high-yield bond analyst is trying to develop an equation using financial ratios to estimate the probability of a company defaulting on its bonds. Since the analyst is using data over different economic time periods, there is concern about whether the variance is constant over time. A technique that can be used to develop this equation is: A) multiple linear regression adjusting for heteroskedasticity. B) dummy variable regression. C) discriminant analysis. D) logit modeling. Your answer: A was incorrect. The correct answer was D) logit modeling. The only one of the possible answers that estimates a probability of a discrete outcome is logit modeling. What is a discrete outcome?

You use logit modelling for discrete data. Financial ratios change and are not linear. In order to develop a linear equation you need to convert it (take the ln). Discrete outcome means that you are testing a yes or no - probability Ordinary outcomes are something like a ranking, score

the fact that is qualitative gives it to be C or D but c as omster said would be for yes/no scenarios

Like in normal regression… what are you usually testing? You’re testing stuff like inreases in sales, changes in costs, gdp growth… you’re not testing stuff like… will i pass the cfa exam… or are sales going to be more than 1000000000K.