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Applied Statistics I: Basic Bivariate Techniques has been created from the first half of Rebecca M. Warner's popular Applied Statistics: From Bivariate Through Multivariate Techniques. The author's contemporary approach differs from some of the well-worn texts in the market, and reflects current thinking in the field. It spends less time on statistical significance testing, and moves in the direction of the "new statistics" by focusing more on confidence intervals and effect size. Instructors of upper undergraduate or beginning graduate level courses will find that the greater focus on basic concepts such as partition of variance and effect size is more useful to students, particularly as preparation for more advanced courses. Spending less time on statistical significance testing allows for more time to be devoted to more interesting and useful statistics that students will see in journal articles (such as correlation and regression). This introductory statistics text includes examples in SPSS, together with datasets on an accompanying website. A companion study guide reproducing the exercises and examples in R will also be available.
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Rebecca M. Warner's bestselling Applied Statistics: From Bivariate Through Multivariate Techniques has been split into two volumes for ease of use over a two-course sequence. This new multivariate statistics text, Applied Statistics II: Multivariable and Multivariate Techniques, Third Edition is based on chapters from the second half of original book, but with much additional material. This text now provides a distinctive bridge between earlier courses and advanced topics through extensive discussion of statistical control (adding a third variable), a new chapter on the "new statistics", a new chapter on outliers and missing values, and a final chapter that provides an introduction to structural equation modeling. This text provides a solid introduction to concepts such as statistical control, mediation, moderation, and path modeling necessary to students taking intermediate and advanced statistics courses across the social sciences. Examples are provided in SPSS with datasets available on an accompanying website. A companion study guide reproducing the exercises and examples in R will also be available.
(source: Nielsen Book Data)

• Acknowledgments
• Introduction and descriptive statistics
• Introduction to statistics
• Summarizing data : frequency distributions in tables and graphs
• Summarizing data : central tendency
• Summarizing data: variability
• Probability and the foundations of inferential statistics
• Probability, normal distributions, and z scores
• Characteristics of the sample mean
• Hypothesis testing : significance, effect size, and power
• Making inferences about one or two means
• Testing means : one-sample t test with confidence intervals
• Testing means : two-independent-sample t test with confidence intervals
• Testing means : related-samples t test with confidence intervals
• Making inferences about the variability of two or more means
• One-way analysis of variance : between-subjects and within-subjects (repeated-measures) designs
• Two-way analysis of variance : between-subjects factorial design
• Making inferences about patterns, prediction, and nonparametric tests
• Correlation and linear regression
• Chi-square tests : goodness-of-fit and the test for independence
• Afterword
• Appendix A: Basic math review and summation notation
• Appendix B: SPSS general instruction guide
• Appendix C: Statistical tables
• Appendix D: Chapter solutions for even-numbered problems
• Glossary
• References
• Index.

Revised edition of the author's Essential statistics for the behavioral sciences, 2016.
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• List of Figures List of Tables Acknowledgements
• 1 Introduction to ITSA
• 2 ARIMA Algebra
• 3 The Noise Component: N(at)
• 4 The Intervention Component: X(It)
• 5 Auxiliary Modeling Procedures References Index.
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Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. It provides example analyses of social, behavioral, and biomedical time series to illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. Additionally, the book supplements the classic Box-Jenkins-Tiao model-building strategy with recent auxiliary tests for transformation, differencing, and model selection. Not only does the text discuss new developments, including the prospects for widespread adoption of Bayesian hypothesis testing and synthetic control group designs, but it makes optimal use of graphical illustrations in its examples. With forty completed example analyses that demonstrate the implications of model properties, Interrupted Time Series Analysis will be a key inter-disciplinary text in classrooms, workshops, and short-courses for researchers familiar with time series data or cross-sectional regression analysis but limited background in the structure of time series processes and experiments.
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• Chapter 1: Why Space in the Social Sciences?
• Chapter 2: Maps as Displays of Information
• Chapter 3: Interdependency Among Observations
• Chapter 4: Spatially Lagged Dependent Variables
• Chapter 5: Spatial Error Model
• Chapter 6: Extensions.
