Question

Describe five real-world applications in which regression can be used. For each of these applications, describe the y-value and the corresponding feature vector X. Also discuss whether linear regression can be used in each case.

Answer 1

Regression is basicly when you have to predict a value ( mostly continuous) based on some old data set which has that value along with the parameters which are common in that data set and what you have to predict that target value.

1) Stock Market Prediction - based on some 10-12 parameter like what's the current value , what's the day before and a few move as a vector [X1,X2,X3... ] We have to predict the value of he stock at the end of today ( Y).

2)Realstate price prediction - based on the number of rooms (X1), bathroom (2), sq. Foot area(3) and a few more parameter which you personally see before buying a property you can have these as [X1,X2,X3...] And then predict the selling price of a new property which you haven't seen (Y).

3) What's the score of the team at the end of the game, we have per over run rate which they are making, number of wickets down and the previous performance of a cricket team all these are X1,X2,X3 we can choose any of these and predict the score at the end of the game (Y).

4) Your own Height, based on your parents and siblings height at a given age you can predict your age (Y) based on your age(X).

5) Your Salary(Y) after your graduation based on parameters like grades in each semester or overall grades(X), can be a prediction job for regression.

If there is confusion ask me in comments else if you like the solution, then thumps up.