Regression Analysis on Marathon Finishing Times - Research Paper Example

These five variables were taken to be independent variables but the marathon finishing time was taken to be dependent. The main procedure utilized in this study was regression analysis. It was utilized to explain the total variation of the dependent variable, the marathon finishing time. The dependent variable was accompanied by 5 variables, which were tested against the dependent variable to determine how much of the total variation is explained. The analyses also discussed the comparison of the different regression models, and determine which model is the most effective.

In regards to the regression analysis results, it is clearly evident that model 4 and 5 are the strongest model and model 1 being the weakest. Model 2 and 3 does not apply because of the reasons given .Therefore, the number of half marathon run, age and number of days of training a week have an influence on the half marathon finishing time that is related to the athlete performance. The numbers of half marathon run and number of days of training a week have a negative moderate influence on the half marathon finishing time, while the age has a weak positive influence on the half marathon finishing time. . ed criteria, which normally entails perceived potential in relation to perceived athletic career cost and the level of achievements ( Lundqvist and Hassmen2009).

Successful athletic careers are related to achieving individual peak in performance that corresponds to individual environment and resources (Lavallee and Wylleman, 2000).There are factors which are believed to affect the performance of the athlete and this study was to ascertain this basing on few chosen factors among several others ( Lundqvist and Hassmen,2009). The first factor chosen is age which is known to affect the performance of the athlete, the second one is number of sleeping hours, then number of time eating out a week, the number of days of training a week and the number of marathons ran.

All this factors have been supported by research to have an influence to the athlete performance (Lavallee and Wylleman, 2000) and it was my task to prove this on the 37 athletes who are specialized in running half marathon. Regression Analysis The survey conducted was from runners at a local 5k here on Guam. A questionnaire was used to collect the data and the sample was randomly selected. This was important because it represented the population of athlete. The challenge faced during data collection was that some individuals were never willing to respond.

The first regression analysis step was to input all the collected data from the surveys into a spreadsheet. This process allows efficient running of regression models. After all the data was entered, there was formulation of the initial regression model. The first model consists of my Y variable that was the half marathon finishing time and one independent variable that was age. Different models were run to ascertain which model was the strongest as well as to