Graduate Statistics Service Courses

What are graduate statistics service courses?

  • The Mathematics and Statistics Department offers two types of graduate statistics courses. Courses primarily intended for our department’s graduate students have mathematical prerequisites such as would be associated with a bachelor’s or even master’s degree in mathematics. Examples include STAT 684, 685, 686 and 687. All SDSU graduate students who have the necessary prerequisites are welcome to take these courses, but should be aware that the mathematical level will be high. Since it is not reasonable to expect all students to be interested in such courses, we also offer an extensive array of service courses for which the mathematical prerequisite is a course equivalent to SDSU’s MATH 102 College Algebra.

Who teaches graduate statistics service courses?

  • The same statistics faculty members who teach courses for our own graduate students also teach our service courses. They have outstanding credentials, with substantial experience in applied statistics in a variety of disciplines. They include Chris Saunders, Gary Hatfield, Gemechis Djira, Jixiang Wu, Tom Brandenburger and Xijin Ge.

Which graduate statistics courses are service courses?

  • Current service courses include:
    • STAT 535 Applied Bioinformatics
    • STAT 541 Statistical Methods II
    • STAT 545 Nonparametric Statistics
    • STAT 560 Time Series Analysis
    • STAT 601 Modern Applied Statistics I
    • STAT 602 Modern Applied Statistics II
    • STAT 742 Spatial Statistics
    • STAT 661 Design of Experiments I

...and also include:

  • STAT 510 SAS Programming I
  • STAT 514 Introduction to R (1 credit)
  • STAT 515 R Programming
  • STAT 600 Statistical Programming.

All courses are three credits unless otherwise specified

How much undergraduate statistics knowledge do I need before I take these courses?

  • Our non-programming statistics service courses have either STAT 541 or an introductory undergraduate statistics course such as STAT 281 as prerequisite. It is expected that students enrolling in STAT 541 will have taken an introductory undergraduate statistics course.
  • For the programming courses, no prior statistics knowledge is required although some programming experience or at least familiarity with computer basics such as creating and uploading files is expected.
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
  • Spatiotemporal modeling
  • Hierarchical modeling
  • Disease mapping
  • Spatial autocorrelation (global and local)
  • R, R packages, other open source software
Topics in Graduate Statistics Programming Courses
STAT 510 SAS Programming I
Prerequisite: familiarity with computer basics
  • Reading external data files into SAS
  • Creating permanent SAS data sets
  • Formatting and labeling variables
  • Using Excel through SAS
  • Conditional processing
  • Do loops
  • Subsetting and combining data sets
  • Using numeric and character functions
  • Arrays
  • Displaying data and customizing reports
  • Summarizing results
  • Frequencies
  • Tables
  • Graphing
STAT 514 R Programming
Prerequisite: familiarity with computer basics
  • R installation and environment
  • Data structure, management and manipulation
  • Logic statements (if else)
  • Basic loops (for, repeat, while, do until)
  • Use of in‐built functions
  • Basic custom function writing
  • Installation of extensions to R
  • Intermediate programming techniques
    • apply, lapply, sapply
STAT 592 Data Visualization with R
Prerequisite: STAT 514
  • Data importation and formatting
  • Data visualization
    • Base graphics
  • Plot, boxplot, hist, lines, points, etc.
  • Use of par for graphical parameters
  • GGplot2
    • Qplot
    • Basic grammar
    • Plotting with layers
    • Graphical parameters
STAT 600 Statistical Programming
Prerequisite: STAT 541
  • R programming
    • Formatting
    • Aggregation
    • Loops
    • Dynamic report generation (Sweave/Knitr)
    • External data sources
  • SAS programming
    • Defining and using macro variables and macros
    • PROC SQL in Macros
    • Graphing in SAS
Contact us
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Department of Mathematics and Statistics
Physical Address
905 Campanile Ave.
Brookings, SD 57007
Mailing Address
SAME 276, Box 2225
Brookings, SD 57007
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Hours
Mon - Fri: 8:00 a.m.-5:00 p.m.
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