 Book
 1 online resource.
 Preface ................................................................ ........................................................... 1Chapter 1  Basics of regression models ................................................................ .. 21.1. Types and applications of regression models. ................................................................ .............. 21.2. Basic elements of a singleequation linear regression model. ..................................................... 4Chapter 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. .................................................... 33Chapter 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. ............................................ 57Chapter 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 ................................................................ .................... 130Chapter 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. ................................................................ .................... 164Chapter 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. ... 197Chapter 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. ................................................................ ..................................................... 218Chapter 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. ................................................... 246Chapter 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)9783319711553 20180226
(source: Nielsen Book Data)9783319711553 20180226
 Preface ................................................................ ........................................................... 1Chapter 1  Basics of regression models ................................................................ .. 21.1. Types and applications of regression models. ................................................................ .............. 21.2. Basic elements of a singleequation linear regression model. ..................................................... 4Chapter 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. .................................................... 33Chapter 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. ............................................ 57Chapter 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 ................................................................ .................... 130Chapter 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. ................................................................ .................... 164Chapter 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. ... 197Chapter 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. ................................................................ ..................................................... 218Chapter 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. ................................................... 246Chapter 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)9783319711553 20180226
(source: Nielsen Book Data)9783319711553 20180226
 Book
 1 online resource.
 Book
 xxix, 496 pages ; 24 cm
 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)9781483358598 20170508
(source: Nielsen Book Data)9781483358598 20170508
 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)9781483358598 20170508
(source: Nielsen Book Data)9781483358598 20170508
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HA29 .F76812 2018  Unknown 
5. Statistics for the behavioral sciences [2018]
 Book
 xliii, 650 pages, 114 variously numbered pages ; 26 cm
 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.
 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.
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
HA29 .P755 2018  Unknown 
 Book
 274 pages : map, facsimiles ; 21 cm.
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request 
HA4556.5 .A4 A19 2017  Available 
 Book
 8, 455 pages ; 31 cm. + 1 CDROM (4 3/4 in.)
"2015年北京市1%人口抽样调查资料"为2015年北京市1%人口抽样调查数据的汇总加工资料.组织开展2015年全国1%人口抽样调查,将摸清2010年以来人口在数量,素质,结构,分布以及居住等方面的变化情况, 为制定国民经济和社会发展规划提供科学准确的统计信息支持.
"2015年北京市1%人口抽样调查资料"为2015年北京市1%人口抽样调查数据的汇总加工资料.组织开展2015年全国1%人口抽样调查,将摸清2010年以来人口在数量,素质,结构,分布以及居住等方面的变化情况, 为制定国民经济和社会发展规划提供科学准确的统计信息支持.
East Asia Library
East Asia Library  Status 

Find it
Chinese Collection


HA4638 .B44 A163 2017  Unknown 
 Book
 8, 468 pages ; 31 cm. + 1 CDROM (4 3/4 in.)
"2015年天津市1%人口抽样调查资料"为2015年天津市1%人口抽样调查数据的汇总加工资料.组织开展2015年全国1%人口抽样调查,将摸清2010年以来人口在数量,素质,结构,分布以及居住等方面的变化情况, 为制定国民经济和社会发展规划提供科学准确的统计信息支持.
"2015年天津市1%人口抽样调查资料"为2015年天津市1%人口抽样调查数据的汇总加工资料.组织开展2015年全国1%人口抽样调查,将摸清2010年以来人口在数量,素质,结构,分布以及居住等方面的变化情况, 为制定国民经济和社会发展规划提供科学准确的统计信息支持.
