You work for a company in the marketing department. Your manager has tasked you with forecasting sales lay month for the next year. You notice that over the past 12 months sales haye consistently gone up in a linear fashion. so you decide to run a regression the company's sales history. You find that the regression equation for the data is {sales} = 62.123*[time] + 193.623. In August [time = 3] you see the actual sales quantity was 333.1?3. The residual is 332.469. Interpret this residual in terms of the problem. 0 1} The month is 332.469 months less than what we would expect. 0 2} The sales is 332.469 units greater than what we would expect. 0 3} The sales is 333193 units less than what we would expect. 0 4} The month is 332.469 months larger than what we would expect. 0 5} The sales is 332.469 units less than what we would expect