Stat 381: Applied Statistical Methods
Course Syllabus
Calculator Help (TI-83, TI-85, TI-89)
Lia Liu's Stat 381 Course Page, Review Problems and Notes
Math Learning Center schedule for Statistics TAs
Table of Standard Normal CDF probabilities (Z Table)
Regularized Lower Incomplete Gamma Function Table
Chi-Square Probability Table
Table of critical values for Student's T-Distribution
Interactive website for many distributions

Written Homework Assignments

These should be submitted through Blackboard (my preference) or email ( second preference). Should these not be possible, submission on Monday in class is fine. 1 or 2 paragraphs should suffice.
  1. Due before 1/19: Describe the difference between mean, median and mode. Describe a situation for each where it is the most appropriate concept for the "center" of the data.
  2. Due before 1/26: It was stated in lecture that nCr = nPr / r!. Please explain the meaning of this relationship. It is also true that nPr = nCr * r!. Please explain the relationship of combinations and permutations in this way (e.g. in terms of ordering of objects).
  3. Due before 2/2: Discuss the concepts of mutually exclusive events and independent events.
    1. Define the two terms in your own words
    2. Provide an example for each
    3. Discuss why two events cannot be both mutually exclusive AND independent (unless either event is impossible).
  4. Due before 2/9: Please describe the difference between a continuous and a discrete random variable, give an example of each. Also explain the difference between a probability mass function and a probability density function.
  5. Due before 2/23: Please explain in your own words what a Bernoulli random variable is and give an example of something (besides a coin flip) that can be modeled using a Bernoulli distribution. Also interpret the pmf of the Binomial distribution: f(y)=(nCy)pyqn-y, or explain how it is derived. Please give an example of something that can be modeled as a Binomial random variable.
  6. Due before 3/4: Explain what a Poisson process is, and the Poisson distribution. Give an example of something that may be modeled using the Poisson distribution.
  7. Due before 3/11: In the Normal Distribution we have two parameters, μ and σ2. Please explain how these control location and scale, and explain what it means to standardize a random variable X.
  8. Due before 3/18: Please explain the Central Limit Theorem in your own words and why it is important for statistical inference.
  9. Due before 4/15: Please explain what a confidence interval is, and what it means to be (say) 95% confident . Why are sampling distributions so important for this type of inference?

Special Project

An example project, which would probably get a B (pdf) (LaTeX source)

Lecture Notes

Additional Material, Papers and Articles

Quiz and Exam Solutions