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Books on the topic 'Software regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Rothermel, Gregg. Regression test selection for C++ software. Oregon State University, Dept. of Computer Science, 1999.

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2

C, Littell Ramon, and SAS Institute, eds. SAS System for regression. 2nd ed. SAS Institute, 1991.

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3

C, Littell Ramon, ed. SAS System for regression. 3rd ed. SAS Institute, 2000.

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4

Elbaum, Sebastian. Prioritizing test cases for regression testing. Oregon State University, Dept. of Computer Science, 2000.

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5

Bible, John. A unifying framework supporting the analysis and development of safe regression test selection techniques. Oregon State University, Dept. of Computer Science, 1999.

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6

N, Naik Dayanand, ed. Applied multivariate statistics with SAS software. 2nd ed. SAS Institute, 1999.

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7

N, Naik Dayanand, ed. Applied multivariate statistics with SAS software. SAS Institute, 1995.

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8

Khattree, Ravindra. Applied multivariate statistics with SAS software. 2nd ed. SAS Institute, 1999.

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9

Asquith, William H. Site-specific estimation of peak-streamflow frequency using generalized least-squares regression for natural basins in Texas. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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10

Asquith, William H. Site-specific estimation of peak-streamflow frequency using generalized least-squares regression for natural basins in Texas. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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11

Asquith, William H. Site-specific estimation of peak-streamflow frequency using generalized least-squares regression for natural basins in Texas. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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12

Asquith, William H. Site-specific estimation of peak-streamflow frequency using generalized least-squares regression for natural basins in Texas. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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13

Asquith, William H. Site-specific estimation of peak-streamflow frequency using generalized least-squares regression for natural basins in Texas. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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14

Asquith, William H. Site-specific estimation of peak-streamflow frequency using generalized least-squares regression for natural basins in Texas. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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15

Analysis of enzyme kinetic data. Oxford University Press, 1995.

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16

Unix test tools and benchmarks: Methods and tools to design, develop, and execute functional, structural, realiability, and regression tests. Prentice Hall, 1995.

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17

Babeshko, Lyudmila, Mihail Bich, and Irina Orlova. Econometrics and econometric modeling. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141216.

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The textbook covers a wide range of issues related to econometric modeling. Regression models are the core of econometric modeling, so the issues of their evaluation, testing of assumptions, adjustment and verification are given a significant place. Various aspects of multiple regression models are included: multicollinearity, dummy variables, and lag structure of variables. Methods of linearization and estimation of nonlinear models are considered. An apparatus for evaluating systems of simultaneous and apparently unrelated equations is presented. Attention is paid to time series models. Deta

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18

Eckstein, Peter P. Statistik für Wirtschaftswissenschaftler: Eine realdatenbasierte Einführung mit SPSS. 2nd ed. Gabler, 2010.

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19

Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluati

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20

Grigorev, Anatoliy. Methods and algorithms of data processing. INFRA-M Academic Publishing LLC., 2017. http://dx.doi.org/10.12737/22119.

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In this manual some methods and algorithms of data processing, the sequence of the solution of problems of processing and the analysis of data for creation of behavior model of an object taking into account all a component of his mathematical model are considered. Types of technological methods of use of software and hardware for the solution of tasks in this area are described. Algorithms of distributions, regressions of temporary ranks, their transformation for the purpose of receiving mathematical models and the forecast of behavior of information and economic systems (objects) are consider

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21

Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects). The second edition is supplem

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22

Sayre, Kent. Regression testing experiments. 1999.

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23

Completely monotone regression estimates of software failure rates. George Washington University, School of Engineering and Applied Science, Institute for Management Science and Engineering, 1985.

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24

ANOVA and Regression Using Statview Software Course Notes. Sas Inst, 2000.

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25

Anova and Regression Methods Using Jmp Software Course Notes. SAS Institute, Incorporated, 2000.

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26

Muller, Keith E., and Bethel A. Fetterman. Regression and ANOVA : An Integrated Approach Using SAS Software. WA (Wiley-SAS), 2003.

