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Simulations The basics for simulations

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Simulation is a way to model random events, such that simulated outcomes closely match real-world outcomes. By observing simulated outcomes, researchers gain insight on the real world. What is a Simulation?

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Some situations do not lend themselves to precise mathematical treatment. Others may be difficult, time- consuming, or expensive to analyze. In these situations, simulation may approximate real-world results; yet, require less time, effort, and/or money than other approaches. Why a Simulation?

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The steps to creating a Simulation 1- Describe the possible outcomes. 2- Link each outcome to one or more random numbers. 3- Choose a source of random numbers (random # table / calculator). 4- Choose a random number. 5- Based on the random number, note the "simulated" outcome. 6- Repeat steps 4 and 5 multiple times; preferably, until the outcomes show a stable pattern. 7- Analyze the simulated outcomes and report results.

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The Problem: On average, suppose a baseball player hits a home run once in every 10 times at bat, and suppose he gets exactly two "at bats" in every game. Using simulation, estimate the likelihood that the player will hit 2 home runs in a single game. 1- Describe the possible outcomes.

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The Problem: On average, suppose a baseball player hits a home run once in every 10 times at bat, and suppose he gets exactly two "at bats" in every game. Using simulation, estimate the likelihood that the player will hit 2 home runs in a single game. 2- Link each outcome to one or more random numbers.

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The Problem: On average, suppose a baseball player hits a home run once in every 10 times at bat, and suppose he gets exactly two "at bats" in every game. Using simulation, estimate the likelihood that the player will hit 2 home runs in a single game. 3- Choose a source of random numbers (random # table / calculator).

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The Problem: On average, suppose a baseball player hits a home run once in every 10 times at bat, and suppose he gets exactly two "at bats" in every game. Using simulation, estimate the likelihood that the player will hit 2 home runs in a single game. 4- Choose a random number.

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The Problem: On average, suppose a baseball player hits a home run once in every 10 times at bat, and suppose he gets exactly two "at bats" in every game. Using simulation, estimate the likelihood that the player will hit 2 home runs in a single game. 5- Based on the random number, note the "simulated outcome.

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The Problem: On average, suppose a baseball player hits a home run once in every 10 times at bat, and suppose he gets exactly two "at bats" in every game. Using simulation, estimate the likelihood that the player will hit 2 home runs in a single game. 6- Repeat steps 4 and 5 multiple times (lets run it 100 times) preferably, until the outcomes show a stable pattern.

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The Problem: On average, suppose a baseball player hits a home run once in every 10 times at bat, and suppose he gets exactly two "at bats" in every game. Using simulation, estimate the likelihood that the player will hit 2 home runs in a single game.

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The Problem: On average, suppose a baseball player hits a home run once in every 10 times at bat, and suppose he gets exactly two "at bats" in every game. Using simulation, estimate the likelihood that the player will hit 2 home runs in a single game. 7- Analyze the simulated outcomes and report results.

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The Problem: You want each of the pictures of Lebron, Payton and Serena. Remember last class? When you check your wallet you find you can only afford 4 boxes of cereal. What is the probability that you will get all 3 pictures? Run the simulation at least 20 times.

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