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AP Statistics Final Project. Philadelphia Phillies Attendance. Kevin Carter, Devon Dundore, Ryan Smith. About the Phils. Oldest one-named, one-city franchise in all professional American sports First game played on May 1, 1883 2 World Series Victories (1980, 2008). About the Bank. - PowerPoint PPT Presentation
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AP Statistics Final Project
Philadelphia Phillies Attendance
Kevin Carter, Devon Dundore, Ryan Smith
• Oldest one-named, one-city franchise in all professional American sports
• First game played on May 1, 1883
• 2 World Series Victories (1980, 2008)
About the Phils
• Built in 2004• 43,651 seats• Sold out 73 times in
2009• Biggest attendance
46,208• 2008- Celebrated first
World Series since 1980
About the Bank
Studied Phillies attendance from 2004-2009 depending on…
- Weather (temperature) - Time of day
• Calculator randomly select 10 games from each season• Look up time of first pitch and park attendance of past games
using www.baseball-reference.com and www.fairview.ws
Studying the Statistics
Create scatter plots of comparisons to view LSR and correlation
Conduct a 2 sample t confidence interval for each comparison of statistics
Also, conduct a 1 sample t confidence interval of the average attendance at Citizens Bank Park
Tests and Data Analysis cont.
Exploratory Data Analysis
40 50 60 70 80 90 100 11015000
20000
25000
30000
35000
40000
45000
50000
Temperature Vs. Attendance
Temperature (°F)
Atten
danc
e
Residual Plot
-20
0
20
20 25 30 35 40 45 50
Attendance (thousands)
Attendance (thousands)
Temperature = 7.11e-05Attendance + 74
Correlation= .04622Coefficient of Determination= .0021LSR: Attendance=30.0423(Temperature)+35012- Weak (scattered)- Very slightly positiveResidual plot is scatter so LSR is a decent fit
Analysis
• .21% of the change in attendance is due to the change in temperature
• Temperature seems to have practically no relationship or effect on Phillies game attendance
Data Conclusion
Exploratory Data Analysis
1 1 10:00
0:00
Start Time Vs. Attendance
Series1
Game Start Time
Atten
danc
e
Residual Plot
-20
-10
0
10
12 14 16 18 20
Start_Time
Start_Time
Correlation= -.118Coefficient of determination= .014LSR: Attendance= -419.731(Start)+44841- Weak (slightly scattered)- Slight negative slopeResidual Plot is scatter so LSR is a good fit
Analysis
• 1.2% of the change in attendance is due to the change in start time of the game
• Start time seems to have practically no relationship or effect on Phillies game attendance
Data Conclusion
Use linear regression t tests for both comparisons to test the hypothesis that…
Beta= 0 or Beta>0 (temperature)
Beta=0 or Beta>0 (time of day)
Tests and Data Analysis
STATE
- SRS- True relationship is
linear
CHECK
-Checks out-Assume (scatter plots)
*Sample size of 60 games
Test 1 (temperature)
t= b/SEb
t= .3524 (df=58)P(t> .3524|df=58)= .36.36>.05 so…We fail to reject the null hypothesis because the
p-value is greater than .05. We have sufficient evidence that the slope of
the LSR line is not greater than zero. The weather does not have a great effect on
Phillies game attendance.
Mean+/- t-score(Stand. Dev. of Stat.)= (35201.9, 39256.2)We are 95% sure that population difference of
means lies between 35201.9 and 39256.2 people attending the game.
STATE- SRS- True relationship is linear
Test 2 (time of day)CHECK-Checks out-Assume (scatter plots)
*Sample size of 60 games
t= b/SEb
t= -.9085 (df=58)P(t>-.9085|df=58)= .82.82>.05 so…We fail to reject the null hypothesis because the
p-value is greater than .05.We have sufficient evidence that the slope of
the LSR line is not greater than zero.The start time of the game does not have a great
effect on the Phillies attendance.
Mean+/- t-score(Stand. Dev. Of Stat.)= (35260, 39314.6)We are 95% sure that the population difference
of means lies between 35260 and 39314.6 people attending the game.
• Attendance can be affected by other things (team being played, pitcher, star ball players, promotions, ticket pricing)
• Phillies were better and more popular during some year than others
• Data included many more night game times than afternoon games
Bias/Error
Personal Opinions
• We would have thought that our data would have a had a better correlation.
• We feel that our own decisions to go to a game is somewhat effected by time and temperature. (Rainy day = colder weather)
• We feel that there was to much bias to our data.
Conclusion
• In conclusion, we can say that time of day and temperature has no relation to the attendance of a Philadelphia Phillies baseball game. Either nothing or something else is effecting the attendance of these games.
Q&A