**Question : 3 What describes a true limitation of Logistic Regression method?**

**1. It does not handle redundant variables well.****2. It does not handle missing values well.****3. It does not handle correlated variables well.****4. It does not have explanatory values.**

Correct Answer : 2

Exp : Logistic regression extends the ideas of linear regression to the situation where the dependent variable, Y, is categorical. We can think of a categorical variable as dividing the observations into classes. For example, if Y denotes a recommendation on holding/selling/buying a stock, we have a categorical variable with three categories. We can think of each of the stocks in the dataset (the observations) as belonging to one of three classes: the hold class, the sell class, and the buy class. Logistic regression can be used for classifying a new observation, where the class is unknown, into one of the classes, based on the values of its predictor variables (called classification). It can also be used in data (where the class is known) to find similarities between observations within each class in terms of the predictor variables (called profiling). For example, a logistic regression model can be built to determine if a person will or will not purchase a new automobile in the next 12 months. The training set could include input variables for a person's age, income, and gender as well as the age of an existing automobile. The training set would also include the outcome variable on whether the person purchased a new automobile over a 12-month period. The logistic regression model provides the likelihood or probability of a person making a purchase in the next 12 months. After examining a few more use cases for logistic regression, the remaining portion of this chapter examines how to build and evaluate a logistic regression model. Logistic regression attempts to predict outcomes based on a set of independent variables, but if researchers include the wrong independent variables, the model will have little to no predictive value. For example, if college admissions decisions depend more on letters of recommendation than test scores, and researchers don't include a measure for letters of recommendation in their data set, then the logit model will not provide useful or accurate predictions. This means that logistic regression is not a useful tool unless researchers have already identified all the relevant independent variables.