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 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.
(source: Nielsen Book Data) 9781506386300 20180409
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HA29 .P75 2019  Unknown 
 Hjellbrekke, Johs, author.
 London ; New York : Routledge, Taylor & Francis Group, 2019.
 Description
 Book — xvii, 118 pages : illustrations ; 24 cm
 Summary

 Preface
 1. Geometric Data Analysis
 2. Correspondence Analysis
 3. Multiple Correspondence Analysis
 4. Passive and Supplementary Points, Supplementary Variables and Structured Data Analysis
 5. MCA and Ascending Hierarchical Cluster Analysis
 6. Constructing Spaces
 7. Analyzing SubGroups: ClassSpecific MCA
 Appendix: Softwares for Doing MCA.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781138699717 20180910
 Online
Green Library
Green Library  Status 

Find it Jonsson Social Sciences Reading Room: New books  
HA29 .H65445 2019  Unknown 
3. 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.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781544328836 20180813
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
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.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781526402493 20180521
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HA29 .W3325 2019  Unknown 
 Welc, Jacek, author.
 Cham, Switzerland : Springer, [2018]
 Description
 Book — 1 online resource.
 Summary

 Preface ................................................................ ........................................................... 1
 Chapter 1  Basics of regression models ................................................................ .. 21.1. Types and applications of regression models. ................................................................ .............. 21.2. Basic elements of a singleequation linear regression model. ..................................................... 4
 Chapter 2  Relevance of outlying and influential observations for regression analysis ................................................................ ..................................... 72.1. Nature and dangers of univariate and multivariate outlying observations. ................................ 72.2. Tools for detection of outlying observations. ................................................................ ............. 192.3. Recommended procedure for detection of outlying and influential observations. .................... 322.4. Dealing with detected outlying and influential observations. .................................................... 33
 Chapter 3  Basic procedure for multiple regression model building ............. 353.1. Introduction. ................................................................ ............................................................... 353.2. Preliminary specification of the model. ................................................................ ...................... 353.3. Detection of potential outliers in the dataset. ................................................................ ........... 403.4. Selection of explanatory variables (from the set of candidates). ............................................... 483.5. Interpretation of the obtained regression' structural parameters. ............................................ 57
 Chapter 4  Verification of multiple regression model ...................................... 604.1. Introduction. ................................................................ ............................................................... 604.2. Testing general statistical significance of the whole model: F test. ........................................... 614.3. Testing the normality of regression residuals' distribution. ....................................................... 634.4. Testing the autocorrelation of regression residuals. ................................................................ .. 724.5. Testing the heteroscedasticity of regression residuals. .............................................................. 874.6. Testing the symmetry of regression residuals. ................................................................ ........... 974.7. Testing the randomness of regression residuals. ................................................................ ..... 1064.8. Testing the specification of the model: Ramsey's RESET test. ................................................. 1154.9. Testing the multicollinearity of explanatory variables. ............................................................ 1214.10. What to do if the model is not correct? ................................................................ .................. 1254.11. Summary of verification of our model ................................................................ .................... 130
 Chapter 5  Common adjustments to multiple regressions .............................. 1325.1. Dealing with qualitative factors by means of dummy variables. ............................................. 1325.2. Modeling seasonality by means of dummy variables. ............................................................. 1365.3. Using dummy variables for outlying observations. ................................................................ .. 1482815.4. Dealing with structural changes in modeled relationships. ..................................................... 1555.5. Dealing with insample nonlinearities. ................................................................ .................... 164
 Chapter 6  Common pitfalls in regression analysis .......................................... 1716.1. Introduction. ................................................................ ............................................................. 1716.2. Distorting impact of multicollinearity on regression parameters. ........................................... 1716.3. Analyzing incomplete regressions. ................................................................ ........................... 1766.4. Spurious regressions and longterm trends. ................................................................ ............. 1806.5. Extrapolating insample relationships too far into outofsample ranges. .............................. 1866.6. Estimating regressions on too narrow ranges of data. ............................................................ 1936.7. Ignoring structural changes within modeled relationships and within individual variables. ... 197
 Chapter 7  Regression analysis of discrete dependent variable .................... 2097.1. The nature and examples of discrete dependent variables. ..................................................... 2097.2. The discriminant analysis. ................................................................ ........................................ 2097.3. The logit function. ................................................................ ..................................................... 218
 Chapter 8  Reallife casestudy: The quarterly sales revenues of Nokia Corporation..................................................... .......................................................... 2238.1. Introduction. ................................................................ ............................................................. 2238.2. Preliminary specification of the model. ................................................................ .................... 2238.3. Detection of potential outliers in the dataset ................................................................ .......... 2258.4. Selection of explanatory variables (from the set of candidates). ............................................. 2318.5. Verification of the obtained model. ................................................................ .......................... 2348.6. Evaluation of the predictive power of the estimated model. ................................................... 246
 Chapter 9  Reallife casestudy: Identifying overvalued and undervalued airlines ................................................................ ........................................................ 2529.1. Introduction. ................................................................ ............................................................. 2529.2. Preliminary specification of the model. ................................................................ .................... 2529.3. Detection of potential outliers in the dataset ................................................................ .......... 2549.4. Selection of explanatory variables (from the set of candidates). ............................................. 2589.5. Verification of the obtained model. ................................................................ .......................... 2599.6. Evaluation of model usefulness in identifying overvalued and undervalued stocks. ............... 268Appendix  Statistical Tables ................................................................ ................... 271A1. Critical values for Fstatistic for k = 0,05............................................................ ..................... 271A2. Critical values for tstatistic. ................................................................ ...................................... 273A3. Critical values for Chisquared statistic. ................................................................ .................... 274282A4. Critical values for Hellwig test. ................................................................ .................................. 275A5. Critical values for symmetry test for k = 0,10. ................................................................ ........ 276A6. Critical values for maximum series length test for k = 0,05. ................................................... 276A7. Critical values for number of series test for k = 0,05. ............................................................. 277.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9783319711553 20180226
 Bors, Douglas Alexander, 1950 author.
 London ; Thousand Oaks, CA : SAGE Publications, 2018.
 Description
 Book — xv, 642 pages : illustrations (some color) ; 24 cm
 Summary

