Introduction

Parents today tell their children that a college education is necessary to gain employment that pays a decent salary. The thinking today is that the days of obtaining a job right out of high school and working up through the ranks without a post-secondary education are gone. Many individuals probably wondering, which field he or she needs to continue his or her studies in so that upon graduation the starting salary is rewarding. How much education is too much? One can make an argument asking if it is worth spending eight years in post-secondary education to make the same amount of money as an accountant who only had to study four years, or the individual who did not complete a college degree but has 20 years of experience. “Persons with doctoral degrees earn an average of $3.4 million during their working life, while those with professional degrees do best at $4.4 million”.(Longley, 2010) Although salaries will fluctuate based on several factors; industry, company, and location. Previously we conducted a test to determine how mean education affected mean salaries of men and women. For this week’s linear regression testing we will determine specifically how much an education has an effect on increasing wages.

In order to put the theory that those with a higher education make more money, we must create a hypothesis statement. For this case, we have two possible hypotheses.

H0: People with a college degree make more money than those without a college degree.

H1: People with a college degree make less than or equal to those without a college degree.

This study will examine over 80 people who have different educations and salaries. A regression hypothesis test will be performed to determine whether the first hypothesis is true, or if we have to reject the first and accept the second statement. If the first hypothesis is true, then it is proved that there is a benefit to having a college education. Certain companies require that applicants have a college degree. The requirement is more to show dedication and ability to stick with a task more than for knowledge.

Regression Analysis
r² / 0.167 / n / 100
r / 0.408 / k / 1
Std. Error / 2.562 / Dep. Var. / Education in Years
ANOVA table
Source / SS / df / MS / F / p-value
Regression / 128.51904415 / 1 / 128.51904415 / 19.58 / 2.50E-05
Residual / 643.19095585 / 98 / 6.56317302
Total / 771.71000000 / 99
Regression output / confidence interval
variables / coefficients / std. error / t (df=98) / p-value / 95% lower / 95% upper / std. coeff.
Intercept / 10.6570 / 0.5339 / 19.960 / 2.67E-36 / 9.5975 / 11.7166 / 0.000
Wage / 0.00006723 / 0.00001519 / 4.425 / 2.50E-05 / 0.00003708 / 0.00009738 / 0.408

Reference

Longley, R. (2011) About.com Lifetime Earnings Soar with Education