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Books on the topic 'Statistica bivariata'

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Published: 10 March 2023

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1

Haung, Xin. Statistics of bivariate extreme values. Amsterdam: Thesis Publishers, 1992.

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2

Diekhoff, George. Statistics for the social and behavioral sciences: Univariate, bivariate, multivariate. Dubuque, IA: Wm. C. Brown Publishers, 1992.

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3

Diekhoff, George. Statistics for the social and behavioral sciences: Univariate, bivariate, and multivariate. IA: Wm. C. Brown, 1992.

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4

K, Kocherlakota, ed. Bivariate discrete distributions. New York: M. Dekker, 1992.

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5

Jacoby, William. Statistical Graphics for Univariate and Bivariate Data. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications Inc., 1997. http://dx.doi.org/10.4135/9781412985963.

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6

Applied statistics: From bivariate through multivariate techniques. Los Angeles: SAGE Publications, 2008.

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7

Applied statistics: From bivariate through multivariate techniques. 2nd ed. Thousand Oaks: SAGE Publications, 2013.

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8

Jacoby, William G. Statistical graphics for univariate and bivariate data. Thousand Oaks, Calif: Sage Publications, 1997.

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9

D, Lai C., ed. Continuous bivariate distributions, emphasising applications. Adelaide, South Australia: Rumsby Scientific Publishing, 1990.

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10

Statistical fundamentals: Using Microsoft Excel for univariate and bivariate analysis. Chesapeake: Watertree Press, 2016.

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11

Denis, Daniel J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119465775.

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12

Moskowitz, Herbert. A polynomial approximation for bivariate normal probabilities. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1986.

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13

Peters, Dawn. A bivariate signed-rank test for the two-sample location problem. Corvallis, Or: Dept. of Statistics, Oregon State University, 1988.

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14

Peters, Dawn. A bivariate signed-rank test for the two-sample location problem. Corvallis, Or: Dept. of Statistics, Oregon State University, 1988.

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15

Czaplewski, Raymond L. Expected value and variance of Moran's bivariate spatial autocorrelation statistic for a permutation test. Fort Collins, Colo. (240 W. Prospect Rd., Fort Collins 80526): U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1993.

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16

Tanabe, Kunio. BNDE, FORTRAN subroutines for computing Bayesian nonparametric univariate and bivariate density estimator. Tokyo: Institute of Statistical Mathematics, 1988.

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17

Koeppl, James W. Robust statistics for spatial analysis: The bivariate normal home range model applied to syntopic populations of two species of ground squirrels. Lawrence, Kan: Museum of Natural History, the University of Kansas, 1985.

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18

Denis, Daniel J. Applied Univariate, Bivariate, and Multivariate Statistics. Wiley & Sons, Incorporated, John, 2015.

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19

Applied Statistics I: Basic Bivariate Techniques. SAGE Publications, Incorporated, 2020.

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20

Applied Univariate, Bivariate, and Multivariate Statistics. Wiley & Sons, Limited, John, 2016.

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21

Denis, Daniel J. Applied Univariate, Bivariate, and Multivariate Statistics. Wiley & Sons, Incorporated, John, 2015.

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22

Denis, Daniel J. Applied Univariate, Bivariate, and Multivariate Statistics. Wiley & Sons, Incorporated, John, 2015.

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23

Tran, Thanh V., and Keith T. Chan. Applied Cross-Cultural Data Analysis for Social Work. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190888510.001.0001.

