I. Abstract

This paper investigates the relationship between the unemployment

rates of College graduates and High School graduates. From this

investigation, it appears that the relationship is moderately weak.

II. Introduction

Many people pursue a degree to escape the inevitability of

unemployment.

It is obvious that many people feel a college education is

important, and more employment opportunities will arise if one has a

degree. On the other hand, can someone be just as successful with only a

High School degree? Is there an association between the Unemployment Rates Essay

of College and High School graduates? This spring quarter I have become

knowledgeable as to how to use the Storm software. Therefore, I am able to

compare data obtained to determine if a certain relationship exists between

the two variables. As a result of using this information, I was able to

accurately state if there was any kind of relationship between the

unemployment rates of College and High School graduates.

III. Discussion of Variables

It might be thought that the unemployment rates of College graduates

and High School graduates are related in that when the unemployment rates

of High School graduates increases, the unemployment rate of College

graduates might be expected to decline or remain steady.

The reason for

being is because it is assumed that having a college degree means greater

job security.

To test this theory, 40 data elements are acquired. Randomness is

sought by selecting the data on the last day of the month for 40

consecutive months starting with January 2001, and ending with April 2004.

This time period includes unemployment rates that are not seasonally

adjusted. The data on the unemployment rates of both College and High

School graduates was found in the U.S.

Department of Labor – Bureau of

Labor Statistics.

IV. Discussion of the Results

The sample is described using a linear regression model. The result is

expressed by the formula: High School (Y) = 2.14 + 1.04 College (X).

The

R-squared at 0.40 suggests that the relationship is moderately weak due to

the fact that R-squared represents a stronger relationship the closer the

number is to 1.

A study of the residual graphs indicates that the relationship is

poor due to curvilinearity for unemployment rates of College graduates and

poor due to violation of both homoscedasticity and linearity assumption for

the unemployment rates of High School graduates. This impacts on the

results by saying that the graphs show that the model does not describe the

data fully.

V. Conclusion

Taken as a whole, this model seems to need more refinement being that

the R-squared is actually fairly moderate at 0.

40. This model might be of

little use in predicting future movements of high school (Y) when college

(X) moves. Particularly interesting is how the unemployment rates for both

College and High School graduates have increased during the years, and that

one if not influenced by the other significantly.

VI. Appendix

When trying to describe a universe such as the relationship between

unemployment rates of high school graduates versus college graduates, one

might take a random sample and expect that the sample adequately represents

the universe. The sample in this study is the unemployment rates for 40

consecutive months of those with simply a High School diploma versus those

who possess a College degree (Bachelor’s Degree or Higher).

Next, measures are taken of the sample, and a model estimated. If the

model is a good estimator of the sample, it is to be expected that the

model is a good estimator of the universe. In this study, the model is not

a good estimator of the sample, and therefore it is not expected to be a

good estimator of the universe.

The model used in this paper is the linear regression model, which attempts

to model the relationship between two variables by fitting a linear

equation to observed data. One variable is considered to be an explanatory

variable, and the other is considered to be a dependent variable (Poole &

O’Farrell 1). There are several research objectives for which the

regression model may be used, but they may be classified into three groups:

(I) the computation of point estimates, (II) the derivation of interval

estimates, and (III) the testing of hypotheses (Poole & O’Farrell 2).

Care has to be taken to observe the assumptions of the model, which are:

1. The mean of the probability distribution of the random error is 0,

E(e) = 0. that is, the average of the errors over an infinitely long

series of experiments is 0 for each setting of the independent .