Introduction:
We want to predict the financial aid to college students using independent variable such as parents’ contribution, high school GPA and gender of the student. We need to see which independent variable can predict the dependent variable which is financial aid to college students. Is parents’ contribution a factor in the amount of financial aid students receive? Is high school GPA a factor of the college financial aid received? How about gender, is it a factor? Maybe it is these factors combined. So, our motive is to find the best model based on these variables.

Data and Measurement:
There are 50 observation in this dataset and none of the data is missing. The dependent variable is financial aid to college students (FINAID) and it is continuous. There are 3 independent variables in this data.
1. parents’ contribution (PARENT) which is continuous variable
2. high school GPA (HSRANK) which is continuous variable
3. Gender (MALE) which is a binary variable
Gender variable is a dummy variable which is coded as 0 and 1. The value “1” represent the male student.

Methodology and Analysis Software:
In this project, we want to predict the financial aid to college students using independent variable such as parents’ contribution, high school GPA and gender of the student so the best method to analyze this is using simple linear regression and multiple linear regression. In simple linear regression, we will try to see which independent variable is playing an important role in predicting the dependent variable and in multiple regression model we will try to see the impact of including all variables in the model to predict the dependent variable. We have used excel for this analysis. The excel add ins “Data Analysis” tool pack is used for the regression analysis.

Question 1: Discrete Random Variables 1.1 (Binomial distribution)...