Here is a Question, I want to know the step-by step answer

Your consulting firm is hired to understand the demand for the Iwatch Series by Apple . Suppose you've collected data on price and quantity for a day for stores selling the new iWatch Series. Let's suppose that you have n = 35 observations, and have calculated the following sample statistics: Price = 300 and Quantity = 32 cov(Price,Quantity)=-20 var(Price)=60, var(Quantity)=100 a. Write out a linear regression model relating quantity to price, where quantity is the dependent variable and price is the independent variable. (You mat want to write beta0 and beta1 as the representation of the parameters) b. Using your sample statistics, what are the OLS estimates for the intercept and slope of this line? Be explicit on how you estimate the coefficients in your model. c. If price goes up by $12, what is the predicted change in quantity purchased? d. Suppose Tim Cook reviews your results claims that you've only collected data from areas where the demand was especially high. If this claim is true, which modeling assumption doesn't hold, and what are the consequences? e. Now, suppose instead that Timothy tells you that he set the price in the following way. In areas where he believed there were many Apple loyalists, he charged a high price, and in areas where he believed there were few Apple loyalists, he charged a low price. If this is true, which modeling assumption doesn't hold, and what are the consequences