Operations Analytics Coursera Quiz Answers – Networking Funda

All Weeks Operations Analytics Coursera Quiz Answers

This course is designed to impact the way you think about transforming data into better decisions. Recent extraordinary improvements in data-collecting technologies have changed the way firms make informed and effective business decisions.

The course on operations analytics, taught by three of Wharton’s leading experts, focuses on how the data can be used to profitably match supply with demand in various business settings. In this course, you will learn how to model future demand uncertainties, how to predict the outcomes of competing policy choices and how to choose the best course of action in the face of risk.

The course will introduce frameworks and ideas that provide insights into a spectrum of real-world business challenges, will teach you methods and software available for tackling these challenges quantitatively as well as the issues involved in gathering the relevant data.

This course is appropriate for beginners and business professionals with no prior analytics experience.

Enroll on Coursera

Operations Analytics Week 01 Quiz Answers

Newsvendor and Forecasting Quiz

Q1. This question relates to concepts covered in Lectures 1 & 2.

You can use any of the excel files posted to work through the question.

Which of the following statements is a true statement about the Newsvendor model?

  • In the Newsvendor model, the ordering decision is made before seeing the customer demand.
  • The Newsvendor model can be only applied to vendors who sell newspapers or magazines.
  • In the Newsvendor model, the decision-maker who decides how much to order, always knows the exact quantity of demand that will happen.
  • The Newsvendor model can be used only if unsold units cannot be salvaged.

Q2. This question relates to concepts covered in Lectures 1 & 2.

You can use any of the excel files posted to work through the question.

Demand at a store can be modeled by a random variable which takes the following values across four different scenarios that occur with following probabilities.

Scenario Low: D1 = 10 with probability p_1=0.1

Scenario Medium 1: D2 = 30 with probability p_2=0.4

Scenario Medium 2: D3 = 60 with probability p_3=0.4

Scenario High: D4 = 90 with probability p_4=0.1

What is the mean of this demand distribution?

  • 10
  • 60
  • 90
  • 30
  • 46
  • 52.18

Q3. This question relates to concepts covered in Lectures 1 & 2.

You can use any of the excel files posted to work through the question.

Consider the demand at a store given by the data in the previous question, i.e., the demand at a store that can be modeled by a random variable with values and corresponding probabilities shown in Q2. Which of the following statements about the standard deviation of the demand distribution is true?

  • The standard deviation is greater than or equal to 30 but less than 40.
  • The standard deviation is greater than or equal to 20 but less than 30.
  • The standard deviation is greater than or equal to 10 but less than 20.
  • The standard deviation cannot be calculated for this data.
  • The standard deviation is greater than or equal to 0 but less than 10.

Q4. This question relates to concepts covered in Lectures 1 & 2.

You can use any of the excel files posted to work through the question.

Suppose we have a data set containing n=100 data points with the following descriptive statistics: mean = 80, and standard deviation = 18. We believe that this data sample comes from a normal distribution. Which of the following choices would you use for forecasting or predicting future demand values?

  • Mean= 80 and standard deviation= 18.
  • Mean= 81.8 and standard deviation= 18.
  • Mean= 81.8 and standard deviation=19.8.
  • Mean= 80 and standard deviation= 19.8.
  • Mean=88 and standard deviation= 19.8.
  • Mean= 88 and standard deviation= 27.

Q5. Using DemandData.xlsx file on the course website, and using the “Moving averages of 13” method, i.e., MA (13), calculate the moving averages for periods 81 through 100. In particular, start with forecast for period 81 using data from the previous 13 periods, i.e., from periods 68 through 80. Continue this process till you generate forecast for period 100. Now calculate errors, the differences between your forecasts and the data. What is the Mean Absolute Deviation of these errors? Choose the closest numerical answer.

  • 9.17
  • 17.51
  • 7.30
  • 34.33
  • 8.46
  • 15.00

Q6. The number of customer requests for car rentals recorded on each day at a Philly Cars rental location is as follows:

DayNumber of Car Rental Requests
Monday20
Tuesday30
Wednesday30
Thursday40
Friday60

Start with Wednesday forecast using two-day moving averages MA(2). Using the errors between forecasted and actual values for Wednesday through Friday, calculate MAPE. Choose the closest answer from the variants below.

  • 15.8%
  • 27.8%
  • 43.6%
  • 9.3%
  • 38.1%
  • 21.3%

Q7. This question relates to concepts covered in Sessions 3 and 4.

You can use any of the excel files posted online to work through the Quiz.

