Week 9: \*\ Bivariate linear regression
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.