新西兰物理学论文代写：足球变量分析
新西兰物理学论文代写：足球变量分析
为了包括其他内在变量，进行了进一步的回归分析。为了捕捉不同类别的效果，创建了虚拟变量来比较这些类别与目标保持是否存在显著的关系，以及它们是否与其他类别不同。使用虚拟变量的一般标准规则是，在最高频率的变量中的类别没有分配一个虚拟变量，但所有其他类别都被分配了一个虚拟变量（数据和统计服务，普林斯顿大学）。虚拟变量用来比较它们在因变量中产生的任何差异，与没有虚拟变量的变量进行比较。例如，分析包括从前锋位置最多的球员数量。因此，该模型不使用任何虚拟变量的前锋位置，但使用虚拟变量的所有其他位置。所以，对于门将位置的模型系数可以解释为影响门将位置上进球的前锋位置得分的差异。如果系数是正的，在统计上是显著的，那么位置对前锋位置的进球数有显著的积极影响。所有其他职位都可以作出类似的解释。
遵循相同的逻辑，虚拟变量已被用于确定其他变量（如国家和团队）的影响。 分析中使用的数据来自网站Ultimate A-League。得分最多的年龄组的计算可以通过直方图完成。分析基于5岁年龄组从15岁开始分配数据。因此，年龄组包括15至19岁，20至24岁等。对整个数据以及一些具有大量数据点的组进行了分析。
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.
