Statistical Modeling

Statistics is the art and science of using sample data to make generalizations about populations. Students who successfully complete this could should be able to:

  • critically consume statistically-based results reported in popular media, recognizing whether reported results reasonably follow from the study and analysis conducted.
  • recognize questions for which the investigative process in statistics would be useful and should be able to answer questions using the investigative process.
  • produce and interpret graphical displays and numerical summaries.
  • recognize and explain the central role of variability in the field of statistics.
  • recognize and explain the central role of randomness in designing studies and drawing conclusions.
  • use statistical models to address a research question.
  • conduct and interpret the results from hypothesis tests and confidence intervals.
  • use and interpret the results from StatKey and Minitab Express.

This course is a combination of a sequence of three one-credit classes on SAS programming (Stat 480, 481, and 482).  The class is organized into three five-week segments.

This is a graduate level course in analysis of variance (ANOVA), including randomization and blocking, single and multiple factor designs, crossed and nested factors, quantitative and qualitative factors, random and fixed effects, split plot and repeated measures designs, crossover designs and analysis of covariance (ANCOVA). This course is cohort-based, which means that there is an established start and end date, and that you will interact with other students throughout the course.

Students completing this course should be able to:

  • Select appropriate methods of multivariate data analysis, given multivariate data and study objectives;
  • Write SAS and/or Minitab programs to carry out multivariate data analyses;
  • Interpret results of multivariate data analyses.

Data mining and statistical learning methods use a variety of computational tools for understanding large, complex datasets. In some cases, the focus is on building models to predict a quantitative or qualitative output based on a collection of inputs. In others, the goal is simply to find relationships and structure from data with no specific output variable. This course takes an applied approach to understand the methodology, motivation, assumptions, strengths, and weaknesses of the most widely applicable methods in this field.

Time series data are intriguing yet complicated information to work with. While this course will provide students with a basic understanding of the nature and basic processes used to analyze such data, you will quickly realize that this is a small first step in being able to confidently understand what trends might exist within a set of data and the complexities of being able to use this information to make predictions or forecasts. Yet, whether it is financial, medical or weather related, this type of data is quite frequently found in much of our daily lives.