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APPENDIX A: <br />FURTHER EXPLANATION OF THE <br />MULTIPLE REGRESSION <br />ANALYSIS <br />As explained in the Report, the multiple regression statistical techniques seek <br />to explore the relationship between a set of independent variables and a <br />dependent variable. The following equation is a way to visualize this relation- <br />ship: <br />DV = f (D, I, O), <br />where DV is the dependent variable; D is a set of demographic variables; I is a <br />set of industry and occupation variables; and O is a set of other independent <br />variables. <br />The estimation process takes this equation and transforms it into: <br />DV=C+(p1*D)+(p2*1)+(p3*O)+It, <br />where C is the constant term; 01, 02 and 03 are coefficients, and µ is the ran- <br />dom error term. <br />The statistical technique seeks to estimate the values of the constant term and <br />the coefficients. <br />In order to complete the estimation, the set of independent variables must be <br />operationalized. For demographic variables, the estimation used race, gender <br />and age. For industry and occupation variables, the relevant industry and occu- <br />pation were utilized. For the other variables, age and education were used. <br />A coefficient was estimated for each independent variable. The broad idea is <br />that a person's wage or earnings is dependent upon the person's race, gender, <br />age, industry, occupation, and education. This analysis used the most recent <br />American Community5urveydata downloaded from the IPUMS website and <br />used data from the State of Indiana 169. Therefore, the coefficient for the new <br />169. IPUMS USA, University of Minnesota, www.ipums.org. <br />0 2020 Colette Holt & Associates, All Rights Reserved. 103 <br />