Mr. Cal Coolator uses a trend line forecasting model to forecast the daily demand for 1-quart water bottles at a convenience store based on the average temperature recorded outside during the day. The demand line is given by

Y=36+4.3*X

where Y = daily number of 1-quart water bottles demanded, and X = ambient temperature measured in degrees Fahrenheit.

What is the demand forecast for 1-quart water bottles on a day when the average temperature is 70 degrees Fahrenheit?

  • 407
  • 380
  • 371
  • 412
  • 353
  • 395
  • 337

Q8. This question relates to concepts covered in Sessions 3 and 4.

You can use any of the excel files posted online to work through the Quiz.

Suppose you are a manager at a ski resort in Utah. The ski resort collects and keeps data on the demand for ski rentals over the past several seasons. Your assistant observes that the demand has a day-of-the-week effect (i.e., demand over a week is highly seasonal or cyclic). He calculates the seasonality factors for all 7 days of the week. The seasonality factors for Monday through Saturday are 0.8, 0.5, 0.4, 0.8, 1.7 and 1.8 respectively. What is the estimate of the seasonality factor for Sunday?

  • 1.0
  • 1.7
  • 0.5
  • 1.8
  • 0.8
  • 0.4
  • 0.9

Q9. This question relates to concepts covered in Sessions 3 and 4.

You can use any of the excel files posted online to work through the Quiz.

The Philadelphia Museum’s estimate of the number of visitors to its outdoor sculpture garden during a quarter (3-month period) shows both seasonality and increasing trend.

The de-seasonalized trend line is D = 770 + 10*t,

where t=1 for Jan 2015 (Winter), t=2 for April 2015 (Spring), t=3 for July 2015 (Summer), t=4 for October 2015 (Fall/Autumn), t=5 for January 2016 and so on.

Suppose the seasonal factors are as follows:

QuarterFactor (Index)
January (Winter)0.8
April (Spring)1.1
July (Summer)1.4
October (Fall/Autumn)0.7

What is the forecast of the number of visitors for Winter (January) 2017?

(Hint: First use the trend line to calculate the de-seasonal forecast and then apply the correct seasonal factor for January 2017).

  • 1232
  • 688
  • 917
  • 957
  • 595
  • 1176

Q10. This question relates to concepts covered in Sessions 3 and 4.

You can use any of the excel files posted online to work through the Quiz.

Sew Good is a fashion apparel chain that sells a variety of clothes in California. Below are the actual demands and the forecasts that Sew Good recorded for 20 of their clothing designs from the past season. In the following problem round all the ratio values to 2 decimal places.

For example, use 0.97 instead of 0.9733 or 0.9689.

Actual DemandForecast
546914
639698
534518
732595
699909
10191063
11741160
10921084
608776
908758
1162938
13002108
739870
9411278
831762
9151270
682524
799708
1281946
640806

The point forecast for Sew Good’s new “mood indigo” blue jeans the company plans to release in the coming season is 1450. Sew Good follows the forecast methodology we used in Session 4, and uses a normal distribution to forecast the demand for “mood indigo” jeans, and fits a demand model by studying the deviations of actual demands from forecasts in the last season using A/F ratios. What value of the descriptive standard deviation should Sew Good use for fitting the normal distribution? Please choose the closest number.

  • 298
  • 55
  • 174
  • 120
  • 332
  • 251

Operations Analytics Week 02 Quiz Answers

Decisions with Low Uncertainty Quiz

Q1. This question relates to the Zooter Example discussed in Sessions 1 and 2, and tests your understanding of the algebraic model formulation. You can answer it without using Excel. Only use Excel if you cannot answer this question otherwise.

Suppose that Zooter considers producing 400 Razor scooters and 800 Navajo scooters. What is the objective function value, in $, corresponding to this solution?

  • 184000
  • 170000
  • 198000
  • 188000
  • 190000

Q2. This question relates to the Zooter Example discussed in Sessions 1 and 2, and tests your understanding of the algebraic model formulation. You can answer it without using Excel. Only use Excel if you cannot answer this question otherwise.

Suppose now that the company considers a production plan that includes 500 Razor scooters and 700 Navajo scooters. How many frame manufacturing hours will this production plan require?

  • 5300
  • 5600
  • 5500
  • 5200
  • 5400

Q3. This question relates to the Zooter Example discussed in Sessions 1 and 2, and tests your understanding of the algebraic model formulation. You can answer it without using Excel. Only use Excel if you cannot answer this question otherwise.

