13
7 9
8 12
8.5 11
3.5 14
6.5 11
6.5 9
10.5 12
8.5 8
8 15
10 12
10 14
9.5 12
9 9
10 9
7.5 13
4.5 13
4.5 15
0.5 11
6.5 7
4.5 10
5.5 11
5 14
6 14
4 13
8 12
10.5 8
6.5 13
8 9
8.5 12
5.5 13
7 11
5 11
3.5 13
5 12
9 12
8.5 10
5 9
9.5 10
3 13
1.5 13
6 9
0.5 11
6.5 12
7.5 8
4.5 12
8 12
9 12
7.5 9
8.5 12
7 12
9.5 11
6.5 12
9.5 6
6 7
8 10
9.5 12
8 10
8 12
9 9
5 3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\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 & 3 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266844&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266844&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266844&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 time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)6.0520.7328.2670
X0.0120.0670.1830.855
- - -
Residual Std. Err. 2.538 on 276 df
Multiple R-sq. 0
Adjusted R-sq. -0.004

\begin{tabular}{lllllllll}
\hline
Linear Regression Model \tabularnewline
Y ~ X \tabularnewline
coefficients: &   \tabularnewline
  & Estimate & Std. Error & t value & Pr(>|t|) \tabularnewline
(Intercept) & 6.052 & 0.732 & 8.267 & 0 \tabularnewline
X & 0.012 & 0.067 & 0.183 & 0.855 \tabularnewline
- - -  &   \tabularnewline
Residual Std. Err.  & 2.538  on  276 df \tabularnewline
Multiple R-sq.  & 0 \tabularnewline
Adjusted R-sq.  & -0.004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266844&T=1

[TABLE]
[ROW][C]Linear Regression Model[/C][/ROW]
[ROW][C]Y ~ X[/C][/ROW]
[ROW][C]coefficients:[/C][C] [/C][/ROW]
[ROW][C] [/C][C]Estimate[/C][C]Std. Error[/C][C]t value[/C][C]Pr(>|t|)[/C][/ROW]
[C](Intercept)[/C][C]6.052[/C][C]0.732[/C][C]8.267[/C][C]0[/C][/ROW]
[C]X[/C][C]0.012[/C][C]0.067[/C][C]0.183[/C][C]0.855[/C][/ROW]
[ROW][C]- - - [/C][C] [/C][/ROW]
[ROW][C]Residual Std. Err. [/C][C]2.538  on  276 df[/C][/ROW]
[ROW][C]Multiple R-sq. [/C][C]0[/C][/ROW]
[ROW][C]Adjusted R-sq. [/C][C]-0.004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266844&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266844&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:

Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)6.0520.7328.2670
X0.0120.0670.1830.855
- - -
Residual Std. Err. 2.538 on 276 df
Multiple R-sq. 0
Adjusted R-sq. -0.004






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
CONFSOFTTOT10.2160.2160.0340.855
Residuals2761777.9286.442 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
CONFSOFTTOT & 1 & 0.216 & 0.216 & 0.034 & 0.855 \tabularnewline
Residuals & 276 & 1777.928 & 6.442 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266844&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]1[/C][C]0.216[/C][C]0.216[/C][C]0.034[/C][C]0.855[/C][/ROW]
[ROW][C]Residuals[/C][C]276[/C][C]1777.928[/C][C]6.442[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266844&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266844&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
CONFSOFTTOT10.2160.2160.0340.855
Residuals2761777.9286.442


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1)
cat2<- as.numeric(par2)
intercept<-as.logical(par3)
x <- t(x)
xdf<-data.frame(t(y))
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
xdf <- data.frame(xdf[[cat1]], xdf[[cat2]])
names(xdf)<-c('Y', 'X')
if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) )
sumlmxdf<-summary(lmxdf)
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
nc <- ncol(sumlmxdf$'coefficients')
nr <- nrow(sumlmxdf$'coefficients')
a<-table.row.start(a)
a<-table.element(a,'Linear Regression Model', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],nc+1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'coefficients:',1,TRUE)
a<-table.element(a, ' ',nc,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
for(i in 1 : nc){
a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE)
}#end header
a<-table.row.end(a)
for(i in 1: nr){
a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE)
for(j in 1 : nc){
a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE)
}
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, '- - - ',1,TRUE)
a<-table.element(a, ' ',nc,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Std. Err. ',1,TRUE)
a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
a<-table.element(a, 'Df',1,TRUE)
a<-table.element(a, 'Sum Sq',1,TRUE)
a<-table.element(a, 'Mean Sq',1,TRUE)
a<-table.element(a, 'F value',1,TRUE)
a<-table.element(a, 'Pr(>F)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, V2,1,TRUE)
a<-table.element(a, anova.xdf$Df[1])
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',1,TRUE)
a<-table.element(a, anova.xdf$Df[2])
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3))
a<-table.element(a, ' ')
a<-table.element(a, ' ')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='regressionplot.png')
plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution')
if(intercept == TRUE) abline(coef(lmxdf), col='red')
if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red')
dev.off()
library(car)
bitmap(file='residualsQQplot.png')
qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit')
dev.off()
bitmap(file='residualsplot.png')
plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit')
dev.off()
bitmap(file='cooksDistanceLmplot.png')
plot.lm(lmxdf, which=4)
dev.off()