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Simple Regression

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A Teaching Topic in Quantitative Methods

Simple Regression

This topic teaches students an important statistical technique that is used to measure or quantify the relationship between two and only two variables. This technique is called simple regression, or bivariate regression. Regression models that have more than one independent variable are examples of multivariate regression analysis, which is usually taught as a separate topic. Regression models are important for public policy because they can be used to test theories, make predictions and test hypothesis about relationships between variables.

Topic Learning Outcome: Students will be able to clearly define simple regression and be able to calculate the level of correlation between two variables using software such as STATA, SPSS or Microsoft EXCEL.

Core Concepts associated with this Topic: Influential Observations; Ordinary Least Squares Estimator; Regression Analysis; Regression Line.

Recommended Readings

Harvard Kennedy School: API 201

Moore, D., McCabe G., & Craig, B. (2009). Introduction to  the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company.  Pp. 545-560.

NYU Wagner: GP 1011

Blustein, J. SPSS: The Wagner Way. Ch. 7.

Healy, J.F. The Essentials of Statistics: A Tool for Social Research (3rd Edition), Wadsworth/Cengage Learning 2013. Ch. 13.

Carleton University: PADM 5114 

Moore, D., McCabe G., & Craig, B. (2009). Introduction to  the Practice of Statistics, Sixth Edition. New York: W. H. Freeman and Company.

Johnson Shoyama Graduate School of Public Policy: JSGS-803

Linda M. Gerber, “Urban Diversity: Riding Composition and Party Support in the Canadian Federal Election of 2004,” Canadian Journal of Urban Research15:2 Supplement (2006), pp. 105-118.

B. Curtis Eaton and Mukesh Eswaran, “Differential Grading Standards and Student Incentives,” Canadian Public Policy 34:2 (June 2008), pp. 215-36. (http://library.usask.ca/scripts/remote?URL=http://www.jstor.org/stable/pdfplus/25463608.pdf?acceptTC=true)

John Richard, Jennifer Hove, and Kemi Afolabi, “Understanding the Aboriginal/Non-Aboriginal Gap in Student Performance” C.D. Howe Institute Commentary, No. 276 (December 2008), available online at http://www.cdhowe.org/pdf/commentary_276.pdf.

University of Toronto: PPG 1004 

Stock, James H. and Mark W. Watson. 2011. Introduction to Econometrics, 3rd ed. Pearson/Addison-Wesley. Ch. 4-7.

Possible Assessment Questions

  1. What is the difference between simple regression and multivariate regression?
  2. Why is simple regression a potentially useful tool for policy analysis?
  3. Describe the possible shortcomings of a simple regression model as a tool for understanding the causal relationship between a dependent and independent variable.
  4. What is omitted variable bias?

Page Created By: Ben Eisen, 26 October 2014.

 


Important Notices
© University of Toronto 2008
School of Public Policy and Governance