Consider a solution R=800 and N=500. Is this solution feasible?

  • Impossible to tell
  • No
  • Yes

Q4. This question relates to the Zooter Example discussed in Sessions 1 and 2, and tests your understanding of the algebraic model formulation. You can answer it without using Excel. Only use Excel if you cannot answer this question otherwise.

In a typical week, Zooter has ample warehouse capacity to store the scooters it manufactures. The company has just learned that one of its warehouses got flooded and will not be available to store scooters produced in the coming week. As a result, Zooter projects that its total storage capacity for the coming week will be limited to 8000 cubic feet. One unit of Razor model requires 6 cubic feet of storage capacity and one unit of Navajo model requires 7 cubic feet of storage capacity. In terms of decision variables R and N, which algebraic expression represents the constraint “the total storage capacity required for the next week’s production cannot exceed 8000 cubic feet”?

  • 6*R + 7*N = 8000
  • 6*R + 7*N ≥ 8000
  • 6*R + 7*N ≤ 8000
  • 7*R + 6*N ≤ 8000
  • 7*R + 6*N = 8000
  • 7*R + 6*N ≥ 8000

Q5. This question relates to the Zooter Example discussed in Sessions 1 and 2, and tests your understanding of the algebraic model formulation. This question requires you to run an optimization using Solver. You can use the file Zooter.xlsx we developed in Session 2.

Zooter’s sales department has revised its projections for the profit contributions of the two scooter models. The department now estimates that the profit contribution for each unit of Razor model will be $135 and the profit contribution for each unit of Navajo model will be $170. If these new profit contributions are used in the Excel optimization model, and the Solver is re-run to find the new optimal solution, what is the new optimal value of the objective function, in $ 1 point

  • 189900
  • 198000
  • 190300
  • 203550
  • 204000

Q6. This question relates to the details of spreadsheet implementation of optimization models in Excel covered in Session 2. You should be able to answer this question using only information provided below, but you can also use Excel.

Below is the picture of an excerpt from an Excel file.

If the formula in cell B4 is “=SUMPRODUCT(A1:B1,A2:B2)”, what is the hidden value in the cell B1?

  • 5.6
  • 5.06
  • 3.08
  • 3.8

Q7. This question relates to the details of spreadsheet implementation of optimization models in Excel covered in Session 2. You should be able to answer this question using only information provided below, but you can also use Excel.

Below is the picture of an excerpt from an Excel file.

The formula in cell C5 is “=SUMPRODUCT($A$1:$C$1,A2:C2)”. If one copies and pastes the formula from the cell C5 into the cell C6, what numerical value will appear in the cell C6?

  • 111
  • 112
  • 105
  • 121
  • 114

Q8. This question relates to the KDGL example introduced in Session 3, and tests your understanding of the algebraic model formulation. You can answer it without using Excel. Only use Excel if you cannot answer this question otherwise.

Suppose that KDGL considers the following shipping policy: it would like to ship integer numbers of tons of product from all warehouses to all distribution centers, and the amounts shipped from a particular warehouse to all three distribution centers must be as close as possible. In particular, it considers the following shipping plan:

  • From the LA warehouse, it wants to ship 5 tons to each of three distributions centers
  • From the Chicago warehouse, it wants to ship 6 tons to Denver distribution center, 7 tons to Austin distribution center, and 7 tons to Washington distribution center
  • From the New York City warehouse, it wants to ship 10 tons to each of three distribution centers

Is this a feasible shipping plan?

  • Yes
  • Impossible to tell
  • No

Q9. This question relates to the KDGL example introduced in Session 3, and tests your understanding of the algebraic model formulation. You can answer it without using Excel. Only use Excel if you cannot answer this question otherwise.

Irrespective of feasibility, what will be the total shipping cost, in $, that KDGL would incur under the shipping plan described in Q8?

  • 8334
  • 8307
  • 8304
  • 7485

Q10. This question relates to the KDGL example introduced in Session 3, and tests your understanding of the algebraic model formulation. You can answer it without using Excel. Only use Excel if you cannot answer this question otherwise.

KDGL considers the following additional constraint for its shipping plan: the total amount shipped to the Washington distribution center cannot be less than the total amount shipped to the Denver distribution center. Which algebraic expression below corresponds to this constraint?

