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.

(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.

hi there!

first we need to find out how much they are adding to each number to make the next number.

ex: 4 + 4 = 8, 8 + 8 = 16, and so on.

so continuing with 32,

32 + 32 = 64

64 + 64 = 128

128 + 128 = 256

256 + 256 = 512

4, 8, 16, 32, 64, 128, 256, 512

the 8th term of the sequence will be 512

hope this ! ~