The bootstrap method is based on resampling of the original random sample drawn from a population with an unknown distribution. In the article it was shown that because of the progress in computer technology resampling is actually unnecessary if the sample size is not too large. It is possible to automatically generate all possible resamples and calculate all realizations of the required statistic. The obtained distribution can be used in point or interval estimation of population parameters or in testing hypotheses. We should stress that in the exact bootstrap method the entire space of resamples is used and therefore there is no additional bias which results from resampling. The method was used to estimate mean and variance. The comparison of the obtained distributions with the limit distributions confirmed the accuracy of the exact bootstrap method. In order to compare the exact bootstrap method with the basic method (with random sampling) probability that 1,000 resamples would allow for estimating a parameter with a given accuracy was calculated. There is little chance of obtaining the desired accuracy, which is an argument supporting the use of the exact method. Random sampling may be interpreted as discretization of a continuous variable.

The aim of the research presented in the article was to compare the income from commercial farms with national averages for wages in EU countries. Average income in farms and national averages by economic classes (KS6 classification) were compared. In 12 EU countries, the average income from farms was higher than national wage averages, and in 15 countries it was lower. An increase in average agricultural income is usually a result of a general increase in the economic class. The economically strongest farms, with incomes that are higher than national average wages, are those that are owned first in Western, and then in Southern, EU countries, with the smallest numbers found in post-Communist countries. Among new EU members, however, the preservation of income parity in agriculture is easier than in the case of old members.

The primary aim of this study was to assess and compare EU countries in terms of the use of renewable energy sources in road transport. The following research tasks were undertaken to realize this aim: (1) a review of the literature concerning the negative externalities in road transport, the concept of sustainable development, and legal regulations referring to the utilization of renewable energy sources; (2) presentation of changes in energy consumption (both traditional and renewable) in road transport in EU countries in the years 2008–2019; and (3) identification of leaders among the EU countries in terms of consumption of renewable energy sources in road transport. The aim and tasks were realized using the literature review and TOPSIS method as well as descriptive, tabular, and graphic methods. The analysis was conducted for 28 EU countries according to the status for 2019. The period of 2008–2019 was investigated. Sources of materials included literature on the subject and Eurostat data. Although renewable energy sources accounted for as little as 6% of total energy consumption in road transport in EU countries in 2019, this is a significant topical issue. It results from the direction in which changes need to be implemented in terms of energy generation in this area of human activity. It turned out that blended biodiesel and blended biogasoline were the most commonly used fuels originating from renewable sources. The application of the TOPSIS method resulted in the identification of five groups of EU member countries, which differed in terms of the degree of utilization of renewable energy sources in road transport. Luxemburg, Sweden, and Austria were leaders in this respect. In turn, Malta, Estonia, and Croatia were characterized by very low consumption of renewable energy. The greatest progress in the utilization of renewable energy sources in road transport was recorded in Sweden, Finland, and Bulgaria (changes in the relative closeness to the ideal solution from 0.15 to 0.27), while the greatest reduction in relation to other countries was observed in Austria, Germany, and Lithuania (changes from −0.35 to −0.22).

The aim of the study presented in the paper was to assess the level of investment in farms in European Union countries. The assessment applied the reproduction rate, fixed-asset reproduction rate and the investment rate. Studies were performed for all farms and farms divided into classes according to economic size (KS6 classification). The study covered years 2007–2016. As significant changes were observed in individual years, the study period was divided into two equal sub-periods, for which average values of the ratios were determined. The studies showed that the level of investment in farms as taken jointly for all EU countries is not enough to reproduce their assets in both sub-periods. What needs to be positively assessed is the fact that the values of all indicators grow as the economic size of farms grows. The level of reproduction and multiplication of property in Polish farms is lower than the EU average for economically weaker farms, but it is usually higher for the stronger ones.

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