In Regression analysis, what score is generated from a linear combination of the inputs?

In regression analysis, particularly when discussing logistic regression, the score generated from a linear combination of the inputs is known as the logit. The logit is defined as the natural logarithm of the odds of the dependent variable being in a particular category. Specifically, in logistic regression, you model the probability that a certain outcome occurs, and this outcome is expressed in terms of odds. By taking the logarithm of the odds, you obtain the logit score, which is the combination of your predictors weighted by their respective coefficients.

The logit function allows for handling binary dependent variables effectively, transforming the probabilities into a continuous range that can be modeled linearly. It maps the probabilities from the interval (0, 1) onto the entire real number line. This transformation is key in logistic regression and helps in interpreting how changes in the input variables influence the likelihood of an outcome occurring.

On the other hand, options like "Log," "Odds Ratio," and "Quantile" serve different purposes within statistical analysis. The "Log" refers to the mathematical function itself, "Odds Ratio" is a measure of association between an exposure and an outcome, often used in case-control studies, and "Quantile" pertains to the division of a probability distribution