Continuous random variables (pdf, cdf, moments); sampling distributions; Central Limit Theorem; frequentist inference (estimation, intervals); Bayesian inference (estimation, intervals); simulation.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 240 & MATH 113
 RecommendedStat 123 and Stat 124.
 TaughtFall, Winter
 ProgramsContaining STAT 340
Course Outcomes

Named Continuous Distributions

Understand assumptions and properties of named continuous univariate distributions: normal, beta, gamma, exponential

Solve Problems

Solve problems using joint, marginal, conditional pmf and pdf


Calculate expectation of linear combinations, covariance, correlation


Calculate transformations of jointly distributed random variables

Maximum Likelihood

Solve for the maximum likelihood estimator from a SRS

Normal Distribution

Derive properties of maximum likelihood estimators from SRS of normal distribution

Law of Large Numbers

Apply convergence in probability and distribution to prove the Law of Large Numbers and the Central Limit Theorem

Central Limit Theorem

Derive the 100(1-alpha)% from the pivot derived from the Central Limit Theorem


Derive the probability of Type I and Type II Error for simple hypothesis test using test statistic derived from the Central Limit Theorem