# Topics Covered in Graduate Statistics Service Courses

## STAT 541 Statistical Methods II

Prerequisite: STAT 281 or equivalent

• Check here to see if you are ready for STAT 541.
• Simple and multiple linear regressions
• ANOVA for one or multiple factors
• Design of experiments
• Linear models with categorical data
• Models with categorical response variable

## STAT 535 Applied Bioinformatics

Prerequisite: STAT 281 or equivalent

• Analyzing and interpreting genomics data
• Finding online genomics resources
• BLAST searches
• Manipulating/editing and aligning DNA sequences
• Analyzing and interpreting DNA microarray data
• Other current techniques of bioinformatics analysis

## STAT 545 Nonparametric Statistics

Prerequisite: STAT 281 or equivalent

• Necessary statistics and probability background
• Tests based on the Binomial Distribution
• Contingency tables
• Introduction to categorical data analysis
• Methods based on ranks
• Two or more independent samples
• Matched pairs
• Nonparametric regression
• Balanced incomplete block design
• Kolmogorov‐Smirnov and related tests

## STAT 560 Time Series Analysis

Prerequisite: STAT 541

• Background needed for forecasting, including autocorrelation, data transformations, forecasting, evaluating and monitoring a model
• Regression analysis as applied to forecasting
• Exponential smoothing methods for modeling time series data and forecasting
• Autoregressive Integrated Moving Average (ARIMA) Models aka Box‐Jenkins models
• Transfer functions and intervention models

## STAT 601 Modern Applied Statistics I

Prerequisite: STAT 541; STAT 700 or STAT 514

• Introduction to Statistical Graphics and GGplot
• Logistic Regression I
• Generalized Linear Models
• Density Estimation
• Recursive Partitioning
• Generalized Additive Models and Spline Models
• Survival Analysis
• Longitudinal Data Analysis and Mixed Models
• Multiple Comparisons
• False Discovery Rates
• Simultaneous Inference
• Meta‐Analysis

## STAT 602 Modern Applied Statistics II

Prerequisite: STAT 701

• Introduction to Statistical Learning
• Introduction to Classification
• Resampling Methods
• Model Selection
• “Moving Beyond Linearity”
• Tree‐Based Methods
• Support Vector Machines
• ROC curves
• Clustering/Unsupervised Learning

## STAT 661 Design of Experiments I

Prerequisite: STAT 541

• Analysis of variance
• Block designs
• Fixed and random effects
• Split plots and other experimental designs. Includes
• Use of SAS proc GLM, Mixed, etc…

## STAT 731 Survival Analysis

Prerequisite: STAT 541

• Conduct and analysis of Clinical trials
• Randomized clinical trials
• Ethical issues in clinical trials
• Dose‐escalation methods
• Parallel, crossover, and adaptive designs
• Sample size determinations
• Design and analysis of group sequential trials
• Meta‐analysis
• Survival data analysis

## STAT 742 Spatial Statistics

Prerequisite: STAT 541

• Geostatistics (variograms, kriging, regression)
• Lattice data (gridded data, computer images)
• Point processes
• Spatio‐temporal modeling
• Hierarchical modeling
• Disease mapping
• Spatial autocorrelation (global and local)
• R, R packages, other open source software