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Learn about logistic regression in R with data from the American National Election Study 2012 /

This work introduces readers to logistic regression, often simply called logit. This technique allows researchers to evaluate whether a dichotomous dependent variable is a function of one or more independent variables. The logit model is most commonly estimated via maximum likelihood estimation (MLE...

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Bibliographic Details
Corporate Author: Howard W. Odum Institute for Research in Social Science (associated with work.)
Format: eBook
Language:English
Published: London : SAGE Publications, 2015.
Subjects:
Logistic regression analysis.
Presidents > United States > Election > 2012 > Statistics.
Research > Statistical methods > Case studies.
Research > Statistical methods.
Logistic Models
Régression logistique.
Recherche > Méthodes statistiques > Études de cas.
Recherche > Méthodes statistiques.
Presidents > Election.
United States.
2012
Case studies.
Statistics.
Online Access:Connect to the full text of the electronic book
Description
Summary:This work introduces readers to logistic regression, often simply called logit. This technique allows researchers to evaluate whether a dichotomous dependent variable is a function of one or more independent variables. The logit model is most commonly estimated via maximum likelihood estimation (MLE). This example uses a subset of data from the 2012 American National Election Study. It presents an analysis of whether survey respondents reported voting for Barack Obama or Mitt Romney for U.S. President in 2012 and whether that vote choice can be predicted by several factors, including a respondent's race/ethnicity and how they feel about the Democratic and Republican Parties. An analysis like this allows researchers to test theories regarding what types of factors predict voting behaviour.
Physical Description:1 online resource : illustrations (black and white, and colour)
Audience:Specialized.
ISBN:9781473937949
1473937949