Unemployment RatesI. Abstract
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Unemployment RatesI. 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.
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 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.
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.
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
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Regression analysis, Econometrics, Estimation theory, Parametric statistics, Least squares, Linear regression, Coefficient of determination, Errors and residuals, Statistics, Heteroscedasticity, Estimator, Correlation and dependence
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