Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Simple Regression Y ~ X.wasp
Title produced by softwareSimple Linear Regression
Date of computationTue, 10 Dec 2013 05:27:27 -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/2013/Dec/10/t1386671538taoaqou3rga5nzp.htm/, Retrieved Thu, 28 Mar 2024 17:25:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231854, Retrieved Thu, 28 Mar 2024 17:25:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Simple Linear Regression] [] [2013-12-10 10:27:27] [9e6a405f514733ea23d87e4507d39d29] [Current]
Feedback Forum

Post a new message
Dataseries X:
119.992 157.302
122.4 148.65
116.682 131.111
116.676 137.871
116.014 141.781
120.552 131.162
120.267 137.244
107.332 113.84
95.73 132.068
95.056 120.103
88.333 112.24
91.904 115.871
136.926 159.866
139.173 179.139
152.845 163.305
142.167 217.455
144.188 349.259
168.778 232.181
153.046 175.829
156.405 189.398
153.848 165.738
153.88 172.86
167.93 193.221
173.917 192.735
163.656 200.841
104.4 206.002
171.041 208.313
146.845 208.701
155.358 227.383
162.568 198.346
197.076 206.896
199.228 209.512
198.383 215.203
202.266 211.604
203.184 211.526
201.464 210.565
177.876 192.921
176.17 185.604
180.198 201.249
187.733 202.324
186.163 197.724
184.055 196.537
237.226 247.326
241.404 248.834
243.439 250.912
242.852 255.034
245.51 262.09
252.455 261.487
122.188 128.611
122.964 130.049
124.445 135.069
126.344 134.231
128.001 138.052
129.336 139.867
108.807 134.656
109.86 126.358
110.417 131.067
117.274 129.916
116.879 131.897
114.847 271.314
209.144 237.494
223.365 238.987
222.236 231.345
228.832 234.619
229.401 252.221
228.969 239.541
140.341 159.774
136.969 166.607
143.533 162.215
148.09 162.824
142.729 162.408
136.358 176.595
120.08 139.71
112.014 588.518
110.793 128.101
110.707 122.611
112.876 148.826
110.568 125.394
95.385 102.145
100.77 115.697
96.106 108.664
95.605 107.715
100.96 110.019
98.804 102.305
176.858 205.56
180.978 200.125
178.222 202.45
176.281 227.381
173.898 211.35
179.711 225.93
166.605 206.008
151.955 163.335
148.272 164.989
152.125 161.469
157.821 172.975
157.447 163.267
159.116 168.913
125.036 143.946
125.791 140.557
126.512 141.756
125.641 141.068
128.451 150.449
139.224 586.567
150.258 154.609
154.003 160.267
149.689 160.368
155.078 163.736
151.884 157.765
151.989 157.339
193.03 208.9
200.714 223.982
208.519 220.315
204.664 221.3
210.141 232.706
206.327 226.355
151.872 492.892
158.219 442.557
170.756 450.247
178.285 442.824
217.116 233.481
128.94 479.697
176.824 215.293
138.19 203.522
182.018 197.173
156.239 195.107
145.174 198.109
138.145 197.238
166.888 198.966
119.031 127.533
120.078 126.632
120.289 128.143
120.256 125.306
119.056 125.213
118.747 123.723
106.516 112.777
110.453 127.611
113.4 133.344
113.166 130.27
112.239 126.609
116.15 131.731
170.368 268.796
208.083 253.792
198.458 219.29
202.805 231.508
202.544 241.35
223.361 263.872
169.774 191.759
183.52 216.814
188.62 216.302
202.632 565.74
186.695 211.961
192.818 224.429
198.116 233.099
121.345 139.644
119.1 128.442
117.87 127.349
122.336 142.369
117.963 134.209
126.144 154.284
127.93 138.752
114.238 124.393
115.322 135.738
114.554 126.778
112.15 131.669
102.273 142.83
236.2 244.663
237.323 243.709
260.105 264.919
197.569 217.627
240.301 245.135
244.99 272.21
112.547 133.374
110.739 113.597
113.715 116.443
117.004 144.466
115.38 123.109
116.388 129.038
151.737 190.204
148.79 158.359
148.143 155.982
150.44 163.441
148.462 161.078
149.818 163.417
117.226 123.925
116.848 217.552
116.286 177.291
116.556 592.03
116.342 581.289
114.563 119.167
201.774 262.707
174.188 230.978
209.516 253.017
174.688 240.005
198.764 396.961
214.289 260.277




