The analysis of how junior athletes choose their summer training camps

THE ANALYSIS OF HOW JUNIOR ATHLETES CHOOSE THEIR SUMMER TRAINING CAMPS Introduction The focus of this study is to gain an insight into the choice of summer training camps by junior athletes. A recent survey was conducted which consisted of 16 separate variables that could have influenced the selection process. The questionnaire was completed by 936 first year students that were selected randomly. They were asked to score how much each variable influenced their decision in a scale from 0 to 100. The students attended four camps in different regions of the country, namely: 227 students in Dudmore, 301 in Oldgate, 159 students in Keithwood, 251 in Stockleigh. The aim is to find whether there are any significant differences among these regions as a result of recently conducted survey. Therefore using the SAS package the data was analysed. The main objective of this study is to find are there any significant differences among these regions and what conclusions can be found. Statistical analysis First of all I found outliers by performing summary statistics for the given data. I found that in Keithwood there was a variable called N, however all the variables in this column should be either F (Female) or M (Male). This outlier was omitted. I removed from Training Excellence column values 145 and 112 as these are not from the scale required (0-100). I also removed couple of values that were very different from the majority of the data. Further summary statistics was performed to see what differences are between two genders in each region. The results were as follows: Dudmore had 114 females and 113 males; Oldgate had 62 females and 239 males; Keithwood had 120 females and 36 males; Stockleigh had 62 females and 189 males. Therefore in the graphs above we see that huge differences in number of males and females amongst all the regions. Just Oldgate and Stockleigh have similar number of both and. Also it is noticeable that in Dudmore equality in sex is the best, as there is nearly the same number of the both females and males. Moreover, after omitting the outliers I had a look on differences in “Training Excellence”, “Coaching” and “Advertised”. By looking at the summary statistics, I found that there were no differences amongst these factors with respect to sex in all the regions (mean, standard deviation, minimum, maximum values, also lower and upper quartile along with median were almost the same). Whisker plots and histograms showed the same conclusion: even if there are significant differences between number of males and females in the regions, this does not impact differences in reasons of selection over the mentioned factors. By going further into analysis, I noticed that rest of the data is scored, therefore I am using non-parametric tests, regardless if it is normally distributed or not (although after running distribution analysis I found that all variables are normally distributed, this can be useful in upcoming analysis aspects). So now I apply non-parametric test for “Training excellence”, in particular I use Kruskal-Wallis test as I compare more than 2 groups of variables. I do not test equality of variances as we know that data is scored. There are a few assumptions that have to be made: • The 3 samples have been independently and randomly selected from their respective populations; • For Chi-Square approximation condition is satisfied, there are more than five observations in each sample; • Tied observations (when all responses are ranked in ascending order) are assigned the average value of the ranks. Therefore I test the following hypothesis: H0: the distributions are identical; HA: not all of the distributions are the same. It is found that p F Squares Square Model 2 247498 123749 2631.20

Daugiau informacijos...

★ Klientai rekomenduoja

Šį rašto darbą rekomenduoja mūsų klientai. Ką tai reiškia?

Mūsų svetainėje pateikiama dešimtys tūkstančių skirtingų rašto darbų, kuriuos įkėlė daugybė moksleivių ir studentų su skirtingais gabumais. Būtent šis rašto darbas yra patikrintas specialistų ir rekomenduojamas kitų klientų, kurie po atsisiuntimo įvertino šį mokslo darbą teigiamai. Todėl galite būti tikri, kad šis pasirinkimas geriausias!