AP Stats Course Project

**David Richardson**

**Markesia Hardnett**

**3B stats**

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AP Stats Course Project

**David Richardson**

**Markesia Hardnett**

**3B stats**

Empty text

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**Introduction**

**Topic Question: Is Home Court Advantage real?**

**This topic sparked our ****interest because we enjoy watching the sport of basketball and we feel as though teams perform better at their home court.**

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5 W's

Who? Teams in the NBA

What? The number of games they win home court.

When? 2017-2018 season

Where? United States

Why? To know if teams perform better in their home court.

Summary of Topic Data Collection

As stated in the previous slide we wanted to know if NBA teams perform better at home than away.

The way we collected our data on the internet using the ESPN website and the 2017-2018 season.

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Summary of Data Cont'd

We used 15 teams both from the Eastern Conference and Western Conference. Here we subtracted the number of home game wins from the number of away game wins, to find the difference. The first 7 are Eastern and the the last 8 are Western.

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Box & Whisker plot

Population size: 15

Median: 6

Minimum: -1

Maximum: 19

First quartile: 1

Third quartile: 8

Interquartile Range: 7

Outlier: 19

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Using 'Phantoms' to present our data

P - parameters

H - hypotheses

A - assumptions

N - name your test

T - find your test statistic

O - obtain your p-value

M - make a decision (reject or fail to reject)

S - state a conclusion in the context of the problem

Parameters

Our parameters are 0.761115 and 0.00398142

Hypotheses

Hypothesis: Null(Ho:) - P1=P2 : This means that the proportion Of Home wins is equal to Proportion of Home losses.

Alternative (Ha) P1>P2 : This means the Proportion of home wins is greater than the proportion of home losses

**Assumptions**

Our assumption is normality (nearly normal).We believe distributions are symmetric around the center and follow a â€˜bell-shapedâ€™ distribution

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Naming the Test

It was obvious to us that way more home teams won, so we decided to test it using the 2 proportion z Test. we was able to use this type of test because we could find the proportion of both wins and losses

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Test statistic

P1>P2 n1= 664

X1 = 377

Z = 4.939

P hat1 = 0.5678 n2= 664

X2= 287

P=< .00001

P hat2 =0.4322

É‘ = .05

Obtaining our P-Value

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Research Results

P1>P2 n1 = 664

X1 = 377 Z = 4.939

P hat1 = 0.5678 n2= 664

X2= 287 P=< .00001

P hat2 =0.4322 É‘ = .05

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Further information presented is data strictly based of the Number of Home Wins & Away Wins. (Not the difference between the two)

Pearson R

The value of R is 0.764. R is close to 1 meaning his is a strong positive correlation, which means that high X variable scores go with high Y variable scores.

We use Pearson R to measure the strength of the linear relationship between two variables

(Home & Away Game Wins)

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Least Square Regression

y = 0.7611x + 0.003981

There were a few errors

We used Desmos to show

the line that best fits.

Sum of X = 377

Sum of Y = 287

Mean X = 25.1333

Mean Y = 19.1333

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One-sample T-test

The spurs home win percent was 33%

The NBA average home Wins percentage was 59.9% in 2017-2018 season.

DF: 29 (30 teams in the NBA)

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Conclusion

It is concluded that we reject the null hypothesis and accept the alternative hypothesis.

NBA teams do perform better at their home courts then away