# task problem set 3 ( need asap ) 2-23-14

## Project Description:

task problem set 3

due date: feb 23, 2014 23:59:59 max points: 90

details: complete problems 4.1, 4.3, 4.5, 4.27, 4.31., and 4.37 in the textbook.
please ensure that the excel file includes the associated cell computations; this information is needed in order to receive full credit for these problems.
you are not required to submit this task to turnitin, unless otherwise directed by your instructor. if so directed, refer to the student success center for directions. only word documents can be submitted to turnitin.
4.1 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
a) forecast the demand for the week of october 12 using a 3-week moving average.

b) 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.

c) compute the forecast for the week of october 12 using exponential smoothing with a forecast for august 31 of 360 and a 5 .2.

4.2
year 1 2 3 4 5 6 7 8 9 10 11
demand 7 9 5 9 13 8 12 13 9 11 7
a) plot the above data on a graph. do you observe any trend,cycles, or random variations?
b) starting in year 4 and going to year 12, forecast demand using a 3-year moving average. plot your forecast on the same graph as the original data.
c) starting in year 4 and going to year 12, forecast demand using a 3-year moving average with weights of .1, .3, and .6, using .6 for the most recent year. plot this forecast on the same graph.
d) as you compare forecasts with the original data, which seems to give the better results?

4.3 refer to problem 4.2. develop a forecast for years 2 through 12 using exponential smoothing with a 5 .4 and a forecast for year 1 of 6. plot your new forecast on a graph with the actual data and the naive forecast. based on a visual inspection, which forecast is better?

4.5 the carbondale hospital is considering the purchase
4.5 the carbondale hospital is considering the purchase of a new ambulance. the decision will rest partly on the anticipated mileage to be driven next year. the miles driven during the past 5 years are as follows:
year mileage
1 3,000
2 4,000
3 3,400
4 3,800
5 3,700
a) forecast the mileage for next year (6th year) using a 2-year moving average.
b) find the mad based on the 2-year moving average. (hint: you will have only 3 years of matched data.)
c) use a weighted 2-year moving average with weights of .4 and .6 to forecast next year’s mileage. (the weight of .6 is for the most recent year.) what mad results from using this approach to forecasting? (hint: you will have only 3 years of matched data.)
d) compute the forecast for year 6 using exponential smoothing, an initial forecast for year 1 of 3,000 miles, and a 5 .5.

4.27 george kyparisis owns a company that manufactures
sailboats. actual demand for george’s sailboats during each of
the past four seasons was as follows:
year
season 1 2 3 4
winter 1,400 1,200 1,000 900
spring 1,500 1,400 1,600 1,500
summer 1,000 2,100 2,000 1,900
fall 600 750 650 500
george has forecasted that annual demand for his sailboats in year 5 will equal 5,600 sailboats. based on this data and the multiplicative seasonal model, what will the demand level be for george’s sailboats in the spring of year 5?

4.31 café michigan’s 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 these 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?

4.37 sales of tablet computers at ted glickman’s electronics store in washington, d.c., over the past 10 weeks are shown in the table below:
week demand week demand
1 20 6 29
2 21 7 36
3 28 8 22
4 37 9 25
5 25 10 28
a) forecast demand for each week, including week 10, using exponential smoothing with a 5 .5 (initial forecast 5 20).
b) compute the mad.
c) compute the tracking signal.
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