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Learn to test for heteroscedasticity in SPSS with data from the China Health and Nutrition Survey (2006).

Contributor(s): Material type: TextPublisher: London : SAGE Publications, 2015Description: 1 online resource : illustrations (black and white, and colour)Content type:
  • text
  • still image
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781473947931 (online resource) :
Subject(s): DDC classification:
  • 001.422 23
Online resources: This dataset example introduces testing for heteroscedasticity following a linear regression analysis, which rests on several assumptions: one is that the variance of the residuals from the model is constant and unrelated to the independent variable(s). Constant variance is homoscedasticity, while non-constant variance is called heteroscedasticity. In this example, a simple regression model is estimated using a subset of data from the 2006 China Health and Nutrition Survey. It presents an analysis of whether systolic blood pressure is a linear function of a person's age.
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This dataset example introduces testing for heteroscedasticity following a linear regression analysis, which rests on several assumptions: one is that the variance of the residuals from the model is constant and unrelated to the independent variable(s). Constant variance is homoscedasticity, while non-constant variance is called heteroscedasticity. In this example, a simple regression model is estimated using a subset of data from the 2006 China Health and Nutrition Survey. It presents an analysis of whether systolic blood pressure is a linear function of a person's age.

Specialized.

Description based on online resource; title from home page (viewed on September 16, 2015).

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