Question 1

What is the main goal of linear regression?

To cluster data into groups To find a line that best fits the data for prediction To convert categorical data to numerical format To reduce the number of features

Question 2

In the equation y' = ax + b, what do 'a' and 'b' represent?

a = intercept, b = slope a = residual, b = mean a = slope, b = y-intercept a = standard deviation, b = mean

Question 3

Which method is typically used to find the best fit line in linear regression?

Gradient descent Least squares K-means clustering Cross-entropy

Question 4

What does the 'least squares' method minimize?

The sum of all feature values The difference between x and y The sum of squared residuals The number of data points

Question 5

Which of the following best describes the meaning of the slope 'a' in the linear equation y' = ax + b?

It represents the predicted value It is the difference between actual and predicted values It indicates how much y changes for a unit change in x It is the point where the line intersects the y-axis

Question 6

In linear regression, how does y' (y prime) differ from y?

y' is the actual value from the dataset, while y is the predicted value by the model y' is the predicted value by the model, while y is the actual observed value y' is the average of all y values, while y is the output from the model There is no difference; y and y' are always the same

Question 7

What are 'residuals' in the context of linear regression?

The slope and intercept values The average of all data points The difference between predicted and actual values The minimum and maximum values in the dataset

Question 8

Which of the following best describes the fitting process in linear regression?

Maximizing the angle of the line Finding a line that goes through all data points Calculating the slope from standard deviation Adjusting parameters to minimize prediction error

Question 9

Why is it important to start with an initial line when training a linear regression model?

To give the model a starting point for adjusting parameters To eliminate the need for gradient descent To ensure the line perfectly fits all data points at the beginning To avoid using the actual dataset during training

Question 10

What does linear regression allow us to do once a line is fitted to data?

Randomize the input data Generate 3D plots Predict new output values for unseen inputs Classify categorical labels

Linear Regression