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Sensitivity
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# of correctly predicted buyers/ actual buyers
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Specificity
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# of correctly predicted non buyers/ # of actual non-buyers
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False Positives
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# of incorrectly predicted buyers/ # predicted buyers
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False Negatives
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# of incorrectly predicted non-buyers/ # of predicted Non-buyers
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The Normality Assumption is tested by
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The plot of the cumulative distribution of the residuals vs. the cumulative standardized normal distribution
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When it comes to choice, how come we can't use a linear regression model?
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1. Since the choice variable has only two values, the error term cannot be normally distributed. 2. It would be preferable if the model predictions represent the probability that choice= yes, but this is not so in linear regression.
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Odds ratio:
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P/(1-p)
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Marginal Effect
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B*p(1-p)
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Percent Concordant
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If the predicted purchase probability for the non-buyer is lower than the predicted prchase probability for the buyer
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Percent Discordant
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The observations are discordant if the predicted purchase probability for the non-buyer is higher than the predicted purchase probability for the buyer
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Models with what type of concordant are preferred?
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Models with high concordant and low discordant are preffered over models with low concordant and high discordant
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What is the characterization of the prediction from a logit model?
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It predicted predicted purchase probability
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What best describes an ROC Curve?
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A graph of a model's sensitivity vs. 1-specificity
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What is the purpose of the logit model
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To examine associative relationships between a dependent variable and one or more independent variables
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What is a logit model
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A special type of regression in which the dependent variable is limited to a small set of possible values
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