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1

Berthold, M. Guide to intelligent data analysis: How to intelligently make sense of real data. Springer, 2010.

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2

Hailperin, Max. Load balancing using time series analysis for soft real time systems with statistically periodic loads. Dept. of Computer Science, Stanford University, 1993.

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3

Orlova, Ekaterina. Research and Development (R&D): data analysis and cost-effectiveness. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/1959274.

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The textbook outlines the methodological foundations of statistical analysis, modeling and forecasting of data characterizing research and development (R&D) both at the level of individual organizations and at the level of industries and regional systems. Examples of the analysis of empirical data on R&D are given, which allow us to get an idea of the scope of statistical data processing methods in the analysis of real economic processes. A set of methods for the economic justification of R&D projects, depending on their type and stage of the life cycle, is presented. Examples of e
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4

Hale, Robert L. MYSTAT: Statistical applications. Course Technology, 1992.

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5

Kiselev, I︠U︡. V. (I︠U︡riĭ Vasilʹevich), ed. Statistical methods of geophysical data processing. World Scientific, 2010.

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6

McCuen, Richard H. Microcomputer applications in statistical hydrology. Prentice Hall, 1993.

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7

A, Aitkin Murray, ed. Statistical modelling in GLIM. Clarendon Press, 1989.

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8

Nazarov, Dmitriy, and Anton Kopnin. Information technologies in professional activity: data mining and business analytics. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2110964.

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The textbook offers a comprehensive look covering a wide range of topics related to the role of information technology in the modern world. Starting with an overview of the history of information technology and its evolution, the book introduces the reader to professions in the field of information and digital technologies, emphasizing them in various professional fields. The content of the textbook includes a detailed analysis of key concepts such as Data Science, Data Mining and Machine Learning, and their role in healthcare, law, education, science and business. The textbook provides a deta
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9

Thisted, Ronald A. Elements of statistical computing. Chapman and Hall, 1988.

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10

Froeschl, Karl. Metadata management in statistical information processing: A unified framework for metadata-based processing of statistical data aggregates. Springer, 1997.

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11

Jan, Wikander, and Svensson Bertil 1954-, eds. Real-time systems in mechatronic applications. Kluwer Academic Publishers, 1998.

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12

Goyal, M. Computer-based numerical & statistical techniques. Infinity Science Press, 2007.

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13

United States. Bureau of the Census. 1980 census of population and housing: History : Data processing : Data products. U.S. Department of Commerce, Bureau of the Census, 1989.

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14

-P, Tsai Jeffrey J., ed. Distributed real-time systems: Monitoring, visualization, debugging, and analysis. Wiley, 1996.

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15

1943-, Crowder M. J., ed. Statistical analysis of reliability data. Chapman & Hall, 1994.

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16

Winson-Geideman, Kimberly, Andy Krause, Clifford A. Lipscomb, and Nick Evangelopoulos. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling. CRC Press LLC, 2017.

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17

Statistics with R: Solving Problems Using Real-World Data. SAGE Publications, Incorporated, 2020.

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18

Winson-Geideman, Kimberly, Andy Krause, Clifford A. Lipscomb, and Nick Evangelopoulos. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling. Taylor & Francis Group, 2017.

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19

Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling. CRC Press LLC, 2017.

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20

Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling. CRC Press LLC, 2017.

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21

Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling. Taylor & Francis Group, 2017.

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22

Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling. CRC Press LLC, 2017.

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23

Boobier, Tony. Analytics for Insurance: The Real Business of Big Data. Wiley & Sons, Limited, John, 2016.

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24

Boobier, Tony. Analytics for Insurance: The Real Business of Big Data. Wiley & Sons, Incorporated, John, 2016.

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25

Boobier, Tony. Analytics for Insurance: The Real Business of Big Data. Wiley & Sons, Incorporated, John, 2016.

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26

Boobier, Tony. Analytics for Insurance: The Real Business of Big Data. Wiley & Sons, Limited, John, 2016.

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27

Analyzing Compositional Data With R. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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28

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Capstone: Application of NLTS to Real-World Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0010.

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This chapter investigates the use of Extreme Value Statistics (EVS) to probabilistically model extreme events separated as unstructured noise in signal processing. We apply a version of EVS that computes the likelihood of extreme discrepancies exceeding a selected threshold value within a given time interval. In theory, exceedances follow a Generalized Pareto (GP) distribution, and we run diagnostics to determine how well the data actually fit this distribution. If we find a reasonable fit, we can invert the GP distribution to solve for quantiles providing a useful noise diagnostic: return lev
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29

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Data Preprocessing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0006.

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Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test th
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30

NumPy 1.5: Beginner's guide : an action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples. Packt Publishing, 2011.

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31

Majewski, Miroslaw. Getting Started with Mupad. Springer, 2008.

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32

Getting Started with MuPAD. Springer Berlin Heidelberg, 2006.

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33

Sprent, Peter. Data Driven Statistical Methods. CRC Press LLC, 2019.

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34

Sprent, Peter. Data Driven Statistical Methods. CRC Press LLC, 2019.

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35

From, Steven, Samantha Folken, Andrew W. Swift, and Larry Stephens. Learning Statistical Methods Using Statistical Software. Kendall Hunt Publishing Company, 2014.

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36

Troyan, Vladimir, and Yurii Kiselev. Statistical Methods of Geophysical Data Processing. World Scientific Publishing Co Pte Ltd, 2010.

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37

Troyan, Vladimir. Statistical Methods of Geophysical Data Processing. World Scientific Publishing Co Pte Ltd, 2010.

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38

Gupta, Amitava, Anil Kumar Chandra, and Peter Luksch. Real-Time and Distributed Real-Time Systems: Theory and Applications. Taylor & Francis Group, 2016.

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39

Gupta, Amitava, Anil Kumar Chandra, and Peter Luksch. Real-Time and Distributed Real-Time Systems: Theory and Applications. Taylor & Francis Group, 2016.

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40

Gupta, Amitava, Anil Kumar Chandra, and Peter Luksch. Real-Time and Distributed Real-Time Systems: Theory and Applications. Taylor & Francis Group, 2016.

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41

Gupta, Amitava, Anil Kumar Chandra, and Peter Luksch. Real-Time and Distributed Real-Time Systems: Theory and Applications. Taylor & Francis Group, 2016.

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42

Gupta, Amitava, Anil Kumar Chandra, and Peter Luksch. Real-Time and Distributed Real-Time Systems: Theory and Applications. Taylor & Francis Group, 2016.

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43

Gupta, Sumit, and Shilpi Saxena. Real-Time Big Data Analytics. Packt Publishing, Limited, 2016.

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44

Piersol, Allan G., and Julius S. Bendat. Random Data. Wiley & Sons, Incorporated, John, 2011.

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45

Clarke, Robert B., and Edward W. Minium. Elements of Statistical Reasoning. John Wiley & Sons Inc, 1994.

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46

Minum. Elements in Statistical Reasoning. John Wiley & Sons Inc, 1994.

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47

Andrews, D. F., and J. E. Stafford. Symbolic Computation for Statistical Inference (Oxford Statistical Science Series). Oxford University Press, USA, 2000.

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48

Javidi, Bahram, and Joseph L. Horner. Real-Time Optical Information Processing. Elsevier Science & Technology Books, 2012.

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49

Barthes, Angela, and Michel Larini. Quantitative and Statistical Data in Education: From Data Collection to Data Processing. Wiley & Sons, Incorporated, John, 2018.

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50

Barthes, Angela, and Michel Larini. Quantitative and Statistical Data in Education: From Data Collection to Data Processing. Wiley & Sons, Incorporated, John, 2018.

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