 Part I: The Foundations
 Chapter 1: Overview The general framework Recognizing randomness Lies, damn lies, and statistics Testing for randomness Research design and key concepts Paradoxes
 Chapter 2: Descriptive Statistics Numerical Scales Histograms Measures of Central Tendency: Measurement Data Measures of Spread: Measurement Data What creates Variance? Measures of Central Tendency: Categorical Data Measures of Spread: Categorical Data Unbiased Estimators Practical SPSS Summary
 Chapter 3: Probability Approaches to probability Frequency histograms and probability The asymptotic trend The terminology of probability The laws of probability Bayes' Rule Continuous variables and probability The standard normal distribution The standard normal distribution and probability Using the ztables Part II: Basic Research Designs
 Chapter 4: Categorical data and hypothesis testing The binomial distribution Hypothesis testing with the binomial distribution Conducting the binomial test with SPSS Null hypothesis testing The x2 goodnessoffit test The x2 goodnessoffit test with more than twocategories Conducting the x2 goodnessoffit test with SPSS Power and the x2 goodnessoffit test G test Can a failure to reject indicate support for a model?
 Chapter 5: Testing for a Difference: Two Conditions Building on the zscore Testing a single sample Independentsamples ttest ttest assumptions Pairsamples ttest Confidence limits and intervals Randomization test and bootstrapping Nonparametric tests
 Chapter 6: Observational studies: Two categorical variables x2 goodnessoffit test reviewed x2 test of independence The phi coefficient Necessary assumptions x2 test of independence SPSS example Power, sample size, and the x2 test of independence The thirdvariable problem Multicategory nominal variables Tests of independence with ordinal variables
 Chapter 7: Observational studies: Two measurement variables Tests of association for categorical data reviewed The scatterplot Covariance The PearsonProduct Moment Correlation Coefficient Simple regression analysis The Ordinary Least Squares Regression Line (OLS) The assumptions necessary for valid correlation and regression coefficients
 Chapter 8: Testing for a difference: Multiple betweensubject conditions (ANOVA) Reviewing the ttest and the x2 test of independence The logic of ANOVA: Two unbiased estimates of o2 ANOVA and the Ftest Standardized effect sizes and the Ftest Using SPPS to run an ANOVA Ftest: Betweensubjects design The thirdvariable problem: Analysis of covariance (ANCOVA) Nonparametric alternatives
 Chapter 9: Testing for a difference: Multiple relatedsamples Reviewing the betweensubject ANOVA and the ttest The logic of the randomized block design Running a randomized block design with SPSS The logic of the repeatedmeasures design Running a repeatedmeasures design with SPSS Nonparametric alternatives
 Chapter 10: Testing for specific differences: Planned and unplanned tests A priori versus post hoc tests Percomparison versus familywise error rates Planned comparisons: A priori test Testing for polynomial trends Unplanned comparisons: Post hoc tests Nonparametric followup comparisons Part III: Analyzing Complex Designs
 Chapter 11: Testing for Differences: ANOVA and Factorial Designs Reviewing the independentsamples ANOVA The logic of factorial designs: Two betweensubject independent variables Main and simple effects Two BetweenSubject Factorial ANOVA with SPSS Fixed versus random factors Analyzing a mixeddesign ANOVA with SPSS Nonparametric alternatives
 Chapter 12: Multiple Regression Regression revisited Introducing a second predictor A detailed example Issues concerning normality Missing data Testing for linearity and homoscedasticity A multiple regression: The first pass Addressing multicollinearity Interactions What can go wrong?
 Chapter 13: Factor analysis What is factor analysis? Correlation coefficients revisited The correlation matrix and PCA The component matrix The rotated component matrix A detailed example Choosing a method of rotation Sample size requirements Hierarchical multiple factor analysis The effects of variable selection.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781446298480 20180530
 Online
 Sueyoshi, T. (Toshiyuki), 1954 author.
 Hoboken, NJ, USA : John Wiley & Sons, Inc., 2018.
 Description
 Book — xviii, 699 pages : illustrations ; 24 cm.
 Summary