East Asia Library
East Asia Library  Status 

Find it
Chinese Collection


HA4638 .T54 A173 2017  Unknown 
9. America's diverse population : a comparison of race, ethnicity, and social class in graphic detail [2017]
 Book
 xvi, 390 pages : color charts, maps ; 28 cm
The composition of the American population is rapidly changing from a white, male dominated society to one that is so diverse it will soon be without any single, dominant race, ethnicity or gender. The dramatic demographic shifts in American society have provoked many false claims and distortions of facts that have fueled demagoguery, as occurred during the 2016 presidential campaign. Access to unvarnished facts about people different than youbut who are becoming your neighborsis more critical now than ever. This book was created to provide a single source of easily accessible factsobtained primarily from U.S. government agenciescomparing characteristics of race, ethnicity and gender in graphic format to enhance comprehension, as only visual presentations can achieve. Virtually all major socioeconomic topics are covered, including geographic distribution of populations, birth rates, health, wealth, poverty, income, employment, crime, incarcerations, social behaviors, education and political preferences. Included are past and future trends for many characteristics, as are comparisons between foreignborn, natural citizens, legal and undocumented immigrants. Special Features: *Socioeconomic characteristics between races, ethnicities, and genders in America *Comparisons include: health, education, wealth, poverty, income, employment, crime, incarcerations, social behaviors, geographic distributions, and political preferences *Includes foreignborn and natural citizens, lawful and undocumented immigrants *All data are graphically displayed for easy visualization and comprehension *Attributed sources for all data include web addresses to enable additional research *Only factual data are presented without editorial comments or opinions Interesting facts found in America's Diverse Population include: *More than oneineight persons residing in the U.S. in 2015 were born elsewhere. *Approximately oneinfour persons with "Green Cards" resided in California in 2013. *Over three million temporary workers were admitted into the U.S. in 2014. *In 2009 over twothirds of convictions of undocumented immigrants were for violations of immigration law, and two percent for crimes against persons. *Approximately oneofthree Black or African American children live in households with both married parents, compared with ninetyfive percent of Asian households. *In 2013 oneofeight high school age Hispanic or Latino females reported they were forced to have sex. *In 2015 ninetyseven percent of kindergarten teachers were women. *Approximately onehalf of all maids and housekeeping cleaners in 2015 were Hispanic or Latino women. *In 2015, almost oneinfour Asian females held a Master's degree, the highest rate of any race or ethnicity. *In 2013, the number of NonHispanic White children in the U.S., grades K8, fell below fifty percent of students for the first time.
(source: Nielsen Book Data)9781598889147 20171121
(source: Nielsen Book Data)9781598889147 20171121
The composition of the American population is rapidly changing from a white, male dominated society to one that is so diverse it will soon be without any single, dominant race, ethnicity or gender. The dramatic demographic shifts in American society have provoked many false claims and distortions of facts that have fueled demagoguery, as occurred during the 2016 presidential campaign. Access to unvarnished facts about people different than youbut who are becoming your neighborsis more critical now than ever. This book was created to provide a single source of easily accessible factsobtained primarily from U.S. government agenciescomparing characteristics of race, ethnicity and gender in graphic format to enhance comprehension, as only visual presentations can achieve. Virtually all major socioeconomic topics are covered, including geographic distribution of populations, birth rates, health, wealth, poverty, income, employment, crime, incarcerations, social behaviors, education and political preferences. Included are past and future trends for many characteristics, as are comparisons between foreignborn, natural citizens, legal and undocumented immigrants. Special Features: *Socioeconomic characteristics between races, ethnicities, and genders in America *Comparisons include: health, education, wealth, poverty, income, employment, crime, incarcerations, social behaviors, geographic distributions, and political preferences *Includes foreignborn and natural citizens, lawful and undocumented immigrants *All data are graphically displayed for easy visualization and comprehension *Attributed sources for all data include web addresses to enable additional research *Only factual data are presented without editorial comments or opinions Interesting facts found in America's Diverse Population include: *More than oneineight persons residing in the U.S. in 2015 were born elsewhere. *Approximately oneinfour persons with "Green Cards" resided in California in 2013. *Over three million temporary workers were admitted into the U.S. in 2014. *In 2009 over twothirds of convictions of undocumented immigrants were for violations of immigration law, and two percent for crimes against persons. *Approximately oneofthree Black or African American children live in households with both married parents, compared with ninetyfive percent of Asian households. *In 2013 oneofeight high school age Hispanic or Latino females reported they were forced to have sex. *In 2015 ninetyseven percent of kindergarten teachers were women. *Approximately onehalf of all maids and housekeeping cleaners in 2015 were Hispanic or Latino women. *In 2015, almost oneinfour Asian females held a Master's degree, the highest rate of any race or ethnicity. *In 2013, the number of NonHispanic White children in the U.S., grades K8, fell below fifty percent of students for the first time.
