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.
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This example is a hypothetical scenario using actual data.
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This example is a hypothetical scenario using actual data.
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The case is a hypothetical scenario using actual data.
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Harvard Business School Case 9602103.
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© 2012 Springer Science+Business Media, LLC
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Fraser, C. (2012). Logit Regression for Bounded Responses. In: Business Statistics for Competitive Advantage with Excel 2010. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9857-6_13
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DOI: https://doi.org/10.1007/978-1-4419-9857-6_13
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