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

## Presentation on theme: "Running Statistics Rachel Passman, Ciara Gilligan, and Ryan Biemuller."— Presentation transcript:

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) 17 th 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

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

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

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

Ho: The observed frequency distribution of type of running shoe fits the expected distribution. Ha: The observed frequency distribution of type of running shoe doesnt fit the expected distribution. x 2 = (obs-exp) 2 / exp = 24.00 Assumptions: 1.SRS 1. assumed 2.All expected counts are greater than or equal to 52. check P(x^2>24I df=6)= 0.00052

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 doesnt fit the expected distribution.

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: 1.2 independent SRS 1. assumed 2.All expected counts are greater than or equal to 52. no, but cont. x 2 = (obs-exp) 2 / exp = 3. 741 P(x^2>3.741I df=6)= 0.71

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

Ho: p=0.50 Ha: p<0.50 Assumptions: 1.SRS1. assumed 2.Np2. (53x 0.50) n(1-p) >10 (53x0.50)> 10 (no, but cont.) 3.Pop > 10n3. pop> 10x53 Z= p-p/ =-2.335 ^ P(z<-2.335)= 0.02

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.

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)

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 Didnt include people running at home or at gym – Didnt ask questions

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.