# logistic regression

Logistic Regression estimates the probability of binary outcome as a function of independent variables.
An example is the probability that a borrower will default as a function of his credit score,
income, loan size and his current debts. In other words, Logistic Regression is a statistical method
for analyzing dataset in which there are one or more independent variables (predictor) that determine
the outcome of a dichotomous dependent variable such as YES or NO. Dichotomous is an outcome variable
with the possibility of only two alternative outcomes such as TRUE or FALSE.
Binary logistic regression model is used to estimate the probability of the binary response of dependent
variable based on one or more independent or predictor variables. Logistic regression uses categorical
predictor to predict the binary dependent outcome.
Question:
In assessing the predictive power of categorical predictors of a binary outcome,
should logistic regression be used?
Requirements:
Another way to frame the question is:
Can logistic regression be used to predict
categorical outcome?
If yes, then how can logistic regression be used to predict
categorical outcome?
If no, why?
Start by defining Logistic regression
Define binary logistic regression.
Describe the predictive power of  categorical variable on binary outcome.
Explain the usefulness of logistic regression in Big Data Analytics.
Provide examples.