(source: Nielsen Book Data)9781598889147 20171121
(source: Nielsen Book Data)9781598889147 20171121
10. Applied spatial modelling and planning [2017]
 Book
 xxiv, 362 pages, 8 unnumbered pages of plates : illustrations (some color), color maps ; 24 cm.
 Chapter 1: Introduction John Lombard, Eli Stern and Graham Clarke Chapter 2: Applied spatial modelling: 'big science' and 'best practice' challenges Sir Alan Wilson Part I Dynamics of economic space Chapter 3: An exploratory analysis of new firm formation in New England Jitendra Parajuli and Kingsley E. Haynes Chapter 4: The impacts of policy changes on overseas human captial in Australia: the implementation of the 485 graduate visa Jonathan Corcoran, Francisco Rowe, Alessandra Faggian, and Robert Stimson Chapter 5: On the three 'laws' of spatial interaction and a string theory finale: perspectives from social physics with examples in the digital and retail economy Robert G.V. Baker Part II Housing and settlements Chapter 6: Spatial typology of the private rental housing market at neighbourhood scale: the case of South East Queensland, Australia Yan Liu, David Wadley and Jonathan Corcoran Chapter 7: Optimal kernel and bandwidth specifications for geographically weighted regression: an evaluation using automated valuation models (AVMS) for mass real estate appraisal Paul E. Bidanset and John R. Lombard Chapter 8: Spatial search: new settlements for Israel's ultraorthodox population Eliahu Stern Chapter 9: A tale of two earthquakes: dynamic agentbased simulation of urban resilience A. Yair Grinberger and Daniel Felsenstein Part III Population dynamics and population ageing Chapter 10: The United Kingdom's multiethnic future: how fast is it arriving? Philip Rees, Pia Wohland and Paul Norman Chapter 11: Decomposition of life expectancy at older ages and prospects for ageing populations Leslie Mayhew and David Smith Chapter 12: Using agentbased modelling to understand crime phenomena Nick Addis Part IV Health care planning and analysis Chapter 13: Modelling the impact of new community hospitals on access to health care Holly Shulman, Graham Clarke and Mark Birkin Chapter 14: SimSALUD  towards a health decision support system for regional planning Melanie N. Tomintz and Victor M. GarciaBarrios Chapter 15: Smallscale agentbased modelling of infectious disease transmission: an example in a primary school Mike Bithell Chapter 16: Exploring smallarea geographies of obesity in the UK: evidence from the UK Women's Cohort Study Michelle A. Morris, Graham Clarke, Kimberley L. Edwards, Claire Hulme and Janet E. Cade Chapter 17: Examining the sociospatial determinants of depression in the UK Karyn Morrissey, Peter Kinderman, Eleanor Pontin, Sara Tai and Matthias Schwannauer Part V Environmental modelling Chapter 18: A case study of flooding in the Limpopo River Basin, XaiXai, Mozambique Robert Fligg and Joana Barros Chapter 19: A computational framework for mitigating land degradation: a synergy of knowledge from physical geography and geoinformatics Tal Svoray.
 (source: Nielsen Book Data)9781138925700 20170321
(source: Nielsen Book Data)9781138925700 20170321
 Chapter 1: Introduction John Lombard, Eli Stern and Graham Clarke Chapter 2: Applied spatial modelling: 'big science' and 'best practice' challenges Sir Alan Wilson Part I Dynamics of economic space Chapter 3: An exploratory analysis of new firm formation in New England Jitendra Parajuli and Kingsley E. Haynes Chapter 4: The impacts of policy changes on overseas human captial in Australia: the implementation of the 485 graduate visa Jonathan Corcoran, Francisco Rowe, Alessandra Faggian, and Robert Stimson Chapter 5: On the three 'laws' of spatial interaction and a string theory finale: perspectives from social physics with examples in the digital and retail economy Robert G.V. Baker Part II Housing and settlements Chapter 6: Spatial typology of the private rental housing market at neighbourhood scale: the case of South East Queensland, Australia Yan Liu, David Wadley and Jonathan Corcoran Chapter 7: Optimal kernel and bandwidth specifications for geographically weighted regression: an evaluation using automated valuation models (AVMS) for mass real estate appraisal Paul E. Bidanset and John R. Lombard Chapter 8: Spatial search: new settlements for Israel's ultraorthodox population Eliahu Stern Chapter 9: A tale of two earthquakes: dynamic agentbased simulation of urban resilience A. Yair Grinberger and Daniel Felsenstein Part III Population dynamics and population ageing Chapter 10: The United Kingdom's multiethnic future: how fast is it arriving? Philip Rees, Pia Wohland and Paul Norman Chapter 11: Decomposition of life expectancy at older ages and prospects for ageing populations Leslie