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Testing for Heteroscedasticity and the Canadian Fuel Consumption Report (2015): Highway Fuel Consumption and Engine Size in Canada

Dataset
By: The Odum Institute Published: 2015 | Product: SAGE Research Methods Datasets
Data Type: Numeric
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Abstract

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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About This Dataset
Data Source Citation

Natural Resources Canada. (2015). Fuel consumption ratings [Data file]. Retrieved from http://data.gc.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64

Full title of originating dataset

2015 – Fuel Consumption Ratings

Data author(s) and affiliations

Natural Resources Canada, Government of Canada

Data Universe

Vehicles for retail sale in Canada

Funding sources/suppliers

Natural Resources Canada

Dataset contains information licensed under the Open Government Licence – Canada (http://open.canada.ca/en/open-government-licence-canada)

Data collection dates

2015

Unit of analysis

Vehicles

Location covered by data

Canada

Links to SRM content
  • 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
List of variables

year

Year

make

Make

model

Model

class

Class

enginesize

Engine Size

fuelusecity

Fuel Use City

fuelusehwy

Fuel Use Highway

fueluseboth

Fuel Use Combined

mpgboth

MPG Combined

co2emissions

CO2 Emissions

cylinders468

Num of Cylinders

cylinder4

4-cylinder

cylinder6

6-cylinder

cylinder8

8-cylinder

trans2

Transmission

fuel

Fuel Type