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231854&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231854&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231854&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)118.4736.4818.2840
X0.1810.036.0810
- - -
Residual Std. Err. 38.015 on 193 df
Multiple R-sq. 0.161
Adjusted R-sq. 0.156

\begin{tabular}{lllllllll}
\hline
Linear Regression Model \tabularnewline
Y ~ X \tabularnewline
coefficients: &   \tabularnewline
  & Estimate & Std. Error & t value & Pr(>|t|) \tabularnewline
(Intercept) & 118.473 & 6.48 & 18.284 & 0 \tabularnewline
X & 0.181 & 0.03 & 6.081 & 0 \tabularnewline
- - -  &   \tabularnewline
Residual Std. Err.  & 38.015  on  193 df \tabularnewline
Multiple R-sq.  & 0.161 \tabularnewline
Adjusted R-sq.  & 0.156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231854&T=1

[TABLE]
[ROW][C]Linear Regression Model[/C][/ROW]
[ROW][C]Y ~ X[/C][/ROW]
[ROW][C]coefficients:[/C][C] [/C][/ROW]
[ROW][C] [/C][C]Estimate[/C][C]Std. Error[/C][C]t value[/C][C]Pr(>|t|)[/C][/ROW]
[C](Intercept)[/C][C]118.473[/C][C]6.48[/C][C]18.284[/C][C]0[/C][/ROW]
[C]X[/C][C]0.181[/C][C]0.03[/C][C]6.081[/C][C]0[/C][/ROW]
[ROW][C]- - - [/C][C] [/C][/ROW]
[ROW][C]Residual Std. Err. [/C][C]38.015  on  193 df[/C][/ROW]
[ROW][C]Multiple R-sq. [/C][C]0.161[/C][/ROW]
[ROW][C]Adjusted R-sq. [/C][C]0.156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231854&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231854&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)118.4736.4818.2840
X0.1810.036.0810
- - -
Residual Std. Err. 38.015 on 193 df
Multiple R-sq. 0.161
Adjusted R-sq. 0.156







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MDVP:Fhi(Hz)153437.91453437.91436.9780
Residuals193278910.7541445.133

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MDVP:Fhi(Hz) & 1 & 53437.914 & 53437.914 & 36.978 & 0 \tabularnewline
Residuals & 193 & 278910.754 & 1445.133 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231854&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]MDVP:Fhi(Hz)[/C][C]1[/C][C]53437.914[/C][C]53437.914[/C][C]36.978[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]193[/C][C]278910.754[/C][C]1445.133[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231854&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231854&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MDVP:Fhi(Hz)153437.91453437.91436.9780
Residuals193278910.7541445.133



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1)
cat2<- as.numeric(par2)
intercept<-as.logical(par3)
x <- t(x)
xdf<-data.frame(t(y))
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
xdf <- data.frame(xdf[[cat1]], xdf[[cat2]])
names(xdf)<-c('Y', 'X')
if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) )
sumlmxdf<-summary(lmxdf)
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
nc <- ncol(sumlmxdf$'coefficients')
nr <- nrow(sumlmxdf$'coefficients')
a<-table.row.start(a)
a<-table.element(a,'Linear Regression Model', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],nc+1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'coefficients:',1,TRUE)
a<-table.element(a, ' ',nc,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
for(i in 1 : nc){
a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE)
}#end header
a<-table.row.end(a)
for(i in 1: nr){
a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE)
for(j in 1 : nc){
a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE)
}
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, '- - - ',1,TRUE)
a<-table.element(a, ' ',nc,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Std. Err. ',1,TRUE)
a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
a<-table.element(a, 'Df',1,TRUE)
a<-table.element(a, 'Sum Sq',1,TRUE)
a<-table.element(a, 'Mean Sq',1,TRUE)
a<-table.element(a, 'F value',1,TRUE)
a<-table.element(a, 'Pr(>F)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, V2,1,TRUE)
a<-table.element(a, anova.xdf$Df[1])
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',1,TRUE)
a<-table.element(a, anova.xdf$Df[2])
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3))
a<-table.element(a, ' ')
a<-table.element(a, ' ')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='regressionplot.png')
plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution')
if(intercept == TRUE) abline(coef(lmxdf), col='red')
if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red')
dev.off()
library(car)
bitmap(file='residualsQQplot.png')
qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit')
dev.off()
bitmap(file='residualsplot.png')
plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit')
dev.off()
bitmap(file='cooksDistanceLmplot.png')
plot.lm(lmxdf, which=4)
dev.off()