This dataset example introduces readers to testing for heteroscedasticity following a linear regression analysis. Linear regression rests on several assumptions, one of which is that the variance of the residuals from the model is constant and unrelated to the independent variable(s). Constant variance is called homoscedasticity, while non-constant variance is called heteroscedasticity. In this example, we estimate a simple regression model using a subset of data from the 2015 Fuel Consumption Report from Natural Resources Canada. It presents an analysis of whether the size of an automobile’s engine predicts the highway fuel consumption of that automobile. After performing the regression, we show how to examine the results for evidence of heteroscedasticity. Understanding the factors that predict fuel consumption is important for making decisions about transportation ...
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Natural Resources Canada. (2015). Fuel consumption ratings [Data file]. Retrieved from http://data.gc.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64
2015 – Fuel Consumption Ratings
Natural Resources Canada, Government of Canada
Vehicles for retail sale in Canada
Natural Resources Canada
Dataset contains information licensed under the Open Government Licence – Canada (http://open.canada.ca/en/open-government-licence-canada)
- Sweeney, K. (2004). Heteroskedasticity. In M. S. Lewis-Beck, A. Bryman, & T. Futing Liao (Eds.), The SAGE encyclopedia of social science research methods (pp. 459–460). Thousand Oaks, CA: Sage Publications, Inc. doi: http://dx.doi.org/10.4135/9781412950589.n392
- Kaufman, R.L. (2013). Heteroskedasticity in regression: Detection and correction. Thousand Oaks, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781452270128
Fuel Use City
Fuel Use Highway
Fuel Use Combined
Num of Cylinders