Hoppy Peanut Butter Case Facts Hoppy Peanut Butter is one among five major players in Southern Florida There are six million households in the market Consumers make 1.2 trips/week to the grocery store The marketing department of Hoppy Peanut Butter has identified four possible strategies for increasing profitability in southern Florida sales region including Increasing advertising Either offering discount coupons for the product alone Offering discount coupons for the product
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Exhibit 4: Distribution of sample means Answer 1c): Probability that 125 or more passengers board the plane Exhibit 5: Steps from Crystal Ball to obtain probability of 125 or more passengers boarding the plane Exhibit 6: Steps from Crystal Ball to obtain probability of 125 or more passengers boarding the plane Exhibit 7: Forecast chart showing the probability of 125 or more passengers boarding the plane Therefore the probability of 125 or more passengers boarding the plane equals to
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000=$34,200) 2. Assuming Mr. Jaeger chooses to wait to harvest the grapes, what is the probability that the grapes will end up with the botrytis mold, and how much money will he make if that occurs? There is a 40% chance that it would lead to the development of the botrytis mold, if the storm did strike. Moreover, the probability of storm would hit the Napa Valley is 50%. Therefore, the probability that the grapes will end up with the botrytis mold is 20%. The wholesale price for a botrytised
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exposed about Paul, the octopus, forecasting accurate results for six of Germany’s World cup games. Learning about probabilities in class, we are trying how we can interpret rules and terms about probability to Paul the octopus selecting the winning team. In the BBC article, Sarah Shenker states, “[Paul] had a 1/64 chance of predicting six correct outcomes.” The author reaches the probability of 1/64 by multiplying the chances of predicting for each game. For example, in the article, Paul had a 1/2 chance
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1. The Hurwicz criterion is a compromise between the maximax and maximin criteria. The Hurwicz criterion is the weighted sum of the maximax and maximin. So, the answer is true. 2. The minimin criterion is optimistic. The minimin criterion is NOT applied to the profit payoff table. It is applied to the cost table. In this situation we want to minimize cost as much as possible. So, if we can do it this is optimistic criterion. 3. A business owner is trying to decide whether to buy, rent
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wait in line to be unloaded. Size of team | Expected time in queue | 2 | .8 hrs | 3 | .125 hrs | 4 | .067 hrs | 5 | .030 hrs | Step 3: Immediate Unload Probability | | Question 3: For each of the four work team sizes, what is the probability that a truck cannot be unloaded immediately? Size of team | Probability that truck must wait | 2 | 80% | 3 | 50% | 4 | 40% | 5 | 30% | Step 4: Lowest-Cost Team | | Question 4: Which of the four work team sizes results in the
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© Orangetree Business Solutions Private Limited, 2012 No part of this book should be referenced or copied without the prior permission of the company. A FEW WORDS TO THE STUDENTS Analytics is becoming a popular tool for managerial decision making. It‘s still not so widespread in countries like India, but in the west it has become a standard practice. Previously studying analytics involved an in depth knowledge of statistics and programming languages. But widespread availability of statistical
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drug licensing decision. The maximum bid price that Merck should offer LAB should be lower than or equal to the total expected value of the project, which is $14 million. The expected value of the licensing arrangement is calculated based on the probability of success and failure of each stage during the FDA approval process; but it is not the cash flows that Merck or LAB will receive in the future. Therefore, $14 million is the maximum bid price that Merck should offer to license Davanrik. Merck has
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large ones on aggregate terms, while commercial and savings banks perform better on their respective traditional markets. Finally, I find increasing discrepancies between riskneutral and actual default probabilities since June 2007 and discuss their possible sources. Keywords: Credit risk, Probability of default, Asset Pricing, Mean-Variance allocation, Stochastic Discount Factor, Value at Risk. JEL: G21, G12, G11, C32, D81, G28. This paper is the sole responsibility of its author. The views represented
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sample mean x in place of μ? For example, suppose that the average hours for 100 randomlyselected US residents was x = 6.34. Can we conclude that the average hours for all US residents (μ) is 6.34? Can we conclude that μ > 6? Fortunately, we can use probability theory to understand how the process of taking a random sample will blur the information in a population. But first, we need to understand why and how the information is blurred. Sampling Variability Although the average social networking hours
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