Match each concept with its definition. a) Bayes Theorem likelihood that an event of a random experiment will occur b) Central Limit Theorem c) correlation coefficient rejecting the null hypothesis when the null hypothesis is true. d) definition of independence The sum of n independently distributed random variables will tend to be normally distributed as n becomes large. e) exponential random variables f) F-distribution _ difference between the observed and the predicted value of some variable. e) mutually exclusive events outcome is not known with certainty in advance h) operating characteristic curve ) overfitting exact significance level of a statistical test; probability of obtaining a test statistic that is at least as extreme as the one observed if the null hypothesis is true. j) p-value k) probability set of all possible outcomes from a random experiment I) random experiment m) residual Pr(A|B) = Pr(3lA) PríA) Pr(B) n) Rule of Total Probability _Pr(A|B) = Pr(A) o) statistic _failure to reject the null hypothesis when the null hypothesis is false p) Sample space q) Type I error Pr(B) = Pr (B|A) Pr(A) + Pr(B|A') Pr(A') r) Type Il error summary value calculated from a sample of ahservations; usually an estimator of a population parameter ANB = 9

Define Probabilities and Moments of Continuous Random Variables?

Assume that hybridization experiments are conducted with peas having the property that for offspring, there is a 0.25 probability that a pea has green pods. Assume that the offspring peas are randomly selected in groups of 40. What is the mean

The probability that a trainee will remain with a company is 0.6. the probability that am employee earnsbmore than k10,000 per month is 0.5. the probability that an employee who is a trainee remained with the company or who earns more than k10,000 per month is 0.7. what is the probability that an employee earns more than k10,000 per month given that he is a trainee who stayed with the company