# Linear Regression II

Welcome to your quiz on Linear Regression II.

Q1. True- False: Overfitting is more likely when you have a huge amount of data to train?
Q2. Which of the following statement is true about the sum of residuals of A and B?

Below graphs show two fitted regression lines (A & B) on randomly generated data. Now, I want to find the sum of residuals in both cases A and B.Note:1.

Note:1. The scale is same in both graphs for both axis.2. The x-axis is independent variable and Y-axis is the dependent variable. Q3. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penality x.

Choose the option which describes bias in best manner:

Q4. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penality x.

What will happen when you apply a very large penalty?

Q5. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penality x.

What will happen when you apply very large penalty in case of Lasso?

Q6. Which of the following statement is true about outliers in Linear regression?

Q7. Suppose you plotted a scatter plot of the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
Q8. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another term, it perfectly fits the data.

What will happen when you fit degree 4 polynomial in linear regression?

Q9. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another term, it perfectly fits the data.

What will happen when you fit degree 2 polynomial in linear regression?

Q10. Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another terms it perfectly fits the data.

In terms of bias and variance. Which of the following is true when you fit degree 2 polynomial?

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