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 Warner, Rebecca M., author.
 Third edition  Thousand Oaks, California : SAGE Publications, Inc., [2021]
 Description
 Book — xxiv, 623 pages : illustrations ; 26 cm
 Summary

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 wellworn 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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HA31.35 .W37 2021  Unknown 
 Warner, Rebecca M., author.
 Third edition  Thousand Oaks, California : SAGE Publications, Inc., [2021]
 Description
 Book — xxiii, 682 pages : illustrations ; 26 cm
 Summary

Rebecca M. Warner's bestselling Applied Statistics: From Bivariate Through Multivariate Techniques has been split into two volumes for ease of use over a twocourse 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.
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HA31.35 .W37 2021B  Unknown 
 Privitera, Gregory J., author.
 Second edition.  Thousand Oaks, California : SAGE Publications, Inc., [2019]
 Description
 Book — xli, 596 pages ; 26 cm
 Summary

 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 : onesample t test with confidence intervals
 Testing means : twoindependentsample t test with confidence intervals
 Testing means : relatedsamples t test with confidence intervals
 Making inferences about the variability of two or more means
 Oneway analysis of variance : betweensubjects and withinsubjects (repeatedmeasures) designs
 Twoway analysis of variance : betweensubjects factorial design
 Making inferences about patterns, prediction, and nonparametric tests
 Correlation and linear regression
 Chisquare tests : goodnessoffit 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 evennumbered problems
 Glossary
 References
 Index.
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HA29 .P75 2019  Unknown 
4. Interrupted time series analysis [2019]
 McDowall, David, 1949 author.
 New York, NY : Oxford University Press, [2019]
 Description
 Book — xviii, 180 pages : illustrations ; 24 cm
 Summary

 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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HA30.3 .M34 2019  Unknown 
5. Spatial regression models [2019]
 Ward, Michael Don, 1948 author.
 Second edition.  Thousand Oaks, California : SAGE Publications, [2019]
 Description
 Book — xv, 112 pages ; 22 cm.
 Summary

 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.
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HA30.6 .W37 2019  Unknown 
 Wagner, William E. (William Edward) author.
 Thousand Oaks, California : SAGE Publications, Inc., [2019]
 Description
 Book — xiii, 213 pages ; 23 cm
 Summary

 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 NonNormal Distribution Characteristics Data Transformations for Dealing With NonNormal 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 ChiSquare Test of Independence The Independent Samples t Test OneWay 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 Taub 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 ChiSquare Appendix D. Critical Values for F Statistic Appendix E. Glossary About the Authors.
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HA29 .W3325 2019  Unknown 
 Frieman, Jerome, author.
 Thousand Oaks, California : SAGE Publications, Inc., [2018]
 Description
 Book — xxix, 496 pages ; 24 cm
 Summary

 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 StemandLeaf Displays LetterValue 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 NonNormal Populations The Sample Mean and Sample Median Are LEstimators 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) MEstimators: 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 pValues, 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 OneTailed 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 tTests for Testing for Differences Between Population Means Student's ttest Distribution of the Independent Groups tStatistic when H0 Is True Distribution of the Independent Groups tStatistic When H0 Is False Factors That Affect the Power of the Independent Groups tTest 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 SingleParticipant Experimental Designs Summary Conceptual Exercises Additional Resources
 Chapter 10: Exploring the Relationship Between Two Variables: Correlation Measuring the Degree of Relationship Between Two IntervalScale 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 tTest 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 FTest 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 TwoFactor Analysis of Variance Testing Hypotheses With TwoFactor 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 tDistribution 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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HA29 .F76812 2018  Unknown 
 Flora, David B., author.
 London ; Thousand Oaks, California : SAGE Publications, 2018.
 Description
 Book — xvi, 456 pages : illustrations ; 26 cm
 Summary

 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.
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HA29 .F56 2018  Unknown 
9. Statistics for the behavioral sciences [2018]
 Privitera, Gregory J., author.
 Third edition.  Thousand Oaks, California : SAGE Publications, Inc., [2018]
 Description
 Book — xliii, 650 pages, 114 variously numbered pages ; 26 cm
 Summary

 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 : onesample and twoindependentsample t tests
 Testing means : the relatedsamples T test
 Estimation and confidence intervals
 Making inferences about the variability of two or more means
 Analysis of variance: oneway betweensubjects design
 Analysis of variance : oneway withinsubjects (repeatedmeasures) design
 Analysis of variance: twoway betweensubjects factorial design
 Making inferences about patterns, frequencies, and ordinal data
 Correlation
 Linear regression and multiple regression
 Nonparametric tests: chisquare 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 evennumbered problems
 Glossary
 References.
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HA29 .P755 2018  Unknown 
10. Thinking through statistics [2018]
 Martin, John Levi, 1964 author.
 Chicago ; London : The University of Chicago Press, 2018.
 Description
 Book — xiv, 362 pages : illustrations, maps ; 24 cm
 Summary

