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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 computationTue, 15 Nov 2011 06:06:09 -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/2011/Nov/15/t1321355178vajzir33s30bzen.htm/, Retrieved Fri, 29 Mar 2024 05:13:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=142723, Retrieved Fri, 29 Mar 2024 05:13:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
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]
-  MPD  [Linear Regression Graphical Model Validation] [] [2011-11-15 10:30:33] [77e355412ccdb651b3c7eae41c3da865]
- R P       [Linear Regression Graphical Model Validation] [] [2011-11-15 11:06:09] [5f7ae77ad4c15dc18491c39fdf8bddde] [Current]
-             [Linear Regression Graphical Model Validation] [] [2011-11-15 11:07:33] [aefb5c2d4042694c5b6b82f93ac1885a]
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Dataseries X:
22
16
22
21
14
13
18
18
15
16
25
32
20
28
23
35
20
22
15
23
23
22
14
26
24
18
15
23
17
22
25
28
30
20
20
20
26
16
8
20
20
25
16
18
17
22
26
24
24
20
12
4
26
18
11
16
29
20
23
20
27
18
27
22
18
10
15
23
20
15
20
16
20
20
22
24
20
19
18
21
18
25
21
23
8
20
22
19
22
23
20
21
19
20
20
16
20
19
33
19
27
21
20
19
12
17
18
20
16
20
23
26
1
20
11
22
19
0
16
8
23
26
18
13
8
19
29
28
26
27
30
17
16
19
24
16
22
18
21
22
9
21
21
21
25
25
16
25
0
0
0
0
0
0
16
27
0
0
0
5
1
23
0
12
Dataseries Y:
68
56
30
70
29
43
66
22
52
34
87
107
57
89
41
122
75
45
40
86
68
76
48
104
83
63
40
83
56
71
93
72
107
75
72
62
90
40
18
75
55
63
55
47
23
69
66
84
73
70
42
7
95
61
32
56
108
71
78
69
85
47
50
76
56
21
56
68
66
52
65
51
72
50
75
81
75
61
62
61
55
60
79
32
27
59
82
71
32
80
36
76
57
73
71
60
67
56
123
65
87
62
37
64
17
28
48
64
43
60
87
75
0
54
30
47
49
0
29
9
78
90
50
24
21
71
102
83
89
83
104
60
48
71
81
50
82
45
67
59
12
57
62
78
73
89
51
84
0
0
0
0
0
0
34
55
0
0
0
13
4
31
0
19




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=142723&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=142723&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=142723&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'Gwilym Jenkins' @ jenkins.wessa.net







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term-5.032827069506082.65477602454982-1.89576334235560.0597720030299025
slope3.317529215358930.13323325042126424.90015971890940

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & -5.03282706950608 & 2.65477602454982 & -1.8957633423556 & 0.0597720030299025 \tabularnewline
slope & 3.31752921535893 & 0.133233250421264 & 24.9001597189094 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=142723&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]-5.03282706950608[/C][C]2.65477602454982[/C][C]-1.8957633423556[/C][C]0.0597720030299025[/C][/ROW]
[ROW][C]slope[/C][C]3.31752921535893[/C][C]0.133233250421264[/C][C]24.9001597189094[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=142723&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=142723&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 term-5.032827069506082.65477602454982-1.89576334235560.0597720030299025
slope3.317529215358930.13323325042126424.90015971890940



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')