# Part euro. · An interpretation of the values of

Part
2

·
A brief
description of the meaning of the data and comment on each variable (turnover,
employees and companies)

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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?