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One of the most important indicators characterizing the level of economic and social development of society is the income of the population, as material goods and the amount of money received or created by citizens over a certain period of time. Since the situation in the sphere of income is under constant social tension, and the main component that characterizes the standard of living of the population is the material situation, therefore, research related not only to theoretical, but also practical aspects of the standard of living is important and relevant. The article analyzes the incomes of the population of Ukraine with the help of statistical analysis and the use of computer technologies. To study the incomes of the population of Ukraine, the latest technologies and methods of data analysis, which are used to identify hidden regularities in large data sets, as well as methods and tools of statistical analysis, were used. The main procedures of data analysis were calculated and characterized with the help of modern computer technologies, namely, using one of the methods of statistical analysis of the income of the population - descriptive statistics, which was carried out with the help of Microsoft Excel software. The results of the conducted research show that the existing inequality in terms of income and expenses of the population requires assistance to solve this problem from the side of state bodies, overcoming corruption, socialization of the population, and business. This will provide an opportunity to make effective decisions and develop development programs to increase the population's income and reduce poverty in Ukraine.

Purpose. To determine and substantiate practical aspects of statistical analysis application for management results of the morphological description of cutting lettuce (Lactuca sativa L.) varieties when identifying them during corresponding phenological phases of growth and development. Methods. Field study, analytical approach based on descriptive statistics and cluster analysis. Results. Quantitative values of display of such morphological characters as leaf rosette diameter, lettuce head size, leaf blade thickness and its venation were determined for the Lactuca sativa L. varieties. Statistical indices of four morphological characters of randomized sampling frame of seven cutting lettuce varie­ties were determined and the results of statistical analysis were interpreted. Cutting lettuce of loose leaf and capitate varieties was identified during corresponding phenological phases of growth and development. The most suitable method for clustering cutting lettuce varieties was defined. The results of clustering were interpreted. It was found that ‘Hodar’ variety differed greatly from others, ‘Dumka’ and ‘Olzhych’ varieties were the most similar. Conclusions. The results of the identification allowed to establish that capitate lettuce varieties were similar in the following combinations: ‘Bona’ and ‘Dyvohray’, ‘Olzhych’ and ‘Dumka’. According to the duration of interphase periods, it can be noted that such varieties as ‘Dumka’ and ‘Dyvohrai’ had the highest rate of maturation in comparison with ‘Bona’ and ‘Hodar’ varieties, and the lowest one as compared to the ‘Olzhych’ variety

An important factor of impact on the development and living abilities of small and medium-sized innovation enterprises, including startups, is the opportunity to evaluate their investment attractiveness. The main reason for the «failure» of such enterprises is the lack of instrumentarium to forecast the potential number of their customers, and therefore their financial results. The article suggests the number of projected customers as an indicator for evaluation of the investment attractiveness of small innovation enterprises. The authors propose to use a number of mathematical models on the basis of the instrumentarium of descriptive statistics and simulation modeling. The proposed models are built on the basis of the hypothesis of normality of the distribution law of random amounts of income clients and allow forecasting with high accuracy in relation to the day of week, and therefore evaluating the investment risks for potential investors.