Solution Manual For Applied Statistics: From Bivariate Through Multivariate Techniques Second Edition by Rebecca (Becky) M. (Margaret) Warner

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Product details:

• ISBN-10 ‏ : ‎ 141299134X
• ISBN-13 ‏ : ‎ 978-1412991346
• Author: Rebecca M. Warner

Rebecca M. Warner′s Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.

Table Of Contents:

1. The New Statistics Required Background What is the New Statistics? Common Misinterpretations of  Values Problems with NHST Logic The Replication Crises Common Misuses of NHST The Replication Crisis Some Proposed Remedies for NHST Problems Review of Confidence Intervals Effect Size Brief Introduction to Meta-Analysis Recommendations for Better Research and Analysis Summary2. Advanced Data Screening: Outliers and Missing Values Introduction Variable Names and File Management Sources of Bias Screening Sample Data Possible Remedy for Skewness: Nonlinear Data Transformations Identification of Outliers Handling Outliers Testing Linearity Assumptions Evaluation of Other Assumptions Specific to Analyses Describing Amount of Missing Data How Missing Data Arise Patterns in Missing Data Empirical Example: Detecting Type a Missingness Possible Remedies for Missing Data Empirical Example: Multiple Imputation to Replace Missing Values Data Screening Checklist Reporting Guidelines Summary Appendix 2 A Brief Note About Zero Inflated Binomial or Poisson Regression3. Statistical Control: How an X, Y Association Can Change When a Control Variable is Added What is Statistical Control? First Research Example: Controlling for a Categorical X2 Variable Assumptions for Partial Correlation Between X1 and Y, Controlling for X2 Notation for Partial Correlation Computing Partial Correlation: Use of Bivariate Regressions to Remove Variance Predictable by X2 from Both X1 and Y Partial Correlation Makes No Sense if There is An X1 x X2 Interaction Computation of Partial r From Bivariate Pearson Correlations Significance Tests, Confidence Intervals, and Statistical Power for Partial Correlations Comparing Outcomes for ry1.2 and ry1 Introduction to Path Models Possible Paths Among X1, Y, and X2 One Possible Model: X1 and Y are Not Related Whether You Control for X2 or Not Possible Model: Correlation Between X1 and Y is the Same Whether X2 is Statistically Controlled or Not (X2 is Irrelevant to the X1, Y Relationship) When You Control for X2, Correlation Between X1 and Y Drops to 0 When You Control for X2, the Correlation Between X1 and Y Becomes Smaller (But Does not Drop to 0 or Change Sign) Some Forms of Suppression: When You Control for X2, r1y.2 Becomes Larger Than r1y or Opposite in Sign to r1y. “None of the Above Results Section Summary4. Partition of Variance in Regression Introduction Hypothetical Research Example Graphic Representation of Regression Plane Semipartial  Correlation Partition of Variance In Y in Regression with Two Predictors Assumptions for Regression With Two Predictors Formulas for Regression With Two Predictors SPSS Regression Conceptual Basis: Factors that Affect the Magnitude and Sign of ? and b in Regression With Two Predictors Tracing Rules for Path Models Comparison of Equations for ?, b, pr, and sr Nature of Predictive Relationships Effect Size Information in Regression with Two Predictors Statistical Power Issues in Planning a Study Results Summary5. Multiple Regression Research Questions Empirical Example Screening for Violations of Assumptions Issues in Planning a Study Computation of Regression Coefficients with k Predictor Variables Methods of Entry for Predictor Variables Variance Partitioning in Standard Regression Versus Hierarchical and Statistical Regression Significance Test for an Overall Regression Model Significance Tests for Individual Predictors in Multiple Regression Effect Size Changes

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