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.
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.
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.
Box & Whisker plot
Population size: 15
First quartile: 1
Third quartile: 8
Interquartile Range: 7
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
Our parameters are 0.761115 and 0.00398142
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
Our assumption is normality (nearly normal).We believe distributions are symmetric around the center and follow a ‘bell-shaped’ distribution
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
P1>P2 n1= 664
X1 = 377
Z = 4.939
P hat1 = 0.5678 n2= 664
P hat2 =0.4322
ɑ = .05
Obtaining our P-Value
Caption goes here.
P1>P2 n1 = 664
X1 = 377 Z = 4.939
P hat1 = 0.5678 n2= 664
X2= 287 P=< .00001
P hat2 =0.4322 ɑ = .05
Further information presented is data strictly based of the Number of Home Wins & Away Wins. (Not the difference between the two)
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)
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
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)
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