Testing statistical hypotheses with the use of visualization tools in RStudio

Denezhkina, I. E.; Zadadaev, S. A.

doi:10.33278/sae-2018.eng.150-152

Cited by 2 publications

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“…To model the level of distortion of activity parameters, we use a probabilistic approach [22][23][24]. Let H 1 is a hypothesis that an activity was carried out, and the alternative hypothesis H 2 means that the activity was not carried out.…”

Section: Hypotheses and Interpretation Of Resultsmentioning

confidence: 99%

Data preprocessing for machine analysis of sales representatives’ key performance indicators

Vladova¹,

Shek²

2021

Bus. Inform.

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Significant transformation of the operational activity of product and service distributors is driven by changes in data-receiving and processing technology. At present, the work of these companies’ representatives is digitized to a large extent: for example, the road time, the number and places of meetings with customers are automatically recorded. At the same time, the productivity of managers who do not make direct sales is usually evaluated with the help of surveys, experts and costly double visits, although the existence of large data samples makes possible the use of statistical analysis to identify both insufficient and inflated values of performance indicators. Source data: a relational database that accumulates information about 28 categorical, quantitative, geolocation and temporal parameters of sale representatives’ activities for the last year. Based on available data, we created synthetic features (the latitude and longitude features produced the index, region, street, and house features; based upon identifiers we calculated the sum of activities of sales representatives; according to temporary features we defined the season of the year, the day of the week and the period of day features). The methodology for statistical analysis consists of three main stages: collection and processing of primary data; summary and grouping processed information; setting statistical hypotheses and interpreting the results. A probabilistic approach was used to model the level of distortion of sale representatives’ activities. As a result, with the built tag cloud we highlighted: the most popular season for advertising campaigns; the most productive departments and sale representatives; days of the week with the largest number of contacts to customers. We established a significant number of records about meetings with clients at the weekends. As a result of the data mining, we made a statistical hypothesis about the possibility of identifying the sale representatives who distort the number and parameters of meetings. A set of synthetic integer, real and categorical features was created to identify hidden relationships. Doubtful data (such as working at weekends or at night) were revealed. The resulting aggregated dataset is grouped by a sale representative’s activity ID and the distribution of this feature is plotted. For each sale representative, integer and real features are summarized and outliers that characterize inefficient performance or distortion of data have been detected. Thus, the presence of a large sample of data on the history of movements and activities allowed us to evaluate the productivity of the distribution company’s sales representatives based upon indirect features.

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Section: Hypotheses and Interpretation Of Resultsmentioning

confidence: 99%

Data preprocessing for machine analysis of sales representatives’ key performance indicators

Vladova¹,

Shek²

2021

Bus. Inform.

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Opportunities for a New Content and Methodological Line “Big Data Analysis” to Modernize the Training System of the Future Economist

Vlasov

Karasev

Синчуков

2021

Statistika i ekonominka

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The aim of the study is to apply the theory of pedagogical technologies to reveal the possibilities of the new content and methodological line “Big Data Analysis” in the aspect of modernizing the training system of the future economist.Materials and methods. During the study, theoretical and empirical research methods were used, in particular, a theoretical analysis of methods for structuring the content of education and managing the educational and cognitive activities of higher school students based on technological goal-setting and identifying a sequence of tasks, according to the content and methods of solving graduates close to future professional activities; studying the products of pedagogical activities of lecturers of higher education and experimental work, including the method of pedagogical experiment.Results. The necessity of modernization of the system of professional training of the future economist in the context of the development of data science through the selection and implementation of a new content-methodological line “Big Data Analysis” is substantiated. It points to the demand for methods of pedagogical design and the theory of pedagogical technologies for the methodologically expedient inclusion of elements of Big Data theory in the practice of professional training of future bachelors of economics. At the same time, attention is paid to both the content of already developed academic disciplines “Probability theory and mathematical statistics”, “Decision theory”, “System’s analysis in Economics”, “Instrumental methods in Economics”, and the setting of new professionally significant academic disciplines related to quantitative justification of the decisions made. The article presents and methodically describes the components of the content-methodological line “Big Data Analysis”: firstly, a sequence of six micro-goals that allow setting the implementation of this contentmethodological line in the language of educational and cognitive activities of the future bachelor of economics and taking into account the capabilities of new digital tools that support Big Data analysis models; secondly, five didactic modules that can be used to form individual educational trajectories of students of economic bachelor’s degree. Six types of application tasks are presented and characterized, which are of fundamental importance for the implementation of this content-methodological line. These tasks include the following: “Application problem for the analysis of Big Data on the RapidMiner platform”; “Data clustering application”; “Applied problem of soft and hard clustering”; “Applied classification problem”; “Applied problem on the application of methods for finding association rules”; “Applied problem for text mining”.Conclusion. The approach proposed by the authors to structuring the content of professional training of the future bachelor of economics allows us to maintain the balance of four educational components of the content and methodological line “Big Data Analysis”: experience in cognitive and creative activities, experience in implementing standard methods of activity and emotional and value relations (ideals of entrepreneurship, value orientations and motives of economic activity, etc.) The material presented in this article can be useful for lecturers of the higher economic school, as well as for everyone who is interested in the modern achievements of data science.

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Testing statistical hypotheses with the use of visualization tools in RStudio

Cited by 2 publications

References 0 publications

Data preprocessing for machine analysis of sales representatives’ key performance indicators

Data preprocessing for machine analysis of sales representatives’ key performance indicators

Opportunities for a New Content and Methodological Line “Big Data Analysis” to Modernize the Training System of the Future Economist

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