STAT230 Lecture 1: Stat 230 Lec 1 - Intro

STAT230 Lecture 2: Stat 230 Lec 2 - Probability Model

STAT230 Lecture 3: Stat230 Lec 3 Probability Model & Counting techniques

STAT230 Lecture 5: Stat 230 Lec 5 - Arrangement

STAT230 Lecture 7: Stat 230 Lec 7 - Unions, Intersections, Compliment

STAT230 Lecture 8: Stat 230 - Lec 8 Independent and dependent events

STAT230 Lecture 9: Stat 230 Lec 9 - Conditional Probability

STAT230 Lecture 11: Stat 230 Lec 11 - Probability Function

STAT230 Lecture 12: Stat 230 Lec12- Cummulative Distribution Function

STAT230 Lecture 13: Stat 230 Lec 13 - Distribution Methods

STAT230 Lecture 17: Stat 230 Lecture 17 - Distribution Model

STAT230 Lecture 18: Stat 230 Lec18 - Poisson Distribution

STAT230 Lecture 20: Stat 230 Lec 20 - Expectation of a Random Variable

STAT230 Lecture 21: Stat 230 Lec 21 - Expectation function

STAT230 Lecture 22: Stat 230 Lec 22 - Variance

STAT230 Lecture 23: Stat 230 Lec 23 - Chapter 8

STAT230 Lecture 24: Stat 230 Lec 24 - Chapter 8 Continuous Random Variable

STAT230 Lecture 25: Stat 230 Lec 25 - Chapter 8 Examples

STAT230 Lecture 26: Stat 230 Lec 26 - PDF and Uniform Distribution

STAT230 Lecture 28: Stat 230 Lec 28 - Exponential Distribution

STAT230 Lecture 29: Stat 230 Lec 29 - Working with CDF

STAT230 Lecture 30: Stat 230 Lec 30

STAT230 Lecture 32: Stat 230 Lec 32

STAT230 Lecture 33: Stat 230 Lec 33

STAT230 Lecture 35: Stat 230 Lec 35

STAT230 Lecture 37: Stat 230 Lec 37

STAT230 Lecture 38: Stat 230 Lec 38

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Covers the theory of stratified, ratio, and regression methods of estimation and cluster and double sampling. Includes the concept of sufficiency and its applications from finite populations, nonsampling errors, estimation of response bias and of optimum number of interviewers, and sampling inspection. 

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