Positive/Negative Correlation Between A Student’s Alcohol Consumption - Sociology Assignment Help

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Assignment Task

 


Introduction 
The dataset that we will be analyzing contains information about students residing in Portugal that are enrolled in secondary school. The information was obtained by conducting a survey of students that were enrolled in math or Portuguese language courses. The dataset contains demographic information about the students that could be used to analyze the outcome of their grades. Gender, social activities, and study information are listed for each student. This specific dataset was last updated four years ago. 

Problem of Interest 
? We will be looking to predict the final grade of a student and determine which factors, such as alcohol consumption, study time, free time, weekend alcohol consumption, etc. Have a direct influence on a student’s grade. 


Variables of Interest 

  •  Free time 
  •  DAlc - workday alcohol consumption 
  •  WAlc - weekend alcohol consumption 
  •  Study time 
  •  Absences 
  •  # of past failures 


Analytical Method(s) 

  •  We will use regression analysis in order to see if there is a positive/negative correlation between a student’s alcohol consumption, study time, free time, and course grade. 
  •  We will perform an explanatory analysis to determine if there are other relevant features (e.g. number of absences, parent's job and education, alcohol consumption) that influence student performance, besides past evaluations. 
  •  Using data mining techniques, we will determine the relationship between alcohol consumption and their final grade. After eliminating outliers, we will reach a conclusion that will help us determine their relationship. 
  •  We will come up with scatterplots and statistical models to determine the strength of relationships.
  •  G3 (final grade) & DAlc 
  •  G3 (final grade) & walk 
  •  conduct an explanatory data analysis, and get used to working with your data set. Your steps may include data pre-processing methods such as data transformation, handling outliers and missing values
     


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