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Preface | |

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Introduction to Statistical Analysis | |

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Why Study Statistics? | |

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Thinking Statistically | |

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Descriptive and Inferential Statistics | |

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Descriptive Statistics | |

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Inferential Statistics | |

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Statistics and Error | |

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Parametric and Nonparametric Statistics | |

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Operationalization | |

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Reliability and Validity | |

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Measurement | |

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Dependent and Independent Variables | |

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Nominal Level | |

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Ordinal Level | |

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Interval Level | |

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Ratio Level | |

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The Role of Statistics in Science | |

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Summary | |

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Practice Application: Variables and Levels of Measurement | |

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Problems | |

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Presenting and Summarizing Data | |

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Types of Frequency Distributions | |

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Interpreting Cumulative Frequencies | |

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Frequency Distribution of Grouped Data | |

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Limits, Sizes, and Midpoints of Class Intervals | |

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Advantages and Disadvantages of Grouping Data | |

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Bar Graphs and Pie Charts | |

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Histograms and Frequency Polygons | |

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Numerical Summation of Data: Percentages, Proportions, and Ratios | |

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Summary | |

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Practice Application: Displaying and Summarizing Data | |

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Problems | |

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Central Tendency and Dispersion | |

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Measures of Central Tendency | |

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Mode | |

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Median | |

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Computing the Median with Grouped Data | |

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The Mean | |

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Computing the Mean from Grouped Data | |

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A Research Example | |

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Choosing a Measure of Central Tendency | |

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Measures of Dispersion | |

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Range | |

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Standard Deviation | |

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Computational Formula for s | |

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Variability and Variance | |

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Computing the Standard Deviation from Grouped Data | |

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Coefficient of Variation | |

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Index of Qualitative Variation | |

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Summary | |

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Practice Application: Central Tendency and Dispersion | |

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Reference | |

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Problems | |

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Probability and the Normal Curve | |

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Probability | |

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The Multiplication Rule | |

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The Addition Rule | |

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Theoretical Probability Distributions | |

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The Normal Curve | |

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Different Kinds of Curves | |

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The Standard Normal Curve | |

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The z Scores | |

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Finding Area of the Curve Below the Mean | |

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Summary | |

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Practice Application: The Normal Curve and z Scores | |

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Reference | |

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Problems | |

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The Sampling Distribution and Estimation Procedures | |

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Sampling | |

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Simple Random Sampling | |

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Stratified Random Sampling | |

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The Sampling Distribution | |

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The Central Limit Theorem | |

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Standard Error of the Sampling Distribution | |

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Point and Interval Estimates | |

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Confidence Intervals and Alpha Levels | |

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Calculating Confidence Intervals | |

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Sampling and Confidence Intervals | |

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Interval Estimates for Proportions | |

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Estimating Sample Size | |

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Estimating Sample Size for Proportions | |

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Summary | |

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Practice Application: The Sampling Distribution and Estimation | |

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Problems | |

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Hypothesis Testing: Interval/Ratio Data | |

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The Logic of Hypothesis Testing | |

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The Evidence and Statistical Significance | |

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Errors in Hypothesis Testing | |

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One Sample z Test | |

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Decision Rule | |

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The t Test | |

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Degrees of Freedom | |

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The t Distribution | |

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Directional Hypotheses: One- and Two-Tailed Tests | |

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Computing t | |

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t Test for Correlated (Dependent) Means | |

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Effects of Sample Variance on H[subscript 0] Decision | |

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Large Sample t Test: A Computer Example | |

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Interpreting the Printout | |

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Calculating t with Unequal Variances | |

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Testing Hypotheses for Single-Sample Proportions | |

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Statistical Versus Substantive Significance, and Strength of Association | |

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Summary | |

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Practice Application: t Test | |

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Problems | |

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Analysis of Variance | |

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Assumptions of Analysis of Variance | |

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The Basic Logic of ANOVA | |

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The Idea of Variance Revisited | |

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The Advantage of ANOVA over Multiple Tests | |

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The F Distribution | |

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An Example of ANOVA | |

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Determining Statistical Significance: Mean Square and the F Ratio | |

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ETA Squared | |

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Multiple Comparisons: The Scheffe Test | |

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Two-Way Analysis of Variance | |

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Determining Statistical Significance | |

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Significance Levels | |

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Understanding Interaction | |

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A Research Example of a Significant Interaction Effect | |

