Probability and Inference 1

Probability and Inference 1
Set theory; probability; principles of counting; random variables; mathematical expectation; sampling distributions; point estimation.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 121 & MATH 112; or STAT 121 & MATH 113; or STAT 201 & MATH 112
 RecommendedMath 113.
 TaughtFall, Winter, Spring
 ProgramsContaining STAT 240
Course Outcomes: 

Set Theory and Basic Set Operations

Apply fundamentals of set theory and basic set operations

Discrete Sample Space

Enumerate a discrete sample space with counting techniques

Bayes Theorem

Solve problems using axioms of probability, conditional probability, independence, and Bayes theorem

Random Variables

Solve problems with the pdf, cdf, moments of discrete univariate random variables

Discrete Distributions

Understand the assumptions and properties of the named discrete distributions (Bernoulli, binomial, Poisson, geometric)

Maximum Likelihood

Solve for the maximum likelihood estimator

Sampling Distribution

Derive the sampling distribution for the proportion for SRS with and without replacement

Solve Problems

Solve problems with the pdf, cdf, qf, moments of continuous distributions