Question 1

The binomial distribution is widely used in business and economic applications involving the probability of discrete occurrences.

Accepted Answer

A) True 
 B)False

Question 2

Given a binomial variable with n = 40 and the probability of a success on any given trial of 0.8,the mean of the probability distribution would be 0.4.

Accepted Answer

A) True 
 B)False

Question 3

Calculate the variances of X and Y.

Accepted Answer

Var(X)= 0.

Question 4

Flipping a coin is an example of a random variable.

Accepted Answer

A) True 
 B)False

Question 5

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING:
The manager of a movie rental store was interested in examining the relationship between the weekly take-home pay for a family and the amount that family spends weekly on recreational activities.The following output was generated using Minitab:
Covariances
   
Let X = weekly take-home pay,and Y = amount spent weekly on recreational activities.
-Identify the variance of weekly take-home pay.

Accepted Answer

The answer of THE NEXT QUESTIONS ARE BASED ON THE...

Question 6

The Poisson distribution can be used to approximate the binomial distribution when the number of trials n is large,and the probability P is small.

Accepted Answer

A) True 
 B)False

Question 7

Calculate the expected values of X and Y.

Accepted Answer

E(X)= 1.7

Question 8

You can use the Poisson distribution to approximate the binomial distribution with mean nP when P is small and n is large,such that nP is of only moderate size (preferably nP ≤ 7).

Accepted Answer

A) True 
 B)False

Question 9

If the covariance between two variables is zero,then the two variables are statistically independent.

Accepted Answer

A) True 
 B)False

Question 10

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING:
The following table presents the probability distribution function for the number of claims processed per hour at an insurance agency.
  
-What is the variance of the number of claims processed?

Accepted Answer

A) 3.6211 
B) 2.0819 
C) 1.4428 
D) 1.9029 
A) 3.6211 
B) 2.0819 
C) 1.4428 
D) 1.9029

Question 11

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING:
Suppose you know that the number of complaints coming into a phone center averages 4.2 every ten minutes.Assume that the number of calls follows the Poisson distribution.
-In a box of 16 chocolates,there are four chocolates with coconut filling.What is the probability of choosing four chocolates,none of which have coconut fillings?

Accepted Answer

A) 0.272 
B) 0.264 
C) 0.248 
D) 0.236 
A) 0.272 
B) 0.264 
C) 0.248 
D) 0.236

Question 12

Develop a realistic example of a pair of random variables for which you would expect to find negative covariance.

Accepted Answer

Price of c

Question 13

An important generalization of the Bernoulli distribution concerns the case where a random experiment with infinite outcomes is repeatedly independent.

Accepted Answer

A) True 
 B)False

Question 14

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING:
Consider the following probability distribution function.
  
-What is P(X ≥ 2)?

Accepted Answer

0.23 + 0.1

Question 15

Let the random variable Y = a + bX.Then σY =   
σX

Accepted Answer

A) True 
 B)False

Question 16

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING:
The number of people arriving at a bicycle repair shop follows a Poisson distribution with an average of 5 arrivals per hour.Let X represent the number of people arriving per hour.
-What is the probability that seven people arrive at the bike repair shop in a one hour period of time?

Accepted Answer

P(X = 7)= @#IMG-DLM& = 0.1044

Question 17

Which of the following is an example of a continuous random variable?

Accepted Answer

A) The number of cars in a parking lot. 
B) The number of repairs at a computer shop over the course of the week. 
C) The total points scored in a basketball game. 
D) The weight of a bag of potatoes. 
A) The number of cars in a parking lot. 
B) The number of repairs at a computer shop over the course of the week. 
C) The total points scored in a basketball game. 
D) The weight of a bag of potatoes.

Question 18

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING:
The following table displays the joint probability distribution of two discrete random variables X and Y.  
-Compute the variance for the linear function W = 2X + Y.

Accepted Answer

@#IMG-DLM& @#IMG-DLM& @#IMG-DLM& = (2)² @#IMG-DLM& + (1

Question 19

What is the probability that X is farther than one standard deviation from the mean?

Accepted Answer

μ ± σ = 1 ± 6.333 ,or -5.633 to 7.633,so

Question 20

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING:
The probability that a person catches a cold during the cold and flu season is 0.4.Assume that 10 people are chosen at random.
-What is the probability that four or more of them will catch a cold?

