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Group 8 Masatoshi Hirokawa, Han Liu, Christian Mundo, Ashley Arlotti, Jingyu Nie, and Aygul Nagaeva. Study of the Forbes 500. What?. The Forbes 500 includes the top companies of each major industry
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Group 8 Masatoshi Hirokawa, Han Liu, Christian Mundo, Ashley Arlotti, Jingyu Nie, and Aygul Nagaeva Study of the Forbes 500
What? • The Forbes 500 includes the top companies of each major industry • Data includes sales, profits, market-value, assets, cash-flow, and employees of each company • Industries included are energy, finance, transportation, hi-tech, manufacturing, communication, medical, and retail
Why? • Understanding the correlation of variables in an industry gives insight to: • Health • Growth • Value
How? • To understand the correlation we used Ordinary Least Squares to create regression equations • If values were questionable further analysis is done through White’s test (Testing for heteroskedasticity)
Variables Used • Facts about companies selected from the Forbes 500 list for 1986. This is a 1/10 systematic sample from the alphabetical list of companies. (Data found at: http://lib.stat.cmu.edu/DASL/Datafiles/Companies.html) • Sales: Amount of sales (in millions) • Assets: Amount of assets (in millions) • Market_Value: Market Value of the company (in millions) • Profits: Profits (in millions) • Cash_Flow: Cash Flow (in millions) • Employees: Number of employees (in thousands) • Sector: Type of market the company is associated with
Regression Before Grouping • Dummies: • Other • Energy • Finance • Transportation • Hi-Tech • Manufacturing • Communication • Medical • Retail
Dropping Variables • All remaining variables are significant • Only dum3 is left, representing the financial sector
Adding Profits • Until this point we have left profits out of the regression because of its relationship with sales
Regression of Profits vs Sales • This is the regression performed with profits as the dependent variable and Sales as the independent variable
White’s Test Since the Profits vs Sales regression had a negative correlation coefficient we did extra analysis with the White’s Test to find heteroskedasticity in the residuals
Regression Before Grouping • Dummies: • Other • Energy • Finance • Transportation • Hi-Tech • Manufacturing • Communication • Medical • Retail
Wald’s Test In order to group all of the Dummy variables we used a Wald’s Test from the equation: SALES = C(1)*MARKET + C(2)*EMPLOYEE + C(3)*CASHFLOW + C(4)*ASSETS + C(5)*ENERGYD2 + C(6)*FINANCED3 + C(7)*TRANSPORTATIOND4 + C(8)*HITECHD5 + C(9)*MANUFACTURINGD6 + C(10)*COMMUCATIOND7 + C(11)*MEDICALD8 + C(12)*RETAILD9
Regression After Grouping • Dummy Negative includes finance (dum3), hi-tech (dum5), Communication (dum7), and Medical (dum8) • Dummy Positive includes energy (dum2), transportation (dum4), manufacturing (dum6), and retail (dum9)
Final Equation • SALES = 61.25792336(EMPLOYEE) + 0.222356458(ASSETS) + 1.405446264(CASHFLOW) - 897.3202179(DUMNEG )+ 710.3874293(DUMPOS) • Every 1000 employees generates about 61 million dollars in sales • Every dollar in assets gives .22 in sales • Every dollar of cash flow correlates to 1.4 in sales • Depending on the sector, there is a negative or positive effect on sales
Conclusion • The only insignificant variable in determining the number of sales for a company or industry is market-value • Profits is negatively correlated with number of sales which could be because of its heteroskedastic error or increase in production cost • Grouping dummy variables for sector together helped to make a more significant regression.