Question

List three types of regression and a real-world example of each. Why do you think regression is so critical in statistics?

Answer 1

Answers:

There are a lot of types of regression but we need only three regression with example.

1. Linear Regression

It is the simplest form of regression. It is a technique in which the dependent variable is continuous in nature. The relationship between the dependent variable and independent variables is assumed to be linear in nature.

Examples:

1. Linear Regression can be used to predict the sale of products in the future based on past buying behaviour.

2. Sports analyst use linear regression to predict the number of runs or goals a player would score in the coming matches based on previous performances.

3. Economists use Linear Regression to predict the economic growth of a country or state.

2. Logistic Regression

In logistic regression, the dependent variable is binary in nature (having two categories). Independent variables can be continuous or binary. In multinomial logistic regression, you can have more than two categories in your dependent variable.

Examples:

1. To predict whether a person will buy a car or not?

2. To know whether the tumor is malignant or not?

3. HR Analytics: IT firms recruit a large number of people, but one of the problems they encounter is after accepting the job offer many candidates do not join. So, this results in cost over-runs because they have to repeat the entire process again. Now when you get an application, can you actually predict whether that applicant is likely to join the organization (Binary Outcome - Join / Not Join).

3. Poisson Regression

Poisson regression is used when the dependent variable has count data.

Application of Poisson Regression -

1. Predicting the number of calls in customer care related to a particular product
2. Estimating the number of emergency service calls during an event

Examples:

1. The number of aircraft/road accidents at any time interval.
2. The number of arrivals in any service facility like at an ATM, bank, railway station, petrol pump,…
3. The number of patients in a doctor's clinic.
4. The number of beds occupied in a large hospital or hotel.