Linear Regression

Welcome to your quiz on Linear Regression I.

Q1. True-False: Linear Regression is a supervised machine learning algorithm.
Q2. True-False: It is possible to design a Linear regression algorithm using a neural network?
Q3. Which of the following methods do we use to find the best fit line for data in Linear Regression?
Q4. Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
Q5. True-False: Lasso Regularization can be used for variable selection in Linear Regression.
Q6. Which of the following is true about Residuals?

Q7. Suppose that we have N independent variables (X1,X2… Xn) and the dependent variable is Y.

Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data.
You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.
Which of the following is true for X1?


Q8. Looking at below two characteristics, which of the following option is the correct for Pearson correlation between V1 and V2?

If you are given the two variables V1 and V2 and they are following below two characteristics.
1. If V1 increases then V2 also increases
2. If V1 decreases then V2 behavior is unknown

Q9. Suppose Pearson correlation between V1 and V2 is zero. In such case, is it right to conclude that V1 and V2 do not have any relation between them?

Q10. Which of the following offsets, do we use in linear regression’s least square line fit? Suppose horizontal axis is independent variable and vertical axis is dependent variable?









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