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Libri sul tema "Statistical inferences"

Autore: Grafiati

Pubblicato: 25 gennaio 2025

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1

Miller, Douglas R. Making statistical inferences about software reliability. Hampton, Va: Langley Research Center, 1988.

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2

United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., a cura di. Making statistical inferences about software reliability. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.

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3

MacRae, Sandy. Drawing inferences from statistical data: Tutor notes. Leicester: British Psychological Society, 1994.

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4

Uebersax, John. Validity inferences from interobserver agreement. Santa Monica, CA: Rand, 1989.

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5

Howard, Wainer, e Educational Testing Service, a cura di. Drawing inferences from self-selected samples. Mahwah, N.J: Lawrence Erlbaum Associates, 2000.

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6

He, Hua, Pan Wu e Ding-Geng Chen, a cura di. Statistical Causal Inferences and Their Applications in Public Health Research. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41259-7.

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7

Gaba, Anil. Using survey data in inferences about purchase behaviour. Fontainebleau, France: INSEAD, 1990.

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8

Pelosi, Marilyn K. Doing statistics for business with Excel: Data, inferences, and decision making. New York: John Wiley, 1999.

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9

Schklar, Jason. Who killed Mrs. Prob(ability)'s dog?: Drawing inferences from statistical evidence. Chicago, Ill: American Bar Foundation, 1998.

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10

W, Roberts Carl, a cura di. Text analysis for the social sciences: Methods for drawing statistical inferences from texts and transcripts. Mahwah NJ: Erlbaum, 1997.

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11

Bromek, Tadeusz, e Elżbieta Pleszczyńska, a cura di. Statistical Inference. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0575-7.

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12

Panik, Michael J. Statistical Inference. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118309773.

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13

L, Berger Roger, a cura di. Statistical inference. Pacific Grove, Calif: Brooks/Cole Pub. Co., 1990.

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14

Silvey, S. D. Statistical inference. London: Chapman and Hall, 1988.

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15

Garthwaite, Paul H. Statistical inference. London: Prentice Hall, 1995.

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16

Garthwaite, Paul H. Statistical inference. 2^a ed. Oxford: Oxford University Press, 2002.

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17

Oakes, Michael W. Statistical inference. Chestnut Hill, MA: Epidemiology Resources Inc., 1990.

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18

L, Berger Roger, a cura di. Statistical Inference. 2^a ed. Australia: Thomson Learning, 2002.

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19

T, Jolliffe I., e Jones Byron 1951-, a cura di. Statistical inference. Oxford: Oxford University Press, 2002.

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20

Deshmukh, Shailaja, e Madhuri Kulkarni. Asymptotic Statistical Inference. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9003-0.

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21

Caliński, T., e W. Klonecki, a cura di. Linear Statistical Inference. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4615-7353-1.

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22

Held, Leonhard, e Daniel Sabanés Bové. Applied Statistical Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-37887-4.

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23

Gibbons, Jean Dickinson, e Subhabrata Chakraborti. Nonparametric Statistical Inference. 6th edition. | Boca Raton : CRC Press, 2021.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315110479.

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24

Silvapulle, Mervyn J., e Pranab K. Sen. Constrained Statistical Inference. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2001. http://dx.doi.org/10.1002/9781118165614.

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25

Boos, Dennis D., e L. A. Stefanski. Essential Statistical Inference. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4818-1.

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26

Dickhaus, Thorsten. Simultaneous Statistical Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45182-9.

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27

Paolella, Marc S., a cura di. Fundamental Statistical Inference. Chichester, UK: John Wiley & Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119417897.

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28

Barnett, Vic, a cura di. Comparative Statistical Inference. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1999. http://dx.doi.org/10.1002/9780470316955.

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29

Drawing Inferences from Statistical Data (Psychology). Blackwell Publishing Limited, 1994.

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30

MacRae, Sandy. Drawing Inferences from Statistical Data (Open Learning Units: Statistics). BPS Blackwell, 1994.

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31

Hesamian, Gholamreza. Fuzzy Statistical Inferences Based on Fuzzy Random Variables. Taylor & Francis Group, 2022.

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32

Hesamian, Gholamreza. Fuzzy Statistical Inferences Based on Fuzzy Random Variables. Taylor & Francis Group, 2022.

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33

Basawa, Ishwar V. Statistical Inferences for Stochasic Processes: Theory and Methods. Elsevier Science & Technology Books, 2014.