• (source: Nielsen Book Data)

Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including: mapping data on spatial units, exploratory spatial data analysis, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social sciences examples based on real data, Michael D. Ward and Kristian Skrede Gleditsch illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.
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• Acknowledgments
• Chapter 1: Brief Introduction to Research in the Social, Behavioral, and Health Sciences What Is the Purpose of Research? How Is Research Done? Scientific Method and Hypothesis Testing Inductive Research Deductive Research Research Designs
• Chapter 2: Variables and Measurement Variables and Data Levels of Variable Measurement Types of Relationships Research Design and Measurement Quality
• Chapter 3: How to Sample and Collect Data for Analysis Why Use a Sample? Probability Sampling Methods Nonprobability Sampling Methods Validating a Sample Split Ballot Designs How and Where Are Data Collected Today?
• Chapter 4: Data Frequencies and Distributions Univariate Frequencies and Relative Frequencies Cumulative Percentages and Percentiles Frequencies for Quantitative Data Univariate Distributions The Normal Distribution Non-Normal Distribution Characteristics Data Transformations for Dealing With Non-Normal Distributions Bivariate Frequencies
• Chapter 5: Using and Interpreting Univariate and Bivariate Visualizations Univariate Data Visualization Bivariate Data Visualization
• Chapter 6: Central Tendency and Variability Understanding How to Calculate and Interpret Measures of Central Tendency Understanding How Individuals in a Distribution Vary Around a Central Tendency
• Chapter 7: What Are z Scores, and Why Are They Important? What Is a z Score? How to Calculate a z Score The Standard Normal Table Working With the Standard Normal Distribution to Calculate z Scores, Raw Scores, and Percentiles Confidence Intervals
• Chapter 8: Hypothesis Testing and Statistical Significance Null and Alternative Hypotheses Statistical Significance Test Statistic Distributions Choosing a Test of Statistical Significance The Chi-Square Test of Independence The Independent Samples t Test One-Way Analysis of Variance
• Chapter 9: How to Measure the Relationship Between Nominal and Ordinal Variables Choosing the Correct Measure of Association Trying to Reduce Error (PRE Statistics) Calculating and Interpreting Lambda Calculating and Interpreting Gamma Calculating and Interpreting Somers' d Calculating and Interpreting Kendall's Tau-b Interpreting PRE Statistics Overview
• Chapter 10: Effect Size Effect Size Choosing an Effect Size
• Chapter 11: How to Interpret and Report Regression Results What Is a Regression? Correlation Bivariate Regression Coefficient of Determination (r2) Multiple Regression Logistic Regression
• Chapter 12: Indices, Typologies, and Scales Indices, Typologies, and Scales Defined and Explained Appendix A. The Standard Normal Table Appendix B. Critical Values for t Statistic Appendix C. Critical Values for Chi-Square Appendix D. Critical Values for F Statistic Appendix E. Glossary About the Authors.
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Focusing on descriptive statistics, and some more advanced topics such as tests of significance, measures of association, and regression analysis, this brief, inexpensive text is the perfect companion to help those students who have not yet taken an introductory statistics course or are confused by the statistics used in the articles they are reading.
