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Data X:
87.28 255 87.28 280.2 87.09 299.9 86.92 339.2 87.59 374.2 90.72 393.5 90.69 389.2 90.3 381.7 89.55 375.2 88.94 369 88.41 357.4 87.82 352.1 87.07 346.5 86.82 342.9 86.4 340.3 86.02 328.3 85.66 322.9 85.32 314.3 85 308.9 84.67 294 83.94 285.6 82.83 281.2 81.95 280.3 81.19 278.8 80.48 274.5 78.86 270.4 69.47 263.4 68.77 259.9 70.06 258 73.95 262.7 75.8 284.7 77.79 311.3 81.57 322.1 83.07 327 84.34 331.3 85.1 333.3 85.25 321.4 84.26 327 83.63 320 86.44 314.7 85.3 316.7 84.1 314.4 83.36 321.3 82.48 318.2 81.58 307.2 80.47 301.3 79.34 287.5 82.13 277.7 81.69 274.4 80.7 258.8 79.88 253.3 79.16 251 78.38 248.4 77.42 249.5 76.47 246.1 75.46 244.5 74.48 243.6 78.27 244 80.7 240.8 79.91 249.8 78.75 248 77.78 259.4 81.14 260.5 81.08 260.8 80.03 261.3 78.91 259.5 78.01 256.6 76.9 257.9 75.97 256.5 81.93 254.2 80.27 253.3 78.67 253.8 77.42 255.5 76.16 257.1 74.7 257.3 76.39 253.2 76.04 252.8 74.65 252 73.29 250.7 71.79 252.2 74.39 250 74.91 251 74.54 253.4 73.08 251.2 72.75 255.6 71.32 261.1 70.38 258.9 70.35 259.9 70.01 261.2 69.36 264.7 67.77 267.1 69.26 266.4 69.8 267.7 68.38 268.6 67.62 267.5 68.39 268.5 66.95 268.5 65.21 270.5 66.64 270.9 63.45 270.1 60.66 269.3 62.34 269.8 60.32 270.1 58.64 264.9 60.46 263.7 58.59 264.8 61.87 263.7 61.85 255.9 67.44 276.2 77.06 360.1 91.74 380.5 93.15 373.7 94.15 369.8 93.11 366.6 91.51 359.3 89.96 345.8 88.16 326.2 86.98 324.5 88.03 328.1 86.24 327.5 84.65 324.4 83.23 316.5 81.7 310.9 80.25 301.5 78.8 291.7 77.51 290.4 76.2 287.4 75.04 277.7 74 281.6 75.49 288 77.14 276 76.15 272.9 76.27 283 78.19 283.3 76.49 276.8 77.31 284.5 76.65 282.7 74.99 281.2 73.51 287.4 72.07 283.1 70.59 284 71.96 285.5 76.29 289.2 74.86 292.5 74.93 296.4 71.9 305.2 71.01 303.9 77.47 311.5 75.78 316.3 76.6 316.7 76.07 322.5 74.57 317.1 73.02 309.8 72.65 303.8 73.16 290.3 71.53 293.7 69.78 291.7 67.98 296.5 69.96 289.1 72.16 288.5 70.47 293.8 68.86 297.7 67.37 305.4 65.87 302.7 72.16 302.5 71.34 303 69.93 294.5 68.44 294.1 67.16 294.5 66.01 297.1 67.25 289.4 70.91 292.4 69.75 287.9 68.59 286.6 67.48 280.5 66.31 272.4 64.81 269.2 66.58 270.6 65.97 267.3 64.7 262.5 64.7 266.8 60.94 268.8 59.08 263.1 58.42 261.2 57.77 266 57.11 262.5 53.31 265.2 49.96 261.3 49.4 253.7 48.84 249.2 48.3 239.1 47.74 236.4 47.24 235.2 46.76 245.2 46.29 246.2 48.9 247.7 49.23 251.4 48.53 253.3 48.03 254.8 54.34 250 53.79 249.3 53.24 241.5 52.96 243.3 52.17 248 51.7 253 58.55 252.9 78.2 251.5 77.03 251.6 76.19 253.5 77.15 259.8 75.87 334.1 95.47 448 109.67 445.8 112.28 445 112.01 448.2 107.93 438.2 105.96 439.8 105.06 423.4 102.98 410.8 102.2 408.4 105.23 406.7 101.85 405.9 99.89 402.7 96.23 405.1 94.76 399.6 91.51 386.5 91.63 381.4 91.54 375.2 85.23 357.7 87.83 359 87.38 355 84.44 352.7 85.19 344.4 84.03 343.8 86.73 338 102.52 339 104.45 333.3 106.98 334.4 107.02 328.3 99.26 330.7 94.45 330 113.44 331.6 157.33 351.2 147.38 389.4 171.89 410.9 171.95 442.8 132.71 462.8 126.02 466.9 121.18 461.7 115.45 439.2 110.48 430.3 117.85 416.1 117.63 402.5 124.65 397.3 109.59 403.3 111.27 395.9 99.78 387.8 98.21 378.6 99.2 377.1 97.97 370.4 89.55 362 87.91 350.3 93.34 348.2 94.42 344.6 93.2 343.5 90.29 342.8 91.46 347.6 89.98 346.6 88.35 349.5 88.41 342.1 82.44 342 79.89 342.8 75.69 339.3 75.66 348.2 84.5 333.7 96.73 334.7 87.48 354 82.39 367.7 83.48 363.3 79.31 358.4 78.16 353.1 72.77 343.1 72.45 344.6 68.46 344.4 67.62 333.9 68.76 331.7 70.07 324.3 68.55 321.2 65.3 322.4 58.96 321.7 59.17 320.5 62.37 312.8 66.28 309.7 55.62 315.6 55.23 309.7 55.85 304.6 56.75 302.5 50.89 301.5 53.88 298.8 52.95 291.3 55.08 293.6 53.61 294.6 58.78 285.9 61.85 297.6 55.91 301.1 53.32 293.8 46.41 297.7 44.57 292.9 50 292.1 50 287.2 53.36 288.2 46.23 283.8 50.45 299.9 49.07 292.4 45.85 293.3 48.45 300.8 49.96 293.7 46.53 293.1 50.51 294.4 47.58 292.1 48.05 291.9 46.84 282.5 47.67 277.9 49.16 287.5 55.54 289.2 55.82 285.6 58.22 293.2 56.19 290.8 57.77 283.1 63.19 275 54.76 287.8 55.74 287.8 62.54 287.4 61.39 284 69.6 277.8 79.23 277.6 80 304.9 93.68 294 107.63 300.9 100.18 324 97.3 332.9 90.45 341.6 80.64 333.4 80.58 348.2 75.82 344.7 85.59 344.7 89.35 329.3 89.42 323.5 104.73 323.2 95.32 317.4 89.27 330.1 90.44 329.2 86.97 334.9 79.98 315.8 81.22 315.4 87.35 319.6 83.64 317.3 82.22 313.8 94.4 315.8 102.18 311.3
Names of X columns:
X1 X2
Column number of first sample
Column number of second sample
Confidence
Alternative
greater
two.sided
less
greater
Are observations paired?
paired
unpaired
paired
Null Hypothesis
Chart options
Title:
R Code
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) a<-table.element(a,paste('Wicoxon rank sum test 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 <i>Distributions</i> 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 <i>Distributional Shape</i> 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')
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Big Analytics Cloud Computing Center
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