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Summary | |

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Practice Application | |

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Problems | |

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Hypothesis Testing with Categorical Data: Chi-Square Test | |

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Table Construction | |

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Putting Percentages in Tables | |

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Assumptions for the Use of Chi-Square | |

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The Chi-Square Distribution | |

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Yates' Correction for Continuity | |

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Chi-Square Distribution and Goodness of Fit | |

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Chi-Square-Based Measures of Association | |

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Sample Size and Chi-Square | |

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Contingency Coefficient | |

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Cramer's V | |

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A Computer Example of Chi-Square | |

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Kruskal-Wallis One-Way Analysis of Variance | |

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Summary | |

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Practice Application: Chi-Square | |

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Reference | |

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Problems | |

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Nonparametric Measures of Association | |

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The Idea of Association | |

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Does an Association Exist? | |

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What Is the Strength of the Association? | |

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What Is the Direction of the Association? | |

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Proportional Reduction in Error | |

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The Concept of Paired Cases | |

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A Computer Example | |

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Gamma | |

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Lambda | |

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Somer's d | |

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Tau-B | |

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The Odd's Ratio and Yule's Q | |

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Spearman's Rank Order Correlation | |

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Which Test of Association Should We Use? | |

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Summary | |

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Practice Application: Nonparametric Measures of Association | |

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Reference | |

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Problems | |

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Elaboration of Tabular Data | |

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Causal Analysis | |

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Criteria for Causality | |

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Association | |

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Temporal Order | |

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Spuriousness | |

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Necessary Cause | |

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Sufficient Cause | |

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Necessary and Sufficient Cause | |

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A Statistical Demonstration of Cause-and-Effect Relationships | |

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Multivariate Contingency Analysis | |

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Introducing a Third Variable | |

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Explanation and Interpretation | |

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Illustrating Elaboration Outcomes | |

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Controlling for One Variable | |

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Further Elaboration: Two Control Variables | |

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Partial Gamma | |

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When Not to Compute Partial Gamma | |

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Problems with Tabular Elaboration | |

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Summary | |

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Practice Application: Bivariate Elaboration | |

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Reference | |

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Problems | |

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Bivariate Correlation and Regression | |

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Preliminary Investigation: The Scattergram | |

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The Slope | |

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The Intercept | |

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The Pearson Correlation Coefficient | |

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Covariance and Correlation | |

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Partitioning r Squared and Sum of Squares | |

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Standard Error of the Estimate | |

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Standard Error of r | |

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Significance Testing for Pearson's r | |

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The Interrelationship of b, r, and [beta] | |

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Summarizing Properties of r, b, and [beta] | |

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Summarizing Prediction Formulas | |

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A Computer Example of Bivariate Correlation and Regression | |

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Practice Application: Bivariate Correlation and Regression | |

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Practice Application: Bivariate Correlation and Regression | |

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Reference | |

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Problems | |

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Multivariate Correlation and Regression | |

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Partial Correlation | |

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Computing Partial Correlations | |

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Computer Example and Interpretation | |

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Second-Order Partials: Controlling for Two Independent Variables | |

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The Multiple Correlation Coefficient | |

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Multiple Regression | |

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The Unstandardized Partial Slope | |

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The Standardized Slope ([beta]) | |

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A Computer Example of Multiple Regression and Interpretation | |

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Summary Statistics: Multiple R, R[superscript 2], s[subscript Y.X], and ANOVA | |

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The Predictor Variables: b, [beta], and t | |

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A Visual Representation of Multiple Regression | |

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Dummy Variable Regression | |

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Regression and Interaction | |

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Summary | |

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Practice Application: Partial Correlation | |

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Problems | |

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Introduction to Logistic Regression | |

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An Example of Logit Regression | |

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Interpretation: Probabilities and Odds | |

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Assessing the Model Fit | |

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Multiple Logistic Regression | |

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Summary | |

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Practice Application: Logistic Regression | |

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Problem | |

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Statistical Tables | |

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Answers to Odd Numbered Problems | |

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Chapter 1 | |

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Chapter 2 | |

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Chapter 3 | |

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Chapter 4 | |

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Chapter 5 | |

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Chapter 6 | |

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Chapter 7 | |

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Chapter 8 | |

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Chapter 9 | |

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Chapter 10 | |

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Chapter 11 | |

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Chapter 12 | |

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Chapter 13 | |

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Glossary | |

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Index | |