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• Preface About the Authors Prologue PART I * GETTING STARTED
• Chapter 1: The Big Picture Models The Classical Statistical Model Designing Experiments and Analyzing Data Summary Questions Raised by the Use of the Classical Statistical Model Conceptual Exercises
• Chapter 2: Examining Our Data: An Introduction to Some of the Techniques of Exploratory Data Analysis Descriptive Statistics Histograms Exploratory Data Analysis Quantile Plots Stem-and-Leaf Displays Letter-Value Displays Box Plots Did My Data Come From a Normal Distribution? Why Should We Care About Looking at Our Data? Summary Conceptual Exercises PART II * THE BEHAVIOR OF DATA
• Chapter 3: Properties of Distributions: The Building Blocks of Statistical Inference The Effects of Adding a Constant or Multiplying by a Constant The Standard Score Transformation The Effects of Adding or Subtracting Scores From Two Different Distributions The Distribution of Sample Means The Central Limit Theorem Averaging Means and Variances Expected Value Theorems on Expected Value Summary Conceptual Exercises PART III * THE BASICS OF STATISTICAL INFERENCE: DRAWING CONCLUSIONS FROM OUR DATA
• Chapter 4: Estimating Parameters of Populations From Sample Data Statistical Inference With the Classical Statistical Model Criteria for Selecting Estimators of Population Parameters Maximum Likelihood Estimation Confidence Intervals Beyond Normal Distributions and Estimating Population Means Summary Conceptual Exercises
• Chapter 5: Resistant Estimators of Parameters A Closer Look at Sampling From Non-Normal Populations The Sample Mean and Sample Median Are L-Estimators Measuring the Influence of Outliers on Estimates of Location and Spread ?-Trimmed Means as Resistant and Efficient Estimators of Location Winsorizing: Another Way to Create a Resistant Estimator of Location Applying These Resistant Estimators to Our Data Resistant Estimators of Spread Applying These Resistant Estimators to Our Data (Part 2) M-Estimators: Another Approach to Finding Resistant Estimators of Location Which Estimator of Location Should I Use? Resampling Methods for Constructing Confidence Intervals A Final Caveat Summary Conceptual Exercises
• Chapter 6: General Principles of Hypothesis Testing Experimental and Statistical Hypotheses Estimating Parameters The Criterion for Evaluating Our Statistical Hypotheses Creating Our Test Statistic Drawing Conclusions About Our Null Hypothesis But Suppose H0 Is False? Errors in Hypothesis Testing Power and Power Functions The Use of Power Functions p-Values, a, and Alpha (Type I) Errors: What They Do and Do Not Mean A Word of Caution About Attempting to Estimate the Power of a Hypothesis Test After the Data Have Been Collected Is It Ever Appropriate to Use a One-Tailed Hypothesis Test? What Should We Mean When We Say Our Results Are Statistically Significant? A Final Word Summary Conceptual Exercises PART IV * SPECIFIC TECHNIQUES TO ANSWER SPECIFIC QUESTIONS
• Chapter 7: The Independent Groups t-Tests for Testing for Differences Between Population Means Student's t-test Distribution of the Independent Groups t-Statistic when H0 Is True Distribution of the Independent Groups t-Statistic When H0 Is False Factors That Affect the Power of the Independent Groups t-Test The Assumption Behind the Homogeneity of Variance Assumption Graphical Methods for Comparing Two Groups Suppose the Population Variances Are Not Equal? Standardized Group Differences as Estimators of Effect Size Robust Hypothesis Testing Resistant Estimates of Effect Size Summary Conceptual Exercises
• Chapter 8: Testing Hypotheses When the Dependent Variable Consists of Frequencies of Scores in Various Categories Classifying Data Testing Hypotheses When the Dependent Variable Consists of Only Two Possibilities The Binomial Distribution Testing Hypotheses About the Parameter p in a Binomial Experiment The Normal Distribution Approximation to the Binomial Distribution Testing Hypotheses About the Difference Between Two Binomial Parameters (p1 - p2) Testing Hypotheses in Which the Dependent Variable Consists of Two or More Categories Summary Conceptual Exercises
• Chapter 9: The Randomization/Permutation Model: An Alternative to the Classical Statistical Model for Testing Hypotheses About Treatment Effects The Assumptions Underlying the Classical Statistical Model The Assumptions Underlying the Randomization Model Hypotheses for Both Models The Exact Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior The Approximate Randomization Test for Testing Hypotheses About the Effects of Different Treatments on Behavior Using the Randomization Model to Investigate Possible Effects of Treatments Single-Participant Experimental Designs Summary Conceptual Exercises Additional Resources
• Chapter 10: Exploring the Relationship Between Two Variables: Correlation Measuring the Degree of Relationship Between Two Interval-Scale Variables Randomization (Permutation) Model for Testing Hypotheses About the Relationship Between Two Variables The Bivariate Normal Distribution Model for Testing Hypotheses About Population Correlations Creating a Confidence Interval for the Population Correlation Using the Bivariate Normal Distribution Model Bootstrap Confidence Intervals for the Population Correlation Unbiased Estimators of the Population Correlation Robust Estimators of Correlation Assessing the Relationship Between Two Nominal Variables The Fisher Exact Probability Test for 2 x 2 Contingency Tables With Small Sample Sizes Correlation Coefficients for Nominal Data in Contingency Tables Summary Conceptual Exercises
