A statistical analysis of world rankings for the population happiness level was conducted to find ways to stimulate sustainable development. The research is based on data from an annual Gallup World survey called the World Happiness Report. Various data describing the population's happiness level through direct (GDP per Capita, Social support, Life expectancy) and indirect well-being indicators have been studied. The visualization methods, graphical mapping in Cartesian and polar coordinate systems and primary statistical processing of numerical data were used. We have used descriptive statistics, histograms, and cumulative constructions. Linear smoothing was performed, and the GDP per Capita rating trend was used to establish. Since the level of economic development and well-being of the population does not correspond to its happiness, it is recommended to include the implementation of the population's socio-historical, cultural and psychological traditions.

Sustainable Development by a Statistical Analysis of Country Rankings by the Population Happiness Level

A survey of consumers' opinions of women's clothing was obtained from reviews and comments during online sales. The high popularity of clothing and footwear as a segment of the electronic market is considered. Correlation analysis of survey data was performed, correlation coefficients were calculated, a correlation matrix was constructed, and autocorrelation was established, establishing how consumers perceive the offered products and services in the clothing sales segment. Cluster data analysis was performed. Dendrograms of clothing sales responses were constructed and analyzed due to the conclusions obtained from various research methods of the clothing sales segment on the Internet, recommendations for improving the clothing sales system, and proposals for developing new marketing measures.

Cluster Analysis of Exclamations and Comments on E-Commerce Products

A data set of 550 elements was analyzed during the study, containing data on books: title, author, number of reviews, price, and rating. Smoothing methods were used to average local data for further forecasting, in which non-systematic elements replace each other. Clear graphs without sharp peaks were obtained. Based on which the methods of moving average, weighted moving average, exponential smoothing and median filtering, it was found that the books that became bestsellers in 2009-2011 did not have a high response rate. While in the following 2012 to 2019, there was an increase in the number of reviews; the average was 25,000 per book. Examining the correlation between Reviews and Prices, we found a relationship that indicates that books priced above $ 60 typically have no more than 10,000 reviews. Books priced from $ 40 to $ 60 have an average of no more than 20,000 reviews. Books up to $ 20 have different reviews, but mostly this value does not exceed 40,000, and those books that have more are the exception rather than the rule. The clustering method was applied to the 12 authors who wrote the bestsellers, and after normalizing the table and building proximity tables, we identified clusters.

Intelligent Analysis of Best-Selling Books Statistics on Amazon

The proposed approach of modelling and identifying the current situation is illustrated by e-tourism, which is advisable to expand and apply in other areas. Implementing this approach will create intelligent systems and services that respond quickly to changes in the environment. An example of an intellectual tourist service provided for tourists is developed.

Intellectual Tourist Service with the Situation Context Processing

This paper presents a statistical analysis of existing trends in the spread of programming language libraries based on data set studies. The various problems that arise when using specific libraries of different programming languages for certain periods, the most common is the month, are studied and analyzed. The results of the study of existing trends in the spread of programming language libraries, collected in the studied dataset, are presented graphically, set key descriptive characteristics, taking into account the correlation of data. Trends in the behavior of the studied indicators using the methods of smoothing time series are determined. A cluster analysis of programming language libraries was performed, making it possible to group data by clusters and form appropriate data groups for ranking programming language libraries.

Statistical Analysis of the Popularity of Programming Language Libraries Based on StackOverflow Queries

The need to study the demand for bicycle sharing based on regression models of data analysis and prediction of results was investigated. To substantiate possibility of work of the main processes of the information systems, UML diagrams were created. To determine the peaks in demand for bicycles in a certain period of time, it was proposed to use regression models of data analysis. The proposed decision trees were recommended for modeling new datasets in Smart Cities, especially Lviv.

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Mariya Maslak

Papers

Analysis of the Demand for Bicycle Use in a Smart City Based on Machine Learning.