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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, 15 Nov 2011 04:10:29 -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/t1321348562de70ntd1jc1qcdb.htm/, Retrieved Sat, 20 Apr 2024 09:46:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=142536, Retrieved Sat, 20 Apr 2024 09:46:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordscholesterol vs gewicht
Estimated Impact108

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] [mini-tutorial] [2011-11-15 09:10:29] [065e524ef27b3ebe8baf73e00eb8c266] [Current]
- R PD      [Linear Regression Graphical Model Validation] [Paper] [2011-12-16 14:28:53] [227e53f633d125e3e89f625705633e7f]
- RMPD      [Harrell-Davis Quantiles] [Paper] [2011-12-16 14:33:43] [227e53f633d125e3e89f625705633e7f]
- RMPD      [Central Tendency] [Paper] [2011-12-16 14:37:31] [227e53f633d125e3e89f625705633e7f]
- RMPD      [Pearson Correlation] [Paper] [2011-12-16 14:50:31] [227e53f633d125e3e89f625705633e7f]
- R           [Pearson Correlation] [Paper] [2011-12-16 14:53:46] [227e53f633d125e3e89f625705633e7f]
- RM D        [Cronbach Alpha] [Paper] [2011-12-16 15:04:42] [227e53f633d125e3e89f625705633e7f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
83
79
92
83
92
103
82
86
106
79
86
76
108
82
108
118
127
123
72
105
63
86
58
59
100
100
78
94
105
89
101
92
105
76
80
66
117
94
107
110
110
106
94
71
101
84
89
119
97
82
89
70
101
81
74
107
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83
95
82
88
74
104
73
73
81
79
83
111
138
81
107
66
81
74
96
86
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73
71
64
79
60
111
107
90
98
77
93
68
74
70
80
81
72
81
92
81
78
92
92
107
98
86
77
96
104
77
65
61
117
84
69
85
116
115
55
64
117
68
104
66
70
89
79
70
63
79
62
94
83
118
62
78
83
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84
76
100
80
98
89
98
88
81
88
75
77
88
65
69
76
53
82
67
84
112
91
104
79
90
60
79
99
68
79
107
114
81
83
61
82
134
102
132
72
72
102
92
94
86
84
66
129
88
109
84
73
86
113
88
90
82
111
73
91
72
111
108
83
81
111
69
106
115
132
78
90
132
91
115
65
77
71
74
76
115
100
70
71
74
60
58
105
105
100
74
77
77
109
68
67
96
86
85
91
104
94
67
79
73
93
87
66
79
94
84
67
121
82
116
83
66
66
71
83
93
83
112
79
135
85
91
103
77
70
53
85
88
65
119
93
84
70
64
63
152
83
66
83
106
85
85
84
78
94
82
66
69
83
83
124
101
113
107
83
79
85
62
83
101
60
86
101
73
70
88
74
105
82
83
90
70
56
70
79
127
96
101
81
93
92
79
78
68
92
73
61
73
108
88
66
59
61
55
119
89
68
125
66
82
101
104
63
63
63
98
90
97
74
63
102
90
79
74
89
70
77
78
70
95
100
64
90
109
89
66
88
108
97
66
85
79
95
62
76
69
105
74
96
83
76
83
68
68
76
73
71
74
78
95
103
82
67
77
93
76
85
68
81
87
67
78
93
87
77
79
84
114
84
105
117
94
65
75
73
117
75
85
83
63
80
73
86
103
67
83
55
90
123
98
188
76
72
65
99
86
81
99
58
90
97
85
79
110
92
88
56
71
110
77
66
114
76
109
125
74
89
100
77
99
88
96
87
101
78
119
105
80
69
89
69
66
70
83
81
73
100
67
108
105
119
91
102
100
76
100
86
103
93
57
115
82
99
63
166
74
86
87
101
73
102
97
86
116
81
140
93
111
115
70
68
77
81
64
111
126
97
72
88
78
94
78
70
96
99
79
102
89
79
94
89
94
63
76
55
83
70
81
130
64
77
85
104
72
70
84
106
118
70
74
75
95
71
81
103
56
91
54
76
101
83
62
67
91
91
97
63
80
71
83
88
74
89
65
103
122
123
98
97
69
90
85
60
97
111
105
72
123
129
83
86
88
50
62
79
99
82
71
124
81
83
121
65
61
65
71
71
62
74
63
75
82
94
107
94
91
102
73
107
74
80
112
71
78
69
88
106
56
118
90
76
83
77
122
85
67
93
59
92
58
115
96
120
87
118
87
101
55
98
83
102
79
Dataseries Y:
Download CSV
Histogram 
96
91
108
95
105
117
94
98
120
91
100
90
123