• Chapter 11: Exploring the Relationship Between Two Variables: The Linear Regression Model Assumptions for the Linear Regression Model Estimating Parameters With the Linear Regression Model Regression and Prediction Variance and Correlation Testing Hypotheses With the Linear Regression Model Summary Conceptual Exercises
• Chapter 12: A Closer Look at Linear Regression The Importance of Looking at Our Data Using Residuals to Check Assumptions Testing Whether the Relationship Between Two Variables Is Linear The Correlation Ratio: An Alternate Way to Measure the Degree of Relationship and Test for a Linear Relationship Where Do We Go From Here? When the Relationship Is Not Linear The Effects of Outliers on Regression Robust Alternatives to the Method of Least Squares A Quick Peek at Multiple Regression Summary Conceptual Exercises
• Chapter 13: Another Way to Scale the Size of Treatment Effects The Point Biserial Correlation Coefficient and the t-Test Advantages and Disadvantages of Estimating Effect Sizes With Correlation Coefficients or Standardized Group Difference Measures Confidence Intervals for Effect Size Estimates Final Comments on the Use of Effect Size Estimators Summary Conceptual Exercises
• Chapter 14: Analysis of Variance for Testing for Differences Between Population Means What Are the Sources of Variation in Our Experiments? Experimental and Statistical Hypotheses Estimating Variances When There Are More Than Two Conditions in Your Experiment Assumptions for Analysis of Variance Testing Hypotheses About Differences Among Population Means With Analysis of Variance Factors That Affect the Power of the F-Test in Analysis of Variance Relational Effect Size Measures for Analysis of Variance Randomization Tests for Testing for Differential Effects of Three or More Treatments Using ANOVA to Study the Effects of More Than One Factor on Behavior Partitioning Variance for a Two-Factor Analysis of Variance Testing Hypotheses With Two-Factor Analysis of Variance Testing Hypotheses About Differences Among Population Means With Analysis of Variance Dealing With Unequal Sample Sizes in Factorial Designs Summary Conceptual Exercises
• Chapter 15: Multiple Regression and Beyond Overview of the General Linear Model Approach Regression Simple Versus Multiple Regression Multiple Regression Types of Multiple Regression Interactions in Multiple Regression Continuous x Continuous Interactions Categorical x Continuous Interactions Categorical x Categorical Interactions: ANOVA Versus Regression Summary Conceptual Exercises Epilogue Appendices A. Some Useful Rules of Algebra B. Rules of Summation C. Logarithms D. The Inverse of the Cumulative Normal Distribution E. The Unit Normal Distribution F. The t-Distribution G. The Fisher r to zr Transformation H. Critical Values for F With Alpha = .05 I. The Chi Square Distribution References Index.
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This unique intermediate/advanced statistics text uses real research on antisocial behaviors, such as cyberbullying, stereotyping, prejudice, and discrimination, to help readers across the social and behavioral sciences understand the underlying theory behind statistical methods. By presenting examples and principles of statistics within the context of these timely issues, authors Jerome Frieman, Donald A. Saucier, and Stuart S. Miller show how the results of analyses can be used to answer research questions. New techniques for data analysis and a wide range of topics are covered, including how to deal with "messy data" and the importance of engaging in exploratory data analysis.
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• 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.
• (source: Nielsen Book Data)

Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. 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 behavioural sciences.
(source: Nielsen Book Data)

• About the author
• Acknowledgments
• Preface to the instructor
• To the student : how to use spss with this book
• Introduction and descriptive statistics
• Introduction to statistics
• Summarizing data: frequency distributions in tables and graphs
• Summarizing data: central tendency
• Summarizing data: variability
• Probability and the foundations of inferential statistics
• Probability
• Probability, normal distributions, and Z scores
• Probability and sampling distributions
• Making inferences about one or two means
• Hypothesis testing: significance, effect size, and power
• Testing means : one-sample and two-independent-sample t tests
• Testing means : the related-samples T test
• Estimation and confidence intervals
• Making inferences about the variability of two or more means
• Analysis of variance: one-way between-subjects design
• Analysis of variance : one-way within-subjects (repeated-measures) design
• Analysis of variance: two-way between-subjects factorial design
• Making inferences about patterns, frequencies, and ordinal data
• Correlation
• Linear regression and multiple regression
• Nonparametric tests: chi-square tests
• Nonparametric tests: tests for ordinal data
• Afterword
• Appendix A. Basic math review and summation notation
• Appendix B. SPSSs general instructions guide
• Appendix C. Statistical tables
• Appendix D. Chapter solutions for even-numbered problems
• Glossary
• References.

