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Required Courses

Introductory Courses (One course must be taken from the following):

*** Please note that the current offerings may have changed. All interested students are encouraged to consult with their adviser for further information.***

Applied Statistics 300: Introduction to Psychological Statistics

  • Same as home course Psych 300: Introductory Psychological Statistics.
  • Course is typically taught in Fall and Spring semesters.

Descriptive statistics including correlation and regression. Inferential statistics including nonparametric and parametric tests of significance through two-way analysis of variance. Course emphasizes underlying logic and is not primarily mathematical, though knowledge of elementary algebra is essential.

Applied Statistics 320: Introduction to Statistics for Pre-Health Majors

  • Course is home based in Applied Statistics.
  • Course is typically taught in Spring semester.

This course is an introduction to basic statistical analysis for undergraduate students who intend to pursue a career in medicine and/or public health. Students will be introduced to core statistical tools used to study health outcomes. Topics include: measurement, descriptive analysis, correlation, graphical analysis, hypothesis testing, confidence intervals, analysis of variance, and regression analysis. Major components of the course include learning how to collect, manage, and analyze data using computer software and how to effectively communicate to others results from statistical analyses. The second aspect of the course is focused on the statistical package, R (free for downloading). R is an implementation of the S language (the default computational tool for research statisticians). R is the most powerful, extensively featured, and capable statistical computing tool available and the Rcmdr package in R provides a friendly "point-and-click" environment.

Applied Statistics 363: Quantitative Political Methodology

  • Graduate equivalent is Applied Statistics 563.
  • Same as home course Political Science 363: Quantitative Political Methodology.
  • Course typically taught in Fall semester.

This is an introduction to research methodology and quantitative analysis for social scientists. Students will be introduced to the logic of social scientific inquiry, and to the basic statistical tools used to study politics. Students will learn and apply the following to answer substantive questions: measurement, descriptive analysis, correlation, graphical analysis, hypothesis testing, confidence intervals, analysis of variance, and regression analysis. Major components of the course include learning how to collect, manage, and analyze data using computer software, and how to effectively communicate to others results from statistical analyses. Students will work collaboratively on research projects where they pose their own questions, design a study, collect and analyze the data, and present their findings in a research paper. Additional work may be required for graduate students.

Applied Statistics 2200: Elementary Probability and Statistics

  • Graduate equivalent is Applied Statistics 5200.
  • Same as home course Math 2200: Elementary Probability and Statistics.
  • Course typically taught in Fall semester.

An elementary introduction to probability and statistics. Discrete and continuous random variables, mean and variance, hypothesis testing and confidence limits, nonparametric methods, Student's t, analysis of variance (ANOVA),(multiple) regression and contingency tables. Graphing calculator with statistical distribution functions (such as the TI-83) is required. Prerequisite: Math 131.
  

Intermediate Courses (One course must be taken from the following):


Applied Statistics 301: Experimental Psychology

  • Same as home course Psychology 301
  • Course typically taught in Spring semester.

This course provides training in the logic and techniques of psychological research so as to provide students with experience in the design of psychology experiments and interpretation of results. Topics include experimental design and control, library research, quantitative treatment of data, graphical presentation of results, and clarity of scientific writing. Lectures focus on general principles of experimentation while the laboratory sections provide an introduction to a range of psychological phenomena through hands-on experience in experimentation. Each student also completes an independent research project of his or her own design. Declared Psychology majors will have priority. Others must obtain departmental approval. (4 units)

Applied Statistics 364: Intermediate Applied Statistics: Linear Models

  • Graduate equivalent is Applied Statistics 564.
  • Course is home based in Applied Statistics.
  • Course typically taught in Spring semester.

This course provides a detailed introduction to linear statistical models, the workhorse of applied statistics. Building on foundations in basic probability theory and central limit theorems, linear statistical models can be built from first principles to describe data and/or formalize statistical inference. The remainder of the course will evaluate the robustness to deviations from the basic assumptions and thus generalize the core principles of the linear regression model. Prerequisites: Applied Statistics 330.

Applied Statistics 413: Introduction to Econometrics

  • Graduate equivalent is Applied Statistics 413.
  • Same as home course Economics 413: Introduction to Econometrics.
  • Course typically taught in Fall and Spring semesters.

Course provides a basic working knowledge of econometrics. Topics include: translation of economic theory into statistical models, statistical foundations of econometrics, preregression analysis bivariate and multiple regression techniques, hypothesis testing, multicollinearity, specification error, autocorrelation, errors in variables, identification, and simultaneous estimation. Prerequisite, Econ 103B and 104B and Math 2200 or equivalent.