Question

Question 1 (10 points). (a) Explain the difference between linear and logistic regression. (Explain at least two differences and one similarity in detail). (b) Give an example of a modeling problem (ie, using X to predict Y, where X and Y are real life examples) for each type.

Answer 1

Answer:

(a) Linear regression is used to estimate dependent variable which is continuous by using a independent variable set. Logistic regression we predict the dependent variable which is categorical using a set of independent variables.

(b) Finding the relationship between the Number of doors in the house vs the number of openings. Suppose that the number of door is a dependent variable X and the number of openings is an independent variable Y.

Step-by-step explanation:

(a) Linear regression is used to estimate dependent variable which is continuous by using a independent variable set .whereas In the logistic regression we predict the dependent variable which is categorical using a set of independent variables. Linear regression is regression problem solving method while logistic regression is having use for solving the classification problem.

(b) Example: Finding the relationship between the Number of doors in the house vs the number of openings. Suppose that the number of door is a dependent variable X and the number of openings is an independent variable Y.

If I am to predict that increasing or reducing the X will have an effect on the input variable X or by how much we will make a regression to find the variance that define the relationship or strong relationship status between them. I will run the regression on any computing software and check the stats result to measure the relationship and plots.