Prediction of Salaries Based on Years of Education 
Statistical Topic:
In previous analyses we have studied dealt with testing difference in means. Prediction and its statistical analysis, regression, deal with measures of relationships between two variables, x and y. The variable of interest is the dependent variable (y) and the variable used to predict the dependent is the independent variable (x).  For this data set, the dependent variable is salaries since this is the variable we wish to study.  Variables which might affect the salary could be gender, field of study in school or years of education.  For descriptive statistics, the graphical form of data description is the scatter plot with the dependent variable on the y-axis and the independent variable on the x-axis.  The numerical statistics we compute are the correlation between the two variables and the linear regression line.
Student Issue:
Many occupations require students to have a bachelor's degree.  Starting salaries of students offered by employers are related not only to the value of the skills and knowledge learned in college but also to the supply and demand for qualified individuals.  Gender, type of major as well as level of degree such as bachelors, master's and doctoral also determine starting student will make when they finish their degrees.
Data Set:
Input your best estimate of the starting salary you would accept after finishing your degree into Table 1. Estimated starting salaries for classmates including the field of study, gender and years of full-time education.  
Research Question:
Can starting salary be predicted using years of full-time education?
Statistical Techniques: 
  1. Draw, label and title a scatterplot that describes the research question.  Find the least squares linear regression line that fits this data set.  Draw the estimated linear regression line on the scatter plot and label this line with the equation.  Explain in writing what this equation means.
  2. Compute the errors and predicted values for five of the data values.
  3. Compute the correlation coefficient between these two variables.  Explain in writing what this correlation coefficient means (don't forget the sign!).
Social Commentary:
  1. Does the prestige of the university from which you received your degree affect your starting salary?
  2. Would gender or race affect your expected starting salary?  How would you find out if it did?