Question 1

  1. A box contains 54 red marbles, 9 white marbles, and 75 blue marbles. If a marble is randomly selected from the box, what is the probability that it is red or blue? Express your answer as a fraction or a decimal number rounded to four decimal places

The probability can be expressed as probability of red, plus the probability of blue marbles. \[ \frac{54}{54+9+75}+\frac{75}{54+9+75} \]

(p1<-round( (54/(54+9+75))+(75/(54+9+75)),2))
## [1] 0.93

Question 2

  1. You are going to play mini golf. A ball machine that contains 19 green golf balls, 20 red golf balls, 24 blue golf balls, and 17 yellow golf balls, randomly gives you your ball. What is the probability that you end up with a red golf ball? Express your answer as a simplified fraction or a decimal rounded to four decimal places.

this is simply the number of yellow golf balls over the total

\[ \frac{20}{19+20+24+17} \]

(p2<-round(20/(19+20+24+17),2))
## [1] 0.25

Question 3

  1. A pizza delivery company classifies its customers by gender and location of residence. The research department has gathered data from a random sample of 1399 customers. The data is summarized in the table below.

Converting table into dataframe:

library(knitr)

Housing <- c("Apartment","Dorm","With Parent(s)","Sorority/Fraternity House","Other")
Males <- c(81,116,215,130,129)
Females <- c(228,79,252,97,72)
df <- data.frame(Housing,Males,Females)
colnames(df) <- c("Housing Type","Males","Females")
kable(df,full_width = F,caption = "Gender and Residence of Customers")
Gender and Residence of Customers
Housing Type Males Females
Apartment 81 228
Dorm 116 79
With Parent(s) 215 252
Sorority/Fraternity House 130 97
Other 129 72

What is the probability that a customer is not male or does not live with parents? Write your answer as a fraction or a decimal number rounded to four decimal places.

we can take our two probabilities, we have to make sure that the intersection of the probabilities are accounted for so we are not double counting. in this case, for probability that they will not live with parents, we will not double count the 252 for females. this probability is going to include every part of the table except males who live with parents (215/total)

\[ 1 - \frac{215}{1399} \]

sum=81+116+215+130+129+228+79+252+97+72


round(1-215/1399, 2)
## [1] 0.85

Question 4

  1. Determine if the following events are independent. Going to the gym and Losing weight.

The following events are dependent. People frequently go to the gym to do cardio and exercise in order to lose weight

Question 5

  1. A veggie wrap at City Subs is composed of 3 different vegetables and 3 different condiments wrapped up in a tortilla. If there are 8 vegetables, 7 condiments, and 3 types of tortilla available, how many different veggie wraps can be made?
 choose(8,3) * choose(7,3) * choose(3,1)
## [1] 5880

Question 6

  1. Determine if the following events are independent. Jeff runs out of gas on the way to work. Liz watches the evening news.

These events are independent.

Question 7

7)The newly elected president needs to decide the remaining 8 spots available in the cabinet he/she is appointing. If there are 14 eligible candidates for these positions (where rank matters), how many different ways can the members of the cabinet be appointed?

We can compute this via the following function

permutations <- function(n,k)
  {
  choose(n,k) * factorial(k)
  }
permutations(14, 8)
## [1] 121080960

Question 8

  1. A bag contains 9 red, 4 orange, and 9 green jellybeans. What is the probability of reaching into the bag and randomly withdrawing 4 jellybeans such that the number of red ones is 0, the number of orange ones is 1, and the number of green ones is 3? Write your answer as a fraction or a decimal number rounded to four decimal places
p8_n<-choose(9,0)*choose(4,1)*choose(9,3)
p8_k<-choose((9+4+9),4)
p8<-p8_n/p8_k
round(p8, 4)
## [1] 0.0459

Question 9

  1. Evaluate
factorial(11)/factorial(7)
## [1] 7920

\[ \frac{11!}{7!}=\frac{11\cdot10\cdot9\cdot8\cdot7!}{7!}=11\cdot10\cdot9\cdot8=7920 \]

Question 10

  1. Describe the complement of the given event. 67% of subscribers to a fitness magazine are over the age of 34

33% of subscribers are not over the age of 34

Question 11

  1. If you throw exactly three heads in four tosses of a coin you win $97. If not, you pay me $30. Step 1. Find the expected value of the proposition. Round your answer to two decimal places.
w <- pbinom(3, size=4, prob=0.5) - pbinom(2, size=4, prob=0.5)
l <- 1 - w
w1<-w*97
l1<-l*30
4*0.5^4*(97) + (1+4+6+1)* 0.5^4 * (-30)
## [1] 1.75

Step 2. If you played this game 559 times how much would you expect to win or lose? (Losses must be entered as negative.)

round(559*(w1-l1), 2)
## [1] 978.25

Question 12

  1. Flip a coin 9 times. If you get 4 tails or less, I will pay you $23. Otherwise you pay me $26. Step 1. Find the expected value of the proposition. Round your answer to two decimal places.
(9*2*7+3*4*7+9*4+9+1)*0.5^9*(23-26)
## [1] -1.5

Step 2. If you played this game 994 times how much would you expect to win or lose? (Losses must be entered as negative.)

994 * (-1.5)
## [1] -1491

Question 13

    1. The sensitivity and specificity of the polygraph has been a subject of study and debate for years. A 2001 study of the use of polygraph for screening purposes suggested that the probability of detecting a liar was .59 (sensitivity) and that the probability of detecting a “truth teller” was .90 (specificity). We estimate that about 20% of individuals selected for the screening polygraph will lie.
prob_Liar <- 0.2
prob_Truth <- 0.8
senstivity <- 0.59
specificity <- 0.90
prob_detect_liar <- 0.59 * prob_Liar
prob_detect_truth <- 0.90 * prob_Truth
prob_false_detect_liar <- (1-0.59)*prob_Liar
prob_false_detect_truth <- (1-0.9)*prob_Truth
  1. What is the probability that an individual is actually a liar given that the polygraph detected him/her as such? (Show me the table or the formulaic solution or both.)
prob_detect_liar /(prob_detect_liar + prob_false_detect_truth)
## [1] 0.5959596
  1. What is the probability that an individual is actually a truth-teller given that the polygraph detected him/her as such? (Show me the table or the formulaic solution or both.)
prob_detect_truth / (prob_detect_truth + prob_false_detect_liar)
## [1] 0.8977556
  1. What is the probability that a randomly selected individual is either a liar or was identified as a liar by the polygraph? Be sure to write the probability statement.

We should consider use of inclusion exclusion formula

\[ P(liar\bigcup { detect\_ liar)=P(liar)+P(detect\_ liar)-P(liar\bigcap { detect\_ liar)}} \\ =0.2+0.59-0.118\\ =0.672 \]