Course Syllabus

 Course Description

This course provides graduate-level instruction in the application of statistical analysis to issues in public and environmental affairs and related fields. It is designed to assist students in learning the methods by which statistical analysis is carried out, as well as the basic theory that enables and constrains the application of statistics to real world data. The course emphasizes practical aspects of applying such methods, appropriately interpreting the results of these statistical analysis tools, and gaining a meaningful understanding of how statistical analysis can be misused or erroneously executed (either intentionally or unintentionally). As such, the course will address descriptive statistics, statistical inference, the nature of random variables, sampling distributions, point and interval estimation of parameters (mean, standard deviation, etc.), hypothesis testing, analysis of variance, and bivariate and multivariate regression. Although these are traditional topics for an introductory statistics course, the emphasis in V506 will be on appropriately applying these techniques and extracting meaningful information from unstructured data. Use of computer tools for carrying out statistical analysis (primarily R) will also be a major emphasis.

 Learning Outcomes 

The primary learning outcomes for the course are to enable students to:

  • apply statistical tools to problem-solving in the social sciences,
  • understand differences in how one uses data, e.g., description and/or causal inference,
  • improve decision-making through statistical analysis,
  • explain the nature and use of distributions,
  • generalize sample data to larger populations,
  • explain the properties and limitations of estimators and hypothesis tests,
  • measure the nature and strength of relationship between variables,
  • transform raw data into useful information, and
  • carry out statistical analysis using computer tools.

 Course Requirements 

Required Text and Materials

  • Required Textbook: Diez, David M.; Çetinkaya-Rundel, Mine; and Barr, Christopher D. OpenIntro Statistics. 4th edition. 2019.  

    Additional required readings will be available as PDFs or links through Canvas.

  • Required Materials:
    • A pencil with an eraser
    • A scientific calculator (simple, non-graphing, $10-$15 is best)
    • A WIFI-enabled laptop (for lab sessions and in-class exams); this will be used to access R software and RStudio

 Course Assessment & Grades

Major assignments for this course will include two in-class exams (midterm and final) and five homework exercises. These major assignments, in addition to class attendance, will constitute your final grade.