Application of Statistical Procedures

The level of unemployment is a matter of significant concern to both the general public and political leaders. In order to develop effective policies to address the unemployment issue, it is necessary to understand how the unemployment rate is related to other factors. It is this problem which is addressed in this research.

A total of six hypotheses were formulated for, and tested in the performance of this research. These hypotheses were stated as follows:

1. It is hypothesized that the rate of civilian unemployment in the United States changes linearly and inversely to changes in the gross national product.

2. It is hypothesized that changes in the unemployment rate are more extreme for nonwhites than for whites.

3. It is hypothesized that changes in the unemployment rate are more extreme for females than for males.

4. It is hypothesized that changes in the unemployment rate are more extreme for persons under the age of 20 years old in the economy than for those persons aged 20 years old and older.

5. It is hypothesized that changes in the unemployment rate are more extreme for part-time workers than for full-time workers.

6. It is hypothesized that changes in the unemployment rate are more extreme

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sing of the relative effect upon the dependent variable of each of the independent variables; an assessing of the combined effects of the independent variables on the dependent variable; and an assessment of the interactions between all of the variables. A major difference between the analysis of variance procedure and regression analysis is that, in analysis of variance, the emphasis is on analysis of the variations in the independent variable, as opposed to the joint interaction of the variations in dependent and independent variables. one result of this difference in emphasis is that, in regression analysis, both the independent and the dependent variables must be measured on an interval scale, whereas, in analysis of variance procedures, only the dependent variable is required to be measured on an interval scale (Nie, Hull, Jenkins, Steinbrenner & Bent, 1975). Simple regression analysis involves the analysis of the relationship between one dependent variable and one explanatory, or independent, variable. Multiple regression, on the other hand, is a . . . general statistical technique through which one can analyze the relationship between a dependent or criterion variable and a set of independent or predictor variables.
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Approximate Word count = 1749
Approximate Pages = 7 (250 words per page)

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