Gain the statistics skills you need for the social sciences with this accessible introductory guide Statistical Methods for the Social Sciences, 5th Edition, Global Edition, by Alan Agresti, introduces you to statistical methods used in social science disciplines with no previous knowledge of statistics necessary. With an emphasis on concepts and applications, the book requires only a minimal mathematical background, maintaining a low technical level throughout to make it accessible to beginners. The 5th edition has a strong focus on real examples to help you learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. This approach also helps you understand how to apply your learning to the real world. This edition also emphasises the interpretation of software output rather than the formulas for performing analysis, reflecting advances in statistical software - which are more frequently used by social scientists to analyse data today. Other updates include: Numerous homework exercises included in each chapter.Updated data in most exercises.New sections, such as that on maximum likelihood estimation in chapter 5New examples ask students to use applets to help them learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests.The text also relies more on applets for finding tail probabilities from distributions such as the Normal, t, and chi-squared. With a wide array of learning features and the latest available information, this text will equip you with the knowledge you need to succeed in your course - an ideal companion for students majoring in social science disciplines.
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Help your students gain statistics skills for the social sciences with this accessible text Statistical Methods for the Social Sciences introduces your students to the subject in a low-technical way with no statistics knowledge necessary. This edition presents the latest information in a way ideal for two-semester courses in social science.
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Preface Acknowledgments IntroductionSampling and MeasurementDescriptive StatisticsProbability DistributionsStatistical Inference: EstimationStatistical Inference: Significance TestsComparison of Two GroupsAnalyzing Association between Categorical VariablesLinear Regression and CorrelationIntroduction to Multivariate RelationshipsMultiple Regression and CorrelationRegression with Categorical Predictors: Analysis of Variance MethodsMultiple Regression with Quantitative and Categorical PredictorsModel Building with Multiple RegressionLogistical Regression: Modeling Categorical Responses Appendix: R, Stata, SPSS, and SAS for Statistical Analyses Answers to Select Odd-Numbered Exercises Bibliography Credits Index
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Hallmark features of this title A structure and tone that helps your students cover material quickly while avoiding confusion. Emphasis on concepts and applications.Logistic regression is explained in a less technical way so it's understood by students of all levels. A host of learning features aid comprehension and retention of the material. Comparing Two Groups chapter introduces ideas of bivariate analysis, discusses how to compare two groups with a difference or a ratio of two parameters, and shows the general formula for finding a standard error of a difference between two independent estimates.Confidence intervals present methods for proportion before the mean. This allows students to learn the basic concept of a confidence interval without facing too many topics all at once.
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New and updated features of this edition Updated material reflects the latest available information and developments. Greater integration of statistical software. Software output shown now uses R and Stata instead of only SAS and SPSS. The text appendix provides instructions about the basic use of these software packages.ANOVA coverage has been reorganized to put more emphasis on using regression models with dummy variables to handle categorical explanatory variables.Emphasis on concepts on advanced topics underlines the importance of interpreting output from computer packages rather than complex computing formulas. New sections and chapters. New examples and exercises ask students to use applets to help them learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests.Chapter 5 has a new section that introduces maximum likelihood estimation and the bootstrap method.Chapter 13 on regression modelling now has a new section using case studies to illustrate how research studies commonly use regression with both types of explanatory variables. The chapter also has a new section introducing linear mixed models.Chapter 14 contains a new section on robust regression covering standard errors and nonparametric regression.
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Produktdetaljer

ISBN
9781292220314
Publisert
2017
Utgave
5. utgave
Utgiver
Vendor
Pearson Education Limited
Vekt
960 gr
Høyde
252 mm
Bredde
202 mm
Dybde
24 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
568

Forfatter

Biographical note

Alan Agresti is a Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He taught statistics there for 38 years, including the development of e-courses in statistical methods for social science students and three courses in categorical data analysis.

He is the author of more than 100 refereed articles and six texts, including Statistical Methods for the Social Sciences (Pearson, 5th edition, 2018) and An Introduction to Categorical Data Analysis (Wiley, 3rd edition, 2019). Alan has also received teaching awards from the University of Florida and an Excellence in Writing award from John Wiley & Sons.