I will pay for the following essay European Agribusiness. The essay is to be 11 pages with three to five sources, with in-text citations and a reference page.Download file to see previous pages... To

I will pay for the following essay European Agribusiness. The essay is to be 11 pages with three to five sources, with in-text citations and a reference page.

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 &amp. 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 &amp. 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.