  • XLW + XCW + XNW ≥ XLD + XCD + XND
  • XLD + XCD + XND ≤ 20
  • XLW + XCW + XNW ≥ 10
  • XLW + XCW + XNWXLD + XCD + XND

Operations Analytics Week 03 Quiz Answers

Risk and Evaluation Quiz

Q1. This question relates to the Data Plan Example covered in Sessions 1 and 2. The “old plan” refers to the “Family Share” plan, and the “new plan” refers to the “Superior Share” plan. You can answer this question without using Excel. You should involve Excel only if you cannot answer this questions otherwise.

Suppose that in a particular month the consultant’s data usage turned out to be 24GB. Let PFS be the amount, in $, she will have to pay for this data usage under the old plan and PSS be the amount, in $, she will have to pay for this data usage under the new plan. What is the value of the difference PSS – PFS?

  • 240
  • 0
  • -240
  • 20
  • -20
  • -220
  • 220

Q2. This question relates to the Data Plan Example covered in Sessions 1 and 2. The “old plan” refers to the “Family Share” plan, and the “new plan” refers to the “Superior Share” plan. You can answer this question without using Excel. You should involve Excel only if you cannot answer this questions otherwise.

Suppose that in a particular month the consultant’s data usage is U, in GB. Let PFS be the amount, in $, she will have to pay for this data usage under the old plan and PSS be the amount, in $, she will have to pay for this data usage under the new plan. For which values of U will she pay more under the old plan than under the new plan, i.e., for which values of U the difference PSS – PFS is negative?

  • For any U below 16
  • For any U
  • For no value of U. In other words, she will always pay more under the new plan.
  • For any U above 28
  • For any U above 16 but below 28

Q3. This question relates to the Data Plan Example covered in Sessions 1 and 2. The “old plan” refers to the “Family Share” plan, and the “new plan” refers to the “Superior Share” plan. You can answer this question without using Excel. You should involve Excel only if you cannot answer this questions otherwise.

In one of the past months, our consultant had to pay $260 for her data usage under the old plan. How much, in $, would she have to pay for the same amount of monthly data usage under the new plan?

  • 220
  • 210
  • 230
  • 250
  • 200
  • 240

Q4. This question relates to the Data Plan Example covered in Sessions 1 and 2. The “old plan” refers to the “Family Share” plan, and the “new plan” refers to the “Superior Share” plan. You can answer this question without using Excel. You should involve Excel only if you cannot answer this questions otherwise.

Suppose that our consultant subscribes to the new data plan and, in a particular month, she has to pay $310 for her data usage under this plan. How much data, in GB, does she use in that month?

  • 10
  • 50
  • 20
  • 40
  • 30

Q5. This question relates to the Data Plan Example covered in Sessions 1 and 2. The “old plan” refers to the “Family Share” plan, and the “new plan” refers to the “Superior Share” plan. You can answer this question without using Excel. You should involve Excel only if you cannot answer this questions otherwise.

John Datum is another customer of the same wireless company. Just like our consultant, John is trying to compare the old plan and the new plan. Having looked at his past monthly data usage values, John, unlike our consultant, has decided to use a simple scenario approach to model his future data usage. In particular, John estimates that his monthly data usage Ū (in GB) can take one of three values, Ū1 = 15, Ū2 = 21 and Ū3 = 24, each value being equally likely (i.e., each having a probability of 1/3 associated with it). What is the expected value of John’s monthly payments, in $, under the old plan?

  • 180
  • 195
  • 150
  • 225
  • 200

Q6. This question relates to the Data Plan Example covered in Sessions 1 and 2. The “old plan” refers to the “Family Share” plan, and the “new plan” refers to the “Superior Share” plan. You can answer this question without using Excel. You should involve Excel only if you cannot answer this questions otherwise.

Referring to the scenario in Question 5, what is the expected value of John’s monthly payments, in $, under the new plan?

  • 190
  • 185
  • 167.5
  • 197.5
  • 160

Q7. You can answer this question using only the information provided below. You can also use the file DataPlan10.xlsx we created in Session 2 to answer the question.

Suppose that, in evaluating the old and the new data plan for our consultant, we set-up a simulation with n=5 simulation runs and use Excel to generate the following random instances of her data usage, in GB: 14.6, 27.4, 19.6, 30.8 and 25.6. Calculate the corresponding 5 values of the monthly payments under the old plan. What is the sample mean, in $, of these payment values? Choose the closest answer from the choices below.

  • 240
  • 236
  • 231
  • 210
  • 263

Q8. You can answer this question using only the information provided below. You can also use the file DataPlan10.xlsx we created in Session 2 to answer the question.

