Managerial Decision Modeling With Spreadsheets 3rd Edition Barry Render - Solutions

Discover comprehensive solutions for "Managerial Decision Modeling With Spreadsheets 3rd Edition" by Barry Render. Access an extensive array of questions and answers, providing an insightful answers key and solutions manual to enhance your understanding. Our solutions PDF covers solved problems with chapter solutions and step-by-step answers, perfect for those seeking detailed explanations. Utilize the instructor manual and test bank for a complete learning experience. This invaluable textbook resource is available for free download, offering unparalleled assistance for students and educators alike. Explore our online platform for all your academic needs.

Fisheries Management Many models have been developed for the reproduction of fisheries and the effect of fish harvesting on fish populations. A very simplified version is provided here, calibrated for the Pacific Halibut Fishery.23 This model is described by a few equations (where t refers to the
Portfolio Management Exercise7-3.xlsx contains annual data for the inflation-adjusted returns on stocks, Treasury bills, and Treasury bonds from 1928–2018. Imagine that you have $100,000 to invest in these three funds and that you are currently 60 years old.a. Build a simulation model to show the
Fuel Costs vs. Labor Costs Suppose a truck gets 11 miles per gallon if it travels at an average speed of 50 miles per hour. Fuel efficiency is reduced by 0.15 mpg for each increase in the speed of 1 mph, up to 60 mph. Fuel efficiency is reduced by 0.2 mpg for each 1 mph in the range from 60 to 70
Snowfall at a Ski Area Data for the years 2009–2017 for the Jackson Hole, Wyoming, ski area reveals that the average seasonal (October 1–April 15) snowfall is 1048 cm (413 inches), but ranged from a low of 735 cm to a high of 1562 cm—the data is in Exercise6-8.xlsx. Snowfall is critical to
Supermarket Checkout Suppose that customers arrive at a supermarket cash register at the rate of 45 per hour. Each customer takes an average of 2.85 minutes to check out, although this varies (it follows a Lognormal distribution with a mean of 2.85 minutes and a standard deviation of 3 minutes).a.
Oil Spills As the world depends on oil that is harder to reach (e.g., from Alaska, deep in the Gulf of Mexico, etc.), the threat of oil spills is ever present.The data in Table 6.3 is adapted from a report issued by the Bureau of Ocean Energy Management about oil spills in the US Outer Continental
Robocall Marketing Robocalls have become a major problem for consumers and telecommunications companies. They represent the largest category of complaints filed with the Federal Communications Commission(FCC) and are the subject of several potential legislative acts.Estimates for January 2019 are
Utility Company Repair Vehicles Consider a utility company with a fleet of 50 repair vehicles. Each day, assume that the number of hours a vehicle will be used per trip follows a Lognormal distribution (with an average of 4 hours and a standard deviation of 2 hours). The number of repair trips
Hospital Bed Utilization The Centers for Disease Control and Prevention reports that the average rate of discharge from hospital stays was 1139.6 per 10,000 people in 2010. The average length of stay was 4.5 days. Consider a medium-sized city with a population of 500,000 and five hospitals, each
The Olympic Swimsuit Controversy On Feb. 13, 2008, the LZR Racer swimsuit was introduced that was claimed to reduce race times by 1%–2%. By August 14, 2008, 62 world swimming records had been broken. Exercise5-8.xlsx provides winning times for the Olympic Gold medals in two sports: the men’s
New Car Sales Exercise5-7.xlsx has monthly data on total new car registrations in the EU15 countries from 2002 through 2018 and US car sales data from 1976 through 2018.12 You wish to forecast the number of sales for the last month of 2018 and first 6 months of 2019.a. Visually investigate the time
Peak Oil Predictions that world oil production will peak and decline have been a recurring theme. The theory was originally described by M. King Hubbert, who had predicted that US oil production (from conventional fields) would peak around the year 1970. Exercise5-6.xlsx contains monthly data on
Moore’s Law Moore’s Law refers to the empirical observation (by Gordon Moore, cofounder of Intel) that the number of transistors on a chip doubles every 2 years. Exercise5-5.xlsx contains data on CPUs and GPUs for the surface areas of chips and the number of transistors that fit on them.a. The
E-Commerce and Retail Sales Exercise5-4.xlsx has monthly retail sales data for E-commerce and non-E-commerce sales. Clearly, E-commerce makes up an increasing portion of retail sales activity. Ignore inflation until part (d).a. Construct time series models for the two series independently of each
Drug Overdoses Exercise5-1.xlsx contains monthly data on overdose deaths for a number of drugs.8a. Build an appropriate time series model for total opioid overdose deaths for the 12 months starting in August 2018.Discuss whether there is a trend and/or seasonality and any adjustments you made as a
Time Use Exercise4-8.xlsx contains a subset of data from the 2017 American Time Use Survey conducted by the US Bureau of Labor Statistics.This extensive survey contains detailed diary information about how a variety of people spend their time. This excerpted data has only a few variables out of
Warranties Revisited Consider Exercise 3.7 concerning tire failures and warranties. There are five sources of uncertainty: tread wearout, sidewall failure, steel shear of tread, punctures, and sale of car. Assume that the correlation between sidewall failure and steel shear of tread is 0.8, between
Global Warming and Economic Development Carbon emissions (in particular, carbon dioxide) and their relationship with global climate change is a major issue, fraught with uncertainties. Exercise4-6.xlsx has data on per capita carbon emissions, per capita income (GDP, in $US), and total population
Automobile Driving Habits Exercise4.4.xlsx has data from the 2009 National Transportation Survey for 64,861 gasoline engine automobile annual miles driven and average mpg fuel efficiency.41a. Fit a parametric copula to these variables.b. Fit an empirical copula to this data.c. Estimate the fraction
Portfolio Correlation Exercise4-3.xlsx contains data for the weekly changes in Bitcoin prices (BTC) and the Dow Jones Industrial Average (DJI) over the decade of 2010–2019.a. Fit a distribution for each index’s weekly price change and a parametric copula for the relationship between the two.