• Introduction
• Know your data
• Selectivity
• Misspecification and control
• Where is the variance?
• Opportunity knocks
• Time and space
• When the world knows more about the processes than you do
• Too good to be true.

Simply put, Thinking Through Statistics is a primer on how to maintain rigorous data standards in social science work. But don't let that daunt you. With clever examples and witty takeaways, John Levi Martin proves himself to be a most affable tour guide through these scholarly waters. Martin lays out the fundamental vocabulary of sociological statistics-from probability to null models-and illustrates common pitfalls to avoid in quantitative research. He encourages readers to hunker down with the data, using a combination of visual models and simulations to outline the threats to accuracy and validity in a conventional researcher's work. Thinking Through Statistics gives social science practitioners accessible insight into troves of wisdom that would normally have to be earned through arduous trial and error, and it does so with a lighthearted approach that ensures this field guide is anything but stodgy.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Getting Started
• 3. Temporal Analysis with Cross-Sectional Data
• 4. Analysis of Data on Durations
• 5. Analysis of Repeated Contacts Data
• 6. Conclusion Bibliography Index.
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Across the social sciences, there is widespread agreement that quantitative longitudinal research designs offer analysts powerful scientific data resources. But, to date, many texts on analysing longitudinal social analysis surveys have been written from a statistical, rather than a social science data analysis perspective and they lack adequate coverage of common practical challenges associated with social science data analyses. This book provides a practical and up-to-date introduction to influential approaches to quantitative longitudinal data analysis in the social sciences. The book introduces definitions and terms, explains the relative attractions of such a longitudinal design, and offers an introduction to the main techniques of analysis, explaining their requirements, statistical properties and their substantive contribution. The book is designed for postgraduates and researchers across the social sciences considering the use of quantitative longitudinal data resources in their research. It will also be an excellent text for undergraduate and postgraduate courses on advanced quantitative methods.
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• 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1[-]1.1. WhatIsStatistics? . . . . . . . . . . . . . . . . . . . . . 1[-]1.2. StatisticalModels . . . . . . . . . . . . . . . . . . . . . 2[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 12[-]Application: Cox Regression . . . . . . . . . . . . . . . . . 15[-]2. DescriptiveStatistics . . . . . . . . . . . . . . . . . . . . . . 21[-]2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 21[-]2.2. UnivariateSamples . . . . . . . . . . . . . . . . . . . . . 21[-]2.3. Correlation . . . . . . . . . . . . . . . . . . . . . . . . 32[-]2.4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . 38[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 39[-]Application: Benford's Law . . . . . . . . . . . . . . . . . 41[-]3. Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . 45[-]3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 45[-]3.2. MeanSquareError . . . . . . . . . . . . . . . . . . . . . 46[-]3.3. Maximum Likelihood Estimators . . . . . . . . . . . . . . . 54[-]3.4. MethodofMomentsEstimators . . . . . . . . . . . . . . . . 72[-]3.5. BayesEstimators . . . . . . . . . . . . . . . . . . . . . . 75[-]3.6. M-Estimators . . . . . . . . . . . . . . . . . . . . . . . 88[-]3.7. Summary . . . . . . . . . . . . . . . . . . . . . . . . . 93[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 94[-]Application: Twin Studies . . . . . . . . . . . . . . . . . 100[-]4. Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . 105[-]4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 105[-]4.2. Null Hypothesis and Alternative Hypothesis . . . . . . . . . . 105[-]4.3. SampleSizeandCriticalRegion . . . . . . . . . . . . . . 107[-]4.4. Testing with p-Values . . . . . . . . . . . . . . . . . . . 121[-]4.5. StatisticalSignificance . . . . . . . . . . . . . . . . . . 126[-]4.6. SomeStandardTests . . . . . . . . . . . . . . . . . . . 127[-]4.7. Likelihood Ratio Tests . . . . . . . . . . . . . . . . . . 