Question 1: Which one is the correct Linear regression assumption?

(A) Linear regression assumes the input and output variables are not noisy

(B) Linear regression will over-fit your data when you have highly correlated input variables

(C) The residuals (true target value − predicted target value) of the data are normally distributed and independent from each other

(D) All of the above

Question 2: For a Linear Regression model, we choose the coefficients and the bias term by minimizing the _____.

(A) Loss function

(B) Error function

(C) Cost function

(D) All of the above

Question 3: Which parameter determines the size of the improvement step to take on each iteration of Gradient Descent?

(A) learning rate

(B) epoch

(C) batch size

(D) regularization parameter

Question 4: In a simple linear regression problem, a single input variable (x) and a single output variable (y), the linear equation would be y = ax + b; where a and b are _______ and ________ respectively.

(A) bias Coefficient, feature coefficient

(B) feature coefficient, bias Coefficient

(C) slope, y-intercept

(D) y-intercept, slope

Question 5: In a linear regression model, which technique can find the coefficients?

(A) Ordinary Least Squares

(B) Gradient Descent

(C) Regularization

(D) All of the above