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• Analysis of Labor Market Indicators in the Northern Great Plain Region in 2018 and 2022

  1-18

  Lilla Csástyu

  Views:

  34

  In my study, I analyze the labor market indicators of the North Great Plain statistical region for the years 2018 and 2022 in order to map the regional labour market characteristics based on the indicators. One method of the analysis is the Beveridge curve. This complex analysis method graphically illustrates the evolution of the relationship between the unemployment rate and the proportion of vacant positions typical of the region under investigation. The results of the analysis can draw attention to possible problems in the labour market in the region. I examine the secondary statistical data in parallel with the available related literature.

• Analysis of Driving Data of Lorries on a Certain Route

  347-352

  Levente Czégé

  Attila Vámosi

  Imre Kocsis

  Views:

  107

  In this paper the development of a driving cycle for lorries traveling a certain route is presented. A typical transportation route has been selected, which used by lorries instrumented with the proper data-collecting equipment. We used these on-board units to collect data over a long time period in the real-life traffic. We filtered the collected data, and carried out a statistical evaluation on the basis of the measured data.

• Statistical Evaluation of University Student’s Motivation and Personal Competency with Principal Component and Cluster Analysis

  365-374

  Richárd Nagy

  Péter Balogh

  Views:

  204

  The aim of the research is to do a statistical evaluation of agricultural and rural development engineer student’s motivation and personal competency. It sums up the late generation Y’s characteristics and challenges. To be a successful, graduated employee, not only the skill is needed but the personal competency as well. Altogether, 121 filled out questionnaires were collected from the students which were the prime source of the research. They had to evaluate influential factors to their motivation level and competence. The database was analyzed with descriptive statistic methods, principal component and cluster analysis. Studying the personal competency, five different factors were divided based on Belbin’s team roles and four clusters. The four clusters were established by the five factors. Analyzing the student’s educational motivation four different components were divided: the need of performance, social entertainment, the benefits of learning in the near future and the reach of the financial freedom. Based on the four components, generating clusters was not possible due to the significance level of the K-means cluster analysis because it was higher than 0,05 in every grouping variables.