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Statistics de Alan Agresti

Statistics

Autor Alan Agresti, Christine A. Franklin, Bernhard Klingenberg
en Limba Engleză Foi volante

În contextul programelor universitare de introducere în statistică, Statistics: The Art and Science of Learning from Data, Global Edition se poziționează ca o resursă fundamentală care transformă disciplina dintr-un set de formule rigide într-o știință centrală a vieții moderne. Recomandăm acest manual pentru cursurile de licență unde accentul cade pe alfabetizarea statistică și pe capacitatea de a extrage sens din volume mari de date. Ediția a cincea aduce îmbunătățiri tehnologice semnificative, integrând noi aplicații web și resurse video care facilitează învățarea activă.

Subliniem faptul că, spre deosebire de manualele tradiționale, structura propusă de Alan Agresti, Christine A. Franklin și Bernhard Klingenberg inversează ordinea clasică: conceptele sunt introduse mai întâi prin prisma datelor categorice și a proporțiilor, facilitând o înțelegere intuitivă înainte de trecerea la datele cantitative. Această metodologie reflectă expertiza vastă a lui Agresti, vizibilă și în lucrările sale anterioare precum An Introduction to Categorical Data Analysis sau Statistical Methods for the Social Sciences, Global Edition, unde rigoarea academică este dublată de accesibilitate.

Ca o alternativă la Sullivan, M: Statistics: Informed Decisions Using Data, Glob pentru cursurile de statistică introductivă, acest titlu oferă avantajul unei integrări mai profunde a noilor direcții din Data Science încă din primele capitole. Organizarea cărții ghidează studentul de la colectarea și explorarea datelor (grafice, regresie liniară, variabilitate) către analize complexe, utilizând exemple din lumea reală care mențin un nivel ridicat de implicare. Reținem că această ediție Global Edition de la Pearson este calibrată pentru a răspunde nevoilor curriculare internaționale, oferind un echilibru între teorie și aplicații practice.

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Specificații

ISBN-13: 9780133860825
ISBN-10: 0133860825
Pagini: 816
Ediția:4
Editura: Pearson

De ce să citești această carte

Această lucrare se adresează studenților care doresc să înțeleagă logica din spatele cifrelor, nu doar să aplice algoritmi. Prin parcurgerea ei, cititorul câștigă abilitatea de a pune întrebările potrivite în fața unui set de date și de a evita capcanele interpretărilor grafice eronate. Este o resursă esențială pentru viitorii specialiști care au nevoie de o bază solidă în statistica aplicată, susținută de instrumente digitale moderne.

Despre autor

Alan Agresti este profesor emerit în cadrul Departamentului de Statistică al Universității din Florida, unde a predat timp de 35 de ani. Este un expert recunoscut la nivel mondial în analiza datelor categorice, domeniu în care a publicat peste 100 de articole și texte de referință. Printre lucrările sale fundamentale se numără „Categorical Data Analysis” și „Statistical Methods for the Social Sciences”. Pentru contribuțiile sale remarcabile în educația statistică și cercetare, Agresti este membru ales al American Statistical Association, experiența sa didactică fiind pilonul pe care este construită abordarea conceptuală a acestui manual.

Descriere scurtă

NOTE: This edition features the same content as the traditional text in a convenient, three-hole-punched, loose-leaf version. Books a la Carte also offer a great value this format costs significantly less than a new textbook. Before purchasing, check with your instructor or review your course syllabus to ensure that you select the correct ISBN. Several versions of Pearson's MyLab & Mastering products exist for each title, including customized versions for individual schools, and registrations are not transferable. In addition, you may need a CourseID, provided by your instructor, to register for and use Pearson's MyLab & Mastering products. "For courses in introductory statistics." The Art and Science of Learning from Data "Statistics: The Art and Science of Learning from Data, " Fourth Edition, takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises. The authors believe that it s important for students to learn and analyze both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data. Also available with MyStatLab MyStatLab is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts. For this edition, new web apps with complementary exercises, a tightly integrated video program, and strong exercise coverage enhance student learning. Note: You are purchasing a standalone product; MyLab & Mastering does not come packaged with this content. Students, if interested in purchasing this title with MyLab & Mastering, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. "

