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Try these multiple choice problems.

For the next three problems : The probability that the San Jose Sharks will win any given game is 0.3694 based on a 13 year win history of 382 wins out of 1034 games played (as of a certain date). An upcoming monthly schedule contains 12 games.
Let X size 12{X} {} = the number of games won in that upcoming month.

The expected number of wins for that upcoming month is:

  • 1.67
  • 12
  • 382 1043
  • 4.43

D: 4.43

What is the probability that the San Jose Sharks win 6 games in that upcoming month?

  • 0.1476
  • 0.2336
  • 0.7664
  • 0.8903

A: 0.1476

What is the probability that the San Jose Sharks win at least 5 games in that upcoming month

  • 0.3694
  • 0.5266
  • 0.4734
  • 0.2305

C: 0.4734

For the next two questions : The average number of times per week that Mrs. Plum’s cats wake her up at night because they want to play is 10. We are interested in the number of times her cats wake her up each week.

In words, the random variable X size 12{X} {} =

  • The number of times Mrs. Plum’s cats wake her up each week
  • The number of times Mrs. Plum’s cats wake her up each hour
  • The number of times Mrs. Plum’s cats wake her up each night
  • The number of times Mrs. Plum’s cats wake her up

A: The number of times Mrs. Plum's cats wake her up each week

Find the probability that her cats will wake her up no more than 5 times next week.

  • 0.5000
  • 0.9329
  • 0.0378
  • 0.0671

D: 0.0671

The next three questions refer to the following information. The Sky Train from the terminal to the rental car and long term parking center is supposed to arrive every 8 minutes. The waiting times for the train are known to follow a uniform distribution.

What is the average waiting time (in minutes)?

  1. 0.0000
  2. 2.0000
  3. 3.0000
  4. 4.0000

D

Find the 30th percentile for the waiting times (in minutes).

  1. 2.0000
  2. 2.4000
  3. 2.750
  4. 3.000

B

The probability of waiting more than 7 minutes given a person has waited more than 4 minutes is?

  1. 0.1250
  2. 0.2500
  3. 0.500
  4. 0.7500

B

People visiting video rental stores often rent more than one DVD at a time. The probability distribution for DVD rentals per customer at Video To Go is given below. There is 5 video limit per customer at this store, so nobody ever rents more than 5 DVDs.

x 0 1 2 3 4 5
P(X=x) 0.03 0.50 0.24 ? 0.07 0.04
  • Describe the random variable X in words.
  • Find the probability that a customer rents three DVDs.
  • Find the probability that a customer rents at least 4 DVDs.
  • Find the probability that a customer rents at most 2 DVDs.

Another shop, Entertainment Headquarters, rents DVDs and videogames. The probability distribution for DVD rentals per customer at this shop is given below. They also have a 5 DVD limit per customer.

x 0 1 2 3 4 5
P(X=x) 0.35 0.25 0.20 0.10 0.05 0.05
  • At which store is the expected number of DVDs rented per customer higher?
  • If Video to Go estimates that they will have 300 customers next week, how many DVDs do they expect to rent next week? Answer in sentence form.
  • If Video to Go expects 300 customers next week and Entertainment HQ projects that they will have 420 customers, for which store is the expected number of DVD rentals for next week higher? Explain.
  • Which of the two video stores experiences more variation in the number of DVD rentals per customer? How do you know that?

Partial Answer:
A: X = the number of DVDs a Video to Go customer rents
B: 0.12
C: 0.11
D: 0.77

A game involves selecting a card from a deck of cards and tossing a coin. The deck has 52 cards and 12 cards are "face cards" (Jack, Queen, or King)The coin is a fair coin and is equally likely to land on Heads or Tails

  • If the card is a face card and the coin lands on Heads, you win $6
  • If the card is a face card and the coin lands on Tails, you win $2
  • If the card is not a face card, you lose $2, no matter what the coin shows.
  • Find the expected value for this game (expected net gain or loss).
  • Explain what your calculations indicate about your long-term average profits and losses on this game.
  • Should you play this game to win money?