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34

Hesamian, Gholamreza. Fuzzy Statistical Inferences Based on Fuzzy Random Variables. Taylor & Francis Group, 2022.

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35

Hesamian, Gholamreza. Fuzzy Statistical Inferences Based on Fuzzy Random Variables. Taylor & Francis Group, 2022.

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36

Hesamian, Gholamreza. Fuzzy Statistical Inferences Based on Fuzzy Random Variables. CRC Press LLC, 2022.

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37

Drawing Inferences from Self-Selected Samples. Island Press, 1986.

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38

Wainer, Howard. Drawing Inferences from Self-Selected Samples. Taylor & Francis Group, 2016.

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39

Wainer, Howard. Drawing Inferences from Self-Selected Samples. Springer, 2012.

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40

Wainer, Howard. Drawing Inferences from Self-Selected Samples. Springer New York, 2013.

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41

Drawing Inferences from Self-Selected Samples. Taylor & Francis Group, 2013.

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42

Drawing Inferences from Self-Selected Samples. Taylor & Francis Group, 2013.

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43

Drawing Inferences from Self-Selected Samples. Routledge, 2013.

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44

Hedström, Peter. Studying Mechanisms To Strengthen Causal Inferences In Quantitative Research. A cura di Janet M. Box-Steffensmeier, Henry E. Brady e David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0013.

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Abstract (sommario):

This article emphasizes various ways by which the study of mechanisms can make quantitative research more useful for causal inference. It concentrates on three aspects of the role of mechanisms in causal and statistical inference: how an understanding of the mechanisms at work can improve statistical inference by guiding the specification of the statistical models to be estimated; how mechanisms can strengthen causal inferences by improving our understanding of why individuals do what they do; and how mechanism-based models can strengthen causal inferences by showing why, acting as they do, individuals bring about the social outcomes they do. There has been a surge of interest in mechanism-based explanations, in political science as well as in sociology. Most of this work has been vital and valuable in that it has sought to clarify the distinctiveness of the approach and to apply it empirically.

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45

Ioannidis, John P. A. Statistical Biases in Science Communication. A cura di Kathleen Hall Jamieson, Dan M. Kahan e Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.11.

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Abstract (sommario):

Misuse and misinterpretation of statistics result in statistical biases that affect the quality, clarity, relevance, and implications of communicated scientific information. Statistical tools are often suboptimally used in scientific papers, even in the best journals. The vast majority of published results are statistically significant, and even nonsignificant results are often spun as being important. Inferences based on P-values generate additional misconceptions. It is also common to focus on metrics that are more prone to exaggerated interpretation. Most of these problems are possible to solve or at least improve on. The prevalence of statistical biases has been used in attacks designed to discredit science’s validity. However, the use of rigorous statistical methods and their careful interpretation can be one of the strongest distinguishing features of good science and a powerful tool to sustain science’s integrity.

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46

He, Hua, Ding-Geng (Din) Chen e Pan Wu. Statistical Causal Inferences and Their Applications in Public Health Research. Springer, 2016.

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47

He, Hua, Ding-Geng (Din) Chen e Pan Wu. Statistical Causal Inferences and Their Applications in Public Health Research. Springer, 2018.

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48

He, Hua, Ding-Geng (Din) Chen e Pan Wu. Statistical Causal Inferences and Their Applications in Public Health Research. Springer London, Limited, 2016.

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49

Fan, Cong Cong Michelle. A multiplicative model of the transmission rate and its statistical inferences. 2001.

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50

McCleary, Richard, David McDowall e Bradley J. Bartos. Statistical Conclusion Validity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0006.

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Abstract (sommario):

Chapter 6 addresses the sub-category of internal validity defined by Shadish et al., as statistical conclusion validity, or “validity of inferences about the correlation (covariance) between treatment and outcome.” The common threats to statistical conclusion validity can arise, or become plausible through either model misspecification or through hypothesis testing. The risk of a serious model misspecification is inversely proportional to the length of the time series, for example, and so is the risk of mistating the Type I and Type II error rates. Threats to statistical conclusion validity arise from the classical and modern hybrid significance testing structures, the serious threats that weigh heavily in p-value tests are shown to be undefined in Beyesian tests. While the particularly vexing threats raised by modern null hypothesis testing are resolved through the elimination of the modern null hypothesis test, threats to statistical conclusion validity would inevitably persist and new threats would arise.

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