COCA COLA COMPANY

Research Project

For
ACC 412

Presented to:

Overview of Coca-Cola

Leading the beverage industry for the third consecutive year, Coca-Cola, a common household name known around the world, climbs to the 4th spot in Fortune's 50 Most Admired Companies in the world for year 2012. When it comes to a refreshing cold soda, who does not know of Coca-Cola? The company was established in 1886 in Atlanta, Georgia at the Jacobs' Pharmacy soda fountain by pharmacist John Pemberton. In its humble beginning, a glass of this drink costed only five cents and only 9 glasses of Coca-Cola were sold each day. Since then, Coca-Cola has grown to be a multi-billion dollar company. Employing approximately 139,600 …show more content…

The reason might be due to people spending less money when the interest rate increases in the market.
3. Multiple Regression Method In this analysis, we are using multiple independent variables and they are: GDP, median household, and real interest rates. Figure 3: Revenue, GDP, Median Household and Real Interest Rates Data
Year
Net Sales
(Net revenue)
GDP

Median Household
Real Interest Rate (%)
2011
45,794,000
60,377,500
51,422
1.20
2010
35,119,000
58,106,200
49,445
2.40
2009
30,990,000
55,755,700
49,777
1.40
2008
31,944,000
57,166,200
52,029
2.80
2007
28,857,000
56,114,700
52,673
5.00

Projection of Sales Revenue Equation: Revenue = [2.94249 * GDP] + [525.429 * median household] – [1363776 * interest rate] - 1.58*108 There is a positive correlation between median household and GDP. That is, for each additional GDP in the country, there is an increase of sales revenue by 2.94249 dollars and for each additional median household, the amount of sales revenue increases by 525.429 dollars. On the contrary, a negative relationship exists between real interest rate and revenue. In other words, for each additional percent of interest rate in the US, the amount of sales revenue decreases by 1,363,776 dollars. This is the same result we obtained when we apply the Simple Regression Method. Graph 2: The Relationship between GDP