The variable of interest is X = net gain or loss, in dollars

The face cards J, Q, K (Jack, Queen, King). There are(3)(4) = 12 face cards and 52 – 12 = 40 cards that are not face cards.

We first need to construct the probability distribution for X. We use the card and coin events to determine the probability for each outcome, but we use the monetary value of X to determine the expected value.

Card Event $X net gain or loss P(X)
Face Card and Heads 6 (12/52)(1/2) = 6/52
Face Card and Tails 2 (12/52)(1/2) = 6/52
(Not Face Card) and (H or T) –2 (40/52)(1) = 40/52
  • Expected value = (6)(6/52) + (2)(6/52) + (–2) (40/52) = –32/52
  • Expected value = –$0.62, rounded to the nearest cent
  • If you play this game repeatedly, over a long number of games, you would expect to lost 62 cents per game, on average.
  • You should not play this game to win money because the expected value indicates an expected average loss.

You buy a lottery ticket to a lottery that costs $10 per ticket. There are only 100 tickets available be sold in this lottery. In this lottery there is one $500 prize, 2 $100 prizes and 4 $25 prizes. Find your expected gain or loss.

Start by writing the probability distribution. X is net gain or loss = prize (if any) less $10 cost of ticket

X = $ net gain or loss P(X)
$500–$10=$490 1/100
$100–$10=$90 2/100
$25–$10=$15 4/100
$0–$10=$–10 93/100)

Expected Value = (490)(1/100) + (90)(2/100) + (15)(4/100) + (–10) (93/100) = –$2. There is an expected loss of $2 per ticket, on average.

A student takes a 10 question true-false quiz, but did not study and randomly guesses each answer. Find the probability that the student passes the quiz with a grade of at least 70% of the questions correct.

  • X = number of questions answered correctly
  • X~B(10, 0.5)
  • We are interested in AT LEAST 70% of 10 questions correct. 70% of 10 is 7. We want to find the probability that X is greater than or equal to 7. The event "at least 7" is the complement of "less than or equal to 6".
  • Using your calculator's distribution menu: 1 – binomcdf(10, .5, 6) gives 0.171875
  • The probability of getting at least 70% of the 10 questions correct when randomly guessing is approximately 0.172

A student takes a 32 question multiple choice exam, but did not study and randomly guesses each answer. Each question has 3 possible choices for the answer. Find the probability that the student guesses more than 75% of the questions correctly.

  • X = number of questions answered correctly
  • X~B(32, 1/3)
  • We are interested in MORE THAN 75% of 32 questions correct. 75% of 32 is 24. We want to find P(x>24). The event "more than 24" is the complement of "less than or equal to 24".
  • Using your calculator's distribution menu: 1 - binomcdf(32, 1/3, 24)
  • P(x>24) = 0.00000026761
  • The probability of getting more than 75% of the 32 questions correct when randomly guessing is very small and practically zero.

Suppose that you are perfoming the probability experiment of rolling one fair six-sided die. Let F be the event of rolling a "4" or a "5". You are interested in how many times you need to roll the die in order to obtain the first “4 or 5” as the outcome.

  • p = probability of success (event F occurs)
  • q = probability of failure (event F does not occur)
  • Write the description of the random variable X. What are the values that X can take on? Find the values of p and q.
  • Find the probability that the first occurrence of event F (rolling a “4” or “5”) is on the second trial.
  • How many trials would you expect until you roll a “4” or “5”?

A: X can take on the values 1, 2, 3, .... p = 2/6, q = 4/6
B: 0.2222
C: 3

**Exercises 38 - 43 contributed by Roberta Bloom

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Source:  OpenStax, Collaborative statistics using spreadsheets. OpenStax CNX. Jan 05, 2016 Download for free at http://legacy.cnx.org/content/col11521/1.23
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