# Course Objectives

The objectives of the course are to introduce the underlying concepts of probability and statistical inference. In particular, this course will provide a foundation in the underlying probability theory and distribution theory required for application of statistical inference. This theory will be built upon in STAT06 and other later courses. It is expected that students will have a solid prerequisite foundation calculus before enrolling in this course.

Topics: We will cover exploratory data analysis, probability theory, conditional probability, independence, Bayes’ theorem, discrete distributions (binomial, hypergeometric, geometric, negative binomial, Poisson), moment generating functions, continuous distributions (uniform, exponential, gamma, normal), mixed distributions, bivariate and multivariate distributions. In addition, we will cover sampling distribution theory, correlation, linear functions of random variables and introduce the Central Limit Theorem.

# Tentative Schedule of Weekly Topics

1 Basic Concepts, Numerical Characteristics, Probability Set Functions
2 Properties of Probability, Methods of Enumeration
3 Random Variables, Probability Density Functions
4 Distribution Functions, Mathematical Expectation
5 Special Mathematical Expectations, Chebyshev’s Inequality
6 Correlation, Correlation Coefficient, Stochastic Independence
7 Discrete Random Variables: binomial, multinomial, and Poisson distributions
8 Continuous Random Variables: Gamma, Chi-Square, and Beta distributions
9 Continuous Random Variables: Normal and Bivariate Normal distributions
10 Sampling Theory, Transformations of Random Variables
11 The t and F distributions, Order Statistics,
12 Moment Generating Function Technique, distribution of the sample mean and variance
13 Limiting Distributions, Stochastic Convergence
14 Limiting Moment Generating Functions
15 Central Limit Theorem

Example Syllabus

Course Descriptions

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