Airfares Exercise4-2.xlsx contains data for all continental US domestic air routes that averaged at least 10 passengers per day for the first quarter of 2009.40 Data is provided for the originating and terminating city(and airport code) for each route, along with the average one-way distance,
Drug Development Consider a potential pharmaceutical drug for treating type 1 diabetes that is ready to enter phase 1 of research and development for FDA approval. Initially, a 60% chance is assumed that the drug will be effective on any individual patient tested in phase 1. Phase 1 is expected to
Let’s Make a Deala. Monty Hall was the host of the long-running television game show, Let’s Make a Deal. The Monty Hall problem is a well-studied probability problem that continues to confound people with the results. At the end of each show, the day’s winner has a final opportunity to win a
Warranties Tires can fail for a number of reasons, such as tread wear-out, sidewall failure, or steel shear of tread. Assume that sidewall failures and steel shear of tread follow Exponential distributions, with the means equal to a design mileage chosen by the manufacturer. Tread wearout follows a
Electricity Prices Exercise3-6a contains the average electricity prices for 2017 across the United States and by retail sector (data is from the Energy Information Administration33), in cents per kilowatt hour.a. Fit distributions to the prices for each sector across the United States (use the
Global Warming Predictions Exercise3-5.xlsx provides several predictions regarding sea level rise(measured in meters) for the year 2100.32 Global climate modeling is complex and there are many probabilistic scenarios developed, each of which accounts for different climate factors (CO2
Beer Sales Exercise3-4.xlsx has 1 year of daily beer sales (in dollars) at a local microbrewerya. What revenue of daily beer sales is exceeded on 5% of days?b. Fit a parametric distribution to daily sales and estimate the 95th percentile of that distribution. What assumption do you make when you
Investment Returns Exercise3-3.xlsx provides the annual returns to stocks (measured by the S&P 500), Treasury bonds, and Treasury bills over the 1928–2018 period. Consider an investment of $100 invested in each of these assets in 2018.a. Fit a distribution to each of these asset’s annual
Household Car Travel in the United States The Federal Highway Administration conducts the National Household Travel Survey (NHTS) every 8 years.31 Exercise3-2.xlsx contains the 2017 data for the sample of households in each state that own cars. For each sampled household in each state, the total
Smoking and Birthweights Exercise3.1.xlsx contains data on the birthweights (in ounces) of 484 babies born to mothers who smoked and 742 babies born to mothers who did not smoke.a. Fit distributions to each set of baby birthweights.b. Low-birthweight babies (defined to be less than 88 ounces and
Project Management Simulation Use the project management model from Exercise 1.7. Projects are notorious for running over budget and taking longer than anticipated.Suppose that each activity time from Table 1.4 is uncertain and follows a PERT distribution with the table numbers being the most
represent the average hourly demands during these times. Represent the uncertainty by multiplying the average demand by a normal distribution (with mean = 1, standard deviation = 0.25). Each hourly demand will be random, and assume that each varies independently of the other hours. Compare the
Peak Load Pricing Simulation In Exercise 1.6, we saw that peak load pricing can increase profitability by leveling use between peak and off-peak periods (thereby reducing the need for expensive capacity). In this exercise, we examine how uncertain demand influences our results. Suppose that the
Network Economics Simulation Consider your network economics model from Exercise 1.5. There are two major sources of uncertainty that should be accounted for.For a new communications technology, the costs will initially not be known with certainty. Assume that the costs may range between $3 and $8
Customer Lifetime Value Simulation Use your LTV model from Exercise 1.4. One source of uncertainty was already presented in Exercise 1.4—the retention rate. Rather than assuming that the average retention rate is experienced with certainty, simulate the actual retention of customers (use the
and standard deviations equal to 20% of the means. Simulate NPV for the two investment decisions, at two discount rates, 5% and 10% (these rates are indicative of the difference between student federal loans and private student loans). What is the probability that the NPV is positive for each
Educational Investment Simulation Use your model for estimating the NPV from Exercise 1.3. One source of uncertainty is the possibility of becoming unemployed.High school graduates have an unemployment rate of 5.3%, while those with college degrees (or higher) average 2.5% (use a Bernoulli
Electric Vehicle Cost Simulation Use your electric vehicle model from Exercise 1.2. Gasoline and electricity prices are highly uncertain. Investigate how this uncertainty impacts the breakeven additional price for the electric engine. Assume that annual change in gasoline price (adjusted for
Health Insurance Choice Simulation Use your health care insurance plan choice model from Exercise 1.1.Health care service utilization varies across individuals for myriad reasons. For each family member, assume the actual number of office visits follows a Poisson distribution, with a mean rate
Project Management A new product development cycle involves several interrelated activities. Some activities can be pursued simultaneously, while some can only be undertaken after others are completed. Table 1.4 shows these stages for a typical new product.What is the expected total time to
Peak Load Pricing Electricity demand varies over time. It is expensive to maintain generating capacity sufficient for meeting the peak demand—especially if that capacity sits idle the rest of the time. In this exercise, assume that there are two hourly demand levels, peak and off-peak.Both are
Customer Lifetime Value Customer lifetime value (LTV) is a concept developed to consider the fact that it generally costs something to acquire customers and that the profits rely on retaining them over time. Consider the hypothetical data in Table 1.3 concerning customer profitability over a 3-year
Electric Vehicles Consumers are becoming more sensitive to concerns about global warming, as well as concerns about their spending on gasoline. In 2016, all electric vehicles accounted for around 1% of US auto sales, but these are projected to reach 22% of sales by 2025. The initial cost for
Learn how to construct and use an Efficient Frontier.
