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 2006 China Health and Nutrition Survey (CHNS) survey of adults. It presents an analysis of whether systolic blood pressure is a linear function of a person’s age. After performing the regression, we show how to examine the results for evidence of heteroscedasticity. High blood pressure is associated with a number of negative health outcomes. Results from an ...
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Carolina Population Center. (2006). China Health and Nutrition Survey [Data file]. Available from http://www.cpc.unc.edu/projects/china
China Health and Nutrition Survey
Carolina Population Center, University of North Carolina at Chapel Hill
National Institute of Nutrition and Food Safety, Chinese Center for Disease Control and Prevention
Adults 18 and older
- National Institutes of Health (R01-HD30880, DK056350, and R01-HD38700)
- National Institute of Nutrition and Food Safety (INFS)
- Chinese Center for Disease Control and Prevention (CCDC)
- Ford Foundation
- National Science Foundation (INT-9215399)
“This research uses data from China Health and Nutrition Survey (CHNS). We thank the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention, Carolina Population Center, the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, and R01-HD38700) and the Fogarty International Center, NIH for financial support for the CHNS data collection and analysis files from 1989 to 2006 and both parties plus the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009 and future surveys.”
Data collection took place over a 3-day period using a multistage, random cluster process to draw a sample of approximately 4,400 households with a total of 26,000 individuals in nine provinces that vary in geography, economic development, public resources, and health indicators.
Provinces: Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Liaoning, Shandong
- 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
SYSTOLIC BLOOD PRESSURE 1
DIASTOLIC BLOOD PRESSURE 1