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

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

- 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.** - 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).** - Due before 2/2:
**Discuss the concepts of mutually exclusive events and independent events.**- Define the two terms in your own words
- Provide an example for each
- Discuss why two events cannot be both mutually exclusive AND independent (unless either event is impossible).

- 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.** - 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)=(**_{n}C_{y})p^{y}q^{n-y}, or explain how it is derived. Please give an example of something that can be modeled as a Binomial random variable. - 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.** - Due before 3/11:
**In the Normal Distribution we have two parameters, μ and σ**^{2}. Please explain how these control location and scale, and explain what it means to standardize a random variable X. - Due before 3/18:
**Please explain the Central Limit Theorem in your own words and why it is important for statistical inference.** - 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?**

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

- Jan 12, 14: Chapter 1 Shirt Data: (pdf) (csv)
- Jan 16: Sections 2.1,2.2
- Jan 21,23: Section 2.3
- Jan 26,28,30: Sections 2.4-2.7
- Feb 2, 4, 6: Sections 3.1-3.3
- Feb 9,11,13: Sections 4.1-4.3
- Feb 18,20,23: Sections 5.2-5.5
- Feb 25-Mar 6: Sections 6.1-6.7
- 2/27 Notes : Buffon's Needles and Normal Distribution
- 3/2 Notes : Normal Distribution
- 3/4 Notes : Gamma Distribution
- 3/4 Notes : Gamma, Exponential and Chi-Squared Distributions
- Mar 9 - Mar 20: Sections 8.1-8.6
- 3/9 Notes : Sampling Distributions
- 3/11 Notes : Central Limit Theorem
- 3/13 Notes : Central Limit Theorem (cont)
- 3/16 Notes : Sampling Distribution of S
^{2} - 3/18 Notes : Central Limit Theorem Examples
- 3/20 Notes : Chi-Squared and t-Distributions
- 3/30 Notes : Review for the Exam
- Apr 3 - Apr 10: Sections 9.1-9.12
- 4/3 Notes : Classical Estimation
- 4/6 Notes : Confdence Interval for the population mean
- 4/8 Notes : More estimation for population means
- 4/10 Notes : Estimation Intervals for population proportions
- Apr 13 - Apr 27: Sections 10.1-10.5,10.8-10.9

- What Randomness Looks Like (from WIRED)
- Shark attacks and the Poisson approximation
- Central Limit Theorem Simulation in Excel