{"description":"Evaluate your understanding of linear regression, line equations, prediction, and least squares fitting.","questions":[{"answer":"To find a line that best fits the data for prediction","number":1,"options":["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":"What is the main goal of linear regression?"},{"answer":"a = slope, b = y-intercept","number":2,"options":["a = intercept, b = slope","a = residual, b = mean","a = slope, b = y-intercept","a = standard deviation, b = mean"],"question":"In the equation y' = ax + b, what do 'a' and 'b' represent?"},{"answer":"Least squares","number":3,"options":["Gradient descent","Least squares","K-means clustering","Cross-entropy"],"question":"Which method is typically used to find the best fit line in linear regression?"},{"answer":"The sum of squared residuals","number":4,"options":["The sum of all feature values","The difference between x and y","The sum of squared residuals","The number of data points"],"question":"What does the 'least squares' method minimize?"},{"answer":"It indicates how much y changes for a unit change in x","number":5,"options":["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":"Which of the following best describes the meaning of the slope 'a' in the linear equation y' = ax + b?"},{"answer":"y' is the predicted value by the model, while y is the actual observed value","number":6,"options":["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":"In linear regression, how does y' (y prime) differ from y?"},{"answer":"The difference between predicted and actual values","number":7,"options":["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":"What are 'residuals' in the context of linear regression?"},{"answer":"Adjusting parameters to minimize prediction error","number":8,"options":["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":"Which of the following best describes the fitting process in linear regression?"},{"answer":"To give the model a starting point for adjusting parameters","number":9,"options":["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":"Why is it important to start with an initial line when training a linear regression model?"},{"answer":"Predict new output values for unseen inputs","number":10,"options":["Randomize the input data","Generate 3D plots","Predict new output values for unseen inputs","Classify categorical labels"],"question":"What does linear regression allow us to do once a line is fitted to data?"}],"title":"Linear Regression"}