Understand potential trade-offs between risk and return.
Understand the difference between Excel’s Solver and RiskOptimizer.
Learn how to combine optimization and simulation.
Learn how to use RiskOptimizer to find optimal solutions.
Appreciate how Excel Tables can be used to find optimal solutions for relatively simple problems.
Understand when and where optimization can be appropriate and useful.
See how Bayesian modeling allows the analysis of uncertainty that will be updated as new information becomes available.
Appreciate the applicability of frequency-severity modeling to a variety of decision problems.
Use Combined distributions to correctly model insurance problems.
Learn how to use Combined distributions in order to conduct frequency-severity modeling.
Understand how to use empirical distributions to model uncertainty when parametric distributions do not adequately represent uncertainty.
Use stress testing to explore granularity of outcomes, particularly in the tails of outcome distributions.
Learn how to use advanced sensitivity analysis to understand how distributions and parameters affect outcomes.
Learn principles for how to model several interrelated time series.
See how to also use and incorporate (expert) judgment into time series models.
Learn how to simulate time series data for future predictions.
Learn how to capture the uncertainty in a time series.
Learn how to fit time series models to data.
Recognize and capture trends, seasonality, and volatility in historical data.
Understand time series forecasting as a data-driven process that assumes a stable underlying pattern in the data.
To appreciate the difference between analyzing and modeling distributions versus time series.
See an example of how to model the uncertainties inherent in regression models.
Learn how to use regression models to capture relationships in your models.
Learn how to fit correlation patterns to data.
Learn how to capture more complex correlations through the use of copulas.
Learn how to include rank-order correlation structures between variables.
Learn how logical functions can be used to capture important relationships.
Appreciate the importance of including and accurately reflecting key relationships in simulation models.
Adopt a structured approach to selecting appropriate probability distributions.
Understand a variety of classes of distributions commonly used to model different types of situations with uncertainty or variability.
Learn ways to estimate and incorporate uncertainty about the appropriate distribution that fits the data. Apply these methods to estimation of the value at risk of a hypothetical portfolio of assets.
See how distributions may be fit to historical data.
Learn how to incorporate expert opinions into spreadsheet models. Apply these methods to a company valuation.
Understand how distributions may be selected based on theoretical considerations and/or expert opinion.
Acquire the basic skills of setting up a (Monte Carlo)simulation model and interpreting its results.
Review the steps of how to build Monte Carlo simulation in Excel spreadsheets.
Learn about Monte Carlo simulation and how it is a good way to consider risk and uncertainty in your models.
Understand the importance of uncertainty for model building and in using models to support decision making.
Appreciate the prevalence of errors in spreadsheets and learn how to prevent them.
Develop good procedures for building and documenting spreadsheet models.
Translate the visualization into a spreadsheet model.
Use visualizations of a situation to sketch out the logic for a model.
1. List the steps of the decision-making process and describe the different types of decision-making environments.
2. Make decisions under uncertainty and under risk.
3. Use Excel to set up and solve problems involving decision tables.
4. Develop accurate and useful decision trees.
5. Use TreePlan to set up and analyze decision tree problems with Excel.
6. Revise probability estimates using Bayesian analysis.
7. Understand the importance and use of utility theory in decision making.
2 Describe what is involved in the decision-making process.
3 What is an alternative? What is an outcome?
4 Discuss the differences between decision making under certainty, decision making under risk, and decision making under uncertainty.
5 State the meanings of EMV and EVPI.
9 What is the purpose of utility theory?
11 How is a utility curve used in selecting the best decision for a particular problem?
12 What is a risk seeker? What is a risk avoider? How do the utility curves for these types of decision makers differ?Problems

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Managerial Decision Modeling With Spreadsheets 3rd Edition Barry Render - Solutions

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