# 新西兰物理学论文代写：足球变量分析

To include the other intrinsic variables, further regressions were carried out. To capture the effect of different categories, dummy variables were created to compare if there exists any significant relation with goal keeping in these categories and that if they were different from the other categories. The general standard rule of using a dummy variable is that the category in a variable having the highest frequency is not assigned a dummy variable but all other categories are assigned a dummy variable (Data and Statistical Services, Princeton University). The dummy variables are used to compare any difference they create in the dependent variable as compared to the variable which has no dummy. For example, the analysis includes the maximum number of players from the striker position. So, the model does not use any dummy variable for the position of a striker but uses a dummy variable for all the other positions. So, the model coefficient for the position of goalkeeper can be interpreted as the difference of impact the goalkeeping position has on goal scoring against the position of striker. If the coefficient is positive and statistically significant, then the position has a significantly positive effect on the number of goals against the position of striker. A similar interpretation can be drawn for all other positions.

Following the same logic, dummy variables have been used to determine the impact of other variables like country and team. The data used in the analysis has been sourced from the website Ultimate A-League。

The calculation of age groups having the most number of goals scored can be done through histograms. The analysis distributes the data based on a 5 year age group starting from 15 years. Thus the age groups would include 15 to 19 years, 20 to 24 years and so on. The analysis has been carried out for the overall data as well as for some of the groups which have significant number of data points.