This preview shows pages 1–5. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: EGN 3443 Probability & Statistics for Engineers
Midterm II (100 points + 5 bonus)
Closed book/notes, 75 minutes November 05, 2008 Name: (in HWKEH
U#:é§3§~’*l355 Pl ase check below, if applicable
APEX Lakeland Sarasota Good luck! «Lat/wwwwﬁmnom'mr «A i
g
g
E
E
5
§
i
i
g
E
g
i; é.
§
E,
5,
g.
3
§
E
E
z
3' Problem 1 {30 points) The number of surface ﬂaws in plastic roll used in the interior of automobiles has a Poisson distribution with
a mean of 0.07 flaws per square foot of plastic roll. Assume an automobile interior contains 9 square feet of
plastic roll. a) What is the probability that there are no surface ﬂaws in an auto’s interior? (15 points)
, f l “3 b) If 10 cars are sold to a rental company7 What is the probability that at most one car has any surface ﬂaws?
{15 points) I my in“, Ami Flt(V; \ mm: Q
“p
%
W
C;
mi}
ﬁx
<’ 7%
any“;
ME
kg .mwnarbuMM/wﬂwv u .y Mnth «vawa/tawzw §
§
§
g
g
i
i E
s
:3:
i
g
E
i
E
E
z
E
i
S
E
; Problem 2 (30 points) The pressure of a consumer pressure washer is normally distributed with a mean of 2510 psi and a standard
deviation of 90 psi. a) If all washers with the pressure less than 2400 or greater than 2600 psi are considered defective7 ﬁnd the
probability that a randomly chosen washer is defective. {15 points) 3L1 was ~IH50‘70' » b) From a ”reverse engineering” standpoint of a quality control manager, determine the’upper andN)/IGWer
pressure speciﬁcations, that are symmetric about the mean, so that 99% of all washers fall in between (and
thus considered to pass quality inspection). (15 points) Problem 3 {30 points) An airline implements a mildly aggressive overbooking policy by selling 338 tickets for an Airbus A330—300
ﬂight to Japan which has capacity of 335 seats. It is estimated that over the past 10 years7 about 97% of all ticketed passengers showed up for the ﬂight A —~ \“L S P “ 03
K r 7 a) What is the probability that the ﬂight will accommodate all ticketed passengers who show up? (10 points) pa: *3): v  WM?) 5 527 3341 (
fax?) 03713}? ,L/3fgjmzlﬂ‘ﬂj ’i/Ez” halt/.53 
i o ' ' I!
\___
l 0m; b) What is the probability that the ﬂight will depart with empty seats? {10 points) , ~ '3 ’1J
00/133) f) péé’g’) CED ‘3 “33$ ’2 l , 3‘3" 1% 235 reﬁt
’“ [ifjjﬂmvmj ifflogxcn) Z MW 3% .333 c) If you are the third person on the standby list (which means you will be the third one to get on the plane if
there are seats available after all ticketed passengers have been accommodated), what is the probability that
you will be able to take the flight? {10 points) Mom) a \« PM?) : (jg _ 33%"?
/~ 3;“).930m)” + [3,L"),o*s!/,a"2l3g0 + (2“szsz 1:30 willmfm] T@ if i
i
E
E
g
i
g
g
i
i
E
E
i i
E
E
i Problem 4 {10 points) Donald Duck is expecting his three nephews for dinner, but not al of them might come. He thinks that the
probabilities of 0, 1, 2, 0r 3 nephews coming are 0.1, 0.2, 0.3, and 0.4, respectively. He must decide how many
meals to cook! Each nephew can eat only one meal and no meal can be shared. Donald himself will not eat.
He uses the following function to determine his proﬁt from this event P($:yaz)=$—2y—227 where
a: = number of nephews who are fed,
3; = number of nephews are not fed because he did not cook enough food, 2 = number of meals wasted because he cooked too many. Use the expected proﬁt criterion to help Mr. Duck with his decision on how many meals to cook (he will cook
the number of meals which gives the largest value of expected proﬁt). X 2‘3, 7..
V f m, “a i“ ﬁtmhy’mgi.) Bonus question ( 5 points) There are three cards on the table: a black card that is black on both sides, a white card that is white on
both sides, and a mixed card that is black on one side and white on the other. You select one card at random
and note that one of its sides is black. What is the probability that the other side is also black? bi m We“ Wm
“N
[3 l 6
ya @SMK i Qr ,{3 n} ...
View
Full
Document
This note was uploaded on 09/03/2009 for the course STATISTICS EGN 3443 taught by Professor Aliakseisavachkin during the Fall '08 term at University of South Florida  Tampa.
 Fall '08
 AliakseiSavachkin

Click to edit the document details