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Learn about multiple regression with dummy variables in SPSS with data rrom the General Social Survey (2012) / The Odum Institute.

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:
  • 9781473937918 (online resource) :
Subject(s): DDC classification:
  • 001.422 23
Online resources: This work 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, and is sometimes called OLS regression. This example uses a subset of data from the 2012 General Social Survey. It presents an analysis of whether a person's weight is a linear function of a number of attributes, including whether or not the person is female and whether or not the person smokes.
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This work 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, and is sometimes called OLS regression. This example uses a subset of data from the 2012 General Social Survey. It presents an analysis of whether a person's weight is a linear function of a number of attributes, including whether or not the person is female and whether or not the person smokes.

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Description based on online resource; title from home page (viewed on May 18, 2015).

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