 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.
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HA29 .M135 2018  Unknown 
 Gayle, Vernon, author.
 London ; New York, NY : Bloomsbury Academic, an imprint of Bloomsbury Publishing Plc, 2018.
 Description
 Book — xiv, 151 pages ; 22 cm.
 Summary

 1. Introduction
 2. Getting Started
 3. Temporal Analysis with CrossSectional Data
 4. Analysis of Data on Durations
 5. Analysis of Repeated Contacts Data
 6. Conclusion Bibliography Index.
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HA29 .G26 2018  Unknown 
12. An introduction to mathematical statistics [2017]
 Inleiding in de statistiek. English
 Bijma, Fetsje, author.
 Amsterdam : Amsterdam University Press, [2017]
 Description
 Book — xi, 368 pages : illustrations ; 24 cm
 Summary

 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. MEstimators . . . . . . . . . . . . . . . . . . . . . . . 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 pValues . . . . . . . . . . . . . . . . . . . 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 BlackScholes . . . . . . . . 169[]5. ConfidenceRegions . . . . . . . . . . . . . . . . . . . . . 174[]5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 174[]5.2. Interpretation of a Confidence Region . . . . . . . . . . . . 174[]5.3. PivotsandNearPivots . . . . . . . . . . . . . . . . . . 177[]5.4. Maximum Likelihood Estimators as NearPivots . . . . . . . . 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.
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HA29 .B55613 2017  Unknown 
 Wilcox, Rand R., author.
 Second edition.  Boca Raton, FL : CRC Press, Taylor & Francis Group, [2017]
 Description
 Book — xxiii, 706 pages ; 29 cm
 Summary

Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a twosemester, graduatelevel 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 indepth 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.
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HA29 .W51367 2017  Unknown 
 Chen, Youhua.
 Sharjah (U.A.E.) : Bentham Science Publishers Ltd., 2015.
 Description
 Book — vi, 153 pages : illustrations ; 25 cm
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HA4005 .C48 2015  Unknown 
15. Introduction to statistical investigations [2015]
 Tintle, Nathan, author.
 Preliminary edition.  Hoboken, NJ : Wiley, [2015]
 Description
 Book — xv, 1296 pages : black & white illustrations ; 28 cm
 Summary

 Preface
 Preliminaries: Introduction to statistical investigations. Introduction to the sixstep 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.
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HA29 .T56 2015  Unknown 
16. Standard deviations : flawed assumptions, tortured data, and other ways to lie with statistics [2014]
 Smith, Gary, 1945
 London : Duckworth, 2014.
 Description
 Book — 306 p. : ill. ; 24 cm
 Summary

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 Prizewinning economist Ronald Coase once cynically observed, 'If you torture data long enough, it will confess.' Lying with statistics is a timehonoured 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 wellintentioned 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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HA29 .S579 2014  Unknown 
 Battersby, Mark, 1945 author.
 Revised edition.  Peterborough, Ontario, Canada : Broadview Press, [2013]
 Description
 Book — viii, 242 pages : illustrations ; 23 cm
 Summary

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.
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HA29 .B3834 2013  Unknown 
18. ProQuest statistical abstract of the United States [2013  ]
 Lanham, Maryland : Bernan, 2012
 Description
 Journal/Periodical — volumes : maps ; 29 cm
 Database topics
 American History; Communication and Journalism; Government Information: United States; Statistical and Numeric Data
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Information Center: Statistics
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HA202 .A483 2018  Inlibrary use 
19. Discovering statistics using R [2012]
 Field, Andy P.
 Los Angeles, Thousand Oaks, Ca ; London : Sage, 2012.
 Description
 Book — xxxiv, 957 p. : ill ; 27 cm.
 Summary

Hot on the heals of the awardwinning and best selling Discovering Statistics Using SPSS, Third Edition, Andy Field has teamed up with Jeremy Miles (coauthor of Discovering Statistics Using SAS) to write Discovering Statistics Using R. Keeping the uniquely humorous and selfdepreciating 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 realworld 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 groundbreaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, hundreds of examples, selfassessment 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 realworld research it should be essential for anyone wanting to learn about statistics using the freelyavailable R software.
(source: Nielsen Book Data)
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HA32 .F539 2012  Unknown 
 Berman, Evan M.
 3rd ed.  Washington, D.C. : CQ Press, c2012.
 Description
 Book — xix, 369 p. : ill. ; 23 cm
 Summary

 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 Chisquare Measures of Association The Ttest Analysis of Variance (ANOVA) Simple Regression Multiple Regression PART FIVE: FURTHER STATISTICS Logistic Regression Time Series Analysis Survey of Other Techniques Appendix: Statistical Tables.
 (source: Nielsen Book Data)
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HA29 .B425 2012  Unknown 