Question 4: Bike Data – Prediction (4a) 2 pts – Predict bike…

Question 4: Bike Data – Prediction (4a) 2 pts – Predict bikes for the test set (bike_data_test) using model1. Display the first six predicted values. (4b) 2.5 pts – Calculate and display the mean squared prediction error (MSPE) for model1. List one limitation of using this metric to evaluate prediction accuracy. (4c) 1 pt – Refit model1 on bike_data_full, and call it model2. Display the summary table for the model. (4c.1) 3 pts – Estimate the 10-fold and leave-one-out cross validation mean prediction squared error (MSPE) for model2. Display these values. Hint: cv.glm() from the boot package uses MSPE as the default cost function. (4c.2) 1 pt – How do these two MSPEs compare to the model1 MSPE from 4b? 

For Xiaomi, positioning with a right pricing strategy is cri…

For Xiaomi, positioning with a right pricing strategy is critical given its relatively latecomer status. Especially, understanding the concept and factors of price elasticity in the market is critical in developing a successful pricing strategy.   Please define price elasticity. (4 points) Please discuss at least five factors that decrease price elasticity (or consumer price sensitivity) in general and briefly describe what they are. (10 points)