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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 computationWed, 07 Dec 2011 14:04:17 -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/Dec/07/t1323284706n2hhbjsdka1hjal.htm/, Retrieved Thu, 02 May 2024 23:20:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152619, Retrieved Thu, 02 May 2024 23:20:25 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

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]
- RMPD  [Linear Regression Graphical Model Validation] [] [2011-11-14 19:43:01] [d623f9be707a26b8ffaece1fc4d5a7ee]
-   PD      [Linear Regression Graphical Model Validation] [] [2011-12-07 19:04:17] [47d38a19087200036e90a9f702d012f8] [Current]
-             [Linear Regression Graphical Model Validation] [] [2011-12-20 21:05:46] [74be16979710d4c4e7c6647856088456]
-             [Linear Regression Graphical Model Validation] [] [2011-12-20 21:05:46] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
124252
98956
98073
106816
41449
76173
177551
22807
126938
61680
72117
79738
57793
91677
64631
106385
161961
112669
114029
124550
105416
72875
81964
104880
76302
96740
93071
78912
35224
90694
125369
80849
104434
65702
108179
63583
95066
62486
31081
94584
87408
68966
88766
57139
90586
109249
33032
96056
146648
80613
87026
5950
131106
32551
31701
91072
159803
143950
112368
82124
144068
162627
55062
95329
105612
62853
125976
79146
108461
99971
77826
22618
84892
92059
77993
104155
109840
238712
67486
68007
48194
134796
38692
93587
56622
15986
113402
97967
74844
136051
50548
112215
59591
59938
137639
143372
138599
174110
135062
175681
130307
139141
44244
43750
48029
95216
92288
94588
197426
151244
139206
106271
1168
71764
25162
45635
101817
855
100174
14116
85008
124254
105793
117129
8773
94747
107549
97392
126893
118850
234853
74783
66089
95684
139537
144253
153824
63995
84891
61263
106221
113587
113864
37238
119906
135096
151611
144645
0
6023
0
0
0
0
77457
62464
0
0
1644
6179
3926
42087
0
87656
Dataseries Y:
Download CSV
Histogram 
25695
19967
14338
34117
9713
10024
39981
1271
30207
18035
21609
19836
9028
21750
10038
30276
34972
19954
28113
18830
37144
17916
16186
19195
29124
29813
20270
26105
9155
18113
40546
10096
32338
2871
36592
4914
30190
18153
12558
32894
24138
16628
26369
14171
8500
11940
7935
19456
21347
24095
26204
2694
20366
3597
5296
29463
35838
42590
38665
19442
25515
51318
11807
24130
34053
22641
18898
24539
21664
21577
16643
3007
18798
24648
20286
23999
26813
14718
16963
16673
14646
31772
9648
23096
7905
4527
37432
21082
30437
36288
12369
23774
8108
15049
36021
30391
30910
40656
35070
47250
36236
29601
10443
7409
18213
40856
36471
26077
24797
6816
25527
22139
238
24459
3913
9895
25902
338
12937
3988
23370
24015
3870
14648
1888
16768
33400
23770
34762
18793
48186
20140
8728
19060
26880
415
38902
17375
31360
15051
16785
15886
28548
2805
34012
19215
34177
32990
0
2065
0
0
0
0
17428
19912
0
0
556
2089
2658
1801
0
16541

Tables (Output of Computation)





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=152619&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=152619&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152619&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term2736.693924187711282.656116941222.133614682876940.0343797039757119
slope0.1950833057123940.012956814021239815.05642555280940

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 2736.69392418771 & 1282.65611694122 & 2.13361468287694 & 0.0343797039757119 \tabularnewline
slope & 0.195083305712394 & 0.0129568140212398 & 15.0564255528094 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152619&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]2736.69392418771[/C][C]1282.65611694122[/C][C]2.13361468287694[/C][C]0.0343797039757119[/C][/ROW]
[ROW][C]slope[/C][C]0.195083305712394[/C][C]0.0129568140212398[/C][C]15.0564255528094[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152619&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152619&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 term2736.693924187711282.656116941222.133614682876940.0343797039757119
slope0.1950833057123940.012956814021239815.05642555280940


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')