Search Results

1 - 2 of 2 items :

  • Author: Magnolia Tilca x
  • Banks, Financial Service Provider, Insurers x
  • Marketing, Sales, Customer Relations x
Clear All Modify Search


We are studying the economic phenomenon of the unemployment in Maramures County of Romania. To obtain plausible conclusions regarding this study we apply different types of regression: the linear regression, polynomial regression, spline and B-spline regression. In this paper we focus on the numerical side of the research and we compare the predicted values, the graphic representation of the evolution, the future predictions and the errors generated by the regressions mentioned above. The calculations are performed in R, a programming language for statistical computing. An implementation in R is given.


The economic crisis, demography, technology, globalization etc. are all factors which will influence the organizational structures and business strategies. A new business strategy will require, among others, that passive Human Resources Management (HRM) change into an active one with a decisive influence upon business. The vision of an active HRM requires that HR information (IT) dedicated systems assist human resources managers in their decision-making. The existing IT systems predominantly manage the salary calculations and, possibly, the employee's professional development, two of the tasks that a human resources manager has to pursue. However, tasks such as assisting, consulting and engaging the human resources in the organization are equally important. IT systems must also develop into these directions. The present paper proposes a solution to measure the performance of human resources by creating an employee performance indicator (EPI). The paper first describes the economic phenomenon involved in the HR performance process, then the mathematical model is formulated, the algorithm is implemented, the solution of the model is analysed from a technical and economic point of view, and finally the decision is made. We use the weighted arithmetic mean to compute the EPI indicator and the correlation formula to establish the degree of relevance between the EPI indicator and the variables involved in the model. An implementation in R is given.