143[-]4.8. ScoreandWaldTests . . . . . . . . . . . . . . . . . . . 150[-]4.9. Multiple Testing . . . . . . . . . . . . . . . . . . . . . 153[-]4.10. Summary . . . . . . . . . . . . . . . . . . . . . . . . 159[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . 160[-]Application: Shares According to Black-Scholes . . . . . . . . 169[-]5. ConfidenceRegions . . . . . . . . . . . . . . . . . . . . . 174[-]5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 174[-]5.2. Interpretation of a Confidence Region . . . . . . . . . . . . 174[-]5.3. PivotsandNear-Pivots . . . . . . . . . . . . . . . . . . 177[-]5.4. Maximum Likelihood Estimators as Near-Pivots . . . . . . . . 181[-]5.5. ConfidenceRegionsandTests . . . . . . . . . . . . . . . 195[-]5.6. Likelihood Ratio Regions . . . . . . . . . . . . . . . . . 198[-]5.7. BayesianConfidenceRegions . . . . . . . . . . . . . . . . 201[-]5.8. Summary . . . . . . . . . . . . . . . . . . . . . . . . 205[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . 206[-]Application: The Salk Vaccine . . . . . . . . . . . . . . . 209[-]6. Optimality Theory . . . . . . . . . . . . . . . . . . . . . . 212[-]6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 212[-]6.2. SufficientStatistics . . . . . . . . . . . . . . . . . . . . 212[-]6.3. EstimationTheory . . . . . . . . . . . . . . . . . . . . 219[-]6.4. TestingTheory . . . . . . . . . . . . . . . . . . . . . 231[-]6.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . 245[-]Exercises . . . . . . . . . . . . . . . . . . . . . . . . 246[-]Application: High Water in Limburg . . . . . . . . . . . . . 250[-]7. RegressionModels . . . . . . . . . . . . . . . . . . . . . . 259[-]7.1. Introduction.
• (source: Nielsen Book Data)

Statistics is the science that focuses on drawing conclusions from data, by modeling and analyzing the data using probabilistic models. In An Introduction to Mathematical Statistics the authors describe key concepts from statistics and give a mathematical basis for important statistical methods. Much attention is paid to the sound application of those methods to data. [-]The three main topics in statistics are estimators, tests, and confidence regions. The authors illustrate these in many examples, with a separate chapter on regression models, including linear regression and analysis of variance. They also discuss the optimality of estimators and tests, as well as the selection of the best-fitting model. [-]Each chapter ends with a case study in which the described statistical methods are applied. This book assumes a basic knowledge of probability theory, calculus, and linear algebra. [-]Several annexes are available for Mathematical Statistics "http://www.aup.nl/mathematical-data">on this page.[-].
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Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a two-semester, graduate-level introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in an introductory course. Hundreds of journal articles make it clear that basic techniques, routinely taught and used, can perform poorly when dealing with skewed distributions, outliers, heteroscedasticity (unequal variances) and curvature. Methods for dealing with these concerns have been derived and can provide a deeper, more accurate and more nuanced understanding of data. A conceptual basis is provided for understanding when and why standard methods can have poor power and yield misleading measures of effect size. Modern techniques for dealing with known concerns are described and illustrated. Features: * Presents an in-depth description of both classic and modern methods * Explains and illustrates why recent advances can provide more power and a deeper understanding of data * Provides numerous illustrations using the software R * Includes an R package with over 1300 functions * Includes a solution manual giving detailed answers to all of the exercises This second edition describes many recent advances relevant to basic techniques. For example, a vast array of new and improved methods is now available for dealing with regression, including substantially improved ANCOVA techniques. The coverage of multiple comparison procedures has been expanded and new ANOVA techniques are described. Rand Wilcox is a professor of psychology at the University of Southern California. He is the author of 13 other statistics books and the creator of the R package WRS. He currently serves as an associate editor for five statistics journals. He is a fellow of the Association for Psychological Science and an elected member of the International Statistical Institute.