Introduces a bold, new model for energy industry pollution prevention and sustainable growth Balancing industrial pollution prevention with economic growth is one of the knottiest problems faced by industry today. This book introduces a novel approach to using data envelopment analysis (DEA) as a powerful tool for achieving that balance in the energy industries the world s largest producers of greenhouse gases. It describes a rigorous framework that integrates elements of the social sciences, corporate strategy, regional economics, energy economics, and environmental policy, and delivers a methodology and a set of strategies for promoting green innovation while solving key managerial challenges to greenhouse gas reduction and business growth. In writing this book the authors have drawn upon their pioneering work and considerable experience in the field to develop an unconventional, holistic approach to using DEA to assess key aspects of sustainability development. The book is divided into two sections, the first of which lays out a conventional framework of DEA as the basis for new research directions. In the second section, the authors delve into conceptual and methodological extensions of conventional DEA for solving problems of environmental assessment in all contemporary energy industry sectors. Introduces a powerful new approach to using DEA to achieve pollution prevention, sustainability, and business growth Covers the fundamentals of DEA, including theory, statistical models, and practical issues of conventional applications of DEA Explores new statistical modeling strategies and explores their economic and business implications Examines applications of DEA to environmental analysis across the complete range of energy industries, including coal, petroleum, shale gas, nuclear energy, renewables, and more Summarizes important studies and nearly 800 peer reviewed articles on energy, the environment, and sustainability Environmental Assessment on Energy and Sustainability by Data Envelopment Analysis is mustreading for researchers, academics, graduate students, and practitioners in the energy industries, as well as government officials and policymakers tasked with regulating the environmental impacts of industrial pollution.
(source: Nielsen Book Data) 9781118979341 20180604
 Online
8. Han'guk ŭi changgi t'onggye [2018]
 한국 의 장기 통계
 Kim, Nangnyŏn, author.
 김 낙년, author.
 Sŏul Tt'ŭkpyŏlsi : Haenam, 2018. 서울 특별시 : 해남, 2018.
 Description
 Book — 2 volumes : charts ; 31 cm
 Online
 Hjellbrekke, Johs, author.
 Abingdon, Oxon ; New York, NY : Routledge, 2018.
 Description
 Book — 1 online resource.
 Summary

