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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 computationThu, 17 Nov 2011 12:05:03 -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/17/t1321549570zm5undijusuw0f8.htm/, Retrieved Fri, 29 Mar 2024 11:26:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145141, Retrieved Fri, 29 Mar 2024 11:26:56 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Linear Regression Graphical Model Validation] [] [2011-11-17 17:05:03] [bd7a66e2f212a6bc9afe853e3942ee45] [Current]
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Dataseries X:
9492
8767
1423
15387
11936
22386
7703
7694
14513
12552
10893
14584
5469
8334
8651
13657
22452
6023
10602
12476
783
16597
5506
5852
6630
4065
12579
10432
50116
8628
3482
7195
11938
15129
5050
0
7537
8665
3710
11128
7980
6851
15966
22214
7713
6154
2928
10805
17412
9243
6656
3585
11016
14667
11708
7619
11853
9540
10081
2574
3772
3886
8984
5956
10878
9852
13736
8033
9776
1536
6948
5846
7318
4898
1358
0
9580
17612
2941
6924
6387
3690
7153
8765
5761
8809
10686
8055
8352
6030
14070
17349
5645
4120
13983
3853
19150
3895
4408
7498
2325
11429
17265
6265
3087
3979
8931
3721
0
6185
14254
7500
5890
0
0
0
10411
28040
103
14377
2781
8759
2229
2423
13656
2790
10255
4845
5264
0
0
0
0
0
0
0
0
7711
797
0
0
4010
5959
2338
Dataseries Y:
45943
59385
7176
63907
112746
218511
51741
60400
63412
125468
50224
81908
35821
71437
59999
64114
49519
14903
56100
11805
33983
77726
47624
44572
61932
47968
64711
59855
130532
71572
55974
73726
44795
24608
65081
0
45624
60122
31241
73314
65398
41061
30085
44881
35236
24791
24886
87233
55304
42244
33820
37861
77191
76157
81220
46992
58593
22923
48977
17919
28651
27666
22528
33493
41603
44299
49670
58803
31404
12185
83343
25915
29352
35557
23272
17647
51193
35126
15563
21057
22050
66490
27190
48592
21764
47480
74948
17111
28728
45984
45664
51855
46737
46743
14189
102297
58066
19349
60378
47671
12992
17528
34532
17934
22361
20460
47949
16231
0
23392
20284
44393
35245
34131
3058
0
71090
24715
50785
33277
43051
61450
24215
25685
32306
15802
27437
34800
47050
11747
6046
41621
6836
11056
510
6669
0
28068
8916
7131
4194
14160
11970
26174




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=145141&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=145141&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145141&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term22447.84917789163221.167582377686.968854803053091.10381259688097e-10
slope2.407583318559680.3141929607429657.662753846765122.59303689631452e-12

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 22447.8491778916 & 3221.16758237768 & 6.96885480305309 & 1.10381259688097e-10 \tabularnewline
slope & 2.40758331855968 & 0.314192960742965 & 7.66275384676512 & 2.59303689631452e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145141&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]22447.8491778916[/C][C]3221.16758237768[/C][C]6.96885480305309[/C][C]1.10381259688097e-10[/C][/ROW]
[ROW][C]slope[/C][C]2.40758331855968[/C][C]0.314192960742965[/C][C]7.66275384676512[/C][C]2.59303689631452e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145141&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145141&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 term22447.84917789163221.167582377686.968854803053091.10381259688097e-10
slope2.407583318559680.3141929607429657.662753846765122.59303689631452e-12



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')