MSUB Mathematics Department
STAT 341 Introduction to Probability & Statistics
Course Description
4 credits
Prerequisite: M 273
Covers discrete and continuous random variables, moment generating functions, statistical inference, and methods of estimation. Topics may vary from year to year.
Learning Outcomes

Define the concepts of sample space, events, probability, independence of events and compute the probability and conditional probability of events, and use laws of probability and Bayes' Rule,

Define and apply the concepts of univariate discrete and continuous random variables and their probability distributions,

Define and apply the concepts of bivariate discrete and continuous random variables and their probability distributions,

Apply Tchebysheff’s Theorem,

Define and apply the concepts of mean, variance and covariance of random variables,

Find and apply momentgenerating functions of random variables,

Apply the concepts of marginal and conditional probability distributions,

Apply the concepts of expected value of a function of random variables,

Apply the concepts of covariance and conditional expectations,

Find the probability distribution of a function of random variables,

Define and apply the concepts of sampling distributions and the central limit theorem,

Calculate the bias and mean square error of point estimators and evaluate the goodness of a point estimator,

Calculate and interpret confidence intervals.
Course Documents