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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, 21 Dec 2012 18:00:43 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356130871ybm2ijz85d2e5ey.htm/, Retrieved Thu, 28 Mar 2024 10:46:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204379, Retrieved Thu, 28 Mar 2024 10:46:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- RMPD  [Simple Linear Regression] [] [2012-12-19 21:56:03] [43bd65bee76289cab2ce37423d405966]
- R P       [Simple Linear Regression] [] [2012-12-21 23:00:43] [71d0353b830738bf84dffbfcdf1408fc] [Current]
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Dataseries X:
13	12
16	11
19	14
15	12
14	21
13	12
19	22
15	11
14	10
15	13
16	10
16	8
16	15
16	14
17	10
15	14
15	14
20	11
18	10
16	13
16	9.5
16	14
19	12
16	14
17	11
17	9
16	11
15	15
16	14
14	13
15	9
12	15
14	10
16	11
14	13
10	8
10	20
14	12
16	10
16	10
16	9
14	14
20	8
14	14
14	11
11	13
14	9
15	11
16	15
14	11
16	10
14	14
12	18
16	14
9	11
14	14.5
16	13
16	9
15	10
16	15
12	20
16	12
16	12
14	14
16	13
17	11
18	17
18	12
12	13
16	14
10	13
14	15
18	13
18	10
16	11
17	19
16	13
16	17
13	13
16	9
16	11
16	9
15	12
15	12
16	13
14	13
16	12
16	15
15	22
12	13
17	15
16	13
15	15
13	12.5
16	11
16	16
16	11
16	11
14	10
16	10
16	16
20	12
15	11
16	16
13	19
17	11
16	16
16	15
12	24
16	14
16	15
17	11
13	15
12	12
18	10
14	14
14	13
13	9
16	15
13	15
16	14
13	11
16	8
15	11
16	11
15	8
17	10
15	11
12	13
16	11
10	20
16	10
12	15
14	12
15	14
13	23
15	14
11	16
12	11
11	12
16	10
15	14
17	12
16	12
10	11
18	12
13	13
16	11
13	19
10	12
15	17
16	9
16	12
14	19
10	18
17	15
13	14
15	11
16	9
12	18
13	16
13	24
12	14
17	20
15	18
10	23
14	12
11	14
13	16
16	18
12	20
16	12
12	12
9	17
12	13
15	9
12	16
12	18
14	10
12	14
16	11
11	9
19	11
15	10
8	11
16	19
17	14
12	12
11	14
11	21
14	13
16	10
12	15
16	16
13	14
15	12
16	19
16	15
14	19
16	13
16	17
14	12
11	11
12	14
15	11
15	13
16	12
16	15
11	14
15	12
12	17
12	11
15	18
15	13
16	17
14	13
17	11
14	12
13	22
15	14
13	12
14	12
15	17
12	9
13	21
8	10
14	11
14	12
11	23
12	13
13	12
10	16
16	9
18	17
13	9
11	14
4	17
13	13
16	11
10	12
12	10
12	19
10	16
13	16
15	14
12	20
14	15
10	23
12	20
12	16
11	14
10	17
12	11
16	13
12	17
14	15
16	21
14	18
13	15
4	8
15	12
11	12
11	22
14	12




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204379&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]5 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=204379&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204379&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 time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)16.4980.59827.6060
X-0.1630.043-3.8420
- - -
Residual Std. Err. 2.394 on 262 df
Multiple R-sq. 0.053
Adjusted R-sq. 0.05

\begin{tabular}{lllllllll}
\hline
Linear Regression Model \tabularnewline
Y ~ X \tabularnewline
coefficients: &   \tabularnewline
  & Estimate & Std. Error & t value & Pr(>|t|) \tabularnewline
(Intercept) & 16.498 & 0.598 & 27.606 & 0 \tabularnewline
X & -0.163 & 0.043 & -3.842 & 0 \tabularnewline
- - -  &   \tabularnewline
Residual Std. Err.  & 2.394  on  262 df \tabularnewline
Multiple R-sq.  & 0.053 \tabularnewline
Adjusted R-sq.  & 0.05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204379&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]16.498[/C][C]0.598[/C][C]27.606[/C][C]0[/C][/ROW]
[C]X[/C][C]-0.163[/C][C]0.043[/C][C]-3.842[/C][C]0[/C][/ROW]
[ROW][C]- - - [/C][C] [/C][/ROW]
[ROW][C]Residual Std. Err. [/C][C]2.394  on  262 df[/C][/ROW]
[ROW][C]Multiple R-sq. [/C][C]0.053[/C][/ROW]
[ROW][C]Adjusted R-sq. [/C][C]0.05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204379&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204379&T=1

As an alternative you can also use a QR Code:  

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

Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)16.4980.59827.6060
X-0.1630.043-3.8420
- - -
Residual Std. Err. 2.394 on 262 df
Multiple R-sq. 0.053
Adjusted R-sq. 0.05







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
depression184.60484.60414.760
Residuals2621501.7595.732

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
depression & 1 & 84.604 & 84.604 & 14.76 & 0 \tabularnewline
Residuals & 262 & 1501.759 & 5.732 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204379&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]depression[/C][C]1[/C][C]84.604[/C][C]84.604[/C][C]14.76[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]262[/C][C]1501.759[/C][C]5.732[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204379&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204379&T=2

As an alternative you can also use a QR Code:  

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

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
depression184.60484.60414.760
Residuals2621501.7595.732



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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()