# Forecasting, Regression

1)

Alternative Good Market Fair Market Poor Market

Single unit 25000 20000 -45000

Duplex 80000 30000 -10000

Triplex 100000 90000 -9000

Quadraplex 150000 50000 -40000

Probability 0.4 0.35 0.25

What is the best decision based on the following table

Single unit

Duplex

Triplex

Quadraplex

2) the following forecasting model has been developed:

Y=36+4.3X

where

y= deman for air conditioners

Xi= outside temperature

What is the demand for air conditioners when the temperature is 70 degrees

a 360

b 403

c 66

d 337

3) Bus and subway ridership is believed to be related to the number of tourists. These are the data for the past 12 years

Year # of tourists in 100,000s ridership in 1,000,000s

1 7 15

2 2 10

3 6 13

4 4 15

5 14 25

6 15 27

7 16 24

8 12 20

9 14 27

10 20 44

11 15 34

12 7 17

The regression model that reflects the relationship of the tourists to ridership is:

a Y=8,0+17.5x

b Y=7.2+2.1x

c Y=5.060+1.593x

d none of the above

4) For the problem above, what is the expected ridership if 10 million tourists visit the city?

a 5.06 million

b 2.1 million

c 23 million

d 20.99 million

Please see the attached file.

Please note: The question in the attached file related to ipod has not been answerd as the question does not have complete data.

https://brainmass.com/business/360-degree-feedback/160390

#### Solution Preview

Please see attached file.

1) Alternative Good Market Fair Market Poor Market

Single unit 25000 20000 -45000

Duplex 80000 30000 -10000

Triplex 100000 90000 -9000

Quadraplex 150000 50000 -40000

Probability 0.4 0.35 0.25

What is the best decision based on the following table

Single unit

Duplex

Triplex

Quadraplex

Answer: Triplex

We calculate the expected value for each alternative.

The alternative for which the expected value is the maximum is the best decision

Expected value = sum of probability of state x value for state

Alternative Expected value

Single ...

#### Solution Summary

Answers multiple choice questions on Forecasting, Regression.