Reading materials | Weekly Q&A | Lecture notes

Key concepts


  • Correlation:
    • Pearson correlation
    • Spearman’s rank correlation
    • Interpretation of correlation coefficient
  • Linear regression:
    • Dependent and independent variable
    • Slope (regression coefficient) and intercept
    • Goodness of fit
    • Residual variation and standard error of the estimate
  • Assumptions of linear regression
    • Errors Normally distributed
    • Homoskedasticity
    • Errors are independent of each other
    • Independent and dependent variables must be interval variables
    • Linear relationship between variables

Before class


Required readings

  • Meier, K. J., Brudney, J. L., & Bohte, J. (2014). Introduction to Regression Analysis. In Applied Statistics for Public and Nonprofit Administration.
  • Meier, K. J., Brudney, J. L., & Bohte, J. (2014). The Assumptions of Linear Regression. In Applied Statistics for Public and Nonprofit Administration.
  • Video Intro to Linear Regression

In class


  • Lecture on key concepts
  • In class practice
    • Paper practice
    • In-class practice
  • Assignment catch-up: Qualitative group project feedback.

After class


Qualitative group project proposal final due next week.