Calculate the 5 values of the monthly payments under the new plan corresponding to the data usage values shown in Q7. What is the sample mean, in $, for these payment values? Choose the closest answer from the choices below.

  • 210
  • 263
  • 240
  • 231
  • 236

Q9. You can answer this question using only the information provided below. You can also use the file DataPlan10.xlsx we created in Session 2 to answer the question.

Suppose that we set-up a simulation with n=5 simulation runs and used a different random seed to generate another 5 random instances of our consultant family’s data usage, in GB: 14.0, 21.7, 26.1, 22.1, 28.6. Calculate the corresponding 5 values of the monthly payments under the old plan. What is the sample standard deviation, in $, of these payment values? Choose the closest answer from the choices below.

  • 95.5
  • 65.5
  • 75.5
  • 55.5
  • 85.5

Q10. You can answer this question using only the information provided below. You can also use the file DataPlan10.xlsx we created in Session 2 to answer the question.

Calculate the 5 values of the monthly payments under the new plan corresponding to the data usage values shown in Q9. What is the sample standard deviation, in $, for these payment values? Choose the closest answer from the choices below.

  • 73
  • 43
  • 53
  • 63
  • 33

Operations Analytics Week 04 Quiz Answers

Decision Tree Analysis Quiz

Q1. Consider the decision tree we constructed for IDEA in Session 1 of week 4. Assume that all of the data used in the example remain the same except for the probability of the market remaining weak or strong. Suppose that, instead of a 0.5 chance that the market is strong, the probability that the market is strong is now 0.6.

Given the probability that the market is strong is 0.6, what is IDEA’s maxi-min decision?

  • IDEA should choose Supplier S. Choosing either Supplier P or no supplier would be worse.
  • IDEA should choose no supplier. Choosing either Supplier S or Supplier P would be worse.
  • None of these
  • IDEA should choose Supplier P. Choosing either Supplier S or no supplier would be worse.
  • IDEA should choose either Supplier S or Supplier P. Choosing no supplier would be worse.

Q2. Consider the decision tree we constructed for IDEA in Session 1 of week 4. Assume that all of the data used in the example remain the same except for the probability of the market remaining weak or strong. Suppose that, instead of a 0.5 chance that the market is strong, the probability that the market is strong is now 0.6.

Given the probability that the market is strong is 0.6, what is IDEA’s maxi-max decision?

  • None of these
  • Choose Supplier P. Choosing either Supplier S or no supplier would be worse.
  • Choose no supplier. Choosing either Supplier S or Supplier P would be worse.
  • Choose either Supplier S or Supplier P. Choosing no supplier would be worse.
  • Choose Supplier S. Choosing either Supplier P or no supplier would be worse.

Q3. Consider the decision tree we constructed for IDEA in Session 1 of week 4. Assume that all of the data used in the example remain the same except for the probability of the market remaining weak or strong. Suppose that, instead of a 0.5 chance that the market is strong, the probability that the market is strong is now 0.6.

Given the probability that the market is strong is 0.6, what is IDEA’s expected-value-maximizing decision?

  • None of these.
  • Choose Supplier P. Choosing either Supplier S or no supplier would be worse.
  • Choose either Supplier S or Supplier P. Choosing no supplier would be worse.
  • Choose no supplier. Choosing either Supplier S or Supplier P would be worse.
  • Choose Supplier S. Choosing either Supplier P or no supplier would be worse.

Q4. Consider the decision tree we constructed for IDEA in Session 1 of week 4. Assume that all of the data used in the example – including the 0.5 probability that the market is strong – remain the same as in Session 1, except for the fixed upfront cost charged by Supplier S. Suppose that, instead of a fixed upfront cost of 0€, Supplier S charges IDEA a fixed upfront cost of 175,000€.

Given Supplier S charges IDEA a fixed upfront cost of 175,000€, what is IDEA’s maxi-min decision?

  • Choose Supplier P. Choosing either Supplier S or no supplier would be worse.
  • None of these.
  • Choose either Supplier S or Supplier P. Choosing no supplier would be worse.
  • Choose no supplier. Choosing either Supplier S or Supplier P would be worse.
  • Choose Supplier S. Choosing either Supplier P or no supplier would be worse.

Q5. Consider the decision tree we constructed for IDEA in Session 1 of week 4. Assume that all of the data used in the example – including the 0.5 probability that the market is strong – remain the same as in Session 1, except for the fixed upfront cost charged by Supplier S. Suppose that, instead of a fixed upfront cost of 0€, Supplier S charges IDEA a fixed upfront cost of 175,000€.

