1. Which of the following step/assumption in regression modeling impacts the trade-off between under-fitting and over-fitting the most.

2. Suppose you have the following data with one real-value input variable & one real-value output variable. What is leave-one out cross validation mean square error in case of linear regression (Y = bX+c)?

3. Which of the following is/ are true about “Maximum Likelihood estimate (MLE)”?
1. MLE may not always exist
2. MLE always exists
3. If MLE exist, it (they) may not be unique
4. If MLE exist, it (they) must be unique

4. Let’s say, a “Linear regression” model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

5. In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model. Which of the following option is true?

6. Which one of the statement is true regarding residuals in regression analysis?

7. Which of the one is true about Heteroskedasticity?

8. Which of the following indicates a fairly strong relationship between X and Y?

9. Which of the following assumptions do we make while deriving linear regression parameters?
1. The true relationship between dependent y and predictor x is linear.
2. The model errors are statistically independent.
3. The errors are normally distributed with a 0 mean and constant standard deviation.
4. The predictor x is non-stochastic and is measured error-free.

10. To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?