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

European Agribusiness - Research Paper ExampleTo understand the perpetration of causality, we derive the statistical 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 otherwise (the independent or explanatory variable). The regression model that establishes a relationship between sales and number of employees can be written as followsand ar parameters of the regression key. 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- evaluate value. Assuming that has an expected value of zero, we can write the regression equation as followsIt 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 destine 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. ... gross revenue = 0.079911 + 0.256194 x Number of Employees Company that least fits the Regression LineCodecompany nameAlphaBetaPredicted Sales (billions)ActualSales (billions) relaxation Figure (billions)1Nestle0.0799110.25619418.2697122.74.4302852Heineken0.0799110.25619410.045878.8-1.245873Groupe Danone0.0799110.2561949.0467168.6-0.446724Unilever0.0799110.25619411.352478.6-2.752475Danish Crown Amba0.0799110.2561946.9715416.5-0.471546Groupe Lactalis0.0799110.2561946.6641086.4-0.264117Associated British Food0.0799110.2561947.3302135.7-1.630218Sudzucker0.0799110.2561945.1013225.80.6986789Carlsberg0.0799110.2561946.6641085.2-1.4641110Scottish & Newcastle0.0799110.2561943.9228284.90.97717211Royal Friesland Foods0.0799110.256194 3.9996864.70.70031412Campina0.0799110.2561941.6939363.61.90606413Oetker Group0.0799110.2561944.0253053.6-0.4253114Barilla0.0799110.2561941.8732723.61.72672815Tate & Lyle0.0799110.2561941.3096453.52.19035516Cadbury Schweppes0.0799110.2561946.100483.4-2.7004817Bongrain0.0799110.2561944.0765443.3-0.7765418Nutreco0.0799110.2561942.0013730.9986319Kerry Group0.0799110.2561944.255883-1.2558820Danisco0.0799110.2561942.7955722.80.00442821Pernod Ricard0.0799110.2561943.2567222.7-0.5567222Ebro Puleva0.0799110.2561941.64269720.357303To 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 individually of the company. This figure is compared to the actual