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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 computationThu, 25 Nov 2010 09:23:05 +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/25/t1290676898n5fu9rzicgfbu8m.htm/, Retrieved Fri, 19 Apr 2024 03:14:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=100651, Retrieved Fri, 19 Apr 2024 03:14:04 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181

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] [] [2010-11-25 09:23:05] [b91d9cfbf8712a09013bf3c2e3081c55] [Current]
-    D      [Linear Regression Graphical Model Validation] [] [2010-11-25 18:35:56] [234dae34fc2a42f724a2786a39cb083b]
-    D      [Linear Regression Graphical Model Validation] [] [2010-11-25 21:09:47] [afd301b68d203992295e6972aed62880]
-    D      [Linear Regression Graphical Model Validation] [Paper - Autocorre...] [2010-11-30 21:57:38] [8677c3f87cec9201607d40be65aa9670]
-    D        [Linear Regression Graphical Model Validation] [Paper - Autocorre...] [2010-11-30 22:15:19] [8677c3f87cec9201607d40be65aa9670]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
83,5
82,68
82,4
82,782566
81,384
79,67
80,43
82,83
84,11
82
81,95
77,35
83
81,8
82,2
83,98
85,81
82,36
82,3
77,238835
81,9
82,035
82,66
79,6
81,75
78,2
81,415
84,1
82,94
84,04
74
81,345
80,7
43,452
79,574
73,412
43,958
78,868
77,28
64,576
75,5
57,354
67,04
67,358
77,28
50,036
75,84
76,3
53,46
50,492
61,402
50,732
45,822
51,988
76,38
56,118
47,46
81,104
48,998
79,37
80,138
75,2
77,856
73,312
74,684
52,356
59,768
78,14
70,892
57,212
60,134
74,686
60,138
73,516
57,154
47,778
62,204
73,472
85,5
72,278
61,024
72
54,55
69,158
72,1
65,264
76,5
73,988
42,94
46,2
76,63
77,06
76,434
60,796
47,886
76,452
56,066
80,7
65,586
75,91
72,274
43
64,876
52,984
63,716
75,58
55,508
47,328
77,142
78,064
60,274
73,714
73,648
82,56
75,8
73,23
47,262
64,822
75,88
43,87
82,6
48,934
52,5
59,622
75,838
81,4
69,204
53,002
74,774
60,004
71,62
76
67,372
51,432
74,06
81,258
79,52
76,41
63,848
41,86
42,062
Dataseries Y:
Download CSV
Histogram 
1,817
1,4
1,7324337
1,59
1,9552
1,33
1,83
1,84
1,98
1,32
1,39
1,34
2,08
1,9
2,88
1,35
1,32
1,13
1,65
2,171569
1,7
2
1,9
1,27
1,35
1,24
1,31
1,38
1,85
1,43
2,18
1,84
2,1
7,151
1,78
2,4116
5,8
2,2732
2,3348
2,9056
1,287
5,5052
2,3368
3,5916
1,18
2,9528
2,265
1,37
6,0754
6,8
3,2674
4,4346
4,654
6,2644
2,425
4,5502
6,3
2,1354
4,5832
1,38
1,518
2,4
2,6258
2,9476
2,723
5,414
5,1436
1,55
2,7976
3,1232
4,7934
1,4212
3,9466
4,2416
5,5176
7,0752
3,8
3,3053333
0,966
2,0568
4,3828
2,36
4,9672
1,8634
2,7
3,2878
1,35
2,231
3,457
5,2
2,7852
1,31
1,453
4,8764
5,6808
2,6524
6,5538
1,41
4,4626
1,7
1,7
5,1942
2,1034
3,6
3,1
2,804
7,02575
5,4266
3,1396
2,5888
3,8918
2,553
3,2952
1,7728
1,31
1,3
5,9356
5,1885714
1,43
6,4768
1,28
6,1208
2,73
4,3464
3,1606
1,115
3,4402
5,2576
1,845
4,9142
1,63
2,03
2,5502
6,685
1,2
2,347
2,03
2,62
5,6052
5,2704
3,7274286

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100651&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]9 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=100651&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100651&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 time9 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 term10.90454403075960.4045719886552526.95328479612500
slope-0.1136717729729950.00573526386872573-19.81979828214080

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 10.9045440307596 & 0.40457198865525 & 26.9532847961250 & 0 \tabularnewline
slope & -0.113671772972995 & 0.00573526386872573 & -19.8197982821408 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100651&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]10.9045440307596[/C][C]0.40457198865525[/C][C]26.9532847961250[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]-0.113671772972995[/C][C]0.00573526386872573[/C][C]-19.8197982821408[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100651&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100651&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 term10.90454403075960.4045719886552526.95328479612500
slope-0.1136717729729950.00573526386872573-19.81979828214080


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')