FORECASTING HOMEWORK

Using the textbook and powerpoints as your guide, complete the following forecasting calculations. Make sure that you show your work (this is important for partial credit).

1. The following gives the number of pints of type B blood used at Woodlawn Hospital in the past 6 weeks:

WEEK OF	PINTS USED
August 31	360
September 7	389
September 14	410
September 21	381
September 28	368
October 5	374

1. Forecast the demand for the week of October 12 using a 3-week moving average

2. Use a 3-week weighted moving average, with weights of .1, .3, and .6, using .6 for the most recent week. Forecast demand for the week of October 12.

2. Consider the following actual and forecast demand levels for Big Mac hamburgers at a local McDonalds restaurant:

DAY	ACTUAL DEMAND	FORECAST DEMAND
Monday	88	88
Tuesday	72	88
Wednesday	68	84
Thursday	48	80
Friday

The forecast for Monday was derived by observing Mondays demand level and setting Mondays forecast level equal to this demand level. Subsequent forecasts were derived by using exponential smoothing with a smoothing constant of 0.25. Using this exponential smoothing method, what is the forecast for Big Mac demand for Friday?

3. Dr. Lillian Fok, a New Orleans psychologist, specializes in treating patients who are agoraphobic (afraid to leave their homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year:

Year	1	2	3	4	5	6	7	8	9	10
# of Patients	36	33	40	41	40	55	60	54	58	61
Robbery Rate per 1,000 Population	58.3	61.1	74.4	75.7	81.1	89.0	101.0	94.8	103.3	116.2

Using linear regression analysis, predict the number of patients Dr. Fok will see in years 11 and 12 as a function of time.

4. Caf Michigans manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of those coffees sold over six different price values:

PRICE	NUMBER SOLD
$ 2.70	760
$ 3.50	510
$ 2.00	980
$ 4.20	250
$ 3.10	320
$ 4.05	480

Using these data, how many mocha latte coffees would be forecast to be sold according to simple linear regression if the price per cup were $2.80?

5. Rhonda Clark, a Slippery Rock, PA real estate developer, has devised a regression model to help determine residential housing prices in northwestern PA. The model was developed using recent sales in a particular neighborhood. The price (Y) of the house is based on the size (square footage = X) of the house. The model is:

Y = 13,473 + 37.65X

The coefficient correlation for the model is 0.63.

1. Use the model to predict the selling price of a house that is 1,860 square feet.

2. If you were going to use multiple regression to develop such a model, what other quantitative variables might you include?