Math 380 - Topics in Math: Computational Statistics with R

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A Course for Spring 2014 Math 380 - Topics in Math: Computational Statistics with R Description MWF, 10:30 AM - 11:20 AM, STURGS 103 This is an upper level undergraduate course in applied statistical computing for data analysis and statistical programming using R software package. Statistical topics and issues in various areas will be investigated with computation in the blend of application and theory via an examples-based approach. Students in this course will focus on building statistical models and developing skills for implementing data analysis with current methods and simulation study along with programming concepts in R. The course will also provide modeling and data analysis experience for students, who have taken a 200 or 300 level statistics or statistics related course, in individually-chosen research topics. Some basic concepts in probability and statistics will be reviewed as well. Students from all majors are welcome. Prerequisites: One statistics course at the 200- or 300- level such as MATH 242, 262, 341 and 361, ECON 305, MGMT 305, BIOL 250, PSYC 250, 251, SOCL 211 and 212, or permission of the instructor. Students who have taken Math 360 and will be taking Math 361 in Spring 2014 are welcome too. Statistical Topics besides R Language for Statistics Topics covered include the following: • (Matrix review) • Simulation in estimation and inference: • Introduction to R as a statistical computing tool o Bootstrapping • Exploratory data analysis (EDA): descriptive and • Categorical data analysis: graphical tools for o One-way, two-way and multi-way tables o Univariate data o Effect size coefficients o Bivariate data • Robust nonparametric linear regression o Multivariate data o Rank-based approach • Linear Regression Models: o Robustness in factor and response space o Simple and multiple linear regression data • Nonlinear regression analysis: MLE, LS and WLS • (Optional) Introduction to various models: Logistic o Model building, checking and refinement regression, Poisson, Multivariate and others. o General Hypothesis Testing • ANOVA designs and regression approach For more information please contact Yusuf Bilgic, South Hall 324A, 245-5484, [email protected] .

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