Mayhew and David Smith Chapter 12: Using agentbased modelling to understand crime phenomena Nick Addis Part IV Health care planning and analysis Chapter 13: Modelling the impact of new community hospitals on access to health care Holly Shulman, Graham Clarke and Mark Birkin Chapter 14: SimSALUD  towards a health decision support system for regional planning Melanie N. Tomintz and Victor M. GarciaBarrios Chapter 15: Smallscale agentbased modelling of infectious disease transmission: an example in a primary school Mike Bithell Chapter 16: Exploring smallarea geographies of obesity in the UK: evidence from the UK Women's Cohort Study Michelle A. Morris, Graham Clarke, Kimberley L. Edwards, Claire Hulme and Janet E. Cade Chapter 17: Examining the sociospatial determinants of depression in the UK Karyn Morrissey, Peter Kinderman, Eleanor Pontin, Sara Tai and Matthias Schwannauer Part V Environmental modelling Chapter 18: A case study of flooding in the Limpopo River Basin, XaiXai, Mozambique Robert Fligg and Joana Barros Chapter 19: A computational framework for mitigating land degradation: a synergy of knowledge from physical geography and geoinformatics Tal Svoray.
 (source: Nielsen Book Data)9781138925700 20170321
(source: Nielsen Book Data)9781138925700 20170321
 Book
 xvi, 356 pages : illustrations ; 25 cm
 Research and statistics
 Introduction to Stata
 Simple (bivariate) regression
 Multiple regression
 Dummyvariable regression
 Interaction/moderation effects using regression
 Linear regression assumptions and diagnostics
 Logistic regression
 Multilevel analysis
 Panel data analysis
 Exploratory factor analysis
 Structural equation modelling and confirmatory factor analysis
 Critical issues.
(source: Nielsen Book Data)9781473913233 20170424
 Research and statistics
 Introduction to Stata
 Simple (bivariate) regression
 Multiple regression
 Dummyvariable regression
 Interaction/moderation effects using regression
 Linear regression assumptions and diagnostics
 Logistic regression
 Multilevel analysis
 Panel data analysis
 Exploratory factor analysis
 Structural equation modelling and confirmatory factor analysis
 Critical issues.
(source: Nielsen Book Data)9781473913233 20170424
12. The butcher, the baker, the candlestick maker : the story of Britain through its census, since 1801 [2017]
 Book
 xi, 340 pages ; 25 cm
 A perfect man
 Censuses, taxation and war
 A hazy snapshot from the air
 The first modern census
 Seamstresses, prostitutes, billiardmarkers and footballers
 Lathes and rapes
 Two parrots, one canary and innumerable mice
 The British babel
 A nation of emigrants
 Aliens
 Empire
 Great War
 The fractured kingdom
 Depression
 Welcome home.
(source: Nielsen Book Data)9781408707012 20170321
 A perfect man
 Censuses, taxation and war
 A hazy snapshot from the air
 The first modern census
 Seamstresses, prostitutes, billiardmarkers and footballers
 Lathes and rapes
 Two parrots, one canary and innumerable mice
 The British babel
 A nation of emigrants
 Aliens
 Empire
 Great War
 The fractured kingdom
 Depression
 Welcome home.
(source: Nielsen Book Data)9781408707012 20170321
13. The butcher, the baker, the candlestickmaker : the story of Britain through its census, since 1801 [2017]
 Book
 xi, 340 pages ; 24 cm
At the beginning of each decade for 200 years the national census has presented a selfportrait of the British Isles. The census has surveyed Britain from the Napoleonic wars to the age of the internet, through the agricultural and industrial revolutions, possession of the biggest empire on earth and the devastation of the 20th century's two world wars. In The Butcher, the Baker, the Candlestick Maker, Roger Hutchinson looks at every census between the first in 1801 and the latest in 2011. He uses this muchloved resource of family historians to paint a vivid picture of a society experiencing unprecedented changes. Hutchinson explores the controversial creation of the British census. He follows its development from a headcount of the population conducted by clerks with quill pens, to a computerised survey which is designed to discover 'the address, place of birth, religion, marital status, ability to speak English and selfperceived national identity of every twentysevenyearold Welshspeaking Sikh metalworker living in Swansea'. All human life is here, from prime ministers to peasants and paupers, from Irish rebels to English patriots, from the last native speakers of Cornish to the first professional footballers, from communities of prostitutes to individuals called 'abecedarians' who made a living from teaching the alphabet. The Butcher, the Baker, the Candlestick Maker is as original and unique as those people and their islands on the cutting edge of Europe.
