This dataset example introduces readers to multiple regression with dummy variables. Multiple regression allows researchers to evaluate whether a continuous dependent variable is a linear function of two or more independent variables. When one (or more) of the independent variables is a categorical variable, the most common method of properly including them in the model is to code them as dummy variables. Dummy variables are dichotomous variables coded as 1 to indicate the presence of some attribute and as 0 to indicate the absence of that attribute. The multiple regression model is most commonly estimated via ordinary least squares (OLS), and is sometimes called OLS regression. This example uses a subset of data from the 2015 Fuel Consumption Report from Natural Resources Canada. It presents ...
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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)
- Multiple regression. (2004). In Cramer, D., & Howitt, D. (Eds.), The SAGE dictionary of statistics. (pp. 106–108). London, England: SAGE Publications, Ltd. Retrieved from http://srmo.sagepub.com/view/the-sage-dictionary-of-statistics/n351.xml
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- Hardy, M.A. (1993). Regression with dummy variables. Newbury Park, CA: SAGE Publications, Inc. doi: http://dx.doi.org/10.4135/9781412985628
Fuel Use City
Fuel Use Highway
Fuel Use Combined
Num of Cylinders