Part

2

·

A brief

description of the meaning of the data and comment on each variable (turnover,

employees and companies)

The data given

in part two is the European food and drink industry graph 2015 which states the

EU member countries such as Austria, Belgium, Bulgaria, etc. then it states the

number of employees in this industry in the specific country, for example there

is 83.3 thousand employees in Austria working in food and drink industry. The

table also shows the number of companies in each country, for example in

Austria there is 3893 companies in food and drink industry and it also shows

the turnover, for example the turnover in Austria in food and drink industry is

22.7 billion euro.

·

An

interpretation of the values of the summary statistics

Mean and median

means the average, however, the difference is that the mean is the central

tendency of a given information in the table. It is dividing the sum of the

values in the set by the number of values in the set. It is commonly called arithmetic

mean. However, the median is the value where

The mode is

showing the data that has been occurring most often in the table, however, the

mode for a number of employees and number of companies is not available because

the numbers for each country are not the same. The mode of turnover is 4 as two

countries Lithuania and Slovakia have the same data in 2015– 4 billion euros.

·

An

interpretation of the values of the correlation coefficient and the coefficient

of determination in the context of the data

The interpretation of the values of the correlation

coefficient for the number of the employees which is a strong uphill linear

relationship. It is strong relationship as both variables are increasing or

decreasing together so that means the coefficient is positive and the line is

also showing the correlation going upward. The correlation coefficient of determination

is 83% and, the points fall very close to the line this shows that relationship

between variables is strong.

For the number of companies there is weak

positive correlation which is 70% which is weak positive correlation. As the

points on the table are falling closely to the line it shows that relationship

is strong between the variables. However, the relationship between them is negative

as there is more than one variables that are increasing and other variables decreasing.

The coefficient of determination is 49%.

·

State the

equations of the regression line in the context of the meaning of the data

·

Interpret the

intercepts and coefficients of the two regression equations in the context of

the meaning of the data variables

The equations of

the regression line are the Y = 0.3025x – 4.8548 so the Y stands for turnover

(billions) which is the predicted values of Y. 0.3025x is the rate that has

been predicted for Y scores for each unit increase in x, and the 4.8548 is the

Y-intercept = level of Y when X is 0. This predicts that for every thousand

employees there is 4.8548 turnover (€ billions).

The equations of the regression

line are the Y = 0.0025x + 18.862 so the Y stands for turnover (billions) which

is the predicted values of Y. 0.0025x is the rate that has been predicted for Y

scores for each unit increase in x, and the 18.862 is the Y-intercept = level

of Y when X is 0.

·

Which regression

equation does explain the dependent variable better? Why?Part

2

·

A brief

description of the meaning of the data and comment on each variable (turnover,

employees and companies)

The data given

in part two is the European food and drink industry graph 2015 which states the

EU member countries such as Austria, Belgium, Bulgaria, etc. then it states the

number of employees in this industry in the specific country, for example there

is 83.3 thousand employees in Austria working in food and drink industry. The

table also shows the number of companies in each country, for example in

Austria there is 3893 companies in food and drink industry and it also shows

the turnover, for example the turnover in Austria in food and drink industry is

22.7 billion euro.

·

An

interpretation of the values of the summary statistics

Mean and median

means the average, however, the difference is that the mean is the central

tendency of a given information in the table. It is dividing the sum of the

values in the set by the number of values in the set. It is commonly called arithmetic

mean. However, the median is the value where

The mode is

showing the data that has been occurring most often in the table, however, the

mode for a number of employees and number of companies is not available because

the numbers for each country are not the same. The mode of turnover is 4 as two

countries Lithuania and Slovakia have the same data in 2015– 4 billion euros.

·

An

interpretation of the values of the correlation coefficient and the coefficient

of determination in the context of the data

The interpretation of the values of the correlation

coefficient for the number of the employees which is a strong uphill linear

relationship. It is strong relationship as both variables are increasing or

decreasing together so that means the coefficient is positive and the line is

also showing the correlation going upward. The correlation coefficient of determination

is 83% and, the points fall very close to the line this shows that relationship

between variables is strong.

For the number of companies there is weak

positive correlation which is 70% which is weak positive correlation. As the

points on the table are falling closely to the line it shows that relationship

is strong between the variables. However, the relationship between them is negative

as there is more than one variables that are increasing and other variables decreasing.

The coefficient of determination is 49%.

·

State the

equations of the regression line in the context of the meaning of the data

·

Interpret the

intercepts and coefficients of the two regression equations in the context of

the meaning of the data variables

The equations of

the regression line are the Y = 0.3025x – 4.8548 so the Y stands for turnover

(billions) which is the predicted values of Y. 0.3025x is the rate that has

been predicted for Y scores for each unit increase in x, and the 4.8548 is the

Y-intercept = level of Y when X is 0. This predicts that for every thousand

employees there is 4.8548 turnover (€ billions).

The equations of the regression

line are the Y = 0.0025x + 18.862 so the Y stands for turnover (billions) which

is the predicted values of Y. 0.0025x is the rate that has been predicted for Y

scores for each unit increase in x, and the 18.862 is the Y-intercept = level

of Y when X is 0.

·

Which regression

equation does explain the dependent variable better? Why?