(source: Nielsen Book Data)9781408707012 20170321
(source: Nielsen Book Data)9781408707012 20170321
At the beginning of each decade for 200 years the national census has presented a selfportrait of the British Isles. The census has surveyed Britain from the Napoleonic wars to the age of the internet, through the agricultural and industrial revolutions, possession of the biggest empire on earth and the devastation of the 20th century's two world wars. In The Butcher, the Baker, the Candlestick Maker, Roger Hutchinson looks at every census between the first in 1801 and the latest in 2011. He uses this muchloved resource of family historians to paint a vivid picture of a society experiencing unprecedented changes. Hutchinson explores the controversial creation of the British census. He follows its development from a headcount of the population conducted by clerks with quill pens, to a computerised survey which is designed to discover 'the address, place of birth, religion, marital status, ability to speak English and selfperceived national identity of every twentysevenyearold Welshspeaking Sikh metalworker living in Swansea'. All human life is here, from prime ministers to peasants and paupers, from Irish rebels to English patriots, from the last native speakers of Cornish to the first professional footballers, from communities of prostitutes to individuals called 'abecedarians' who made a living from teaching the alphabet. The Butcher, the Baker, the Candlestick Maker is as original and unique as those people and their islands on the cutting edge of Europe.
(source: Nielsen Book Data)9781408707012 20170321
(source: Nielsen Book Data)9781408707012 20170321
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request 
HA37 .G72 H88 2017  Available 
14. China ethnic statistical yearbook 2016 [2017]
 Book
 xxv, 391 pages : 2 color maps ; 22 cm
 Chapter 1: Population Growth and Structural Change. Chapter 2: Macroeconomic Growth and Structural Change. Chapter 3: Employment and Income Distribution. Chapter 4: Living Conditions and the Means of Livelihood. Chapter 5: Agricultural Production and Other Rural Activity. Chapter 6: Education, Science and Technological Progress. Chapter 7: Health Care and Social Security. Chapter 8: Entertainment and Other Cultural Activity.
 (source: Nielsen Book Data)9783319491981 20171211
(source: Nielsen Book Data)9783319491981 20171211
 Chapter 1: Population Growth and Structural Change. Chapter 2: Macroeconomic Growth and Structural Change. Chapter 3: Employment and Income Distribution. Chapter 4: Living Conditions and the Means of Livelihood. Chapter 5: Agricultural Production and Other Rural Activity. Chapter 6: Education, Science and Technological Progress. Chapter 7: Health Care and Social Security. Chapter 8: Entertainment and Other Cultural Activity.
 (source: Nielsen Book Data)9783319491981 20171211
(source: Nielsen Book Data)9783319491981 20171211
 Book
 xvii, 356 pages : illustrations ; 25 cm
How could the same person be classified by the US census as black in 1900, mulatto in 1910, and white in 1920? The history of categories used by the US census reflects a country whose identity and selfunderstandingparticularly its social construction of raceis closely tied to the continuous polling on the composition of its population. By tracing the evolution of the categories the United States used to count and classify its population from 1790 to 1940, Paul Schor shows that, far from being simply a reflection of society or a mere instrument of power, censuses are actually complex negotiations between the state, experts, and the population itself. The census is not an administrative or scientific act, but a political one. Counting Americans is a social history exploring the political stakes that pitted various interests and groups of people against each other as population categories were constantly redefined. Utilizing new archival material from the Census Bureau, this study pays needed attention to the long arc of contested changes in race and censusmaking. It traces changes in how race mattered in the United States during the era of legal slavery, through its fraught end, and then during (and past) the period of Jim Crow laws, which set different ethnic groups in conflict. And it shows how those developing policies also provided a template for classifying Asian groups and white ethnic immigrants from southern and eastern Europeand how they continue to influence the newly complicated racial imaginings informing censuses in the second half of the twentieth century and beyond. Focusing in detail on slaves and their descendants, on racialized groups and on immigrants, and on the troubled imposition of U.S. racial categories upon the populations of newly acquired territories, Counting Americans demonstrates that censustaking in the United States has been at its core a political undertaking shaped by racial ideologies that reflect its violent history of colonization, enslavement, segregation and discrimination.
