Logistic Regression estimates the probability 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.
Useful links:
https://www.medcalc.org/manual/logistic_regression.php
What Students Are Saying About Us
.......... Customer ID: 12*** | Rating: ⭐⭐⭐⭐⭐"Honestly, I was afraid to send my paper to you, but splendidwritings.com proved they are a trustworthy service. My essay was done in less than a day, and I received a brilliant piece. I didn’t even believe it was my essay at first 🙂 Great job, thank you!"
.......... Customer ID: 14***| Rating: ⭐⭐⭐⭐⭐
"The company has some nice prices and good content. I ordered a term paper here and got a very good one. I'll keep ordering from this website."
