Ministry of Education and Science, Youth and Sports of Ukraine

Ministry of Education and Science, Youth and Sports of Ukraine

Kharkiv National University of Economics

Department of

Economics and planning of

the enterprise activity

Report

Practical training 3

Theme: “Models of consumers’ choice and dynamics”

Subject: Econometrics

Checked by: Done by:

Toneva K.V. The second-year student of

the faculty of

Management and Marketing

Ocheredko Ksenia

Kharkiv 2012

According to the Lab. 3, it should be determined the influence of such factors as production of cars (X1) and income per capita (X2) on volume of prodution of gum (Y).

In order to solve this task it is necessary to build the production functions:

Y=B₀+B₁X₁+B₂X₂ and Y=B₀X₁^B1X₂^B2

The table in Excel that we have done for the first part (additive model) of the Lab 3 should look like this:

The table in Excel that we have done for the second part (multiple model) of the Lab. 3 should look like this:

The economic model of the dependence between poduction of cars (X1) and volume of prodution of gum (Y) looks like:

The economic model of the dependence between income per capita (X2) and volume of prodution of gum (Y) looks like:

According to the lab. it is also necessary to choose the most appropriate model for predicting the volume of production of gum. To find the model which is better we should perform some calculations and find which of models (additive or multiple model) gives more accurate results:

The coefficient of multiple correlation (Ryx₁x₂) equals to 0,90365 (in additive model) and to 0,903589 (in multiple model) which means that the relationship between resultant and independent variables is close.

The coefficient of pair correlation (ryx₁) equals to 0,537 which means that the relationship between poduction of cars (X1) and volume of prodution of gum (Y) is considerable.

The coefficient of pair correlation (ryx₂) equals to 0,847 which means that the relationship between income per capita (X2) and volume of prodution of gum (Y) is close.

The coefficient of partial correlation (ryx₂x₁) is equal to 0,862 (in additive model) and and to 0,857 (in multiple model) which means that the relations between the indicators is positive and very strong.

With the help of these figures we can check the correlation coefficient on the essentiality by the Fisher’s criteria. As the estimated quantile (U_p) equals to 2,47 (in additive model) and 2,42 (in multiple model) which is more than U_t=1,96, then with the probability of 95% it can be argued that the relationship between the indicators is significant.

The next step is the estimation of accuracy of the model. As the coefficient of variation (V) is equal to 4, 75% (in additive model) and 1,01% (in multiple model), which is less than 5-7%, than it can be assumed that this model can be used.

Elastisity 1 represents the influence of poduction of cars (X1) on volume of prodution of gum (Y). It equals to 0,296 which means that quantity of production of gum response is inelastic to changes in production of cars. In other words, an essential change in cars production will cause not very big changes in gum production.

Elastisity 2 represents the influence of income per capita (X2) on volume of prodution of gum (Y). It is equal to 2,67 and which means that quantity of production of gum response is elastic to changes in income per capita. In other words, changes in income per capita will cause essential changes in gum production.

As the coefficient of variation in the additive model equals to 4,75% that is more than 1,01% in the multiple model, then it can be assumed that the multiple model is more accurate and this economic model is more appropriate in this case.

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