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TEXTBOOKS
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Introduction to Data Science
AN INTRODUCTION TO DATA SCIENCE
Jeffrey S. Saltz • Jeffrey M. Stanton, both of Syracuse University
An Introduction to Data Science is an easy-to-read, gentle introduction for students with a wide range of backgrounds
into the world of data science. Assuming no prior coding experience or a deep understanding of statistics, this book
uses the open-source R programming language to make data science welcoming and accessible for all learners.
CONTENTS Introduction: Data Science, Many Skills / 1. About Data / 2. Identifying Data Problems / 3. Getting Started with R / 4. Follow the Data
/ 5. Rows and Columns / 6. Data Munging / 7. Onward with RStudio / 8. What's My Function? / 9. Beer, Farms, and Peas and the Use of Statistics
/ 10. Sample in a Jar / 11. Storage Wars / 12. Pictures vs. Numbers / 13. Map Mash-Up / 14. Word Perfect / 15. Happy Words? / 16. Lining Up
Our Models / 17. Hi Ho, Hi Ho - Data Mining We Go / 18. What's Your Vector, Victor? / 19. Shiny Apps / 20. Big Data? Big Deal!
PAPERBACK ISBN: 978-1-5063-7753-7 • ©2018 • 288 PAGES •
online
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Intermediate/Advanced Statistics
INTERMEDIATE STATISTICS USING SPSS
Herschel Knapp, University of Southern California
This friendly and approachable guide to real-world statistics is not just about abstract statistical theory or the
derivation or memorization of statistical formulas–it is about applied statistics. Covering the most common statistical
functions, students learn by doing with this truly practical approach to statistics.
CONTENTS PART I. STATISTICAL FUNDAMENTALS / 1. Research Principles / 2. Working in SPSS / Part II: SUMMARIZING VARIABLES / 3. Descriptive
Statistics / Part III: MEASURING DIFFERENCES BETWEEN GROUPS / 4. t Test and Mann-Whitney U Test / 5. ANOVA and Kruskal-Wallis Test / 6.
ANCOVA / 7. MANOVA / Part IV: MEASURING DIFFERENCES OVER TIME / 8. Paired t Test and Wilcoxon Test / 9. ANOVA Repeated Measures / Part
V: MEASURING RELATIONSHIP BETWEEN VARIABLES / 10. Chi-Square / 11. Correlation and Regression: Pearson and Spearman / 12. Multiple
Regression / 13. Logistic Regression / Part VI: DATA HANDLING / 14. Supplemental SPSS Operations
PAPERBACK ISBN: 978-1-5063-7743-8 • ©2018 • 465 PAGES •
online
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UNDERSTANDING STATISTICAL ANALYSIS AND MODELING
Robert Bruhl, University of Illinois at Chicago
Understanding Statistical Analysis and Modeling is for students in the social, behavioral, or managerial sciences
who may need to conduct some form of statistical analysis in their future professional lives, but who are not trained in
mathematics. Robert Bruhl focuses on the logic of statistical analysis, rather than mathematical methods, and while
formulas are introduced after the underlying logic has been explained, the exercises are performed in SPSS.
CONTENTS PART I: RESEARCH DESIGN / 1. "Why" Conduct Research and "Why" Use Statistics? / PART II: DESCRIPTIVE STATISTICS / 2. Methods
of Quantitative Empirical Investigation / 3. The Frequency Distribution Report: Organizing a Set of Observations / 4. The Mode, Median, and Mean:
Describing a Typical Value of a Quantitative Property Observed for a Set of Phenomena / 5. The Variance and Standard Deviation: Describing the
Variability Observed for a Quantitative Property of a Set of Phenomena / 6. The Z-Transformation and Standardization: Using the Standard Deviation
to Compare Observations / PART III: STATISTICAL INFERENCE AND PROBABILITY / 7. The Concept of a Probability / 8. Co-Existing Properties and
Joint Probability Models / 9. Sampling and the Normal Probability Model / PART IV: STATISTICAL INFERENCE / 10. Estimation Studies / 11. The
Chi-Square Statistic: Association Studies Involving Two Qualitative Properties / 12. The t-Test of Statistical Significance: Comparing a Quantitative
Attribute Assessed for Two Different Group / 13. Analysis of Variance (ANOVA): Comparing a Quantitative Attribute Assessed for Several Different
Groups / 14. Correlation Analysis and Linear Regression: Assessing the Co-Variability of Two Quantitative Properties
PAPERBACK ISBN: 978-1-5063-1741-0 • ©2018 • 440 PAGES •
online
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STATISTICAL METHODS FOR THE SOCIAL AND BEHAVIOURAL SCIENCES:
A Model-Based Approach
David B. Flora, York University, Canada
Statistical Methods for the Social and Behavioural Sciences: A Model-Based Approach is the essential guide for
those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling
method for their own data. It is particularly suited to second or advanced courses in statistical methods across the
social and behavioral sciences.
CONTENTS 1. Foundations of Statistical Modeling Demonstrated with Simple Regression / 2. Multiple Regression with Continuous Predictors /
3. Regression with Categorical Predictors / 4. Interactions in Multiple Regression: Models for Moderation / 5. Using Multiple Regression to Model
Mediation and Other Indirect Effects / 6. Introduction to Multilevel Modeling / 7. Basic Matrix Algebra for Statistical Modeling / 8. Exploratory
Factor Analysis / 9. Structural Equation Modeling I: Path Analysis / 10. Structural Equation Modeling II: Latent Variable Models / 11. Growth Curve
Modeling
PAPERBACK ISBN: 978-1-4462-6983-1 • ©2019 • 472 PAGES •
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