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Applied Cross-Cultural Data Analysis for Social Work is a research guide which provides a hands-on approach for learning and understanding data analysis techniques for examining and interpreting data for the purpose of cultural group comparisons. This book aims to provide practical applications in statistical approaches of data analyses that are commonly used in cross-cultural research and evaluation. Readers are presented with step-by-step illustrations in the use of descriptive, bivariate, and multivariate statistics to compare cross-cultural populations using large-scale, population-based survey data. These techniques have important applications in health, mental health, and social science research relevant to social work and other helping professions, especially in providing a framework of evidence to examine health disparities using population-health data. For each statistical approach discussed in this book, we explain the underlying purpose, basic assumptions, types of variables, application of the Stata statistical package, the presentation of statistical findings, and the interpretation of results. Unlike previous guides on statistical approaches and data analysis in social work, this book explains and demonstrates the strategies of cross-cultural data analysis using descriptive and bivariate analysis, multiple regression, additive and multiplicative interaction, mediation, and SEM and HLM for subgroup analysis and cross-cultural comparisons. This book also includes sample syntax from Stata for social work researchers to conduct cross-cultural analysis with their own research.
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24

Warner, Rebecca M. Applied Statistics: From Bivariate Through Multivariate Techniques. SAGE Publications, Incorporated, 2013.

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25

Warner, Rebecca M. Applied Statistics: From Bivariate Through Multivariate Techniques. Sage Publications, Inc, 2007.

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26

Jacoby, William G. Statistical Graphics for Univariate and Bivariate Data. SAGE Publications, Incorporated, 2013.

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27

Micros in Mathematical Education (Mime): Statistics Bivariate Data. John Wiley and Sons Ltd, 1987.

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28

Sims, Randi L. Bivariate Data Analysis: A Practical Guide. Nova Science Publishers, 1999.

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29

Applied Statistics I - International Student Edition: Basic Bivariate Techniques. SAGE Publications, Incorporated, 2020.

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30

Xin, H. Statistics of Bivariate Extreme Values (Tinbergen Institute Research Series). Thesis Pub, 1992.

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31

Denis, Daniel J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics. Wiley & Sons, Incorporated, John, 2018.

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32

Denis, Daniel J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics. Wiley & Sons, Incorporated, John, 2018.

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33

Denis, Daniel J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics. Wiley & Sons, Limited, John, 2018.

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34

Rasco, Danney. R Companion for Applied Statistics I: Basic Bivariate Techniques. SAGE Publications, Incorporated, 2020.

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35

SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics. Wiley & Sons, Incorporated, John, 2018.

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36

Rovai, Alfred P. Statistical Fundamentals: Using Microsoft Excel for Univariate and Bivariate Analysis. Watertree Press, 2014.

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37

Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.

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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particular emphasis on hypothesis testing and significance tests. It discusses ordinary least squares (OLS) regression and cross-tabulation, two of the most widely used statistical analysis techniques in political research. Finally, it explains how to state the null hypothesis, calculate the chi square, and establishing the correlation between the dependent and independent variables.
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38

Diekhoff, George M. Statistics for the Social and Behavioral Science: Univariate, Bivariate, and Multivariate. William C Brown Pub, 1992.

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39

Grade dependence in bivariate mixed data. A uniform approach based on concentration curves. Warszawa: Instytut Podstaw Informatyki Polskiej Akademii Nauk, 1996.

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40

Expected value and variance of Moran's bivariate spatial autocorrelation statistic for a permutation test. Fort Collins, Colo: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1993.

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41

Peters, Dawn. Rank tests for the one- and two-sample bivariate location problems. 1988.

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42

Denis, Daniel J. Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R. Wiley & Sons, Limited, John, 2021.

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43

Denis, Daniel J. Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R. Wiley & Sons, Incorporated, John, 2021.

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44

Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R. Wiley & Sons, Incorporated, John, 2021.

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45

Denis, Daniel J. Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R. Wiley & Sons, Incorporated, John, 2021.

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46

Czaplewski, R. L. Expected value and variance of Moran's bivariate spatial autocorrelation statistic for a permutation test. 1993.

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47

Applied Univariate, Bivariate, and Multivariate Statistics Using Python: A Beginner's Guide to Advanced Data Analysis. Wiley & Sons, Incorporated, John, 2021.

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48

Denis, Daniel J. Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science. Wiley & Sons, Limited, John, 2020.

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49

Denis, Daniel J. Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science. Wiley & Sons, Limited, John, 2020.

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50

Denis, Daniel J. Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science. Wiley & Sons, Incorporated, John, 2020.

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