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Running Statistics

Running Statistics. Rachel Passman, Ciara Gilligan, and Ryan Biemuller. Man started running out of necessity and was used for communication Pheidippides was a messenger who brought news of battle First sign of running as recreation 3200m race at the Olympics in Egypt (3000 B.C)

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Running Statistics

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  1. Running Statistics Rachel Passman, Ciara Gilligan, and Ryan Biemuller

  2. Man started running out of necessity and was used for communication • Pheidippides was a messenger who brought news of battle • First sign of running as recreation • 3200m race at the Olympics in Egypt (3000 B.C) • 17th century, running was used for gambling purposes • Training for running began with Finn Paavo Nurmi and coach Pikala • Saw relationship between work and rest and understood importance of interval training • Training became known as the terrace training

  3. 18th Century • Light weight leather shoe that can grip the ground • 19th Century • Croquet shoe with a rubber sole with a canvas upper with laces • Spiked leather shoes also invented • 20th Century • Created leather strip around shoe to reduce stretching (known today as Keds) • Converse sneakers • 21st Century • modern synthetic shoes are made of lightweight mesh fabric uppers and lightweight synthetic soles • chosen for maximum flexibility and comfort

  4. The proportion of runners who wear legitimate running apparel • What running sneaker is most popular • What running sneaker is most popular within gender

  5. We went to go certain locations and parks such as Meyer Way Park, Turk Park, IPW, and Kemper Park • Supposed to go to stores • Tried to go to parks at two different times • Early Saturday morning • After school Monday • We observed data of runners coming through the parks • Type of sneaker • Type of sneaker vs. gender • Running apparel

  6. Ho: The observed frequency distribution of type of running shoe fits the expected distribution. Ha: The observed frequency distribution of type of running shoe doesn’t fit the expected distribution. Assumptions: SRS 1. assumed All expected counts are greater than or equal to 5 2. check x2 = ∑ (obs-exp)2/ exp = 24.00 P(x^2>24I df=6)= 0.00052

  7. We reject Ho because our p-value is less than alpha which equals 0.05. We have sufficient evidence that the observed frequency distribution of the type of running shoe doesn’t fit the expected distribution.

  8. Ho: There is no association between the type of running shoe and gender variables. Ha: There is an association between the type of running shoe and gender variables. Assumptions: 2 independent SRS 1. assumed All expected counts are greater than or equal to 5 2. no, but cont. x2 = ∑(obs-exp)2/ exp = 3. 741 P(x^2>3.741I df=6)= 0.71

  9. We fail to reject Ho because our p-value is greater than alpha which equals 0.05. We have sufficient evidence that there is no association between the type of running shoe and gender variables

  10. Ho: p=0.50 Ha: p<0.50 Assumptions: • SRS 1. assumed • Np 2. (53x 0.50)n(1-p) >10 (53x0.50) > 10 (no, but cont.) • Pop > 10n 3. pop> 10x53 Z= p-p/ ^ =-2.335 P(z<-2.335)= 0.02

  11. We reject Ho because our p-value is less than alpha which equals 0.05. We have sufficient evidence that the proportion of people who wear legitimate apparel while running is < 0.50.

  12. The most popular brand of shoe is not affected by gender • Most popular and least popular stay constant within both genders • More than 50% of runners do not wear legitimate apparel (wear shorts and tees)

  13. Only analyzed runners in immediate area • Only observed runners in Bucks County • Only went to parks • Runners might not have been as legitimate as runners shopping in the stores • Didn’t include people running at home or at gym • Didn’t ask questions

  14. If we were allowed to be in stores, we believed our legitimate apparel would have changed. • The shoe brands, such as Soucony and Brooks would be more popular • We believed prior to the test that Nike would be most popular and gender would have no affect on type of shoe.

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