Free Statistics

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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 computationTue, 16 Nov 2010 20:43:36 +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/16/t1289940343482x9p1vuuki9lw.htm/, Retrieved Sat, 04 May 2024 21:17:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=96413, Retrieved Sat, 04 May 2024 21:17:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
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] [Colruyt-BEL20] [2010-11-16 20:43:36] [4c7d8c32b2e34fcaa7f14928b91d45ae] [Current]
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Dataseries X:
35,18
35,24
35,00
35,50
36,32
36,04
36,71
36,70
36,17
36,50
36,94
36,42
36,40
36,68
36,54
36,36
36,23
36,39
36,44
36,43
36,47
36,42
36,44
36,44
36,51
36,51
36,26
36,41
36,19
36,45
36,38
36,70
36,69
37,00
37,20
37,28
37,18
37,13
36,87
36,88
36,85
36,87
37,40
37,21
37,40
36,94
36,66
36,59
37,15
37,00
36,47
36,51
36,17
36,62
35,97
36,48
36,34
37,05
37,11
36,92
36,89
36,94
37,19
36,78
36,25
36,67
36,84
36,54
37,09
37,02
37,04
37,47
37,36
37,38
37,18
37,19
37,35
37,33
37,98
37,72
37,75
37,94
37,82
38,07
38,00
38,00
38,00
37,94
38,24
38,52
38,82
38,73
38,58
37,67
37,79
37,65
38,23
38,10
38,46
38,18
38,38
38,78
38,99
38,90
38,88
38,82
38,84
39,16
39,34
39,92
39,54
38,64
38,47
38,01
37,81
38,20
38,31
38,41
38,46
38,59
38,71
38,90
38,65
38,95
38,84
38,97
39,20
39,01
38,78
38,80
39,60
39,42
39,32
39,28
39,49
39,12
39,08
39,45
39,73
39,44
39,34
39,40
39,52
39,60
39,42
39,30
39,43
39,75
39,23
39,39
39,63
39,56
39,31
39,48
39,65
39,27
38,71
38,66
38,78
38,83
38,82
38,62
38,39
38,57
38,60
38,11
38,16
38,40
39,18
39,03
39,12
39,22
39,39
39,77
39,65
39,81
39,79
40,32
40,33
40,48
41,12
41,24
40,82
41,06
40,29
40,18
39,91
39,81
40,25
39,93
39,90
39,95
Dataseries Y:
2518,41
2527,73
2521,10
2502,89
2514,94
2480,65
2514,87
2543,41
2558,77
2592,56
2582,96
2611,03
2600,83
2606,31
2634,16
2613,52
2625,28
2608,80
2631,57
2665,62
2654,85
2639,28
2634,45
2649,34
2649,70
2672,10
2656,93
2665,40
2656,12
2648,46
2680,56
2696,66
2696,96
2668,62
2702,87
2714,81
2708,98
2716,70
2715,16
2687,36
2667,96
2681,07
2660,86
2630,94
2644,14
2656,28
2567,68
2506,19
2561,69
2560,99
2572,46
2504,25
2446,22
2400,78
2296,81
2511,98
2483,53
2540,36
2534,85
2447,17
2439,07
2485,24
2432,85
2386,49
2395,65
2402,45
2329,79
2386,45
2469,56
2460,54
2453,37
2466,00
2464,25
2484,27
2429,67
2412,05
2390,89
2425,68
2463,32
2463,85
2498,24
2509,31
2513,80
2523,36
2528,93
2549,84
2536,22
2494,34
2455,80
2441,65
2461,96
2382,48
2386,53
2336,12
2337,29
2329,60
2389,28
2415,81
2455,99
2462,75
2470,27
2516,26
2522,12
2495,05
2442,75
2431,56
2417,34
2451,93
2501,41
2506,07
2531,75
2557,43
2545,08
2537,90
2517,30
2579,35
2588,92
2587,82
2586,09
2549,82
2571,02
2561,71
2495,93
2505,90
2485,70
2469,46
2518,34
2512,61
2474,08
2452,32
2469,08
2435,96
2424,00
2446,63
2467,55
2460,93
2457,46
2531,76
2537,76
2549,96
2560,24
2538,91
2556,00
2577,46
2569,72
2610,81
2608,85
2603,51
2585,29
2569,22
2601,85
2604,33
2575,16
2570,39
2601,61
2597,73
2603,50
2602,69
2589,73
2579,39
2558,82
2595,93
2613,44
2604,03
2622,78
2635,10
2620,82
2670,38
2662,91
2658,78
2672,38
2666,15
2669,27
2691,78
2691,53
2698,64
2692,74
2672,71
2677,40
2679,07
2688,39
2696,69
2663,84
2710,41
2686,09
2683,42
2702,84
2672,25
2631,64
2624,97
2654,43
2608,06




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96413&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96413&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96413&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Simple Linear Regression
StatisticsEstimateS.D.T-STAT (H0: coeff=0)P-value (two-sided)
constant term2112.06397678918196.63647614933310.74095721276660
slope11.6188186701035.161723707929852.250957108039940.0255338234769629

\begin{tabular}{lllllllll}
\hline
Simple Linear Regression \tabularnewline
Statistics & Estimate & S.D. & T-STAT (H0: coeff=0) & P-value (two-sided) \tabularnewline
constant term & 2112.06397678918 & 196.636476149333 & 10.7409572127666 & 0 \tabularnewline
slope & 11.618818670103 & 5.16172370792985 & 2.25095710803994 & 0.0255338234769629 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96413&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]2112.06397678918[/C][C]196.636476149333[/C][C]10.7409572127666[/C][C]0[/C][/ROW]
[ROW][C]slope[/C][C]11.618818670103[/C][C]5.16172370792985[/C][C]2.25095710803994[/C][C]0.0255338234769629[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96413&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96413&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 term2112.06397678918196.63647614933310.74095721276660
slope11.6188186701035.161723707929852.250957108039940.0255338234769629



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')