Given Supplier S charges IDEA a fixed upfront cost of 175,000€, what is IDEA’s maxi-max decision?

  • Choose no supplier. Choosing either Supplier S or Supplier P would be worse.
  • Choose Supplier S. Choosing either Supplier P or no supplier would be worse.
  • Choose either Supplier S or Supplier P. Choosing no supplier would be worse.
  • None of these.
  • Choose Supplier P. Choosing either Supplier S or no supplier would be worse.

Q6. Consider the decision tree we constructed for IDEA in Session 1 of week 4. Assume that all of the data used in the example – including the 0.5 probability that the market is strong – remain the same as in Session 1, except for the fixed upfront cost charged by Supplier S. Suppose that, instead of a fixed upfront cost of 0€, Supplier S charges IDEA a fixed upfront cost of 175,000€.

Given Supplier S charges IDEA a fixed upfront cost of 175,000€, what is IDEA’s expected-value-maximizing decision?

  • Choose Supplier P. Choosing either Supplier S or no supplier would be worse.
  • Choose either Supplier S or Supplier P. Choosing no supplier would be worse.
  • Choose Supplier S. Choosing either Supplier P or no supplier would be worse.
  • Choose no supplier. Choosing either Supplier S or Supplier P would be worse.
  • None of these

Q7. For this question, consider the following decision problem. A decision maker must choose one of three actions: A, B, or C. The outcome of each action is uncertain, as follows.

  • If action A is chosen, there are two possible outcomes. With probability 0.2 the reward is 100, and with probability 0.8 the reward is 400.
  • If action B is chosen, there are three possible outcomes. With probability 0.25 the reward is 200, with probability 0.5 the reward is 250, and with probability 0.25 the reward is 300.
  • If action C is chosen, there are two possible outcomes. With probability 0.8 the reward is 0, and with probability 0.2 the reward is 500.

To answer questions 7-10, below, draw and then analyze a decision tree that corresponds to the decision-maker’s problem, above.

Which choice is the decision maker’s maxi-min decision?

  • Action B
  • Action C
  • None of these.
  • Action A

Q8. For this question, consider the following decision problem. A decision maker must choose one of three actions: A, B, or C. The outcome of each action is uncertain, as follows.

  • If action A is chosen, there are two possible outcomes. With probability 0.2 the reward is 100, and with probability 0.8 the reward is 400.
  • If action B is chosen, there are three possible outcomes. With probability 0.25 the reward is 200, with probability 0.5 the reward is 250, and with probability 0.25 the reward is 300.
  • If action C is chosen, there are two possible outcomes. With probability 0.8 the reward is 0, and with probability 0.2 the reward is 500.

To answer questions 7-10, below, draw and then analyze a decision tree that corresponds to the decision-maker’s problem, above.

Which choice is the decision maker’s maxi-max decision?

  • None of these.
  • Action C
  • Action B
  • Action A

Q9. For this question, consider the following decision problem. A decision maker must choose one of three actions: A, B, or C. The outcome of each action is uncertain, as follows.

  • If action A is chosen, there are two possible outcomes. With probability 0.2 the reward is 100, and with probability 0.8 the reward is 400.
  • If action B is chosen, there are three possible outcomes. With probability 0.25 the reward is 200, with probability 0.5 the reward is 250, and with probability 0.25 the reward is 300.
  • If action C is chosen, there are two possible outcomes. With probability 0.8 the reward is 0, and with probability 0.2 the reward is 500.

To answer questions 7-10, below, draw and then analyze a decision tree that corresponds to the decision-maker’s problem, above.

Which choice is the decision maker’s expected-value-maximizing decision?

  • Action C
  • None of these
  • Action B
  • Action A

Q10. For this question, consider the following decision problem. A decision maker must choose one of three actions: A, B, or C. The outcome of each action is uncertain, as follows.

  • If action A is chosen, there are two possible outcomes. With probability 0.2 the reward is 100, and with probability 0.8 the reward is 400.
  • If action B is chosen, there are three possible outcomes. With probability 0.25 the reward is 200, with probability 0.5 the reward is 250, and with probability 0.25 the reward is 300.
  • If action C is chosen, there are two possible outcomes. With probability 0.8 the reward is 0, and with probability 0.2 the reward is 500.

To answer questions 7-10, below, draw and then analyze a decision tree that corresponds to the decision-maker’s problem, above.

What is the expected value of the expected-value-maximizing decision?

  • 300
  • 340
  • 200
  • 100
  • 250
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