• Suppose you are interested in predicting the price of a laptop computer based on its various features
• 1. Formulate a multiple regression model that includes all potential explanatory variables and estimate it with the given sample data
• 2. Interpret the parameters estimated in the regression equation Look at the Adjusted R-square. How well does the estimated regression equation fit the data?
• 3. Which of the computer features in your model affect the laptop price? Use the estimated regression equation to predict the price of a laptop with the following features: a 60 megahertz processor, a battery that holds its charge for 240 minutes, 32 megabytes of RAM, a DX chip, a colour monitor, a mouse, a 24-hour toll-free customer service hotline.