Triathlon Case Study The Canada Games represents the highest level of national competition for up and coming Canadian athletes. Alternating between winter and summer, the competition is held once every two years. Niagara will be the proud host of the 2022 Canada Summer Games. In this case study, your group will explore the athletes' information and achievements as given in the "Triathlon" data sheet to make summaries and inferences. Triathlon Events Triathlon debuted at the 2009 Summer Canada Games in PEI and features both Men's and Women's competition. Individual Sprint Triathlon An individual athlete completes a sprint triathlon: 750 m swim -> Transition 1 -> 20 km bike -> Transition 2 -> 5 km run Team Relay A three-person team (all men or all women) in which each athlete completes a super sprint triathlon: 250 m swim -> Transition 1 -> 6.6 km bike -> Transition 2 -> 1.6 km run The three individual super sprint triathlon times are added together for the total team relay time. Data To make computations more manageable, all times should to be converted from their current format of hh:mm:ss to seconds. For example, to convert a time of 0:58:14 in cell F2, click on an empty cell and type = hour(F2)*3600+minute (F2)*60+second(F2) In the home tab, change the Number Format of that cell from "Custom" to "General". The time of 0:58:14 is now converted to 3494 seconds. When providing times in your report, express times in the hh:mm:ss or mm:ss format. Unless stated otherwise in the question, omit athletes who did not finish (DNF) from analysis. Question 5 Does the population mean time to complete a triathlon depend on the event? Consider the four triathlon events: Individual Men, Individual Women, Relay Men, and Relay Women. Use the results from the 2009 Individual Sprint and the total team times from the 2013 and 2017 Team Relay as sample data. a) For each of the four events, construct a separate histogram of total times. b) Perform at the 5% significance level the one-way ANOVA test to compare the population mean times for each of the four events. Should we reject or not reject the claim that there is no difference in population means between each event? Closely examine the histograms in part a). Consider the requirements for an ANOVA test and determine if the result obtained in part b) is in fact valid. Justify your choice