This is an R programming question. I hope there is someone who can solve it.

(on the size of malpractice awards). Comment why this distribution doesn't lend itself to the z-test or t-test. 9.8 The t-statistic has the t-distribution if the X's are normally distributed. What if they are not? Investigate the distribution of the t-statistic if the X's have different distributions. Try short- tailed ones (uniform), long-tailed ones (t-distributed to begin with), Uniform (exponential or log-normal) (For example, If the Xi are nearly normal, but there is a chance of some errors introducing outliers. This can be modeled with where is 1 with high probability and 0 otherwise and Y is of a different distribution. For concreteness, suppose 0, -1 and Y is normal with mean 0, but standard deviation 10 and P(C :. 1) .9. Here is some R code to simulate and investigate. (Please note, the simulations for the suggested distributions should be much simpler.) function (n=10,p=0.95) y = t- > f { = rnorm(n , mean=0 ,sde 1 +9*rbinon(n, 1, 1-p)) (mean(y) - 0)/ (sqrt (var(y))/sqrt(n)) sample = simple. Sin(100 , f) qqplot (sample, rt (100, df#9),main. "sample qqnorm (sample,main-"sample vs. normal");qqline (sample) hist (sample) > > vs. t");qq1 ine (sample) The resulting graphs are shown. First, the graph shows the sample against the t-quantiles. A bad, fit. The normal plot is better but we still see a skew in the histogram due to a single large outlier sample vs. t sample vs. normal Histogram of sample Windows'u Etki Windows'u etkinlesti avarlarna gidin. ample Figure 47: t-statistic for normal data