cw2 <- read.csv("http://www.stat.ufl.edu/~winner/data/cat_water2.csv") attach(cw2); names(cw2) ### Linear Regression ## Complete Model (X1=1 if flow, 0 if still, X2-X9 are dummy vars for cats 1-8 cat.mod1 <- lm(h2o ~ X1+X2+X3+X4+X5+X6+X7+X8+X9) summary(cat.mod1) anova(cat.mod1) confint(cat.mod1) ## Reduced Model (Only Dummy for flow included) cat.mod2 <- lm(h2o ~ X1) summary(cat.mod2) anova(cat.mod2) ## Test for "Cat Effects" anova(cat.mod2, cat.mod1) detach(cw2) cw1 <- read.csv("http://www.stat.ufl.edu/~winner/data/cat_water.csv") attach(cw1); names(cw1) ### Conduct Paired t-test (cw.diff <- flow-still) ### Compute paired differences (mean.cw.d <- mean(cw.diff)) ### Mean of paired difference (sd.cw.d <- sd(cw.diff)) ### SD of paired differences (n.cw.d <- length(cw.diff)) ### n of paired differences (t.cw.d <- mean.cw.d/(sd.cw.d/sqrt(n.cw.d))) ### Paired t-statistic (p.cw.d <- 2*(1-pt(abs(t.cw.d),n.cw.d-1))) ### P-value ## 95% CI for mu_D = mu_F-mu_S mean.cw.d + qt(c(.025,.975),n.cw.d-1)*sd.cw.d/sqrt(n.cw.d) ## Paired t-test using t.test function t.test(flow,still,paired=T)