This course provides a forum for training in statistical consulting. Application of statistical methods to real problems, as well as interpersonal and communication aspects of consulting are explored in the consulting environment. Students enrolled in this class will become eligible to conduct consulting projects as consultants in the Statistical Consulting and Collaboration Services group in the Department of Mathematics and Statistics.

This course will introduce computing tools needed for statistical analysis including data acquisition from database, data exploration and analysis, numerical analysis and result presentation. Advanced topics include parallel computing, simulation and optimization, and package creation. The class will be taught in a modern statistical computing language.

Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building.

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.

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.

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.]

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.

This is a stand-alone independent study designed by the student and faculty sponsor that involves frequent interaction between instructor and student. Qualitative and quantitative enrichment must be evident on the proposed contract before consent is given to undertake the study.

This is a stand-alone independent study designed by the student and faculty sponsor that involves frequent interaction between instructor and student. Qualitative and quantitative enrichment must be evident on the proposed contract before consent is given to undertake the study.