Cuprins

I: GATHERING AND EXPLORING DATA
1. Statistics: The Art and Science of Learning From Data
1.1 Using Datato Answer Statistical Questions
1.2 SampleVersus Population
1.3 OrganizingData, Statistical Software, and the New Field of Data Science
ChapterSummary
ChapterExercises
2. Exploring Data With Graphs and Numerical Summaries
2.1 DifferentTypes of Data
2.2 GraphicalSummaries of Data
2.3 Measuringthe Center of Quantitative Data
2.4 Measuringthe Variability of Quantitative Data
2.5 UsingMeasures of Position to Describe Variability
2.6 LinearTransformations and Standardizing
2.7Recognizing and Avoiding Misuses of Graphical Summaries
ChapterSummary
ChapterExercises
3. Exploring Relationships Between Two Variables
3.1 TheAssociation Between Two Categorical Variables
3.2 TheRelationship Between Two Quantitative Variables
3.3 LinearRegression: Predicting the Outcome of a Variable
3.4 Cautionsin Analyzing Associations
ChapterSummary
ChapterExercises
4. Gathering Data
4.1Experimental and Observational Studies
4.2 Good andPoor Ways to Sample
4.3 Good andPoor Ways to Experiment
4.4 Other Waysto Conduct Experimental and Nonexperimental Studies
ChapterSummary
ChapterExercises
II: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND SAMPLINGDISTRIBUTIONS
5. Probability in Our Daily Lives
5.1 HowProbability Quantifies Randomness
5.2 FindingProbabilities
5.3Conditional Probability
5.4 Applyingthe Probability Rules
ChapterSummary
ChapterExercises
6. Random Variables and Probability Distributions
6.1Summarizing Possible Outcomes and Their Probabilities
6.2Probabilities for Bell-Shaped Distributions
6.3Probabilities When Each Observation Has Two Possible Outcomes
ChapterSummary
ChapterExercises
7. Sampling Distributions
7.1 How SampleProportions Vary Around the Population Proportion
7.2 How SampleMeans Vary Around the Population Mean
7.3 Using theBootstrap to Find Sampling Distributions
ChapterSummary
Chapter Exercises
III: INFERENTIAL STATISTICS
8. Statistical Inference: Confidence Intervals
8.1 Point andInterval Estimates of Population Parameters
8.2 ConfidenceInterval for a Population Proportion
8.3 ConfidenceInterval for a Population Mean
8.4 BootstrapConfidence Intervals
ChapterSummary
ChapterExercises
9. Statistical Inference: Significance Tests AboutHypotheses
9.1 Steps forPerforming a Significance Test
9.2Significance Tests About Proportions
9.3Significance Tests About a Mean
9.4 Decisionsand Types of Errors in Significance Tests
9.5Limitations of Significance Tests
9.6 TheLikelihood of a Type II Error
ChapterSummary
ChapterExercises
10. Comparing Two Groups
10.1Categorical Response: Comparing Two Proportions
10.2 QuantitativeResponse: Comparing Two Means
10.3 ComparingTwo Groups with Bootstrap or Permutation Resampling
10.4 AnalyzingDependent Samples
10.5 Adjustingfor the Effects of Other Variables
ChapterSummary
ChapterExercises
IV: ANALYZING ASSOCIATION AND EXTENDED STATISTICALMETHODS
11. Analyzing the Association Between CategoricalVariables
11.1Independence and Dependence (Association)
11.2 TestingCategorical Variables for Independence
11.3Determining the Strength of the Association
11.4 Using Residualsto Reveal the Pattern of Association
11.5 FishersExact and Permutation Tests
ChapterSummary
ChapterExercises
12. Analyzing the Association Between QuantitativeVariables: Regression Analysis
12.1 ModelingHow Two Variables Are Related
12.2 InferenceAbout Model Parameters and the Association
12.3Describing the Strength of Association
12.4 How theData Vary Around the Regression Line
12.5Exponential Regression: A Model for Nonlinearity
ChapterSummary
ChapterExercises
13. Multiple Regression
13.1 UsingSeveral Variables to Predict a Response
13.2 Extendingthe Correlation and R2 for Multiple Regression
13.3 UsingMultiple Regression to Make Inferences
13.4 Checkinga Regression Model Using Residual Plots
13.5Regression and Categorical Predictors
13.6 Modelinga Categorical Response
ChapterSummary
ChapterExercises
14. Comparing Groups: Analysis of Variance Methods
14.1 One-WayANOVA: Comparing Several Means
14.2Estimating Differences in Groups for a Single Factor
14.3 Two-WayANOVA
ChapterSummary
ChapterExercises
15. Nonparametric Statistics
15.1 CompareTwo Groups by Ranking
15.2Nonparametric Methods for Several Groups and for Matched Pairs
ChapterSummary
ChapterExercises
Appendix
Answers
Index
Index of Applications
Credits

Notă biografică

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.
Christine Franklin is the K-12 Statistics Ambassador for the American Statistical Association and elected ASA Fellow. She has retired from the University of Georgia as the Lothar Tresp Honoratus Honors Professor and Senior Lecturer Emerita in Statistics.
She is the co-author of two textbooks and has published more than 60 journal articles and book chapters. Chris was the lead writer for the American Statistical Association Pre-K-12 Guidelines for the Assessment and Instruction in Statistics Education (GAISE) Framework document, co-chair for the updated Pre-K-12 GAISE II, and chair of the ASA Statistical Education of Teachers (SET) report.
Bernhard Klingenberg is a Professor of Statistics in the Department of Mathematics & Statistics at Williams College, where he has been teaching introductory and advanced statistics classes since 2004. He teaches statistical inference and modelling as well as data visualisation at the Graduate Data Science Program at New College of Florida.
Prof. Klingenberg is responsible for the development of the web apps, which he programs using the R package Shiny. A native of Austria, he frequently returns there to hold visiting positions at universities and gives short courses on categorical data analysis in Europe and the United States. He also enjoys photography, with some of his pictures appearing in this book.