Linear Regression

Linear Regression Analysis

The purpose of this research paper is to use a linear regression analysis test to determine if a significant linear relationship exists between an independent variable X, level/years of education, and a dependent variable Y, salaries earned. “It is used to determine the extent to which there is a linear relationship between a dependent variable and one or more independent variables,” (Statistically Significant Consulting, 2010, para. 1). Learning Team B will be using the salary and education levels from the Wages and Wage Earners Data Set collected through access to the e-source link of University of Phoenix. For this particular test, the dependent variable, Y, will be the salary of the 100 participants and the independent variable, X, will be the education of the 100 participants. This information will be used in a linear regression test to see if there is sufficient evidence to reject the null hypothesis that a higher education equals no difference in salary. This test will analyze the earnings of the workers based on the years of education they have received to see if the slope equals zero. If the slope equals zero, then we will not be able to reject the null hypothesis. This week, Learning Team B will be using linear regression to determine if is a significant linear relationship between the two variables exists.

The information provided from the data set acquired from Lind, Marchal, and Wathen (2008). Statistical Techniques in Business & Economics, 13th edition. New York, NY: McGraw-Hill, entitled, “Wages_2005.xls.” Learning Team B will use this data set and execute the five step hypothesis test to draw a conclusion regarding the study.

The Five Step Hypothesis Test

The null hypothesis for this research paper is that higher education equals no difference in salary. The alternate hypothesis is that the higher education attained will determine the higher salary that an individual might earn.

Step 1

H0 : B 1 = 0...