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Description of Statistical Computation

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 computationTue, 16 Nov 2010 09:49:54 +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/16/t1289900967lj4euy374yyxso7.htm/, Retrieved Thu, 02 May 2024 04:18:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=95304, Retrieved Thu, 02 May 2024 04:18:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160

Tree of Dependent Computations

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] [ass 3 ws6] [2010-11-14 10:45:24] [0e7b3997dca5cf9d94982fb4db7bd3d5]
-    D      [Linear Regression Graphical Model Validation] [mini tutorial] [2010-11-16 09:49:54] [3f56c8f677e988de577e4e00a8180a48] [Current]
-    D        [Linear Regression Graphical Model Validation] [Huwelijken vs Tem...] [2010-12-18 13:15:27] [0e7b3997dca5cf9d94982fb4db7bd3d5]
-    D        [Linear Regression Graphical Model Validation] [huwelijken vs bev...] [2010-12-18 13:19:35] [0e7b3997dca5cf9d94982fb4db7bd3d5]
-    D        [Linear Regression Graphical Model Validation] [huwelijken vs wer...] [2010-12-18 13:22:08] [0e7b3997dca5cf9d94982fb4db7bd3d5]
-    D        [Linear Regression Graphical Model Validation] [huwelijken vs wer...] [2010-12-18 15:55:05] [0e7b3997dca5cf9d94982fb4db7bd3d5]
-    D        [Linear Regression Graphical Model Validation] [Huwelijken vs Tem...] [2010-12-18 15:56:52] [0e7b3997dca5cf9d94982fb4db7bd3d5]
-    D        [Linear Regression Graphical Model Validation] [huwelijken vs bev...] [2010-12-18 15:59:42] [0e7b3997dca5cf9d94982fb4db7bd3d5]
- RMPD        [Kendall tau Correlation Matrix] [Pearson verband h...] [2010-12-18 17:25:13] [0e7b3997dca5cf9d94982fb4db7bd3d5]
- RMPD        [Kendall tau Correlation Matrix] [Pearson verband h...] [2010-12-18 17:29:57] [0e7b3997dca5cf9d94982fb4db7bd3d5]
- RMPD        [Kendall tau Correlation Matrix] [Kendall verband h...] [2010-12-18 17:30:57] [0e7b3997dca5cf9d94982fb4db7bd3d5]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
81,81818182
52,27272727
34,09090909
72,72727273
63,63636364
70,45454545
59,09090909
68,18181818
63,63636364
70,45454545
68,18181818
65,90909091
61,36363636
84,09090909
72,72727273
47,72727273
70,45454545
79,54545455
72,72727273
52,27272727
59,09090909
61,36363636
61,36363636
79,54545455
88,63636364
68,18181818
68,18181818
56,81818182
84,09090909
68,18181818
77,27272727
75
65,90909091
65,90909091
77,27272727
68,18181818
61,36363636
61,36363636
100
59,09090909
70,45454545
70,45454545
47,72727273
72,72727273
63,63636364
52,27272727
65,90909091
63,63636364
75
68,18181818
59,09090909
77,27272727
68,18181818
65,90909091
79,54545455
75
79,54545455
72,72727273
59,09090909
86,36363636
75
52,27272727
61,36363636
72,72727273
65,90909091
70,45454545
65,90909091
63,63636364
72,72727273
52,27272727
77,27272727
65,90909091
79,54545455
68,18181818
63,63636364
65,90909091
56,81818182
56,81818182
63,63636364
61,36363636
75
70,45454545
59,09090909
63,63636364
72,72727273
72,72727273
72,72727273
68,18181818
72,72727273
61,36363636
52,27272727
75
70,45454545
70,45454545
68,18181818
77,27272727
72,72727273
50
70,45454545
79,54545455
70,45454545
63,63636364
70,45454545
56,81818182
68,18181818
59,09090909
75
52,27272727
50
68,18181818
72,72727273
54,54545455
61,36363636
81,81818182
54,54545455
56,81818182
54,54545455
52,27272727
56,81818182
72,72727273
52,27272727
65,90909091
50
68,18181818
52,27272727
61,36363636
61,36363636
63,63636364
68,18181818
63,63636364
63,63636364
52,27272727
52,27272727
61,36363636
54,54545455
70,45454545
52,27272727
72,72727273
61,36363636
63,63636364
77,27272727
56,81818182
70,45454545
61,36363636
54,54545455
63,63636364
61,36363636
79,54545455
84,09090909
63,63636364
68,18181818
63,63636364
75
79,54545455
70,45454545
63,63636364
59,09090909
63,63636364
65,90909091
54,54545455
Dataseries Y:
Download CSV
Histogram 
37
2
3
6
17
14
3
4
18
40
0
35
12
22
50
3
3
16
12
2
4
16
6
0
21
21
2
35
10
4
36
7
17
10
14
7
12
14
45
15
20
16
9
26
15
12
12
11
30
14
24
16
10
2
14
11
16
4
15
0
12
0
13
18
11
13
24
20
12
14
21
21
0
46
2
0
3
3
25
13
18
24
20
24
23
15
7
3
0
35
14
8
0
13
12
21
12
18
6
39
8
25
26
19
4
18
14
18
13
21
4
15
0
1
0
0
10
24
19
12
0
2
8
24
23
42
6
24
18
3
14
0
32
10
4
23
5
18
24
36
40
20
40
33
17
14
40
27
24
4
15
8
43
14
24
3
1
31
12
13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=95304&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=95304&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95304&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 time4 seconds
R Server'George Udny Yule' @ 72.249.76.132






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term-2.556615384797926.23669529791108-0.4099311033608190.682411760103766
slope0.2698293345705380.09337260125588282.889812760288050.00439682668988906

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & -2.55661538479792 & 6.23669529791108 & -0.409931103360819 & 0.682411760103766 \tabularnewline
slope & 0.269829334570538 & 0.0933726012558828 & 2.88981276028805 & 0.00439682668988906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=95304&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]-2.55661538479792[/C][C]6.23669529791108[/C][C]-0.409931103360819[/C][C]0.682411760103766[/C][/ROW]
[ROW][C]slope[/C][C]0.269829334570538[/C][C]0.0933726012558828[/C][C]2.88981276028805[/C][C]0.00439682668988906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=95304&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95304&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:

Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term-2.556615384797926.23669529791108-0.4099311033608190.682411760103766
slope0.2698293345705380.09337260125588282.889812760288050.00439682668988906


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Input Parameters & R Code

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')