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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, 18 Dec 2012 08:27:57 -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/2012/Dec/18/t13558372867r3n6qfn31oatp3.htm/, Retrieved Tue, 16 Apr 2024 06:38:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201415, Retrieved Tue, 16 Apr 2024 06:38:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97

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]
- RM D    [Linear Regression Graphical Model Validation] [] [2012-12-18 13:27:57] [9b45d6b988914ec0ffbfae116e7bcb98] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6945
8459
6459
5078
3455
4922
5342
3976
3493
4189
4239
3588
3628
3462
3503
2754
2892
3696
3600
3377
3322
2899
2513
2405
2317
2322
3174
2715
2759
2219
2808
1970
2210
1947
2365
2683
2877
3125
2172
2447
1753
1921
1883
2226
2678
1933
2137
2093
1742
1953
2294
2146
1647
2527
1944
1900
3585
1567
1999
2975
2437
2197
2076
2151
2118
1498
1997
1730
2058
1715
2392
1697
1727
1706
1600
2088
1665
3015
2443
2626
1625
2588
1877
1573
1928
1669
2432
2977
1821
1869
1607
1990
2166
1569
1277
1867
1500
1457
1683
1641
1330
1401
3066
1866
1776
1751
1497
1366
1821
1974
1655
1359
1234
2193
1384
1745
1530
1907
1243
1445
1543
1490
1255
2487
1209
1525
1431
1251
1105
2065
 928
1870
 748
1067
1342
 836
1133
1244
1098
1656
1310
1228
 765
 782
 769
 798
 582
 585
 480
 826
 442
 868
 394
 379
 595
 808
 657
 547
 206
 422
 113
 158
  44
 216
  67
  15
  37
  20
   6
Dataseries Y:
Download CSV
Histogram 
206
156
140
132
120
109
106
104
101
101
 94
 90
 89
 89
 87
 87
 84
 84
 83
 81
 80
 78
 77
 76
 74
 74
 73
 72
 71
 71
 70
 68
 68
 67
 67
 66
 66
 64
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 63
 62
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 62
 61
 61
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 58
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 57
 57
 57
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 55
 55
 55
 55
 53
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 50
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 50
 48
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 48
 46
 45
 43
 43
 43
 43
 43
 42
 41
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 41
 41
 40
 39
 39
 39
 39
 38
 37
 36
 36
 36
 35
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 34
 34
 34
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 32
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 31
 31
 31
 31
 30
 30
 29
 28
 28
 28
 27
 26
 26
 25
 24
 24
 24
 24
 23
 23
 22
 22
 21
 18
 18
 18
 17
 17
 17
 17
 15
 14
 13
 13
 12
 12
 10
 10
 10
 10
 10
  9
  6
  6
  4
  4
  2
  2
  1
  0
  0
  0
  0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 1 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201415&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201415&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201415&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 time1 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net


Figures (Output of Computation)

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')