How many basketball shots can you make in one minute
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How Many Basketball Shots Can You Make In One Minute?. Chris Torgalski Jin Lee Block One. Introduction.

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Presentation Transcript

Introduction
Introduction

Our experiment was to see how many basketball shots a person could make in one minute. To keep the experiment constant and fair for all people we set the basket at the same height for everyone, had everyone use the same ball, and shot from behind the same line. We broke up the data by whether they play sports competitively, by which partner collected the data, and by the subject’s gender.


Overall data
Overall Data

  • Shape: Bimodal with a right skew

  • Center: Median – 5

  • Range: (1,12)


Normality
Normality

  • Not normal because the data dips at the lower numbers


Partner data
Partner Data

Chris

  • Shape: unimodal, right skewed

  • Center: median - 5

  • Range: (2,9)

    Jin

  • Shape: unimodal, symmetric

  • Center: mean – 5.86

  • Range: (1,12)

    Comparison

  • Jin collected more data and has a greater mean, median and range than Chris does.


Gender data
Gender Data

Female

  • Shape: unimodal, right skewed

  • Center: median - 3

  • Range: (1,9)

    Male

  • Shape: unimodal, symmetric

  • Center: mean – 6.75

  • Range: (2,12)

    Comparison

  • There was more males that participated in the trials than females. Males had a larger mean, range, and median than females did.


Competitive sports data
Competitive Sports Data

No

  • Shape: unimodal, right skewed

  • Center: median - 6

  • Range: (2,12)

    Yes

  • Shape: unimodal, right skewed

  • Center: median– 5

  • Range: (1,11)

    Comparison

  • There was more data collected for people who play sports competitively than people who don’t. People who don’t play sports have a greater mean and median. Both groups share the same range.



Marginal distribution of gender
Marginal Distribution of Gender

  • Males: 20/35 = 57.14%

  • Females: 15/35 = 42.86%


Marginal distribution of playing sports
Marginal Distribution of Playing Sports

  • No: 14/35 = 40%

  • Yes: 21/35 = 60%


Conditional distribution
Conditional Distribution

Association of Variables

  • We can that gender and whether or not someone plays a sport competitively are not independent because their marginal distributions of one variable does not equal the conditional distribution of the other variable.


Bias error
Bias/Error

  • Data collectors could have counted the number of shots made incorrectly

  • People could have shot from in front of the line


Conclusion
Conclusion

  • The sample of 35 people that we collected can lead us to assume that:

    • The amount of shots people will make in one minute will vary but most people will make around five shots

    • Whether or not someone plays a sport does not have a huge effect on the amount of shots made

    • We should have had more evenly distributed data among the categorical values to get more accurate results