Let's look at attendance between the two leagues and draw some conclusions. For purposes parts D and E, we are assuming that separating the data into 2 groups based on League is considered sampling; thus, we have 2 samples (American League and National League) and assume we do NOT know the population standard deviations for both "populations" (we only have 1 overall population, not 2). What was the average attendance in each league? Round your answer to 3 decimal places. American League average attendance mil National League average attendance mil space space Perform a hypothesis test to determine whether there is a difference in the mean attendance of teams in the American League vs teams in the National League. Use a 0.05 level of significance in your testing procedures. Additionally:

Let's look at attendance between the two leagues and draw some conclusions. For purposes parts D and E, we are assuming that separating the data into 2 groups based on League is considered sampling; thus, we have 2 samples (American League and National League) and assume we do NOT know the population standard deviations for both "populations" (we only have 1 overall population, not 2).

What was the average attendance in each league? Round your answer to 3 decimal places.

American League average attendance	mil
National League average attendance	mil

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Perform a hypothesis test to determine whether there is a difference in the mean attendance of teams in the American League vs teams in the National League. Use a 0.05 level of significance in your testing procedures. Additionally:

You will need to know the standard deviations and variances for attendance in each league to conduct this test.
We are assuming that both American and National League attendance figures follow a normal distribution.
American and National league data are samples; thus, the population standard deviations are unknown.

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Hint: When calculating your pooled sample variance and your test statistic, assume the American League data is sample 1 and the National League data is sample 2.

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Decision Rule	-t < or t > = REJECT H0
Value of Pooled Sample Variances
Value of test statistic
Decision: enter Reject or Do Not Reject

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Is there a difference in the mean attendance of teams in the American League vs. teams in the National League assuming a 0.05 level of significance? Enter Yes or No.

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Another idea is to look at how a team's payroll impacts its home attendance. Higher payroll usually signals more higher-skilled players on a roster which should brings fans to the ballpark to see these players.

Create 2 samples based on Team Salary and calculate average attendance, variance of attendance, and standard deviation of attendance for each sample.

Sample 1 = 10 teams with the highest team salary
Sample 2 = 10 teams with the lowest team salary

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Tip: Use Excel's sorting tool to sort the data by salary (highest to lowest), and copy the 10 highest and 10 lowest into a separate part of the spreadsheet for easier analysis.

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	Sample 1	Sample 2
	Top 10 Team Salary	Bottom 10 Team Salary
Sample mean attendance	mil	mil
Sample attendance variance	mil	mil
Sample attendance standard deviation	mil	mil

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Team Salary ($ mil)
ERA Year Stadium Opened Attendance (mil) Net Worth ($ bil)
League
National
Team
HR
BА
Wins
Arizona Diamondbacks
143.32
176
0.235
82
3.72
1998
2.242695
1.21
Atlanta Braves
National
130.6
175
0.257
90
3.75
2017
2.555781
1.625
Baltimore Orioles
American
127.63
188
0.239
47
5.18
1992
1.564192
1.2
Boston Red Sox
Chicago Cubs
Chicago White Sox
Cincinnati Reds
|American
227.4
208
0.268
108
3.75
1912
2.895575
2.8
National
194.26
167
0.258
95
3.65
1914
3.181089
2.9
American
71.84
182
0.241
62
4.84
1991
1.608817
1.5
National
100.31
172
0.254
67
4.63
2003
1.629356
1.01
Cleveland Indians
American
142.8
216
0.259
91
3.77
1994
1.926701
1.045
Colorado Rockies
National
143.97
210
0.256
91
4.33
1995
3.01588
1.1
Detroit Tigers
Houston Astros
American
130.96
135
0.241
64
4.58
2000
1.85697
1.225
American
163.52
205
0.255
103
3.11
2000
2.980549
1.65
Kansas City Royals
Los Angeles Angels
Los Angeles Dodgers
Miami Marlins
Milwaukee Brewers
American
129.94
155
0.245
58
4.94
1973
1.665107
1.015
American
173.78
214
0.242
80
4.15
1966
3.020216
1.8
National
199,58
235
0.25
92
3.38
1962
3.8575
National
91.82
128
0.237
63
4.76
2012
0.811104
National
108.98
218
0.252
96
3.73
2001
2.850875
1.03
Minnesota Twins
American
115.51
166
0.25
78
4.5
2010
1.959197
1.15
New York Mets
National
150.19
170
0.234
77
4.07
2009
2.224995
2.1
New York Yankees
American
179.6
267
0.249
100
3.78
2009
3.482855
4
Oakland Athletics
Philadelphia Phillies
Pittsburgh Pirates
San Diego Padres
San Francisco Giants
Seattle Mariners
St. Louis Cardinals
American
80.32
227
0.252
97
3.81
1966
1.573616
1.02
National
104.3
186
0.234
80
4.14
2004
2.158124
1.7
National
91.03
157
0.254
82
4
2001
1.465316
1.26
National
101.34
162
0.235
66
4.4
2004
2.168536
1.27
American
205.67
176
0.254
89
4.13
2000
2,299489
2.85
National
160.99
133
0.239
73
3.95
1999
3.156185
1.45
National
163.78
205
0.249
88
3.85
2006
3.403587
1.9
|Tampa Bay Rays
American
68.81
150
0.258
90
3.74
1990
1.154973
0.9
Texas Rangers
American
140.63
194
0.24
67
4.92
1994
2.107107
1.6
Toronto Blue Jays
American
150.95
217
0.244
73
4.85
1989
2.325281
1.35
Washington Nationals
National
181.38
191
0.254
82
4.04
2008
2.529604
1.675

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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