Introduction. Correlation and regression analysis. The purpose of the investigation. The correlation analysis between y and every x. Selection of variables x for regression analysis. Simple regression analysis between y and chosen x variables. Multiple correlation and regression analysis between y and x variables. The obtained results and its use. Forecasting and calculation of MSE. Forecast by moving average method. Forecast by exponential smoothing method. Formulation and invesitgation of production planning problem. Formulation of production planning problem. Dual problem. Shadow pricing. Transport problem. Conclusions.

In this course work you will find the most important investigations of quantitative decisions methods. First of all the correlation analysis was done to show the strength of correlation between variables. Then the simple regression analysis took place to show the stochastic relationship between variables and adequacy of equations. After this the multiple regression and correlation analysis were done. The results obtained and the use of it you will find at the end of first part (Correlation and regression analysis). Then some types of forecasting is used to show what production extent can be expected in the future and to choose the most accurate method of forecasting by the help of mean square errors. Then there goes very important part – the production planning. You will find the formulation of production planning problem and also there will be the investigation of created problem. By formulating and solving the dual problem we can clearly find out what are the shadow prices of chosen parts of table. At the end of part three in course work you will find the checkings, weather the obtained shadow prices are correct. The last part of the job was to construct and solve transport problem, which helps us to find the optimal way of transportation of firm’s products

The purpose of correlation analysis is to see the strehgth of correlation between variables y an x and answer the question if there exists the stochastic relationship between variables y and x. This is done by calculating correlation coefficient r and evaluating its significance. When the coefficient is near 0, the correlation is very weak, when the coefficient is near 1, the correlation is said to be strong.

The purpose of regression analysis is to find the formula (analytical expression) of earlier mentioned stochastic relationship between quantitative variables. You can analyze how a single dependent variable is affected by the values of one or more independent variables.This is done through choosing the curve, which can best describe the whole of statistical points. ...