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**Case: The Roulette Wheel Goodness of Fit Tests**

Class 06 07 Case: The Roulette Wheel Goodness of Fit Tests EMBS 11.2

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**What we learned last class**

Probability Distributions have characteristics Descriptive Statistics are used to estimate those characteristics. Location (mean, median, mode) Variability (variance and standard deviation) Shape (skewness) The MEAN is important. Sample mean “value” times n is total value. Measures of variability are under-appreciated

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**Descriptive Statistics Matter**

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**Baseball Statistics A batter came to the plate five times Got a hit**

Struck Out Walked Flied Out Grounded Out WALK is the fault of the pitcher: 1 success in 4 trials Batting Average = ¼ = 0.250 WALK is the fault of the batter: 2 successes in 5 trials On Base Percentage = 2/5 = 0.400

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The Roulette wheel. Surveillance video of 18 hours of play of a roulette wheel in a Reno, Nevada casino 904 spins of the wheel 22,527 bets places

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Outcome Frequency Number of Bets 00 22 354 18 23 518 25 442 19 30 595 1 362 20 24 983 2 450 21 26 447 3 28 357 32 576 4 15 375 746 5 636 461 6 363 521 7 682 703 8 633 27 490 9 503 827 10 484 29 878 11 783 33 695 12 360 31 664 13 525 925 14 649 17 613 340 34 597 16 643 35 627 1,079 36 641 Total 904 22,527

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**First We Examine the Wheel**

H0: The wheel works properly H0: All 38 outcomes have equal probability of occurring H0: P0=P28=P9=…=P2=1/38 HA: they are not all equal Like before, the Hypothesis is about a parameter of a probability distribution

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**Goodness-of-fit Test Calculate the expected counts under H0**

Outcome Observed Expected Distance 00 22 23.79 0.13 25 0.06 1 23 0.03 2 30 1.62 3 28 0.75 4 15 3.25 5 6 20 0.60 7 8 26 0.21 9 10 24 0.00 11 12 21 0.33 13 14 27 0.43 16 17 18 19 32 2.83 1.41 0.96 29 33 3.57 31 1.14 1.94 34 35 36 Total 904 31.20 Calculate the expected counts under H0 Calculate the Distances as (O-E)2/E The sum of the distances is the test statistic. We call it the calculated chi-squared. We reject H0 (and say the results are statistically significant) if the calculated chi-squared statistic is too big. The calculated chi-squared statistic The sum of the distances.

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**We need a p-value! For the lady tasting tea For a GOODNESS OF FIT TEST**

Number correct depends on n and P For the lady tasting tea P(X≥8 │ H0) = 1-binomdist(7,10,.5,true) Pvalue = 0.055 For a GOODNESS OF FIT TEST P(χ2≥ calculated χ2 │H0) = chidist(calculated χ2, dof) dof stands for “degrees of freedom” dof is the parameter of the chi-squared distribution dof here is 37, the number of cells - 1. P(χ2≥ 31.2│H0) = chidist(31.2,37) Pvalue = 0.74 χ2 depends on number of cells - 1. Can also use =chisq.dist.rt(31.2,37) NOT statistically significant.

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WARNING The chi-squared test does not work well when some cells have low expected counts. If some cells have expected counts < 5, combine then with neighboring cells.

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**Roulette Wheel Demonstration**

H0: All 38 are equally likely to get bet on. Ha: The p’s are not equal. (Some segments are more popular than others)

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**H0: The Fill Amounts are Normally Distributed with μ=10.2 and σ=0.16**

Lorex Pharma H0: The Fill Amounts are Normally Distributed with μ=10.2 and σ=0.16 Ha: They are not…

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**Assignment 08 Due Monday, Feb 13**

Youth Soccer (football) teams from several countries compete annually in an important international tournament. The birth months (Jan=1, Feb=2, .. Dec=12) of the 288 boys competing in the 2005 under 16 division showed higher counts for the early months and lower counts for the later months. Formulate and test a relevant hypothesis If you find statistical significance, offer an option about how it came to be that early months are more prevalent. opinion Helsen, W.F., Van Winckel, J., and WIlliams, M., The relative age effect in youth soccer across Europe, Journal of Sports Sciences, June 2005; 23(6):

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**The Data look like….. ID Birth Month 1 5 2 3 285 286 287 288 .**

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