report 4.2.docx - Megan McLoughlin Math 2228 Lab Report 4.2 Using calc streak compute the streak lengths of sim basket 1 Describe the distribution of

report 4.2.docx - Megan McLoughlin Math 2228 Lab Report 4.2...

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Megan McLoughlin Math 2228 10/21/2019 Lab Report 4.2 Using calc streak, compute the streak lengths of sim basket. 1. Describe the distribution of streak lengths. What is the typical streak length for this simulated independent shooter with a 45% shooting percentage? How long is the player’s longest streak of baskets in 133 shots? > outcomes <- c("H", "M") > sim_basket <- resample(outcomes, size = 133, prob = c(0.45, 0.55)) >sim_streak <- calc_streak(sim_basket) The distribution of streak lengths is skewed to the right. They typical streak length for the simulated independent shooter with a 45% shooting percentage is between 0 and 1. The player’s longest streak of baskets in 133 shots is 7. 2. If you were to run the simulation of the independent shooter a second time, how would you expect its streak distribution to compare to the distribution from the question above?
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Unformatted text preview: Exactly the same? Somewhat similar? Totally different? Explain your reasoning. If we ran the stimulation for a second time I would expect the streak distribution to be slightly different then the distribution from question one. This is because we didn’t set a seed value and we are just sampling at random. 3. How does Kobe Bryant’s distribution of streak lengths compare to the distribution of streak lengths for the simulated shooter? Using this comparison, do you have evidence that the hot hand model fits Kobe’s shooting patterns? Explain. > barplot(table(kobe_streak)) > barplot(table(sim_streak)) The distribution of the streak lengths of Kobe Bryannt compared to the streak lengths of the simulated shooter are quite similar. This means that the hot hand model fits with Kobe’s shooting pattern....
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