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.