The data for each runner entered in the Auckland Marathon 2015 was obtained from https://www.aucklandmarathon.co.nz/. This data is owned by the organisers of the Auckland Marathon and can not be used for commercial purposes unless by prior written permission from the organisers.
For each runner, the following was recorded:
time in hours (this is blank if the runner did not compete in the race)
place (this is blank if the runner did not compete in the race)
distance in km (this is blank if the runner did not compete in the race)
mean pace km per hr (this is blank if the runner did not compete in the race)
NB: This data set contains information about the five different races which are part of the Auckland Marathon 2015. It may be necessary to focus on just one of these races for a meaningful investigation, for example if comparing running times for male and female runners (whether as part of a sample-to-population inference or as part of exploring the population data).
The data for each player in the Rugby World Cup 2015 was obtained from http://www.rugbyworldcup.com/. This data is owned by the Rugby World Cup Ltd (RWC) and can not be used for commercial purposes unless by prior written permission from the RWC.
NB: This data set should be used with care for sample-to-population inferenceinvolving comparison, as both categorical variables (team and position) involve a large number of outcomes (16 teams and 11 positions). This means it is not likely that a random sample of 80 players from the population of Rugby World Cup 2015 players, for example, will contain sufficient numbers of players in any two groups for comparison e.g. England vs New Zealand OR forwards vs backs. If you use all the data for NZ and all the data for England to compare the age of players, for example, you will have used all of the data for this population and so there is no need to “make a call” about what is going on “back in the population” 🙂
My advice would be to use this data set for either single variable sampling investigations OR exploratory data analysis for the entire population. There is also something interesting in using the time variable (debut) to explore other variables 🙂