## Applied Statistics: From Bivariate Through Multivariate Techniques: From Bivariate Through Multivariate TechniquesRebecca 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. |

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This book is one tough read. Be sure to have plenty of time to go through the verbose speaking nature of the author. There are good explanations and examples. However, the logic of thought teeters on the edge of too much presented.

### Contents

Preface
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About the Author
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1 Review of Basic Concepts
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2 Basic Statistics Sampling Error and Confidence Intervals
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3 Statistical Significance Testing
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4 Preliminary Data Screening
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5 Comparing Group Means Using the Independent Samples t Test
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6 OneWay BetweenSubjects Analysis of Variance
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17 Analysis of Covariance
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18 Discriminant Analysis
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19 Multivariate Analysis of Variance
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20 Principal Components and Factor Analysis | |

21 Reliability Validity and MultipleItem Scales
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22 Analysis of Repeated Measures
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23 Binary Logistic Regression
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Proportions of Area Under a Standard Normal Curve
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7 Bivariate Pearson Correlation
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8 Alternative Correlation Coefficients
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9 Bivariate Regression
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Preliminary Exploratory Analyses
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11 Multiple Regression with Two Predictor Variables
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12 Dummy Predictor Variables in Multiple Regression
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13 Factorial Analysis of Variance
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14 Multiple Regression with More than Two Predictors
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Tests for Interaction in Multiple Regression
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16 Mediation
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Critical Values for t Distribution | |

Critical Values of F | |

Critical Values of ChiSquare | |

Critical Values of the Pearson Correlation Coefficient | |

Critical Values of the Studentized Range Statistic | |

Appendix G Transformation of r Pearson Correlation to Fisher Z | |

Glossary
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### Other editions - View all

Applied Statistics: From Bivariate Through Multivariate Techniques Rebecca M. Warner Limited preview - 2012 |

Applied Statistics: From Bivariate Through Multivariate Techniques Rebecca M. Warner Limited preview - 2008 |

Applied Statistics: From Bivariate Through Multivariate Techniques Rebecca M. Warner No preview available - 2012 |

### Common terms and phrases

appears in Figure assess assumptions bivariate blood pressure caffeine causal cell Chapter coefficients components compute corresponds covariate Dependent Variable dialog window discriminant function dummy variables equation estimate evaluate example factor analysis factorial ANOVA female gender grand mean group means group membership included independent samples interaction level of measurement linear male matrix mediated methods multiple regression normal distribution null hypothesis obtained one-way ANOVA outcome variable outliers pairs partial correlation participants path Pearson Correlation Pearson’s possible predicted predictor variables proportion of variance quantitative ratio raw score regression analysis reject H0 repeated measures reported represent research situations risk of Type salary sample mean samples t test scatter plot significance tests slope SPSS standard deviation statistical power statistically significant sums of squares Table tion Type I error univariate VSAT weight X1 and X2 z scores