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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, 21 Dec 2010 14:42:24 +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/Dec/21/t129294247661kdcqqrx0pa0v6.htm/, Retrieved Wed, 08 May 2024 17:31:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113627, Retrieved Wed, 08 May 2024 17:31:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

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] [ws6vb] [2010-11-13 11:31:05] [c7506ced21a6c0dca45d37c8a93c80e0]
-    D    [Linear Regression Graphical Model Validation] [tt] [2010-11-13 12:51:28] [4a7069087cf9e0eda253aeed7d8c30d6]
-    D      [Linear Regression Graphical Model Validation] [W6tutorial] [2010-11-13 14:33:02] [c7506ced21a6c0dca45d37c8a93c80e0]
-    D          [Linear Regression Graphical Model Validation] [Spaargeld NL vs L...] [2010-12-21 14:42:24] [a8abc7260f3c847aeac0a796e7895a2e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
143827
145191
146832
148577
149873
151847
153252
154292
155657
156523
156416
156693
160312
160438
160882
161668
164391
168556
169738
170387
171294
172202
172651
172770
178366
180014
181067
182586
184957
186417
188599
189490
190264
191221
191110
190674
195438
196393
197172
198760
200945
203845
204613
205487
206100
206315
206291
207801
211653
211325
211893
212056
214696
217455
218884
219816
219984
219062
218550
218179
222218
222196
223393
223292
226236
228831
228745
229140
229270
229359
230006
228810
232677
232961
234629
235660
240024
243554
244368
244356
245126
246321
246797
246735
251083
251786
252732
255051
259022
261698
263891
265247
262228
263429
264305
266371
273248
275472
278146
279506
283991
286794
288703
289285
288869
286942
285833
284095
289229
289389
290793
291454
294733
293853
294056
293982
293075
292391
Dataseries Y:
Download CSV
Histogram 
9113
9140
9309
9395
10027
10202
10003
9745
9966
10035
9999
9943
10258
10926
10807
10992
11034
10801
10161
10191
10451
10380
10251
10522
10801
10731
10161
9728
9882
9839
9917
10356
10857
10424
10721
10669
10565
10289
10646
10858
10282
10377
10443
10561
10668
10818
10865
10636
10409
10460
10579
10664
10711
11374
11345
11456
11966
12580
13006
13815
14579
14960
14904
16028
17079
15155
16049
15841
15159
14956
15645
15318
15595
16355
15925
16175
15900
15711
15594
15693
16438
17048
17699
17733
19439
20148
20112
18607
18409
18388
19187
17983
18449
19589
19135
19604
20877
23639
22830
21760
21879
21712
21321
21396
22000
22642
24272
24933
25219
25745
26433
27546
30774
32456
30124
30250
31288
31072

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time24 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 & 24 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113627&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]24 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=113627&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113627&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 time24 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 term-11443.94389169661199.79259293879-9.538268496612094.44089209850063e-16
slope0.1206808864315840.0053100811063251922.72675012210880

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & -11443.9438916966 & 1199.79259293879 & -9.53826849661209 & 4.44089209850063e-16 \tabularnewline
slope & 0.120680886431584 & 0.00531008110632519 & 22.7267501221088 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113627&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]-11443.9438916966[/C][C]1199.79259293879[/C][C]-9.53826849661209[/C][C]4.44089209850063e-16[/C][/ROW]
[ROW][C]slope[/C][C]0.120680886431584[/C][C]0.00531008110632519[/C][C]22.7267501221088[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113627&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113627&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 term-11443.94389169661199.79259293879-9.538268496612094.44089209850063e-16
slope0.1206808864315840.0053100811063251922.72675012210880


Figures (Output of Computation)

Figure 1
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Figure 2
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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')