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Author*The author of this computation has been verified*
R Software Modulerwasp_twosampletests_mean.wasp
Title produced by softwarePaired and Unpaired Two Samples Tests about the Mean
Date of computationThu, 27 Jan 2022 12:23:18 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2022/Jan/27/t1643283049s4fl7px3j1cwysg.htm/, Retrieved Mon, 20 May 2024 23:11:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319622, Retrieved Mon, 20 May 2024 23:11:55 +0000
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-       [Paired and Unpaired Two Samples Tests about the Mean] [] [2022-01-27 11:23:18] [9d22051737ca820f26ab852e727e6980] [Current]
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Dataseries X:
233 NA
250 NA
NA 204
236 NA
NA 354
192 NA
NA 294
263 NA
199 NA
168 NA
239 NA
NA 275
266 NA
211 NA
NA 283
NA 219
NA 340
NA 226
247 NA
NA 239
234 NA
233 NA
226 NA
243 NA
199 NA
NA 302
212 NA
175 NA
NA 417
197 NA
NA 198
177 NA
219 NA
273 NA
213 NA
NA 177
NA 304
232 NA
NA 269
NA 360
NA 308
245 NA
208 NA
NA 264
321 NA
325 NA
235 NA
257 NA
NA 216
NA 234
NA 256
302 NA
231 NA
NA 141
NA 252
201 NA
222 NA
260 NA
182 NA
NA 303
NA 265
309 NA
186 NA
203 NA
211 NA
NA 183
222 NA
NA 234
220 NA
NA 209
258 NA
227 NA
204 NA
261 NA
NA 213
NA 250
245 NA
221 NA
205 NA
240 NA
250 NA
308 NA
NA 318
298 NA
NA 265
NA 564
277 NA
197 NA
NA 214
NA 248
255 NA
207 NA
223 NA
NA 288
NA 160
226 NA
NA 394
233 NA
315 NA
246 NA
244 NA
270 NA
NA 195
240 NA
196 NA
NA 211
234 NA
NA 236
NA 244
NA 254
NA 325
126 NA
NA 313
211 NA
262 NA
NA 215
214 NA
193 NA
NA 204
NA 243
NA 303
271 NA
NA 268
NA 267
NA 199
NA 210
204 NA
NA 277
NA 196
NA 269
NA 201
NA 271
295 NA
235 NA
NA 306
NA 269
NA 178
208 NA
201 NA
263 NA
NA 295
303 NA
NA 209
NA 223
NA 197
245 NA
NA 242
NA 240
226 NA
180 NA
228 NA
NA 149
227 NA
NA 278
NA 220
NA 197
253 NA
192 NA
220 NA
221 NA
240 NA
NA 342
157 NA
175 NA
175 NA
286 NA
229 NA
NA 268
254 NA
203 NA
256 NA
229 NA
284 NA
224 NA
206 NA
167 NA
230 NA
335 NA
177 NA
276 NA
353 NA
NA 225
NA 330
230 NA
243 NA
290 NA
253 NA
266 NA
233 NA
172 NA
NA 305
216 NA
188 NA
282 NA
185 NA
326 NA
231 NA
254 NA
267 NA
248 NA
197 NA
258 NA
270 NA
274 NA
NA 164
255 NA
239 NA
NA 258
188 NA
177 NA
229 NA
260 NA
219 NA
NA 307
249 NA
NA 341
NA 263
330 NA
254 NA
256 NA
NA 407
217 NA
282 NA
NA 288
239 NA
174 NA
281 NA
198 NA
288 NA
309 NA
243 NA
289 NA
289 NA
246 NA
322 NA
299 NA
300 NA
293 NA
304 NA
282 NA
269 NA
NA 249
212 NA
274 NA
184 NA
274 NA
NA 409
246 NA
283 NA
254 NA
298 NA
247 NA
NA 294
299 NA
273 NA
309 NA
259 NA
200 NA
NA 244
231 NA
NA 228
230 NA
282 NA
NA 269
206 NA
212 NA
NA 327
149 NA
286 NA
283 NA
249 NA
234 NA
237 NA
234 NA
275 NA
212 NA
218 NA
261 NA
NA 319
166 NA
315 NA
204 NA
218 NA
223 NA
207 NA
311 NA
204 NA
232 NA
335 NA
NA 205
203 NA
318 NA
NA 225
212 NA
169 NA
187 NA
NA 197
176 NA
NA 241
264 NA
193 NA
131 NA
NA 236




