Applied analytical statistics is a course focusing on the tools and techniques used by social scientists to understand, describe and analyse (quantitative) data. It is an introductory level course, though oriented largely towards those who may have had some previous contact with mathematics or statistics in their undergraduate degrees. The focus will be on learning how to apply practical statistics in a social research context (rather than looking at fundamental mathematical foundations of statistical concepts).

During the course, students will make use of the Python programming language, and hence can also expect to build familiarity with the language as they progress through the course, particularly some typical libraries for the analysis and display of data such as Pandas, scikit-learn and matplotlib.

Learning Objectives

At the end of this course students will…

  • Have an understanding of the types of statistical tools available to students to enable them to answer social research questions
  • Have an understanding of how to describe quantitative data
  • Know how to create regression models in the Python programming language and interpret the output
  • Know how to present the results of regression models in research papers.

Topics

  1. Introduction to analytical statistics and descriptive statistics
  2. Linear regression: fitting and interpreting
  3. Statistical significance
  4. Variable selection and model building strategies
  5. Diagnosing (and remedying) problems in linear regression models (I)
  6. Diagnosing (and remedying) problems in linear regression models (I)
  7. Presenting linear regression output, post-estimations etc.
  8. Logistic regression
This page was last modified on 8 October 2018