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Created: Jul 6, 2017
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JMcpherson Project 2
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Introduction: The data collected on the NFl players were taken from Stat Crunch data inventory. This data collected, illustrates the difference in players who plays on offense, Defense and special teams along with their weight ranges. This data was collected with the intent to illumnate the vague controversial difference in weight categories on both sides of the field.  The Data shown includes three different engamenets methods, offense a defense and special teams along with the average weight range within both. The data given contained more than 30 Values. 

Analysis:I believe The best method of center is the mean. In the data, it  begins with the lowest quantitative value being 150. If the median (middle value) is used it will skew it due to the wide range of numbers. The lowest value is 150 and the highest is 364. the mean for Defensive player's weight is 241.3. The mean for Offensive players rate were 250.8 and for special team the means was 213.8. These values contain no outliers hence why i chose mean as best measure of center.To Conclude: 

 To Conclude: The Given data consist of Special Teams, defense and offensive players. It was meant to illustatrate what weight category is most required on both sides of the playing field. As the data shows the Frequent defensive players that weight between 200-250 are normally played on the field, which is interpreted as the most athletic due to the non stop running. On offense the weight class that frequent the field is between 402, and is interpreted as being the most muscle to defend the guy with the ball. There were no given outliers. the sample size of the data were 2,764 players. The boxplot indicated that on average offensive players weigh more than both defensive and special teams.  The standard deviation for  defense was 43.510854, for offense was 48.682432 and for special teams21.937217. 

Result 1: Frequency Table   [Info]
Frequency table results for Bin(var2):
Group: var1=DEF
Count = 1305
Bin(var2)FrequencyRelative Frequency
150 to 2002840.21762452
200 to 2505100.3908046
250 to 3003200.24521073
300 to 3501840.14099617
350 to 40070.0053639847

Frequency table results for Bin(var2):
Group: var1=OFF
Count = 1344
Bin(var2)FrequencyRelative Frequency
150 to 2001980.14732143
200 to 2505530.41145833
250 to 3001900.14136905
300 to 3504020.29910714
350 to 40010.00074404762

Frequency table results for Bin(var2):
Group: var1=ST
Count = 115
Bin(var2)FrequencyRelative Frequency
150 to 200280.24347826
200 to 250780.67826087
250 to 30090.07826087

Result 2: Histogram   [Info]
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Result 3: Summary Stats 1   [Info]
Summary statistics for var3:
Group by: var1
var1MeanMedianModeRangeStd. dev.
DEF241.3233723919021443.510854
OFF250.8556524031520048.682432
ST213.88696211No unique11021.937217

Result 4: 5 # Summary   [Info]
Summary statistics for var3:
Group by: var1
var1MinQ1MedianQ3MaxIQR
DEF15020123927336472
OFF15521024030535595
ST16020021122927029

Result 5: Boxplot   [Info]
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Data set 1. NFL Players 2016   [Info]
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HTML link:
<A href="https://www.statcrunch.com/5.0/viewreport.php?reportid=69256">JMcpherson Project 2</A>

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