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27

Regression and ANOVA: An Integrated Approach Using SAS Software. SAS Publishing, 2002.

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28

Using Econometrics: A Practical Guide and EViews Software Package (5th Edition). 5th ed. Addison Wesley, 2005.

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29

Freund, Rudolf, and Ramon Littell. SAS(r) System for Regression, 3rd Edition. WA (Wiley-SAS), 2000.

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30

Naik, Dayanand N., and Ravindra Khattree. Applied Multivariate Statistics with SAS Software. WA (Wiley-SAS), 1999.

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31

Milliken, George A., Dayanand N. Naik, Walter W. Stroup, Russell D. Wolfinger, Ravindra Khattree, and Ramon C. Littell. Applied Multivariate Statistics with SAS (R) Software. Sas Inst, 1996.

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32

Modern Regression Techniques Using R A Practical Guide For Students And Researchers. Sage Publications (CA), 2009.

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33

Regression Analysis for Categorical Moderators (Methodology In The Social Sciences). The Guilford Press, 2003.

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34

Eckstein, Peter P. Statistik für Wirtschaftswissenschaftler: Eine realdatenbasierte Einführung mit SPSS. Springer Gabler, 2018.

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35

System 8: Programs to assist workers in the earth sciences in using geodetic or Cartesian XYZ data from row column (GSPV85) files; GSPDC, contours, and grids interpolated from triangulated network; GSPCS, graphic sections; GSPUV, univariant statistics and histograms; GSPROB, probability diagrams; GSPXY, regression statistics and XY plots; GSPTD, ternary diagrams; and GSPV85 post-plots, for IBM PC compatible computers. Dept. of the Interior, U.S. Geological Survey, 1992.

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36

Applied plant science experimental design and statistical analysis using the SAS® OnDemand for Academics. CABI, 2021. http://dx.doi.org/10.1079/9781789249927.0000.

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Abstract The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide. The enormous number of design and analysis options available for correctly implementing, analyzing and interpreting research can be overwhelming. Statistical Analysis System (SAS®) is the most widely used statistical software in the world and SAS® OnDemand for Academics is now freely available for academic insttutions. This is a user-friendly guide to statistics using SAS® OnDemand for Academics, ideal for facilitating the design a

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37

Ferreira, Eliel Alves, and João Vicente Zamperion. Excel: Uma ferramenta estatística. Brazil Publishing, 2021. http://dx.doi.org/10.31012/978-65-5861-400-5.

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This study aims to present the concepts and methods of statistical analysis using the Excel software, in a simple way aiming at a greater ease of understanding of students, both undergraduate and graduate, from different areas of knowledge. In Excel, mainly Data Analysis Tools will be used. For a better understanding, there are, in this book, many practical examples applying these tools and their interpretations, which are of paramount importance. In the first chapter, it deals with introductory concepts, such as introduction to Excel, the importance of statistics, concepts and definitions. Be

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38

Witkov, Carey, and Keith Zengel. Chi-Squared Data Analysis and Model Testing for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198847144.001.0001.

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This book is the first to make chi-squared model testing, one of the data analysis methods used to discover the Higgs boson and gravitational waves, accessible to undergraduate students in introductory physics laboratory courses. By including uncertainties in the curve fitting, chi-squared data analysis improves on the centuries old ordinary least squares and linear regression methods and combines best fit parameter estimation and model testing in one method. A toolkit of essential statistical and experimental concepts is developed from the ground up with novel features to interest even those

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39

Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Presenting Medical Statistics from Proposal to Publication. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.001.0001.

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Presenting Medical Statistics from Proposal to Publication (second edition) aims to show readers how to conduct a wide range of statistical analyses from sample size calculations through to multifactorial regressions that are needed in the research process. The second edition of ‘Presenting’ has been revised and updated and now includes Stata, SAS, SPSS, and R. The book shows how to interpret each computer output and illustrates how to present the results and accompanying text in a format suitable for a peer-reviewed journal article or research report. All analyses are illustrated using real d

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