1. The best fit line method for data in Linear Regression?
ANSWER= A)
Explain:- We minimize the least square errors of the model to identify the line of best fit.
2. Which of the following metrics can be used to evaluate a model with a continuous output variable?
ANSWER= C)
Explain:- WIth Continuous target variable case we use mean squared error metric to evaluate the model performance. Remaining options are use in case of a classification problem.
3. Which of the following is true about Residuals ?
ANSWER= A)
Explain:- Residuals refer to the error values of the model. Therefore lower residuals are desired.
4. Which of the following is Not True about Residuals ?
ANSWER= B)
Explain:- Residuals refer to the error values of the model. Therefore lower residuals are desired.
5.FOr a give N independent input variables (X1,X2… Xn) and dependent (target) variable Y a linear regression is fitted for the best fit line using least square error on this data.
The correlation coefficient for one of it’s variable(Say X1) with Y is -0.97. Which of the following is true for X1?
?
ANSWER= B)
Explain:- The absolute value of the correlation coefficient represent strength of the relationship. Given that absolute correlation is very high magnitude (0.97) it means that the relationship is strong between X1 and Y.
6.Given below 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
?
ANSWER= D)
Explain:- The absolute value of the correlation coefficient denotes the strength of the relationship. But here V1 and V2 relationship is not strong by looking characteristics 2.
7. 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?
ANSWER=B)
Explain:- Pearson correlation coefficient between 2 variables might be zero even when they have a relationship between them. If the correlation coefficient is zero,
it just means that that they don’t move together. We can take examples like y=|x| or y=x^2.
8. Which statement is true about outliers in Linear regression?
ANSWER= B)
Explain:- The slope of the regression line will change due to outliers in most of the cases.
9. What does relationship means between residuals and predicted values in linear regression?
ANSWER= A)
Explain:- There should not be any relationship between predicted values and residuals. If there exists any relationship between them,it means that the model has not perfectly captured the information in the data.
10. What will happen with bias and variance as you increase the size of training data in linear regression?
ANSWER= C)
Explain:- The bias would increase while the variance would decrease.
