STAT S200 *Elementary Statistics

3 credits (3+0)
GER. Introduction to concepts and applications of elementary statistical methods. Topics include sampling and data analysis, descriptive statistics, elementary probability, probability and sampling distributions, confidence intervals, hypothesis testing, correlation, and simple linear regression. Recommended: MATH S151 (C 2.00 or better).
Prerequisite: MATH S105 (B 3.00 or better) or placement test.

STAT S210 Introduction to Data Science

3 credits (3+0)
Data science is the science of extracting meaningful information from data. This course provides students with basic tools for working with built-in and publicly available data sets using the open source R programming language. Skills in data manipulation, data visualization, and data wrangling will be developed and applied to data from a variety of subjects. Ethics in data science will be discussed and incorporated into data analysis. Version control and special topics will be included as time permits.
Prerequisite: MATH S151 or STAT S200 or BA S373; or MATH S105 and SSCI S300; or MATH S105 and SOC S325; or instructor permission.

STAT S293 ST:

STAT S373 Probability and Statistics

3 credits (3+0)
A calculus-based course emphasizing theory and applications. Topics include probability, continuous and discrete random variables and their probability distributions, expectation, moment generating functions, joint distributions, functions of random variables, estimations, and an introduction to the study of the power and significance of hypothesis tests.
Prerequisites: MATH S252, C (2.00) or higher.

STAT S400 Statistical Computing with R

2 credits (0+4)
An in-depth introduction to the fundamentals of programming with R, the free open-sourced statistical software. Emphasizes development of skills in preparing user-defined functions and code via topics introduced in traditional elementary statistics courses. Includes descriptive statistics, graphical and quantitative methods for exploring univariate and bivariate data through parametric and nonparametric methods.
Prerequisite: MATH S151 and STAT S200 with C (2.00) or higher, and upper division standing.

STAT S401 Regression and Analysis of Variance

4 credits (3+3)
A study of multiple regression including multiple and partial correlation, the extra sum of squares principle, indicator variables, and model selection techniques. Analysis of variance and covariance for multi-factor studies in completely random and randomized complete block designs, multiple comparisons and orthogonal contrasts. STAT S400 recommended.
Prerequisite: MATH S151 and STAT S200 or equivalent with C (2.00) or higher, and upper division standing.