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Books on the topic 'Binomial distribution'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

von Collani, Elart, and Klaus Dräger. Binomial Distribution Handbook for Scientists and Engineers. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0215-8.

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2

Leemis, Lawrence M. A comparison of approximate interval estimators for the Bernoulli parameter. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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3

Adejumo, Adebowale Olusola. Modelling generalized linear (loglinear) models for raters agreement measure: With complete and missing values cases. New York: Peter Lang, 2006.

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4

Sinclair, Margaret. How to get an A in-- statistics & data analysis: Mean, median & mode, standard deviation, binomial distribution. Toronto: Coles Pub., 1998.

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5

Consortium for Mathematics and Its Applications (U.S.), Chedd-Angier Production Company, American Statistical Association, and Annenberg Media, eds. Against all odds--inside statistics: Disc 3, programs 9-12. S. Burlington, VT: Annenberg Media, 2011.

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6

Daniel, Oladele, and Harry Peach. Probability, Normal and Binomial Distribution. Independently Published, 2019.

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7

Negative Binomial Regression. Cambridfe University Press, 2007.

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8

Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2008.

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9

Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2007.

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10

Negative Binomial Regression. Cambridge, UK: Cambridge University Press, 2007.

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11

Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2011.

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12

Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2011.

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13

Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2011.

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14

Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2012.

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15

Hilbe, Joseph M. Negative Binomial Regression. Cambridge University Press, 2012.

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16

äger, Klaus, and E. von Collani. Binomial Distribution Handbook for Scientists and Engineers. Birkhauser Verlag, 2012.

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17

Collani, E. von, and Klaus Dräger. Binomial Distribution Handbook for Scientists and Engineers. Birkhäuser Boston, 2001.

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18

Dräger, Klaus, and E. Voncollani. Binomial Distribution Handbook for Scientists and Engineers. Springer, 2012.

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19

Negative binomial regression. 2nd ed. Cambridge, UK: Cambridge University Press, 2011.

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20

Binomial Distribution Handbook for Scientists and Engineers. Birkhauser, 2001.

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21

Binomial Distribution Handbook for Scientists and Engineers. Jaico Publishing House, 2005.

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22

Montgomery, David Bruce. Continuous Limit for the Contagious Binomial Distribution. Creative Media Partners, LLC, 2018.

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23

Collani, E. Von, and Klaus Drager. Binomial Distribution Handbook for Scientists and Engineers. Island Press, 2001.

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24

Modelling Generalized Linear-loglinear Models for Raters Agreement Measure: With Complete And Missing Values Cases (Anwendungsorientierte Statistik, Bd. 9). Peter Lang Publishing, 2005.

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25

Modelling Generalized Linear-loglinear Models for Raters Agreement Measure: With Complete And Missing Values Cases (Anwendungsorientierte Statistik). Peter Lang Publishing, 2005.

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26

Greene, William. Functional Form and Heterogeneity in Models for Count Data. Now Publishers Inc, 2007.

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27

M, Amabile Teresa, Singer Jill, Chedd-Angier Production Company, Consortium for Mathematics and Its Applications (U.S.)., and Magic Lantern Communications, eds. Binomial distributions [videorecording]. Oakville, Ont. :bMagic Lantern Communications Ltd, 1989.

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28

Kelly, M. F. An analysis of the single server queueing model with general service time distribution and either compound binomial or compound poisson input. 1985.

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29

Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science. Columbia University Press, 2021.

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30

Dixon, Tim H., and Aubrey Clayton. Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science. Audible Studios on Brilliance Audio, 2021.

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31

Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science. Columbia University Press, 2022.

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32

Kimball, William A. Asymptotic uniform distributions in finite groups with applications to binomial coefficients. 1994.

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33

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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Abstract:

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. Being that in this will be addressed the subjects of population and sample, types of data and their levels of measurement. Then it brings a detailed study of Descriptive Statistics, where it will be studied percentage, construction of graphs, frequency distribution, measures of central tendency and measures of dispersion. In the third chapter, notions of probability, binomial and normal probability distribution will be studied. In the last chapter, Inferential Statistics will be approached, starting with the confidence interval, going through the hypothesis tests (F, Z and t tests), ending with the statistical study of the correlation between variables and simple linear regression. It is worth mentioning that the statistical knowledge covered in this book can be useful for, in addition to students, professionals who want to improve their knowledge in statistics using Excel.

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34

Wright, A. G. Statistical processes. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.003.0004.

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Two statistical processes affect performance: one concerns photon detection at the photocathode (binomial); and the other, gain at each dynode (Poisson). The combined statistical processes dictate resolution, both timing and pulse height. They are best examined using generating functions that are both elegant and capable of providing answers more efficiently than traditional approaches. The requirement for steady and pulsed light sources is an important one for testing and setting up procedures. The use of moments to test the quality of performance is illustrated for a steady DC light source. Amplification provided by a dynode stack is a cascade process, leading to dispersion in gain, and is also ideally handled with generating functions. Theory is developed for essentially continuous pulse height distributions, such as those produced by a multichannel analyser. Arrival time statistics for scintillators are investigated analytically and by Monte Carlo simulation. Treatment is given for dead time and scaling.

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