 Preface
 1. Geometric Data Analysis
 2. Correspondence Analysis
 3. Multiple Correspondence Analysis
 4. Passive and Supplementary Points, Supplementary Variables and Structured Data Analysis
 5. MCA and Ascending Hierarchical Cluster Analysis
 6. Constructing Spaces
 7. Analyzing SubGroups: ClassSpecific MCA
 Appendix: Softwares for Doing MCA.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781315516233 20180723
10. The Palgrave handbook of survey research [2018]
 Cham, Switzerland : Palgrave Macmillan, [2018]
 Description
 Book — 1 online resource.
 Haig, Brian D., 1945 author.
 New York, NY : Oxford University Press, [2018]
 Description
 Book — 1 online resource.
 London ; New York : Routledge, Taylor & Francis Group, 2018.
 Description
 Book — xi, 345 pages : illustrations ; 25 cm.
 Summary

Big Data, gathered together and reanalysed, can be used to form endless variations of our persons  socalled data doubles'. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden wellinformed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance. An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance.  Provided by publisher.
 Online
 Jensen, Pablo, author.
 Paris : Éditions du Seuil, [2018]
 Description
 Book — 314 pages, 1 unnumbered page : illustrations, chart ; 21 cm.
 Summary

 Introduction : Construire un monde commun grâce aux sciences ?
 Cimenter le social
 L'ordre par le nombre
 L'espérance par la planification
 Construire des choses qui tiennent
 Quelle place pour la formalisation?
 Pour aller plus loin
 Comment les sciences naturelles construisent un savoir fiable
 D'où vient la fiabilité des sciences naturelles ?
 La fiabilité, en pratique
 Les abstractions comme outils
 Les abstractions comme résumés
 Pour aller plus loin
 Galilée atil découvert la loi de la chute des corps ?
 La physique de la chute
 Le premier laboratoire
 Le feuillet inédit
 Que veut dire "découvrir" ?
 Les obstacles dus à la matière
 Transformer sans déformer
 Les mathématiques et le monde
 Pour aller plus loin
 Les atomes des physiciens sontils atomiques ?
 Les atomes métaphysiques
 Les qualités des atomes
 À quoi servent les atomes, en pratique?
 Les atomes relient, mais n'expliquent pas
 Pour aller plus loin
 Prédire le temps
 Grâce aux équations de la physique?
 Des résistances
 Une Terre virtuelle apprivoisée
 Prédire le climat
 Pourquoi les climatologues ontils réussi?
 Pour aller plus loin
 Simuler la société ?
 Des quartiers ségrégés impliquentils des habitants racistes ?
 Un modèle utile et dangereux
 Pour aller plus loin
 Physique des élections, piège à c.
 Des rideaux de fumée mathématiques
 Pour aller plus loin
 Compétition économique sur la plage
 Un principe fragile
 Pour aller plus loin
 Sommesnous des fourmis compliquées ?
 Les nids de fourmis, en principe.
 Et en pratique
 Les piétons
 À quoi servent ces modèles?
 Pour aller plus loin
 Des actions sous influence
 Reproduire les inégalités scolaires dans un ordinateur
 Quand le magnétisme aide l'économie
 Pour aller plus loin
 Modéliser les épidémies
 La force d'une épidémie
 Par quels chemins se propage l'épidémie ?
 Intégrer les données digitales
 Quelle place pour la modélisation ?
 Pour aller plus loin
 Prédire l'économie?
 Le futur est comme le passé
 Les actions des atomes sociaux
 Pour aller plus loin
 Prédire grâce aux big data ?
 Prédire le succès des tweets
 Je suis qui je suis
 Le manque de transférabilité
 Qui est au chômage?
 Pour aller plus loin
