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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 computationSat, 22 Dec 2012 10:43:46 -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/22/t1356191054zjxk4ezjqcg6ekp.htm/, Retrieved Thu, 31 Oct 2024 22:52:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204543, Retrieved Thu, 31 Oct 2024 22:52:37 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
- RMPD        [Simple Linear Regression] [simple regression] [2012-12-22 15:43:46] [081b45eff66f9ee50ac0b17603ac2bbc] [Current]
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Dataseries X:
426	7.1	3.2	24776	3
396	7.2	2.9	19814	3
458	7.2	2.7	12738	3
315	7.1	3.1	31566	3
337	6.9	2.7	30111	3
386	6.8	2.6	30019	3
352	6.8	1.8	31934	3
384	6.8	2.3	25826	3
439	6.9	2.2	26835	3.18
397	7.1	1.8	20205	3.25
453	7.2	1.4	17789	3.25
364	7.2	0.3	20520	3.23
367	7.1	0.8	22518	2.92
474	7.1	-0.5	15572	2.25
373	7.2	-2.2	11509	1.62
404	7.5	-2.9	25447	1
385	7.7	-5.1	24090	0.66
365	7.8	-7.2	27786	0.31
366	7.7	-7.9	26195	0.25
421	7.7	-10.9	20516	0.25
354	7.8	-12.7	22759	0.25
367	8	-14	19028	0.25
413	8.1	-15.6	16971	0.25
362	8.1	-16	20036	0.25
385	8	-17.2	22485	0.25
343	8.1	-17.6	18730	0.25
369	8.2	-15.5	14538	0.25
363	8.4	-13.7	27561	0.25
318	8.5	-11.4	25985	0.25
393	8.5	-9.2	34670	0.25
325	8.5	-6.3	32066	0.25
403	8.5	-3.1	27186	0.25
392	8.5	0	29586	0.25
409	8.4	3	21359	0.25
485	8.3	5.4	21553	0.25
423	8.2	7.6	19573	0.25
428	8.1	9.7	24256	0.25
431	7.9	12	22380	0.25
416	7.6	11.6	16167	0.25
330	7.3	10	27297	0.25
314	7.1	10.8	28287	0.25
345	7	11.3	33474	0.39
365	7.1	10.1	28229	0.5
417	7.1	9.4	28785	0.5
356	7.1	9.6	25597	0.65
477	7.3	7.9	18130	0.75
423	7.3	7.3	20198	0.75
386	7.3	6.2	22849	0.75
390	7.2	4.9	23118	0.57
407	7.2	3.6	21925	0.36
398	7.1	2.9	20801	0.25
327	7.1	3.1	18785	0.25
304	7.1	1.7	20659	0.25
378	7.2	0.6	29367	0.25
311	7.3	-0.4	23992	0.25
376	7.4	-1.1	20645	0.25
340	7.4	-2.9	22356	0.08
383	7.5	-2.8	17902	0
467	7.4	-3	15879	0
439	7.4	-3.2	16963	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204543&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204543&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204543&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Linear Regression Model
Y ~ X - 1
coefficients:
EstimateStd. Errort valuePr(>|t|)
X0032.480
- - -
Residual Std. Err. 1.751 on 59 df
Multiple R-sq. 0.947
Adjusted R-sq. 0.946

\begin{tabular}{lllllllll}
\hline
Linear Regression Model \tabularnewline
Y ~ X - 1 \tabularnewline
coefficients: &   \tabularnewline
  & Estimate & Std. Error & t value & Pr(>|t|) \tabularnewline
X & 0 & 0 & 32.48 & 0 \tabularnewline
- - -  &   \tabularnewline
Residual Std. Err.  & 1.751  on  59 df \tabularnewline
Multiple R-sq.  & 0.947 \tabularnewline
Adjusted R-sq.  & 0.946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204543&T=1

[TABLE]
[ROW][C]Linear Regression Model[/C][/ROW]
[ROW][C]Y ~ X - 1[/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]X[/C][C]0[/C][C]0[/C][C]32.48[/C][C]0[/C][/ROW]
[ROW][C]- - - [/C][C] [/C][/ROW]
[ROW][C]Residual Std. Err. [/C][C]1.751  on  59 df[/C][/ROW]
[ROW][C]Multiple R-sq. [/C][C]0.947[/C][/ROW]
[ROW][C]Adjusted R-sq. [/C][C]0.946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204543&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204543&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 - 1
coefficients:
EstimateStd. Errort valuePr(>|t|)
X0032.480
- - -
Residual Std. Err. 1.751 on 59 df
Multiple R-sq. 0.947
Adjusted R-sq. 0.946







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
personenwagens13235.5183235.5181054.9530
Residuals59180.9523.067

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
personenwagens & 1 & 3235.518 & 3235.518 & 1054.953 & 0 \tabularnewline
Residuals & 59 & 180.952 & 3.067 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204543&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]personenwagens[/C][C]1[/C][C]3235.518[/C][C]3235.518[/C][C]1054.953[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]59[/C][C]180.952[/C][C]3.067[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204543&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204543&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)
personenwagens13235.5183235.5181054.9530
Residuals59180.9523.067



Parameters (Session):
par1 = 2 ; par2 = 4 ; par3 = FALSE ;
Parameters (R input):
par1 = 2 ; par2 = 4 ; par3 = FALSE ;
R code (references can be found in the software module):
par3 <- 'FALSE'
par2 <- '4'
par1 <- '2'
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()