The Regression Models with Dummy Explanatory Variables

Manoranjan Pal () and Premananda Bharati ()
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Manoranjan Pal: Indian Statistical Institute, Economic Research Unit
Premananda Bharati: Indian Statistical Institute, Biological Anthropology Unit

Chapter Chapter 8 in Applications of Regression Techniques, 2019, pp 135-153 from Springer

Abstract: Abstract Dummy Variables can be incorporated in regression models just as easily as quantitative variables. As a matter of fact, a regression model may contain regressors that are all exclusively dummy, or qualitative in nature. The results of such a model will be exactly same as the results found by Analysis of Variance (ANOVA) model. The regression model used to assess the statistical significance of the relationship between a quantitative regressand and (all) qualitative or dummy regressors is equivalent to a corresponding ANOVA model. For each qualitative regressor the number of dummy variables introduced must be one less than the no. of categories of that variable. If a qualitative variable has m categories, introduce only (m-1) dummy variables. The category for which no dummy variable is assigned is known as the base, benchmark, control, comparison, reference, or omitted category. And all comparisons are made in relation to the benchmark category. The intercept value represents the mean value of the benchmark category. The coefficients attached to the dummy variables are known as the differential intercept coefficients because they tell by how much the value of the intercept that receives the value of 1 differs from the intercept coefficient of the benchmark category.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-9314-3_8

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DOI: 10.1007/978-981-13-9314-3_8

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