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Logit Regression for Bounded Responses

In: Business Statistics for Competitive Advantage with Excel 2010

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  • Cynthia Fraser

    (University of Virginia)

Abstract

In this chapter we introduce logit regression which accommodates responses that are limited or bounded above and below. For example, the likelihood of trying a new product can neither be negative nor greater than 100%. Market share is similarly limited to the range between 0 and 100%. Indicator 0–1 responses, such as “tried the product or not” and “voted Republican,” reflect probabilities, such as the probability of trying a new product, the probability of winning a game, or the probability of voting Republican. In each of these cases, dependent response must be rescaled, acknowledging these boundaries. The odds ratio rescales probabilities or shares to a corresponding unbounded measure. The logit, or natural logarithm of an odds ratio, rescales responses, producing an S shaped pattern, which reflects greater response among “fence sitters” with probabilities or shares that are mid range.

Suggested Citation

  • Cynthia Fraser, 2012. "Logit Regression for Bounded Responses," Springer Books, in: Business Statistics for Competitive Advantage with Excel 2010, edition 2, chapter 0, pages 435-459, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-9857-6_13
    DOI: 10.1007/978-1-4419-9857-6_13
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