(source: Nielsen Book Data)9780199917853 20170731
(source: Nielsen Book Data)9780199917853 20170731
How could the same person be classified by the US census as black in 1900, mulatto in 1910, and white in 1920? The history of categories used by the US census reflects a country whose identity and selfunderstandingparticularly its social construction of raceis closely tied to the continuous polling on the composition of its population. By tracing the evolution of the categories the United States used to count and classify its population from 1790 to 1940, Paul Schor shows that, far from being simply a reflection of society or a mere instrument of power, censuses are actually complex negotiations between the state, experts, and the population itself. The census is not an administrative or scientific act, but a political one. Counting Americans is a social history exploring the political stakes that pitted various interests and groups of people against each other as population categories were constantly redefined. Utilizing new archival material from the Census Bureau, this study pays needed attention to the long arc of contested changes in race and censusmaking. It traces changes in how race mattered in the United States during the era of legal slavery, through its fraught end, and then during (and past) the period of Jim Crow laws, which set different ethnic groups in conflict. And it shows how those developing policies also provided a template for classifying Asian groups and white ethnic immigrants from southern and eastern Europeand how they continue to influence the newly complicated racial imaginings informing censuses in the second half of the twentieth century and beyond. Focusing in detail on slaves and their descendants, on racialized groups and on immigrants, and on the troubled imposition of U.S. racial categories upon the populations of newly acquired territories, Counting Americans demonstrates that censustaking in the United States has been at its core a political undertaking shaped by racial ideologies that reflect its violent history of colonization, enslavement, segregation and discrimination.
(source: Nielsen Book Data)9780199917853 20170731
(source: Nielsen Book Data)9780199917853 20170731
16. Dernekpazarı nüfus defteri 18341846 [2017]
 Book
 318 pages : facsimiles ; 24 cm
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request 
HA4556.5 .Z9 D476 2017  Available 
 Book
 1 online resource ( xv, 202 pages.) :.
EBSCOhost Access limited to 1 user
 EBSCOhost Access limited to 1 user
 Google Books (Full view)
 Book
 xvii, 216 pages ; 23 cm
 Chapter 1: Introductory Ideas Regression Modeling Control Modeling Modeling Interactions Modeling Linearity With Splines Testing Research Hypotheses Classical Approach to Regression Disadvantages of Classical Approach Discrete Approach to Regression Summary Key Concepts Notes Chapter 2: Basic Statistical Procedures Individual Units and Groups Measurement Level of Measurement Examples for Level of Measurement Count, Sum, and Transformations Mean Proportion and Percentage Odds and Log odds Examples of Means and Log Odds Differences Summary Key Concepts Chapter Exercises Notes Chapter 3: Regression Modeling Basics Difference between Means: The ttest Linear Regression With a TwoCategory Independent Variable Logistic Regression With a TwoCategory Independent Variable Linear Regression With a FourCategory Independent Variable Logistic Regression With a FourCategory Independent Variable Modeling Linear Effect With Dummy Variables Linear Coefficient in Linear Regression Linear Coefficient in Logistic Regression Using Dummy Variables for a Continuous Variable Summary Key Concepts Chapter Exercises Notes Chapter 4: Key Regression Modeling Concepts Unit Vector: Estimating the Intercept Nestedness HigherOrder Differences Constraints Summary Key Concepts Chapter Exercises Notes Chapter 5: Control Modeling Elementary Control Modeling Elaboration for Controlling Demographic Standardization for Controlling Small and Big Models Allocating Influence With Multiple Control Variables OneataTime Without Controls Step Approach OneataTime With Controls Hybrid Approach Nestedness and Constraints Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 6: Modeling Interactions Interactions as Conditional Differences Interactions Between Dummy Variables Interactions Between Dummy Variables and an Interval Variable ThreeWay Interactions Estimating Separate Models Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 7: Modeling Linearity With Splines Dummy Variables Nested in an Interval Variable Introduction to Knotted Spline Variables Spline Variables Nested in an Interval Variable Regression Modeling Using Spline Variables Working With a Continuous Independent Variable Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 8: Conclusion: Testing Research Hypotheses Bivariate Hypothesis/No Controls Bivariate Hypothesis/Unanalyzed Controls Bivariate Hypothesis/Analyzed Controls Hypothesis Involving Interactions Hypothesis Involving Nonlinearity Final Comments Key Concepts Summary Chapter exercises Notes.