97
120
133
141
138
85
121
74
100
67
73
116
115
92
109
120
105
115
105
122
87
94
78
135
113
123
126
127
120
108
83
117
96
103
134
112
93
103
84
116
97
87
122
111
101
109
96
100
90
120
86
85
96
93
95
127
156
94
123
80
98
88
109
98
83
85
85
76
96
72
125
121
103
113
89
108
80
87
84
93
94
87
95
106
94
91
106
103
121
112
98
91
112
118
90
80
73
135
97
82
99
132
130
67
78
131
80
121
79
82
101
91
83
76
93
73
107
97
133
74
93
99
105
95
90
115
95
115
105
115
102
92
101
90
93
102
79
83
89
65
97
78
99
126
106
115
90
104
76
95
114
81
91
120
132
96
95
74
93
152
116
148
84
87
114
106
110
99
96
80
144
102
125
98
87
100
128
102
104
96
127
85
106
85
126
121
94
94
128
82
118
128
149
92
105
146
106
130
77
92
83
88
89
132
113
83
84
88
74
71
119
120
113
88
92
91
123
80
79
109
98
98
106
117
108
79
93
86
106
100
78
92
111
97
77
134
95
134
96
78
79
87
95
110
96
129
93
154
97
109
118
91
83
65
99
103
75
135
109
98
81
78
75
171
96
78
96
121
100
97
99
92
108
96
77
82
94
98
139
116
126
121
96
91
99
74
95
118
74
96
117
85
84
100
88
120
97
94
105
81
70
83
93
141
109
115
97
109
103
95
92
80
109
86
74
84
121
99
76
73
73
66
134
102
81
144
78
95
116
118
77
77
72
113
104
111
86
76
117
103
95
87
105
84
90
90
84
109
113
73
102
124
102
78
103
122
112
76
97
95
106
77
88
85
121
86
113
95
92
97
83
81
90
89
83
89
90
110
116
95
79
87
109
90
96
81
93
101
80
90
108
102
90
93
97
128
99
118
131
110
79
89
84
132
89
100
97
75
94
86
97
117
79
96
71
103
138
113
207
91
85
78
114
98
97
113
69
107
113
100
92
126
106
100
68
85
124
89
80
132
89
122
139
87
103
116
90
115
101
110
103
116
92
136
122
93
81
105
86
75
84
96
96
87
115
81
124
118
136
104
116
114
90
115
98
119
110
69
135
92
112
77
182
85
102
100
118
88
115
114
101
133
93
156
105
128
131
88
81
91
95
78
125
140
114
86
103
92
107
92
83
109
116
93
118
101
91
110
105
111
78
88
70
95
85
95
146
75
89
100
120
85
83
99
121
136
85
88
87
107
87
96
116
67
105
67
90
119
97
72
80
102
105
111
74
96
84
97
102
85
101
77
116
138
140
114
112
81
103
97
73
112
129
117
88
140
146
97
99
104
60
75
91
114
94
82
138
95
98
136
76
74
77
84
84
73
88
75
90
96
110
123
107
106
119
87
123
88
93
127
86
89
84
98
122
68
134
105
88
95
90
137
99
80
108
72
104
70
133
110
136
101
133
102
116
65
113
97
115
94

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'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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=142536&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=142536&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=142536&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'Gwilym Jenkins' @ jenkins.wessa.net






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term9.225608776544190.29458760137594531.31703009038290
slope1.053936136770140.00332635411267891316.8442387877180

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 9.22560877654419 & 0.294587601375945 & 31.3170300903829 & 0 \tabularnewline
slope & 1.05393613677014 & 0.00332635411267891 & 316.844238787718 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=142536&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]9.22560877654419[/C][C]0.294587601375945[/C][C]31.3170300903829[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]1.05393613677014[/C][C]0.00332635411267891[/C][C]316.844238787718[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=142536&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=142536&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 term9.225608776544190.29458760137594531.31703009038290
slope1.053936136770140.00332635411267891316.8442387877180


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')