Stat 381: Applied Statistical Methods
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.
- 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)=(nCy)pyqn-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 σ2. 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
- Mar 9 - Mar 20: Sections 8.1-8.6
- Apr 3 - Apr 10: Sections 9.1-9.12
- Apr 13 - Apr 27: Sections 10.1-10.5,10.8-10.9
Additional Material, Papers and Articles
Quiz and Exam Solutions