1st Dataset: ____________ Difference blood pressure, before and after treatment, n=51: znz1<-c(-12.1, 8.5, -6.1, -15.5, -6.2, 4.0, -12.4, -10.1, 3.8, 4.5, 1.2, -4.0, -6.3, -8.6, -1.6, 2.8, -4.0, 6.3, 1.7, -8.3, 3.1, -0.1, 0.7, -0.2, -1.1, -6.3, -4.5, 3.9, 3.0, 0.3, -7.2, -4.6, -6.9, -7.1, -5.7, -2.3, 3.5, -2.5, -4.3, -4.6, -9.0, -6.0, 4.0, 8.0, 2.6, -5.9, -3.2, -7.1, 0.0, -3.6, -12.4) -12.1, 8.5, -6.1, -15.5, -6.2, 4.0, -12.4, -10.1, 3.8, 4.5, 1.2, -4.0, -6.3, -8.6, -1.6, 2.8, -4.0, 6.3, 1.7, -8.3, 3.1, -0.1, 0.7, -0.2, -1.1, -6.3, -4.5, 3.9, 3.0, 0.3, -7.2, -4.6, -6.9, -7.1, -5.7, -2.3, 3.5, -2.5, -4.3, -4.6, -9.0, -6.0, 4.0, 8.0, 2.6, -5.9, -3.2, -7.1, 0.0, -3.6, -12.4 mean=-2.703922, var=31.31198, sd(znz1)=5.595711 used for t-test, Pooled-t-Test, Sign-Test, QQ-Plot 2nd Dataset: ____________ 31 patients in one study, 41 in second study (Placebo). Data are differences in blood pressure (before and after treatment): znz2: -6.5, -7.7, -6.7, -13.3, -19.9, -18.9, 0.6, 0.9, -12.3, -7.3, -13.9, -11.0, -5.9, -15.8, -11.1, 0.9, -3.4, -16.4, -1.8, -13.3, -18.4, -23.1, -10.5, -12.7, -18.5, -3.8, -0.8, -7.7, -12.0, -7.2, -3.0 c(-6.5, -7.7, -6.7, -13.3, -19.9, -18.9, 0.6, 0.9, -12.3, -7.3, -13.9, -11.0, -5.9, -15.8, -11.1, 0.9, -3.4, -16.4, -1.8, -13.3, -18.4, -23.1, -10.5, -12.7, -18.5, -3.8, -0.8, -7.7, -12.0, -7.2, -3.0) znz3: -2.7, -6.2, -5.8, -19.0, -20.7, -7.1, -15.9, -15.0, -7.0, -11.7, -10.7, -8.4, -10.5, -30.7, -13.3, -13.2, -13.5, -14.5, -1.2, -2.9, -0.3, -9.6, -6.1, -12.6, -9.5, -10.6, -21.5, -3.1, -9.3, -7.8, 2.7, -9.9, -18.8, -23.2, -15.7, -20.3, -4.6, 0.6, -18.7, -16.5, -22.7 c(-2.7, -6.2, -5.8, -19.0, -20.7, -7.1, -15.9, -15.0, -7.0, -11.7, -10.7, -8.4, -10.5, -30.7, -13.3, -13.2, -13.5, -14.5, -1.2, -2.9, -0.3, -9.6, -6.1, -12.6, -9.5, -10.6, -21.5, -3.1, -9.3, -7.8, 2.7, -9.9, -18.8, -23.2, -15.7, -20.3, -4.6, 0.6, -18.7, -16.5, -22.7) mean(znz2): -9.693548 mean(znz3): -11.40244 sd(znz2): 6.627917 sd(znz3): 7.361946 used for 2 sample t-test 3rd Dataset: ____________ 100 people tested in maths, 46 male, 54 female, 22 female fail, 12 male fail. used for Chi2-Independence 4th Dataset: ____________ Data Tschernobyl location<-c(„Pripjet“, „Chistogalovka“, „Lelev“, „Tschernobyl“, „Rudki“, „Orevichi“, „Kiew“, „Tschernikow“, „Tscherkassy“, „Minsk“, „Donezk“, „Wien“, „Oesterreich“, „Stockholm“, „Gaevle“, „SuedBayern“, „Konstanz“, „Irland“, „Stuttgart“, „Chilton“, „Schottland“, „Japan“, „Japan2“) rain<-c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1) dist<-c(3.16, 5.62, 7.94, 14.13, 15.85, 28.18, 89.13, 125.89, 281.84, 316.23, 707.95, 1000.00, 1000.00, 1122.02, 1258.93, 1258.93, 1584.89, 1584.89, 1590.54, 1995.26, 1995.26, 12589.25, 12589.25) bq<-c(5300, 2000, 2100, 2000, 800, 2000, 21, 55, 12, 20, 6, 3, 53, 1.5, 31, 81, 31, 16, 1.5, 1.5, 17, 0.15, 0.85) data<-data.frame(location, rain, dist, bq) > data location rain dist bq 1 Pripjet 0 3.16 5300.00 2 Chistogalovka 0 5.62 2000.00 3 Lelev 0 7.94 2100.00 4 Tschernobyl 0 14.13 2000.00 5 Rudki 0 15.85 800.00 6 Orevichi 0 28.18 2000.00 7 Kiew 0 89.13 21.00 8 Tschernikow 0 125.89 55.00 9 Tscherkassy 0 281.84 12.00 10 Minsk 0 316.23 20.00 11 Donezk 0 707.95 6.00 12 Wien 0 1000.00 3.00 13 Oesterreich 1 1000.00 53.00 14 Stockholm 0 1122.02 1.50 15 Gaevle 1 1258.93 31.00 16 SuedBayern 1 1258.93 81.00 17 Konstanz 1 1584.89 31.00 18 Irland 1 1584.89 16.00 19 Stuttgart 0 1590.54 1.50 20 Chilton 0 1995.26 1.50 21 Schottland 1 1995.26 17.00 22 Japan 0 12589.25 0.15 23 Japan2 1 12589.25 0.85 used for regression, 1 and 2 explanatory variables 5th Dataset: ____________ > growth<-c(33.3,47.8,44.4,42.9,40.9,35.5,35.5,35.4,47.6,38.8,29.6,33.4,32.8,38.8,42.8,38.5,42.4,45.5,38.9,38.9,44.5) > dung<-rep(LETTERS[1:4],c(6,4,5,6)) 1-Way-Anova