Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.

[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. Prerequisites: MATH 132, or 136. (Gen.Ed. R2)

Description not available at this time

In this course, students complete an applied statistics field project that has been solicited from researchers in biological, physical, or social sciences. The instructor supplies applied as well as statistical methodology readings for the students. The readings serve to extend what students have learned in prior classes, and especially to help students learn to apply statistical methodology to problems from real-world applications. The students work in groups of 2, and they have to write a 10-20 page technical report and prepare a poster to summarize the project.

Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)

[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]