Question 1

It is usually not too difficult to predict the shape of the output distribution from the shape(s) of the input distribution(s).

Accepted Answer

A) True 
 B)False

Question 2

The built-in functions in Excel®, along with the RAND function, can be used to generate random numbers from many different types of probability distributions.

Accepted Answer

A) True 
 B)False

Question 3

It is simple to generate a uniformly distributed random number with a minimum and maximum other than 0 and 1. For example, the formula = 150+100   RAND() generates a number uniformly distributed between:

Accepted Answer

A)  100 and 150 
B)  150 and 250 
C)  50 and 100 
D)  50 and 250 
A)  100 and 150 
B)  150 and 250 
C)  50 and 100 
D)  50 and 250

Question 4

Different random numbers generated by the computer are probabilistically dependent. This implies that when we generate a random number in a particular cell, it has some effect on the values of any other random numbers generated in the spreadsheet.

Accepted Answer

A) True 
 B)False

Question 5

Which of the following statements is/are true regarding the normal distribution?

Accepted Answer

A)  It is always the appropriate distribution in simulation modeling. 
B)  It does not permit negative values. 
C)  There is a 95% chance that values will be within ± 2 standard deviations of the mean. 
D)  All of these statements are true. 
A)  It is always the appropriate distribution in simulation modeling. 
B)  It does not permit negative values. 
C)  There is a 95% chance that values will be within ± 2 standard deviations of the mean. 
D)  All of these statements are true.

Question 6

The binomial distribution is a discrete distribution that is applied to situations where n independent and identical "trials" occur, with each trial resulting in a "success" or "failure," and where we want to generate the random number of successes in the n trials.

Accepted Answer

A) True 
 B)False

Question 7

Analysts often plan a simulation so that the confidence interval for the mean of some important output will be sufficiently narrow. The reasoning is that narrow confidence intervals imply more precision about the estimated mean of the output variable.

Accepted Answer

A) True 
 B)False

Question 8

One important special case of bounded distributions is when the only possible values are:

Accepted Answer

A)  zero or one 
B)  uniformly distributed around the mean 
C)  skewed to the right 
D)  nonnegative 
A)  zero or one 
B)  uniformly distributed around the mean 
C)  skewed to the right 
D)  nonnegative

Question 9

Suppose that the demand for cars is normally distributed with mean of 120 and standard deviation of 20. Use @RISK simulation add-in to determine the "best" order quantity; that is, the one that has the largest expected profit. Using the statistics and/or graphs from @RISK, discuss whether this order quantity would not be considered the "best" by the car dealer.

Accepted Answer

@#IMG-DLM& From the mean row, it appears

Question 10

Which of the following statements are false?

Accepted Answer

A)  A probability distribution is discrete if it has a finite number of possible values. 
B)  A probability distribution is continuous if its possible values fall along some continuum. 
C)  An example of a discrete probability distribution is the amount of rain that falls during the month of June in Michigan. 
D)  None of these statements are false. 
A)  A probability distribution is discrete if it has a finite number of possible values. 
B)  A probability distribution is continuous if its possible values fall along some continuum. 
C)  An example of a discrete probability distribution is the amount of rain that falls during the month of June in Michigan. 
D)  None of these statements are false.

Question 11

When n is reasonably large and p isn't too close to 0 or 1, the binomial distribution can be well approximated by which of the following distributions?

Accepted Answer

A)  uniform distribution 
B)  normal distribution 
C)  triangular distribution 
D)  none of these choices 
A)  uniform distribution 
B)  normal distribution 
C)  triangular distribution 
D)  none of these choices

Question 12

Simulation models are particularly useful for:

Accepted Answer

A)  forecasting 
B)  obtaining deterministic outputs 
C)  evaluating constraints 
D)  asking what-if questions 
A)  forecasting 
B)  obtaining deterministic outputs 
C)  evaluating constraints 
D)  asking what-if questions

Question 13

Which of the following statements are false regarding the graph of a continuous probability distribution?

Accepted Answer

A)  It is characterized by a density function, a smooth curve. 
B)  It is a series of spikes. 
C)  The height of the density curve above any point is not actually a probability-that is, it is not necessarily between 0 and 1. 
D)  Heights above the density function indicate relative likelihoods but are not necessarily values between 0 and 1. 
A)  It is characterized by a density function, a smooth curve. 
B)  It is a series of spikes. 
C)  The height of the density curve above any point is not actually a probability-that is, it is not necessarily between 0 and 1. 
D)  Heights above the density function indicate relative likelihoods but are not necessarily values between 0 and 1.

Question 14

When determining the most appropriate input probability distribution in a simulation model, first select the most appropriate family, and then select the most appropriate member of that family.

Accepted Answer

A) True 
 B)False

Question 15

Which of the following statements is/are true regarding the triangular distribution?

Accepted Answer

A)  It is a discrete distribution with a minimum, maximum and most likely value. 
B)  It is more flexible and intuitive than the normal distribution. 
C)  It is a symmetric distribution. 
D)  All of these statements are true. 
A)  It is a discrete distribution with a minimum, maximum and most likely value. 
B)  It is more flexible and intuitive than the normal distribution. 
C)  It is a symmetric distribution. 
D)  All of these statements are true.

Question 16

Spreadsheet simulation modeling is quite similar to the other modeling applications in that it begins with input variables and then relates these with appropriate Excel® formulas to produce output variables of interest.

Accepted Answer

A) True 
 B)False

Question 17

Data tables in spreadsheet simulations are useful for taking a "prototype" simulation and replicating its key results a desired number of times.

Accepted Answer

A) True 
 B)False

Question 18

Which of the following statements are true?

