Question

For major league baseball teams is there relationship between player payrolls and gate money? Here are data for each of the National League teams for the year 2000. The variable denotes the 2000 player payroll (in millions of dollars) and the variable denotes the mean attendance (in thousands of fans) for the 81 home games that year: The data are plotted In the Figure scatter plot: Also given are the products of the player payroll values and mean attendance values for each of the sixteen teams (These products written in the column labelled "ry" may aid in calculations.) Player payroll; Mean (in $1,000,000 attendance, (in thousands) Arizona 87.0 34.81 3028.47 Atlanta 94.5 39.88 3768.66 Chicago Cubs 65.3 34.44 2248.932 Cincinnati 52.6 31.85 1675.31 Colorado 64.8 40.74 2639.952 Florida 30.9 15.06 465.354 Houston 58.3 37.78 2202.574 Los Angeles 105.0 37.16 3901.8 Milwaukee 41.5 19.38 804.27 Montreal 39.5 11.48 453.46 New York Mets 99.8 34.81 3474.038 Philadelphia 53.9 19.88 1071.532 Pittsburgh 36.3 21.60 784.08 San Diego 64.1 29.88 1915.308 San Francisco 59.6 40.99 2443.004 St Louis 80.7 41.23 3327.261

          For major league baseball teams is there relationship between player payrolls and gate money? Here are data for each of the National League teams for the year 2000. The variable denotes the 2000 player payroll (in millions of dollars) and the variable denotes the mean attendance (in thousands of fans) for the 81 home games that year: The data are plotted In the Figure scatter plot: Also given are the products of the player payroll values and mean attendance values for each of the sixteen teams (These products written in the column labelled "ry" may aid in calculations.)
Player payroll; Mean (in $1,000,000 attendance, (in thousands)
Arizona 87.0 34.81 3028.47
Atlanta 94.5 39.88 3768.66
Chicago Cubs 65.3 34.44 2248.932
Cincinnati 52.6 31.85 1675.31
Colorado 64.8 40.74 2639.952
Florida 30.9 15.06 465.354
Houston 58.3 37.78 2202.574
Los Angeles 105.0 37.16 3901.8
Milwaukee 41.5 19.38 804.27
Montreal 39.5 11.48 453.46
New York Mets 99.8 34.81 3474.038
Philadelphia 53.9 19.88 1071.532
Pittsburgh 36.3 21.60 784.08
San Diego 64.1 29.88 1915.308
San Francisco 59.6 40.99 2443.004
St Louis 80.7 41.23 3327.261
        
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Elementary Statistics a Step by Step Approach
Elementary Statistics a Step by Step Approach
Allan G. Bluman 9th Edition
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For major league baseball teams is there relationship between player payrolls and gate money? Here are data for each of the National League teams for the year 2000. The variable denotes the 2000 player payroll (in millions of dollars) and the variable denotes the mean attendance (in thousands of fans) for the 81 home games that year: The data are plotted In the Figure scatter plot: Also given are the products of the player payroll values and mean attendance values for each of the sixteen teams (These products written in the column labelled "ry" may aid in calculations.) Player payroll; Mean (in $1,000,000 attendance, (in thousands) Arizona 87.0 34.81 3028.47 Atlanta 94.5 39.88 3768.66 Chicago Cubs 65.3 34.44 2248.932 Cincinnati 52.6 31.85 1675.31 Colorado 64.8 40.74 2639.952 Florida 30.9 15.06 465.354 Houston 58.3 37.78 2202.574 Los Angeles 105.0 37.16 3901.8 Milwaukee 41.5 19.38 804.27 Montreal 39.5 11.48 453.46 New York Mets 99.8 34.81 3474.038 Philadelphia 53.9 19.88 1071.532 Pittsburgh 36.3 21.60 784.08 San Diego 64.1 29.88 1915.308 San Francisco 59.6 40.99 2443.004 St Louis 80.7 41.23 3327.261
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Refer to the Baseball 2018 data, which report information on the 30 Major League Baseball teams for the 2018 season. Consider the following variables: number of wins, payroll, season attendance, whether the team is in the American or National League, and the number of home runs hit. Prepare a report on the team salaries. Be sure to answer the following questions in your report. Around what values do the data tend to cluster? Specifically, what is the mean team salary? What is the median team salary? Is one measure more representative of the typical team salary than the others? What is the range of the team salaries? What is the standard deviation? About 95% of the salaries are between what two values? Team Salary ($ mil) 143.32 130.6 127.63 227.4 194.26 71.84 100.31 142.8 143.97 130.96 163.52 129.94 173.78 199.58 91.82 108.98 115.51 150.19 179.6 80.32 104.3 91.03 101.34 205.67 160.99 163.78 68.81 140.63 150.95 181.38

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Transcript

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0:00 All right.
00:01 So for this question, we have a data set from the national baseball league.
00:06 And we want to know if there is a relationship between player payrolls and gate money.
00:13 And so we have two columns of data here.
00:18 When i copied and pasted from the question into an excel file, some of the numbers got messed up.
00:26 So hopefully these numbers are.
00:30 Correct.
00:32 If not, we're going to just go through the basics of how to calculate our r squared value.
00:38 So we should be able to reproduce this if any of these numbers are off.
00:44 All you would have to do is just change them.
00:46 And so we have in our x column right here, the player payroll, so that's in thousands of dollars.
00:54 And then we have the mean attendance of the stadium also in thousands.
01:01 And so what we want to do basically is plot these data and then we want to calculate the r squared value, which is our correlation coefficient.
01:12 And we want to determine if there is a relationship, what direction that relationship is, if it's positive or negative, and the strength of the relationship.
01:23 So is it weak, moderate, or strong? and so i just put in the x and y, columns here and then this bottom row here is just the summation of all of these columns and then i also added three additional columns we have this which is x times y we have x squared which is just this value squared and then y squared was just this value squared and so all you have to do is highlight your x and y go to insert click scatter plot and then we have a nice little scatter plot here and then we can actually add a trend line to the data.
02:03 So it looks like there is some type of relationship here, a positive relationship because we have a positive slope.
02:13 But we want to see what the correlation coefficient is for this line to see how well that line fits the data.
02:22 And so to do that, we are going to use this formula here to find r.
02:26 And because we're working in excel, we're going to make excel do all the hard work for us.
02:32 And basically, we're just going to play in our formulas and we're going to get our r value.
02:37 So first thing we want to do is the numerator.
02:40 I like to split up my excel equations into different cells because sometimes, you know, working with a lot of parentheses and functions can kind of get messy.
02:51 We don't want to miss any parentheses.
02:54 And so the top number right here.
02:56 This is our numerator.
02:58 So n is our number of observations.
03:02 So in this case, we have 16 observations.
03:06 So it'll be 16 times the summation of our x times y column, which is c.
03:13 And then we're going to subtract that from the summation of the x column times the summation of the y column, which is a and b...
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