Time-Series Regression; Applicants for MBA Programs Business schools have commonly observed that when job opportunities are down, those interested in business seek to enter MBA programs. To test this hypothesis, we decided to look at the number of applicants for MBA programs and to compare that to the unemployment rate. The applications data are from the Graduate Management Admissions Council (www.gmac.com/gmac) while the unemployment data are taken from the website of the U.S. Bureau of Labor Statistics (www.bls.gov/data/home.htm). We have data for both the total unemployment rate and the rate for college graduates 25 years of age or older. The data for 2000 through 2013 are shown below.
Unemployment Rate for College Grads (%) Unemployment Rate (%) Percentage of Schools Reporting an Increase in MBA Applicants (%)
2000 1.6% 4.0% 63%
2001 2.2 4.6 65
2002 2.9 5.8 84
2003 3.1 6.0 35
2004 2.7 5.5 16
2005 2.3 5.1 20
2006 2.0 4.6 68
2007 2.1 4.7 64
2008 2.3 5.5 80
2009 4.6 9.3 64
2010 4.7 9.6 41
2011 4.3 8.9 28
2012 4.0 8.1 43
2013 3.7 7.4 50
Required Use regression analysis on the above data to determine whether there is a relationship between the unemployment rate and the number of applicants to MBA programs. Use both measures of unemployment and see if there is a difference in your results. State whether you expect the relationship to be positive or negative (inverse).
8-54 Cross-Sectional Regression; Analysis of Rankings (See also Problem 8-55 ) Bloomberg Businessweek magazine periodically publishes a ranking of the Top Companies: Best Places to Work. The ranking gives employers, employees, and potential employees a snapshot of the overall achievements these companies have made in creating a productive and rewarding work environment. The ranking considers a number of Page 306 factors (we will call these independent variables), including surveys of career service directors at U.S. colleges, surveys of the organizations themselves (on pay, benefits, and training programs, etc.), and surveys of undergraduate students. The survey data are used to create a ranking for each independent variable of the top companies. The rankings and survey data for the top 25 organizations are shown next: Career Service Employer Student Director Average Rank Survey Survey Survey Pay 8 55 6 3 6 50 3 22 5 2 50 4 17 11 5 50 5 12 2 24 60 1 1 CH 6 3 13 16 60 7 24 6 35 7 3 8 19 18 45 9 13 12 17 10 17 13 11 5 60 60 60 38 15 28 3 60 Organization Industry Deloitte & Touche/accounting Ernst & Young accounting PricewaterhouseCoopers/accounting KPMG/accounting U.S. State Department/government Goldman Sachs/investment banking Teach for America/nonprofit: government Target/retail JPMorgan/investment banking IBM technology Accenture/consulting General Mills/consumer products Abbott Laboratories/health Walt Disney/hospitality Enterprise Rent-A- Car/transportation General Electric/manufacturing Phillip Morris/consumer products Microsoft technology Prudential insurance Intel/technology Aflac/insurance Verizon Communications/utilities Central Intelligence Agency (CIA government Raytheon/manufacturing Nestlé/consumer products 12 13 33 44 2 36 55 14 60 1 8 40 15 28 4 35 16 SI 16 50 9 19 8 28 17 19 9 18 19 20 55 55 75 50 60 37 9 14 63 55 23 55 37 21 9 62 50 22 IS 49 40 23 45 7 29 24 23 20 39 31 19 44 60 50 25 Required 1. Using regression analysis, develop a regression model to predict company rank based on the four independent variables-employer survey rank, student survey rank, career service director rank, and average pay. Determine which, if any, of these four independent variables are significantly correlated with the overall ranking. Which is the strongest and which is the weakest independent variable, and why? 2. What are some of the limitations of the analysis you have completed in requirement 1?
