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Topics in Graduate Statistics Service Courses

STAT 541 Statistical Methods II

Prerequisite: STAT 281 or equivalent

  • 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