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.
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, Chebyshevs 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
13 Limiting Distributions, Stochastic Convergence
14 Limiting Moment Generating Functions
15 Central Limit Theorem