Accepted Answer

A) 0.715 
B) 0.618 
C) 0.546 
D) 0.312 
A) 0.715 
B) 0.618 
C) 0.546 
D) 0.312

The binomial distribution is widely used in business and economic applications involving the probability of discrete occurrences.

Given a binomial variable with n = 40 and the probability of a success on any given trial of 0.8,the mean of the probability distribution would be 0.4.

Calculate the variances of X and Y.

Flipping a coin is an example of a random variable.

The Poisson distribution can be used to approximate the binomial distribution when the number of trials n is large,and the probability P is small.

Calculate the expected values of X and Y.

You can use the Poisson distribution to approximate the binomial distribution with mean nP when P is small and n is large,such that nP is of only moderate size (preferably nP ≤ 7).

If the covariance between two variables is zero,then the two variables are statistically independent.

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING: The following table presents the probability distribution function for the number of claims processed per hour at an insurance agency. -What is the variance of the number of claims processed?

Develop a realistic example of a pair of random variables for which you would expect to find negative covariance.

An important generalization of the Bernoulli distribution concerns the case where a random experiment with infinite outcomes is repeatedly independent.

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING: Consider the following probability distribution function. -What is P(X ≥ 2)?

Let the random variable Y = a + bX.Then σ_Y = σX

Which of the following is an example of a continuous random variable?

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING: The following table displays the joint probability distribution of two discrete random variables X and Y. -Compute the variance for the linear function W = 2X + Y.

What is the probability that X is farther than one standard deviation from the mean?

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING: The probability that a person catches a cold during the cold and flu season is 0.4.Assume that 10 people are chosen at random. -What is the probability that four or more of them will catch a cold?

Describing Data: Graphical

Describing Data: Numerical

Probability

Continuous Random Variables and Probability Distributions

Sampling and Sampling Distributions

Estimation: Single Population

Estimation: Additional Topics

Hypothesis Testing: Single Population

Hypothesis Testing: Additional Topics

Simple Regression

Multiple Regression

Additional Topics in Regression Analysis

Analysis of Categorical Data

Analysis of Variance

Time-Series Analysis and Forecasting

Additional Topics in Sampling

Filters

Exam 4: Discrete Random Variables and Probability Distributions

The binomial distribution is widely used in business and economic applications involving the probability of discrete occurrences.

Given a binomial variable with n = 40 and the probability of a success on any given trial of 0.8,the mean of the probability distribution would be 0.4.

Calculate the variances of X and Y.

Flipping a coin is an example of a random variable.

The Poisson distribution can be used to approximate the binomial distribution when the number of trials n is large,and the probability P is small.

Calculate the expected values of X and Y.

You can use the Poisson distribution to approximate the binomial distribution with mean nP when P is small and n is large,such that nP is of only moderate size (preferably nP ≤ 7).

If the covariance between two variables is zero,then the two variables are statistically independent.

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING: The following table presents the probability distribution function for the number of claims processed per hour at an insurance agency. -What is the variance of the number of claims processed?

Develop a realistic example of a pair of random variables for which you would expect to find negative covariance.

An important generalization of the Bernoulli distribution concerns the case where a random experiment with infinite outcomes is repeatedly independent.

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING: Consider the following probability distribution function. -What is P(X ≥ 2)?

Let the random variable Y = a + bX.Then σY = σX

Which of the following is an example of a continuous random variable?

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING: The following table displays the joint probability distribution of two discrete random variables X and Y. -Compute the variance for the linear function W = 2X + Y.

What is the probability that X is farther than one standard deviation from the mean?

THE NEXT QUESTIONS ARE BASED ON THE FOLLOWING: The probability that a person catches a cold during the cold and flu season is 0.4.Assume that 10 people are chosen at random. -What is the probability that four or more of them will catch a cold?

Describing Data: Graphical

Describing Data: Numerical

Probability

Continuous Random Variables and Probability Distributions

Sampling and Sampling Distributions

Estimation: Single Population

Estimation: Additional Topics

Hypothesis Testing: Single Population

Hypothesis Testing: Additional Topics

Simple Regression

Multiple Regression

Additional Topics in Regression Analysis

Analysis of Categorical Data

Analysis of Variance

Time-Series Analysis and Forecasting

Additional Topics in Sampling

Filters

Let the random variable Y = a + bX.Then σ_Y = σX