7/28/2019 Lat UASTek Ling

1/1

EVALUASI AKHIR SEMESTER GANJIL 2011-2012

JURUSAN TEKNIK LINGKUNGAN, FTSP - ITS

MATA KULIAH : STATISTIKA (KELAS A)

HARI/TGL : RABU , 4 JANUARI 2012

WAKTU/SIFAT : 100 MENIT/ OPEN BOOK

D O S E N : AGUS SUHARSONO

1. Does product competition always result in lower prices to consumer? Some marketing specialist havesuggested that the relationship between price and the number of competing products is of second

order-that is, that price are high in the presence of either few or many competitors and lowest when

there is a moderate numbers of competitors. In a study of this issue a record was made of the

average price for a six pack of beer and the number of competing brands sold in a certain chain of

supermarkets in 12 different cities. The data are shown in table.

Price($), y Number of Competing Brand,x Price ($), y Number of Competing Brand,x

12.5

14.516.7

15.2

14.2

13.5

4

79

7

5

6

15.6

13.414.2

13.5

12.5

14.5

10

57

9

9

7

a. Fit the first order and the second order to thesedata by using an available regression analysis.

b. Calculate the correlation coefficient rfor this data. Is the correlation between Price and Numberof Competing Brandsignificantly different from zero ? Use = 0.05

c. Plot the data points and simple linear regression graph .2. Sales force management requires the proper adjustment of sales territories to equalize sales

potential among territories as well as the establishment of sales force goals and compensation plan.

To examine this issue, the sales manager for a company that sells office machines and supplies has

recorded the territory sales yfor the past month, the number of accounts, and the number of

years of work experience for a random sample ofn = 20 of her firms sales personal. The data are

shown in the table. It was assumed that a second order model for k = 2 independent variable

appropriately describes the relationship between sales potential and the independent variables

number of accounts and sales potential. Fit the model to these data by

using an available regression analysis. Carefully interpret the result of this analysis.

y(x $1.000) x1 (x $100) x2 (years) y(x $1.000) x1 (x $100) x2 (years)

36.7034.74

22.95

46.76

61.26

21.35

50.32

33.67

65.19

48.76

1514

12

18

24

12

22

14

25

21

1.91.7

1.5

2.6

4.7

1.3

2.5

1.8

4.3

2.4

23.3745.87

33.67

65.19

28.01

32.64

34.54

17.41

21.35

50.32

1019

12

18

13

13

13

15

14

8

1.52.3

1.6

1.8

1.6

1.9

1.7

1.3

1.3

2.5

3.

With hopes of reducing its massive 350 million gallons a year gasoline bill, the U.S. Portal Service hasordered a number electric mail trucks on an experimental basis. Official estimate that savings will

result if they use electric trucks on flat routes and use a special of peak hours rate of recharging. In an

experiment to compare operating costs, = 100 conventional gasoline mail trucks and = 100

electric mail trucks were placed in service for a period of time. The cost per mile for operating the

gasoline trucks averaged = $ 11.70 per mile with variance , while the electric trucks the

mean and variance of the cost per mile were = $ 10.25 and . Do these data

present sufficient evidence to indicate a difference in the mean operating cost between conventional

gasoline and electric powered mail trucks ? Use = 0.05