(source: Nielsen Book Data)

• Preface
• Preliminaries: Introduction to statistical investigations. Introduction to the six-step method section ; Exploring data ; Exploring random processes
• Unit
• 1: Four pillars of inference : strength, size, breadth, and cause. Significance : how strong is the evidence? ; Generalization : how broadly do the results apply? ; Estimation : how large is the effect? ; Causation : can we say what caused the effect?
• Unit
• 2: Comparing two groups. Comparing two proportions ; Comparing two means ; Paired data : one quantitative variable
• Unit
• 3: Analyzing more general situations. Comparing more than two proportions ; Comparing more than two means ; Two quantitative variables
• Appendix: Calculation details
• Appendix: Stratified and cluster samples
• Answers: Answered to starred homework problems.

Did you know that having a messy room will make you racist? Or that human beings possess the ability to postpone death until after important ceremonial occasions? Or that people live three to five years longer if they have positive initials, like ACE? All of these 'facts' have been argued with a straight face by researchers and backed up with reams of data and convincing statistics. As Nobel Prize-winning economist Ronald Coase once cynically observed, 'If you torture data long enough, it will confess.' Lying with statistics is a time-honoured con. In Standard Deviations, economics professor Gary Smith walks us through the various tricks and traps that people use to back up their own crackpot theories. Sometimes, the unscrupulous deliberately try to mislead us. Other times, the well-intentioned are blissfully unaware of the mischief they are committing. Today, data are so plentiful that researchers spend precious little time distinguishing between good, meaningful deductions and total rubbish. Not only do others use data to fool us, we fool ourselves. Drawing on breakthrough research in behavioural economics by luminaries like Daniel Kahneman and Dan Ariely, and taking to task some of the conclusions of Freakonomics author Steven D. Levitt, Standard Deviations demystifies the science behind statistics and brings into stark relief the fraud that surrounds us all.
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We are inundated by scientific and statistical information, but what should we believe? How much should we trust the polls on the latest electoral campaign? When a physician tells us that a diagnosis of cancer is 90% certain or a scientist informs us that recent studies support global warming, what should we conclude? How can we acquire reliable statistical information? Once we have it, how do we evaluate it? Despite the importance of these questions to our lives, many of us have only a vague idea of how to answer them. In this admirably clear and engaging book, Mark Battersby provides a practical guide to thinking critically about scientific and statistical information. The goal of the book is not only to explain how to identify misleading statistical information, but also to give readers the understanding necessary to evaluate and use statistical and statistically based scientific information in their own decision making.
(source: Nielsen Book Data)

Hot on the heals of the award-winning and best selling Discovering Statistics Using SPSS, Third Edition, Andy Field has teamed up with Jeremy Miles (co-author of Discovering Statistics Using SAS) to write Discovering Statistics Using R. Keeping the uniquely humorous and self-depreciating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using the freeware R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next the importance of exploring and graphing data will be discovered, before moving onto statistical tests that are the foundations of the rest of the book (for e.g. correlation and regression). Readers will then stride confidently into intermediate level analyses such as ANOVA, before ending their journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help the reader gain the necessary conceptual understanding of what they're doing, the emphasis is on applying what's learned to playful and real-world examples that should make the experience more fun than expected. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more (at www.sagepub.co.uk/fieldandmilesR). Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.
(source: Nielsen Book Data)

• PART ONE: INTRODUCTION Why Statistics for Public Managers and Analysts? PART TWO: RESEARCH METHODS Research Design Conceptualization and Measurement Measuring and Managing Performance: Present and Future Data Collection PART THREE: DESCRIPTIVE STATISTICS Central Tendency Measures of Dispersion Contingency Tables Getting Results PART FOUR: INFERENTIAL STATISTICS Hypothesis Testing with Chi-square Measures of Association The T-test Analysis of Variance (ANOVA) Simple Regression Multiple Regression PART FIVE: FURTHER STATISTICS Logistic Regression Time Series Analysis Survey of Other Techniques Appendix: Statistical Tables.
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- additional material on performance management, going beyond an emphasis on performance measurement.
(source: Nielsen Book Data)