Accepted Answer

A)  A probability distribution is symmetric (around some point) if the distribution to the left of the point is a mirror image of the distribution to the right of the point. 
B)  We say a distribution is skewed to the right (or positively skewed) if the "longer tail" is the right tail. 
C)  We say a distribution is skewed to the left (or negatively skewed) if the "longer tail" is the left tail. 
D)  All of these choices are correct. 
A)  A probability distribution is symmetric (around some point) if the distribution to the left of the point is a mirror image of the distribution to the right of the point. 
B)  We say a distribution is skewed to the right (or positively skewed) if the "longer tail" is the right tail. 
C)  We say a distribution is skewed to the left (or negatively skewed) if the "longer tail" is the left tail. 
D)  All of these choices are correct.

Question 19

It is common in computer simulations to estimate the mean of some distribution by the average of the simulated observations. The usual practice is then to accompany this estimate with a confidence interval, which indicates the accuracy of the estimate.

Accepted Answer

A) True 
 B)False

Question 20

When we run simulation, the @RISK automatically keeps statistics such as averages and standard deviations, and can also create graphs such as histograms based on the values generated in the output cells in the simulation model.

Accepted Answer

A) True 
 B)False

It is usually not too difficult to predict the shape of the output distribution from the shape(s) of the input distribution(s).

The built-in functions in Excel^®, along with the RAND function, can be used to generate random numbers from many different types of probability distributions.

It is simple to generate a uniformly distributed random number with a minimum and maximum other than 0 and 1. For example, the formula = 150+100 RAND() generates a number uniformly distributed between:

Different random numbers generated by the computer are probabilistically dependent. This implies that when we generate a random number in a particular cell, it has some effect on the values of any other random numbers generated in the spreadsheet.

Which of the following statements is/are true regarding the normal distribution?

The binomial distribution is a discrete distribution that is applied to situations where n independent and identical "trials" occur, with each trial resulting in a "success" or "failure," and where we want to generate the random number of successes in the n trials.

Analysts often plan a simulation so that the confidence interval for the mean of some important output will be sufficiently narrow. The reasoning is that narrow confidence intervals imply more precision about the estimated mean of the output variable.

One important special case of bounded distributions is when the only possible values are:

Which of the following statements are false?

When n is reasonably large and p isn't too close to 0 or 1, the binomial distribution can be well approximated by which of the following distributions?

Simulation models are particularly useful for:

Which of the following statements are false regarding the graph of a continuous probability distribution?

When determining the most appropriate input probability distribution in a simulation model, first select the most appropriate family, and then select the most appropriate member of that family.

Which of the following statements is/are true regarding the triangular distribution?

Spreadsheet simulation modeling is quite similar to the other modeling applications in that it begins with input variables and then relates these with appropriate Excel^® formulas to produce output variables of interest.

Data tables in spreadsheet simulations are useful for taking a "prototype" simulation and replicating its key results a desired number of times.

Which of the following statements are true?

It is common in computer simulations to estimate the mean of some distribution by the average of the simulated observations. The usual practice is then to accompany this estimate with a confidence interval, which indicates the accuracy of the estimate.

When we run simulation, the @RISK automatically keeps statistics such as averages and standard deviations, and can also create graphs such as histograms based on the values generated in the output cells in the simulation model.

Introduction to Business Analytics

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 15: Introduction to Simulation Modeling

It is usually not too difficult to predict the shape of the output distribution from the shape(s) of the input distribution(s).

The built-in functions in Excel®, along with the RAND function, can be used to generate random numbers from many different types of probability distributions.

It is simple to generate a uniformly distributed random number with a minimum and maximum other than 0 and 1. For example, the formula = 150+100 RAND() generates a number uniformly distributed between:

Different random numbers generated by the computer are probabilistically dependent. This implies that when we generate a random number in a particular cell, it has some effect on the values of any other random numbers generated in the spreadsheet.

Which of the following statements is/are true regarding the normal distribution?

The binomial distribution is a discrete distribution that is applied to situations where n independent and identical "trials" occur, with each trial resulting in a "success" or "failure," and where we want to generate the random number of successes in the n trials.

Analysts often plan a simulation so that the confidence interval for the mean of some important output will be sufficiently narrow. The reasoning is that narrow confidence intervals imply more precision about the estimated mean of the output variable.

One important special case of bounded distributions is when the only possible values are:

Which of the following statements are false?

When n is reasonably large and p isn't too close to 0 or 1, the binomial distribution can be well approximated by which of the following distributions?

Simulation models are particularly useful for:

Which of the following statements are false regarding the graph of a continuous probability distribution?

When determining the most appropriate input probability distribution in a simulation model, first select the most appropriate family, and then select the most appropriate member of that family.

Which of the following statements is/are true regarding the triangular distribution?

Spreadsheet simulation modeling is quite similar to the other modeling applications in that it begins with input variables and then relates these with appropriate Excel® formulas to produce output variables of interest.

Data tables in spreadsheet simulations are useful for taking a "prototype" simulation and replicating its key results a desired number of times.

Which of the following statements are true?

It is common in computer simulations to estimate the mean of some distribution by the average of the simulated observations. The usual practice is then to accompany this estimate with a confidence interval, which indicates the accuracy of the estimate.

When we run simulation, the @RISK automatically keeps statistics such as averages and standard deviations, and can also create graphs such as histograms based on the values generated in the output cells in the simulation model.

Introduction to Business Analytics

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Time Series Analysis and Forecasting

Introduction to Optimization Modeling

Optimization Models

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

The built-in functions in Excel^®, along with the RAND function, can be used to generate random numbers from many different types of probability distributions.

Spreadsheet simulation modeling is quite similar to the other modeling applications in that it begins with input variables and then relates these with appropriate Excel^® formulas to produce output variables of interest.