 À quoi servent les modèles de la société ?
 Pour aller plus loin
 Analyser la société
 La naissance des statistiques
 Le Dieu du social jouetil aux dés ?
 Ni société, ni atomes sociaux
 Des différences de moyennes aux différences de faits
 Pour aller plus loin
 Des explications complexes ?
 Les explications générales
 Séparer les effets de différentes causes
 Des causes conjoncturelles, dont l'effet dépend du contexte
 Qui agit ?
 Descriptions, récits
 Oublions les explications scientifiques ?
 Pour aller plus loin
 Les big data, pour comprendre ou coordonner ?
 Peu, c'est mieux
 L'intermédiation numérique
 Pour aller plus loin.
 Online
14. Principles & methods of statistical analysis [2018]
 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.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781483358598 20170508
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
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.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781446269831 20180423
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HA29 .F56 2018  Unknown 
16. 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.
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HA29 .P755 2018  Unknown 
 Salkind, Neil J., author.
 6 edition.  Los Angeles : Sage Publications, [2018]
 Description
 Book — viii, 163 pages : illustrations ; 28 cm
 Summary

 Chapter 1: Statistics or Sadistics? It's Up to You.
 Chapter 2: Means to an End: Computing and Understanding Averages
 Chapter 3: Vive la Difference: Understanding Variability
 Chapter 4: A Picture Really is Worth a Thousand Words
 Chapter 5: Ice Cream and Crime: Computing Correlation Coefficients
 Chapter 6: Just the Truth: An Introduction to Understanding Reliability and Validity
 Chapter 7: Hypotheticals and You: Testing Your Questions
 Chapter 8: Are Your Curves Normal? Probability and Why it Counts
 Chapter 9: Significantly Significant: What it Means for You and Me
 Chapter 10: Only the Lonely: The OneSample zTest
 Chapter 11: t(EA) for Two: Tests Between the Means of Different Groups
 Chapter 12: t(EA) for Two (Again): Tests Between the Means of Related Groups
 Chapter 13: Two Groups Too Many? Try Analysis of Variance
 Chapter 14: Two Too Many Factors: Factorial Analysis of Variance: A Brief Introduction
 Chapter 15: Cousins or Just Good Friends? Testing Relationships Using the Correlation Coefficient
 Chapter 16: Predicting Who'll Win the Super Bowl: Using Linear Regression
 Chapter 17: What to Do When You're Not Normal: ChiSquare and Some Other Nonparametric Tests
 Chapter 18: Some Other (Important) Statistical Procedures You Should Know About
 Chapter 19: Data Mining: An Introduction to Getting the Most Out of Your BIG Data.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781544320052 20180820
 Online
18. 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.
(source: Nielsen Book Data) 9780226567464 20181022
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HA29 .M135 2018  Unknown 
19. Thinking through statistics [2018]
 Martin, John Levi, 1964 author.
 Chicago ; London : The University of Chicago Press, 2018.
 Description
 Book — xiv, 362 pages ; 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
 Conclusion.
(source: Nielsen Book Data) 9780226567464 20181022
 Online
 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.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781472515407 20180508
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HA29 .G26 2018  Unknown 