Topics in multiple linear regression, estimation of model parameters, inferences, diagnostics, model assumptions, ANOVA formulation.
MATH 350: with minimum grade of D- and STAT 355: with minimum grade of D- and (MATH 131: with minimum grade of D- or STAT 150: with minimum grade of D-)
Introduction to elementary sampling concepts. Includes random sampling, stratified sampling, cluster sampling and systematic sampling. Inferences and assumptions are presented for all sampling methods.
MATH 350: with minimum grade of D- and STAT 355: with minimum grade of D- and (MATH 131: with minimum grade of D- or STAT 150: with minimum grade of D-)
This course is an introduction to the elements of data science. Topics include data visualization, data wrangling, statistical learning and predictive analytics, text mining and spatial data.
Individualized investigation under the direct supervision of a faculty member. (Minimum of 37.5 clock hours required per credit hour.)