European Agribusiness Research Paper Example | Topics and Well Written Essays - 2750 words

European Agribusiness - Research Paper Example To understand the direction of causality, we derive the regression equation in the next section. Regression analysis measures the relationship between two variables. It measures how one variable (the dependent variable) depends on the other (the independent or explanatory variable). The regression model that establishes a relationship between sales and number of employees can be written as follows: and are parameters of the regression line. is the intercept of the regression line and is the slope coefficient of the regression line, which measures how sensitive sales is to the number of employees; is a random error term with zero-expected value. Assuming that has an expected value of zero, we can write the regression equation as follows: It can be observed that the alpha is 0.079911 while the beta or slope coefficient of the line is 0.25. This coefficient is significant at the 1 percent level of significance indicating the existence of a strong linear dependence of sales on the number of employees. To determine which company least fits the regression equation, the expected sales is calculated using the regression equation and assuming that sales depend on the number of employees. ... Sales = 0.079911 + 0.256194 x Number of Employees Company that least fits the Regression Line Code company name Alpha Beta Predicted Sales (billions) Actual Sales (billions) Residual Figure (billions) 1 Nestle 0.079911 0.256194 18.26971 22.7 4.430285 2 Heineken 0.079911 0.256194 10.04587 8.8 -1.24587 3 Groupe Danone 0.079911 0.256194 9.046716 8.6 -0.44672 4 Unilever 0.079911 0.256194 11.35247 8.6 -2.75247 5 Danish Crown Amba 0.079911 0.256194 6.971541 6.5 -0.47154 6 Groupe Lactalis 0.079911 0.256194 6.664108 6.4 -0.26411 7 Associated British Food 0.079911 0.256194 7.330213 5.7 -1.63021 8 Sudzucker 0.079911 0.256194 5.101322 5.8 0.698678 9 Carlsberg 0.079911 0.256194 6.664108 5.2 -1.46411 10 Scottish & Newcastle 0.079911 0.256194 3.922828 4.9 0.977172 11 Royal Friesland Foods 0.079911 0.256194 3.999686 4.7 0.700314 12 Campina 0.079911 0.256194 1.693936 3.6 1.906064 13 Oetker Group 0.079911 0.256194 4.025305 3.6 -0.42531 14 Barilla 0.079911 0.256194 1.873272 3.6 1.726728 15 Tate & Lyle 0.079911 0.256194 1.309645 3.5 2.190355 16 Cadbury Schweppes 0.079911 0.256194 6.10048 3.4 -2.70048 17 Bongrain 0.079911 0.256194 4.076544 3.3 -0.77654 18 Nutreco 0.079911 0.256194 2.00137 3 0.99863 19 Kerry Group 0.079911 0.256194 4.25588 3 -1.25588 20 Danisco 0.079911 0.256194 2.795572 2.8 0.004428 21 Pernod Ricard 0.079911 0.256194 3.256722 2.7 -0.55672 22 Ebro Puleva 0.079911 0.256194 1.642697 2 0.357303 To determine which company least fits the regression equation, the expected sales is calculated using the regression equation and assuming that sales depend on the number of employees. We substitute for the number of employees in the regression equation to get the sales figure for each of the company. This figure is compared to the actual