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319622&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319622&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319622&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Two Sample t-test (unpaired)
Mean of Sample 1239.289855072464
Mean of Sample 2261.302083333333
t-stat-3.50293683272192
df301
p-value0.000529966610622663
H0 value0
Alternativetwo.sided
CI Level0.999
CI[-42.8946362911242,-1.1298202306149]
F-test to compare two variances
F-stat0.43203172484399
df206
p-value6.14437983608133e-07
H0 value1
Alternativetwo.sided
CI Level0.999
CI[0.235401756980611,0.754879690689945]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 239.289855072464 \tabularnewline
Mean of Sample 2 & 261.302083333333 \tabularnewline
t-stat & -3.50293683272192 \tabularnewline
df & 301 \tabularnewline
p-value & 0.000529966610622663 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.999 \tabularnewline
CI & [-42.8946362911242,-1.1298202306149] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.43203172484399 \tabularnewline
df & 206 \tabularnewline
p-value & 6.14437983608133e-07 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.999 \tabularnewline
CI & [0.235401756980611,0.754879690689945] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319622&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]239.289855072464[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]261.302083333333[/C][/ROW]
[ROW][C]t-stat[/C][C]-3.50293683272192[/C][/ROW]
[ROW][C]df[/C][C]301[/C][/ROW]
[ROW][C]p-value[/C][C]0.000529966610622663[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.999[/C][/ROW]
[ROW][C]CI[/C][C][-42.8946362911242,-1.1298202306149][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.43203172484399[/C][/ROW]
[ROW][C]df[/C][C]206[/C][/ROW]
[ROW][C]p-value[/C][C]6.14437983608133e-07[/C][/ROW]
[ROW][C]H0 value[/C][C]1[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.999[/C][/ROW]
[ROW][C]CI[/C][C][0.235401756980611,0.754879690689945][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319622&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319622&T=1

As an alternative you can also use a QR Code:  

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

Two Sample t-test (unpaired)
Mean of Sample 1239.289855072464
Mean of Sample 2261.302083333333
t-stat-3.50293683272192
df301
p-value0.000529966610622663
H0 value0
Alternativetwo.sided
CI Level0.999
CI[-42.8946362911242,-1.1298202306149]
F-test to compare two variances
F-stat0.43203172484399
df206
p-value6.14437983608133e-07
H0 value1
Alternativetwo.sided
CI Level0.999
CI[0.235401756980611,0.754879690689945]







Welch Two Sample t-test (unpaired)
Mean of Sample 1239.289855072464
Mean of Sample 2261.302083333333
t-stat-3.02437866568881
df134.394552641249
p-value0.00298541775416255
H0 value0
Alternativetwo.sided
CI Level0.999
CI[-46.4991672063003,2.4747106845612]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 239.289855072464 \tabularnewline
Mean of Sample 2 & 261.302083333333 \tabularnewline
t-stat & -3.02437866568881 \tabularnewline
df & 134.394552641249 \tabularnewline
p-value & 0.00298541775416255 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.999 \tabularnewline
CI & [-46.4991672063003,2.4747106845612] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319622&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]239.289855072464[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]261.302083333333[/C][/ROW]
[ROW][C]t-stat[/C][C]-3.02437866568881[/C][/ROW]
[ROW][C]df[/C][C]134.394552641249[/C][/ROW]
[ROW][C]p-value[/C][C]0.00298541775416255[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.999[/C][/ROW]
[ROW][C]CI[/C][C][-46.4991672063003,2.4747106845612][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319622&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319622&T=2

As an alternative you can also use a QR Code:  

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

Welch Two Sample t-test (unpaired)
Mean of Sample 1239.289855072464
Mean of Sample 2261.302083333333
t-stat-3.02437866568881
df134.394552641249
p-value0.00298541775416255
H0 value0
Alternativetwo.sided
CI Level0.999
CI[-46.4991672063003,2.4747106845612]







Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)
W8070
p-value0.00855508486122403
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.188556763285024
p-value0.0188665177129201
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.136322463768116
p-value0.174622707480172

\begin{tabular}{lllllllll}
\hline
Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired) \tabularnewline
W & 8070 \tabularnewline
p-value & 0.00855508486122403 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.188556763285024 \tabularnewline
p-value & 0.0188665177129201 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.136322463768116 \tabularnewline
p-value & 0.174622707480172 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319622&T=3

[TABLE]
[ROW][C]Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]8070[/C][/ROW]
[ROW][C]p-value[/C][C]0.00855508486122403[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributions of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.188556763285024[/C][/ROW]
[ROW][C]p-value[/C][C]0.0188665177129201[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.136322463768116[/C][/ROW]
[ROW][C]p-value[/C][C]0.174622707480172[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319622&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319622&T=3

As an alternative you can also use a QR Code:  

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

Wilcoxon Rank-Sum Test (Mann–Whitney U test) with continuity correction (unpaired)
W8070
p-value0.00855508486122403
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.188556763285024
p-value0.0188665177129201
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.136322463768116
p-value0.174622707480172



Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.999 ; par4 = two.sided ; par5 = unpaired ; par6 = 0 ;
R code (references can be found in the software module):
par6 <- '0'
par5 <- 'unpaired'
par4 <- 'two.sided'
par3 <- '0,999'
par2 <- '2'
par1 <- '1'
par1 <- as.numeric(par1) #column number of first sample
par2 <- as.numeric(par2) #column number of second sample
par3 <- as.numeric(par3) #confidence (= 1 - alpha)
if (par5 == 'unpaired') paired <- FALSE else paired <- TRUE
par6 <- as.numeric(par6) #H0
z <- t(y)
if (par1 == par2) stop('Please, select two different column numbers')
if (par1 < 1) stop('Please, select a column number greater than zero for the first sample')
if (par2 < 1) stop('Please, select a column number greater than zero for the second sample')
if (par1 > length(z[1,])) stop('The column number for the first sample should be smaller')
if (par2 > length(z[1,])) stop('The column number for the second sample should be smaller')
if (par3 <= 0) stop('The confidence level should be larger than zero')
if (par3 >= 1) stop('The confidence level should be smaller than zero')
(r.t <- t.test(z[,par1],z[,par2],var.equal=TRUE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(v.t <- var.test(z[,par1],z[,par2],conf.level=par3))
(r.w <- t.test(z[,par1],z[,par2],var.equal=FALSE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(w.t <- wilcox.test(z[,par1],z[,par2],alternative=par4,paired=paired,mu=par6,conf.level=par3))
(ks.t <- ks.test(z[,par1],z[,par2],alternative=par4))
m1 <- mean(z[,par1],na.rm=T)
m2 <- mean(z[,par2],na.rm=T)
mdiff <- m1 - m2
newsam1 <- z[!is.na(z[,par1]),par1]
newsam2 <- z[,par2]+mdiff
newsam2 <- newsam2[!is.na(newsam2)]
(ks1.t <- ks.test(newsam1,newsam2,alternative=par4))
mydf <- data.frame(cbind(z[,par1],z[,par2]))
colnames(mydf) <- c('Variable 1','Variable 2')
bitmap(file='test1.png')
boxplot(mydf, notch=TRUE, ylab='value',main=main)
dev.off()
bitmap(file='test2.png')
qqnorm(z[,par1],main='Normal QQplot - Variable 1')
qqline(z[,par1])
dev.off()
bitmap(file='test3.png')
qqnorm(z[,par2],main='Normal QQplot - Variable 2')
qqline(z[,par2])
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.t$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.t$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.t$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.t$conf.int[1],',',r.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-test to compare two variances',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-stat',header=TRUE)
a<-table.element(a,v.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,v.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,v.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,v.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,v.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(v.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',v.t$conf.int[1],',',v.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Welch Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.w$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.w$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.w$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.w$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.w$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.w$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.w$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.w$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.w$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.w$conf.int[1],',',r.w$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
myWlabel <- 'Wilcoxon Signed-Rank Test'
if (par5=='unpaired') myWlabel = 'Wilcoxon Rank-Sum Test (Mann–Whitney U test)'
a<-table.element(a,paste(myWlabel,' with continuity correction (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'W',header=TRUE)
a<-table.element(a,w.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,w.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,w.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,w.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributions of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks1.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks1.t$p.value)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')