Quantitative Methods

I Do q&ohq4t ?- eA

The coellicient ol correlatiou computcd 0"68,

a 300-mile trip that took hirn out of town -5 days. what is thc expected atnount that ld claim as

'!

submittcd a rsement rcquest ltlr should the do?

this model. Should any

other led'l Which ones'? Why'.'

red the undcrgraduate9, +-ts Thirteen stu businr-ss p.ogram\at Rollins Collegc 2 years ago. The following icates what their grade-point averiiges (GPAs) bc.ing in the program for 2 1,ears and whlt each student scored on the SAT

2;100) when he or she was in higli school. grades

Ist a meaningful relationship between AT scorcs'l [l'a studcnt scores a 1 200 on

the SAT. rvhat do you think his or hcr GPA lvill be'l What ahout a student who scores 2400?

STIIDEh{T SAT SCORE GPA STT]DENT SAT SCORE GPA

DrscussloN QUESTIONS AND PROBLEMS 147

(d) If there are no tourists at all, explain the pre-

Major Business Other Business

(b)

(c)

A

R

C

D

E

F

G

1267 2.90 l lll 2.93 1755 3.00 2070 3.45 1824 -1.66 l 170 2.88 1245 2.15

H

T

J

K

L

M

1.{-13 2.5-l 2181 7.22 r 503 1 .99 1839 2.15 2121 3.90 1098 1.60

in Washington, D.C., dur- is believed to be heavily tied visitrn-s the city. During the

data have been obtained:

OF

(a) Using a computer, develop a regression model that could be uscd to predict starting salary based on GPA and major.

(b) Use this model to predict the starting sa1ary fbr a busincss maior with a GPA of 3.0.

(c) What does the model say about the starting sal- ary fbr a business major compared to a nonbusi- ness maior?

(d) Do you believe this model is usetul in predicting the starting salary? Justify your answer. using in-

,-.-\ lomation provided in thc computer output. ( * +-:Z Jhe loll.rwing data cive the sclling price. squarev\---z footage. number of bedrooms, and age of houses

that have sold in a neighborhood in the past 6 months. Develop three regr:ession models to pre-

dict the sclling pricc based upon cach of thc other tactors individually. Which of these is bcst?

$:4-19 ing the to the past I 2 years^

I'EAR

SELLING SQUARE PRICE($) FOOTAGE BEDROOMS

AGE (\,EARS)RIDERS}ilP

(100,000s) 84.000 L670 2

t I 7 15 79,000 1.339 2 212t0

91 .500 1.7 t2 3 3 I 6 13

r 20,000 1,840 3 4 I 4 15-. i -' 127.500 2,300 3 s .j t4 25'\ / 132,soo 2.234 3(r' 15 21u r ' l45.ooo 2.311 3 7 16 2o

, uo,uuu 2,3i't 3 8 12 2t' ,r,,uuu 2,736 -+ 9 I 4 2' ,ur,uuu 2,500 3

l o 20 44 ,72.5t)o 2.500 1 I I I 5 34 l74,ooo 2,4'tg 3 12 7 1' ,r,.uuu 2,400 3

(a) Ptot these data and delermine whether a linear 177,500 3,121 4 model is reasonable. 184,000 2,.500 3

(b) Develop a regression model. 195.500 1.062 :+ (c) What is expected ridcrship il" 10 million iourists 195,000 2,854 3

visit the city? Quantitative Analysis for Manageircn, Twelfth Edition, by Barry Render, Ralph M. Stair, Michael E. Hanna, and Trevor S. Hale. Published by Prentice Hall.

30

25

30

40

l8

30

19

7

It)

1

3

3

I

0

2

10

3

Ior the data in output indicatos

to dcvelop a regression model 4- 19. Exnlain what tlris

' graduation. The starting (GPA), and major (busi-

S

5gl s46,ooo '.'r 3.5

Other Business

Copyright @ 201 5 by Pearson Education, lnc.

(a) If Thomas Williams returns

exam

GPA

model.

salary firr stu- fiom a local unil'ersity

s36,500

2.9

Other

SAI.,ARY

GPA

Major

$42.000 s3 1,500 1.4 /. I