CS计算机代考程序代写 ## —-echo=FALSE———————————————————-

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set.seed(2253)

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p <- c(0.9, 0.95, 0.975) qnorm(p) ## ----eval=FALSE---------------------------------------------------------- ## qnorm(p, 5, 3) ## ----eval=FALSE---------------------------------------------------------- ## qt(p, 5) ## ----eval=FALSE---------------------------------------------------------- ## qchisq(p, 1) ## ----eval=FALSE---------------------------------------------------------- ## # your turn... ## ----eval=FALSE---------------------------------------------------------- ## qf(p, 12, 4) ## ----echo=FALSE---------------------------------------------------------- PTweight <- read.table("PTweight.txt") ## ------------------------------------------------------------------------ x <- PTweight[1:5, 2] n <- length(x) x.bar <- mean(x) s <- sd(x) t <- qt(0.95, n - 1) x.bar + c(-1, 1) * t * s / sqrt(n) ## ------------------------------------------------------------------------ t.test(x, conf.level = 0.90) ## ------------------------------------------------------------------------ y <- PTweight[6:10, 2] # stress group data y.bar <- mean(y) s.p <- sqrt((4 * var(x) + 4 * var(y)) / 8) # pooled sample sd x.bar - y.bar + c(-1, 1) * qt(0.975, df = 8) * s.p * sqrt(1 / 5 + 1 / 5) ## ------------------------------------------------------------------------ t.test(x, y, var.equal = TRUE) ## ------------------------------------------------------------------------ var.test(x, y) ## ------------------------------------------------------------------------ lambda <- 10 B <- 1000 # simulation runs n <- 10 # sample size xbar <- 1:B # we will collect results in these two vectors s2 <- 1:B for (b in 1:B) { # repeat B times for b = 1,...,B x <- rpois(n, lambda) xbar[b] <- mean(x) # compute and store X-bar s2[b] <- var(x) # compute and store S^2 } ## ------------------------------------------------------------------------ mean(xbar) mean(s2) ## ------------------------------------------------------------------------ var(xbar) var(s2) ## ------------------------------------------------------------------------ lambda / n lambda * (2 * n * lambda + n - 1) / (n * (n - 1)) ## ----fig.height=4, fig.width=6------------------------------------------- boxplot(xbar, s2, names = c("X-bar", "S-squared"), col = "lightblue", horizontal = TRUE) # Draw a dashed vertical line showing the true parameter value. abline(v = lambda, lty = 2, lwd = 2, col = "magenta") ## ------------------------------------------------------------------------ invadopodia <- read.table("invadopodia.txt") x1 <- invadopodia[invadopodia$Condition == 1, 2] x2 <- invadopodia[invadopodia$Condition == 2, 2] x.bar1 <- mean(x1) x.bar2 <- mean(x2) ## ------------------------------------------------------------------------ # 95% CI for group 1. x.bar1 + c(-1, 1) * 1.96 * sqrt(x.bar1 / length(x1)) # 95% CI for group 2. x.bar2 + c(-1, 1) * 1.96 * sqrt(x.bar2 / length(x2)) ## ------------------------------------------------------------------------ qnorm(0.95) # use this to obtain a 90% CI qnorm(0.995) # use this to obtain a 99% CI ## ------------------------------------------------------------------------ # 95% CI for difference between groups 1 and 2. x.bar1 - x.bar2 + c(-1, 1) * 1.96 * sqrt(x.bar1 / length(x1) + x.bar2 / length(x2)) ## ------------------------------------------------------------------------ sample(c("tails", "heads"), 10, replace = TRUE) ## ------------------------------------------------------------------------ days <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun") sample(days, 1) ## ------------------------------------------------------------------------ sample(c("tails", "heads"), 10, prob = c(0.2, 0.8), replace = TRUE)