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 moment-generating 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