A linear model fit to predict weekly sales of frozen pizza (in pounds) from the average price ($/unit) charged by a sample of stores in a city in 39 weeks over a three-year period is shown below. Complete parts a through f. Sales = 148,822 - 24,254 Price a) What is the explanatory variable? O Sales O Price b) What is the response variable? O Sales O Price c) What does the slope mean in this context? O A. Price increases by 24,254 dollars per pound. O B. Sales decrease by 24,254 pounds per dollar. O C. Price decreases by 24,254 dollars per pound. O D. Sales increase by 24,254 pounds per dollar.A linear model fit to predict weekly sales of frozen pizza (in pounds) from the average price ($/unit) charged by a sample of stores in a city in 39 weeks over a three-year period is shown below. Complete parts a through f. Sales = 148,822 - 24,254 Price . . . Sales O Price c) What does the slope mean in this context? O A. Price increases by 24,254 dollars per pound. O B. Sales decrease by 24,254 pounds per dollar. O C. Price decreases by 24,254 dollars per pound. O D. Sales increase by 24,254 pounds per dollar. d) What does the y-intercept mean in this context? Is it meaningful? O A. $148,822 is a base value that is not meaningful because the sales will not be 0 pounds. O B. 148,822 pounds is a base value that is meaningful because the price will be $0. O C. 148,822 pounds is a base value that is not meaningful because the price will not be $0. O D. $148,822 is a base value that is meaningful because the sales will be 0 pounds. e) What do you predict the sales to be if the average price charged was $3.50 for a pizza? The predicted sales would be |pounds. f) If the sales for a price of $3.50 turned out to be 60,000 pounds, what would the residual be? The residual would be pounds