Key concepts

• Dependent and independent variable (Meier et al., 2015; Bailey, 2016)
• Slope (regression coefficient) and intercept (Meier et al., 2015; Bailey, 2016)
• Goodness of fit and outliers (Meier et al., 2015; Bailey, 2016)
• Residual variation and standard error of the estimate (Meier et al., 2015)
• Coefficient of determination r-squared (Meier et al., 2015)
• Standard error of the slope (Meier et al., 2015; Bailey, 2016)
• Assumptions of linear regression (Meier et al., 2015)
  • 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. (2015). Applied Statistics for Public and Nonprofit Administration (Ninth edition). Cengage Learning., Chapters 17 & 18
• Video Intro to Linear Regression

Recommended readings

• Bailey, M. A. (2016). Real Stats: Using Econometrics for Political Science and Public Policy (1st edition). Oxford University Press., Chapter 1,Intro, 1.1 & 1.2 - Chapter 3, Intro 3.1, 3.2, 3.3, 3.7 & 3.8
• Wonnacott, T. H., & Wonnacott, R. J. (1990). Introductory Statistics (5th ed). Wiley., Chapters 11.1, 11.2 - Chapter 12 & Chapter 15.1, 15.2
• Hamermesh, D. S., & Parker, A. (2005). Beauty in the Classroom: Instructors’ Pulchritude and Putative Pedagogical Productivity. Economics of Education Review, 24(4), 369–376. https://doi.org/10.1016/j.econedurev.2004.07.013

In class

Take the pre-lecture survey here.

Empirical studies as examples

TBD.