 (source: Nielsen Book Data)9781506303475 20161024
(source: Nielsen Book Data)9781506303475 20161024
 Chapter 1: Introductory Ideas Regression Modeling Control Modeling Modeling Interactions Modeling Linearity With Splines Testing Research Hypotheses Classical Approach to Regression Disadvantages of Classical Approach Discrete Approach to Regression Summary Key Concepts Notes Chapter 2: Basic Statistical Procedures Individual Units and Groups Measurement Level of Measurement Examples for Level of Measurement Count, Sum, and Transformations Mean Proportion and Percentage Odds and Log odds Examples of Means and Log Odds Differences Summary Key Concepts Chapter Exercises Notes Chapter 3: Regression Modeling Basics Difference between Means: The ttest Linear Regression With a TwoCategory Independent Variable Logistic Regression With a TwoCategory Independent Variable Linear Regression With a FourCategory Independent Variable Logistic Regression With a FourCategory Independent Variable Modeling Linear Effect With Dummy Variables Linear Coefficient in Linear Regression Linear Coefficient in Logistic Regression Using Dummy Variables for a Continuous Variable Summary Key Concepts Chapter Exercises Notes Chapter 4: Key Regression Modeling Concepts Unit Vector: Estimating the Intercept Nestedness HigherOrder Differences Constraints Summary Key Concepts Chapter Exercises Notes Chapter 5: Control Modeling Elementary Control Modeling Elaboration for Controlling Demographic Standardization for Controlling Small and Big Models Allocating Influence With Multiple Control Variables OneataTime Without Controls Step Approach OneataTime With Controls Hybrid Approach Nestedness and Constraints Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 6: Modeling Interactions Interactions as Conditional Differences Interactions Between Dummy Variables Interactions Between Dummy Variables and an Interval Variable ThreeWay Interactions Estimating Separate Models Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 7: Modeling Linearity With Splines Dummy Variables Nested in an Interval Variable Introduction to Knotted Spline Variables Spline Variables Nested in an Interval Variable Regression Modeling Using Spline Variables Working With a Continuous Independent Variable Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 8: Conclusion: Testing Research Hypotheses Bivariate Hypothesis/No Controls Bivariate Hypothesis/Unanalyzed Controls Bivariate Hypothesis/Analyzed Controls Hypothesis Involving Interactions Hypothesis Involving Nonlinearity Final Comments Key Concepts Summary Chapter exercises Notes.
 (source: Nielsen Book Data)9781506303475 20161024
(source: Nielsen Book Data)9781506303475 20161024
19. European statistics for European policies : a wealth of data to underpin the Commission's priorities [2017]
 Book
 29 pages : color illustrations ; 21 cm
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request 
HA1107.5 .E89 2017  Available 
 Book
 xiii, 125 pages : illustrations ; 23 cm
 * How to Use This Book * Frequently Asked Questions about Reporting Statistics * Descriptive Information * Reliabilities * Correlation * Nonparametric Statistics * Parametric Statistics * Presenting Results Visually * Conclusion Appendices: * Summary chart of Statistics * What to Report * Abbreviations * Suggested Syntax.
 (source: Nielsen Book Data)9781138638082 20161010
(source: Nielsen Book Data)9781138638082 20161010
 * How to Use This Book * Frequently Asked Questions about Reporting Statistics * Descriptive Information * Reliabilities * Correlation * Nonparametric Statistics * Parametric Statistics * Presenting Results Visually * Conclusion Appendices: * Summary chart of Statistics * What to Report * Abbreviations * Suggested Syntax.
 (source: Nielsen Book Data)9781138638082 20161010
(source: Nielsen Book Data)9781138638082 20161010