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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 computationSun, 07 Nov 2010 10:07:07 +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/07/t12891243352di38qkyonmn2bf.htm/, Retrieved Thu, 28 Mar 2024 10:44:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=92504, Retrieved Thu, 28 Mar 2024 10:44:43 +0000
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Estimated Impact203
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-     [Central Tendency] [Arabica Price in ...] [2008-01-19 12:03:37] [74be16979710d4c4e7c6647856088456]
- RMPD    [Linear Regression Graphical Model Validation] [Tutorial Hypothese 3] [2010-11-07 10:07:07] [59f7d3e7fcb6374015f4e6b9053b0f01] [Current]
-   PD      [Linear Regression Graphical Model Validation] [Paper Enkelvoudig...] [2010-12-05 15:06:27] [56d90b683fcd93137645f9226b43c62b]
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Dataseries X:
85
105
108
92
112,5
112
104
69
94,5
68,5
104
103,5
123,5
93
50,5
89
107
78,5
115
114
85
81
83,5
112
101
103,5
93,5
112
140
83,5
90
84
110,5
96
95
121
99,5
142,5
118
104,5
102,5
89,5
95
98,5
94
108
63,5
84,5
93,5
112
148,5
112
109
91,5
75
84
107
92,5
109,5
84
102,5
106
77
111,5
114
75
73,5
93,5
105
113,5
140
77
84,5
113,5
77,5
117,5
98
112
101
95
81
91
142
98,5
112
116,5
98,5
83,5
133
91,5
72,5
106,5
67
122,5
74
144,5
84
72,5
64
116
84
93,5
111,5
92
115
85
108
108
85
86
110,5
98
105
76,5
84
128
87
128
111
79
90
84
112
93
117
84
99,5
95
84
134
171,5
98,5
118,5
94,5
105
104
83
105,5
84
86
81
94
78,5
119,5
133
119
95
112
75
92
112
98,5
112,5
112,5
108
108
88
106
92
117,5
84
112
100
112
84
127,5
80,5
93,5
86,5
92,5
108,5
121
112
114
84
81
111,5
81
70
140
117
84
112
150,5
147
105
119,5
84
91
101
117,5
121
133
112
91,5
105
111
112
114
91
98
118
115,5
112
112
91
85
112
87,5
118
83,5
116
89
171,5
112
72
150
134,5
97
71,5
73,5
112
75
128
98
84
99
112
79,5
80,5
102,5
76
112
114
140
107,5
87
Dataseries Y:
56,3
62,3
63,3
59
62,5
62,5
59
56,5
62
53,8
61,5
61,5
64,5
58,3
51,3
58,8
65,3
59,5
61,3
63,3
61,8
53,5
58
61,3
63,3
61,5
60,8
59
65,5
56,3
64,3
58
64,3
57,5
57,8
61,5
62,3
61,8
65,3
58,3
62,8
59,3
61,5
62
61,3
62,3
52,8
59,8
59,5
61,3
63,5
64,8
60
59
55,8
57,8
61,3
62,3
64,3
55,5
64,5
60
56,3
58,3
60
54,5
55,8
62,8
60,5
63,3
66,8
60
60,5
64,3
58,3
66,5
65,3
60,5
59,5
59
61,3
61,5
64,8
56,8
66,5
61,5
63
57
65,5
62
56
61,3
55,5
61
54,5
66
56,5
56
51,5
62
63
61
64
61
59,8
61,3
63,3
63,5
61,5
60,3
61,3
64,8
60,5
57,3
59,5
60,8
60,5
67
64,8
50,5
57,5
60,5
61,8
61,3
66,3
53,3
59
57,8
60
68,3
67,5
63,8
65
59,5
66
61,8
57,3
66
56,5
58,3
61
62,8
59,3
67,3
66,3
64,5
60,5
66
57,5
64
68
63,5
69
63,8
66
63,5
59,5
66,3
57
60
57
67,3
62
65
59,5
67,8
58
60
58,5
58,3
61,5
65
66,5
68,5
57
61,5
66,5
52,5
55
71
66,5
58,8
66,3
65,8
71
59,5
69,8
62,5
56,5
57,5
65,3
67,3
67
66
61,8
60
63
60,5
65,5
62
59
61,8
63,3
66
61,8
63
57,5
63
56
60,5
56,8
64
60
69,5
63,3
56,3
72
65,3
60,8
55
55
66,5
56,8
64,8
64,5
58
62,8
63,8
57,8
57,3
63,5
55
66,5
65
61,5
62
59,3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92504&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 Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92504&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92504&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 Ronald Aylmer Fisher' @ 193.190.124.24







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term45.43310063448420.86318554985010452.63422289955370
slope0.1572576072314550.0083683585485207818.79192990114560

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 45.4331006344842 & 0.863185549850104 & 52.6342228995537 & 0 \tabularnewline
slope & 0.157257607231455 & 0.00836835854852078 & 18.7919299011456 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92504&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]45.4331006344842[/C][C]0.863185549850104[/C][C]52.6342228995537[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]0.157257607231455[/C][C]0.00836835854852078[/C][C]18.7919299011456[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92504&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92504&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 term45.43310063448420.86318554985010452.63422289955370
slope0.1572576072314550.0083683585485207818.79192990114560



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')