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# ACS Sample Industries

Question # 00011454
Subject: Statistics
Due on: 05/21/2014
Posted On: 04/03/2014 02:47 PM

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i. Describe each regression in words and as an equation. Describehow you created your “new” variable
ii. Describe what the coefficient on the business degree indicatorvariable means in regression A versus regressions C and D.
iii. Interpret all of the other coefficients in Regression C.
iv. Compare the adjusted R2 between Regressions A, B, C and D andinterpret the comparison.

a. A critic of business education argues that the wage premium associatedwith business education is explained entirely by the fact that people withbusiness degrees work longer hours. Use the regression results to providean argument for or against this premise.
b. Are business degrees the only types of degrees that earn a premium inthis industry? Which degree fields are associated with the highest wages?
Which degree fields are associated with the lowest wages? What explainsthis pattern?
c. Describe the limitations of your analysis, as well as what additionalinformation would be useful to collect and study in the future. If youcould add one or two variables to your analysis, what would they be andwhy?

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#### ACS Sample Industries

Tutorial # 00011043
Posted On: 04/03/2014 02:53 PM
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Solutions-_ACS_Sample_Industries.xls (2453.5 KB)
Paper_-_ACS_Sample_Industries.docx (42.31 KB)
Preview: associated xxxx the xxxxxxx wages Which xxxxxx fields are xxxxxxxxxx with xxx xxxxxx wages? xxx other degree xxxxxx are associated xxxx the xxxxxx xxxxx What xxxxxxxx this pattern?The xxxxxxxxxx DB explains xxxx pattern xxx xxxxxxxxxx coefficients xxx these degree xxxxxx Indicator Business, xxxxxxxxx Social xxxxxxxx xxxxx Liberal xxxxx and Indicator xxxxxx 16544 85, xxxxx 17, xxx xxxx 99 xxxxxxx this pattern x Describe the xxxxxxxxxxx of xxxx xxxxxxxxx as xxxx as what xxxxxxxxxx information would xx useful xx xxxxxxx and xxxxx in the xxxxxx If you xxxxx add xxx xx two xxxxxxxxx to your xxxxxxxxx what would xxxx be xxx xxxxxx all xxxxx regression models xxxx are fitted, xxx information xx xxx variable xxxxxxxxxxx of current xxxxxxxxxxxxx was fixed xx real xxxxxx xx these xxxxxxxxxx models were xxxxxxx to only xxx field xx xxxx estate, xxx useful to xxxxxxx the individuals xxxx business xxxxxxx xxx other xxxxxxx in general xxxx the information xx the xxxxxxxx xxxxxxxx of xxxxxxxxxxxx was not xxxx Generally wage xxxxxx varies xxxx xxxxxxxx to xxxxxxxx based upon xxxxx influential variables xx make xxxx xxxxx generalized, xx need to xxxxxxx the information xx these xxx xxxxxxxxx to xxxxxxxxxx Indicator STEM xxx Age are xxx other xxxxxxxxx xxxxx can xx added.....
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