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| The Researcher's Question | |
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| The Researcher's Question | |
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| Isen, Clark, and Schwartz (1976) | |
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| A Lasting Friendship | |
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| Other Examples of Statistics | |
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| International Banking | |
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| What Makes a Person Hardy? | |
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| A Bit about Tools: Scales of Measurement | |
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| The Trail Ahead | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Thinking about Influences on Behavior | |
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| Scores and Influences | |
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| Categorizing Influences | |
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| Example of a Nuisance Variable | |
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| Examples of Confounds | |
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| Between-Group versus Within-Group Influences | |
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| IV Effects and Error | |
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| The Trail Ahead | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Describing Influences on Behavior | |
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| The Mean as Central Tendency | |
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| Using Symbols | |
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| A Problem with the Mean | |
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| The Median | |
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| The Mode | |
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| Central Tendency and the IV Effect | |
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| Central Tendency Isn't Everything | |
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| Looking at a Set of Scores: Frequency Distributions | |
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| Frequency Distributions Have Shapes | |
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| Measuring the Spread of the Distribution | |
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| Total Size of the Distribution: The Range | |
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| A Better Measure: Variability about the Mean | |
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| Standard Deviation (SD) | |
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| Remember the Meaning: Average Deviation | |
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| Variability and Error | |
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| Viewing Data from an Experiment | |
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| Reference | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Looking at Populations | |
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| Samples and Populations | |
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| Normal Distributions and Standard Normal Distributions | |
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| Recycling Distributions | |
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| An Example | |
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| Using Distributions | |
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| Cumulative Distributions | |
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| Being Precise: A More Exact Normal Distribution | |
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| Review | |
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| Exercises | |
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| Answers | |
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| How Accurate Are Sample Means? | |
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| Two Determinants of Accuracy of Sample Means | |
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| An Example | |
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| Looking at a Distribution of Sample Means | |
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| Summarize What You Have Learned | |
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| Putting the Pieces in Perspective | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Answering the Researcher's Question by Turning It Upside Down | |
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| The Null Hypothesis | |
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| An Experiment with Nothingness | |
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| Expanding the Null Hypothesis: The Sampling Distribution of the Difference Between Means | |
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| Using the SDODBM | |
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| A Borderline Between the Likely and Unlikely Regions | |
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| Let's Talk About Our Decision | |
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| Probability Statements | |
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| Practice | |
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| Calculating the SE[subscript diff] | |
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| Perspective | |
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| Another View: The Big Decision | |
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| Type I Errors | |
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| Type II Errors | |
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| A 2 [times] 2 Decision Box | |
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| Power: Avoiding Type II Errors | |
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| Review | |
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| Exercises | |
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| Answers | |
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| A Recipe for Answering the Researcher's Question | |
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| The Big Picture | |
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| Calculating t Values | |
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| Step-By-Step Method for Calculating t | |
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| Expanding the Idea of t | |
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| t[subscript obtained] and t[subscript critical] | |
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| t with Different Sample Sizes | |
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| Measure of Total Sample Size: Degrees of Freedom | |
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| Finding t[subscript critical] in a t-Table | |
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| Putting the Idea to Use | |
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| Conducting the t-test: Summary | |
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| One-Tailed Tests | |
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| Using the One-Tailed Test | |
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| Variation in the Value of p | |
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| Reporting t-Tests in Articles | |
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| What Happens with a Borderline p-Value? | |
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| The Meaning of Statistical Decisions | |
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| "Significance" Is a Lousy Term | |
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| A Big t Means Little | |
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| You Never Prove the Null Hypothesis | |
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| Relations Relevant to Statistical Decisions | |
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| Effect Size | |
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| Power | |
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| Putting This Together: t-Values and Research Design | |
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| Effects of Confounds | |
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| Some More Details | |
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| Standard Error of the Difference, Part 2: Unequal Sample Size | |
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| You've Learned about One Type of t-Test: t-Test for Independent Samples | |
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| t-Test for Dependent Samples | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Expanding Our Thinking to n Dimensions: Analysis of Variance | |
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| Gosset and Fisher | |
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| Influences of a Score, Part 1: Reminder from the t-Test | |
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| Influences of a Score, Part 2 | |
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| The Model | |
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| Rearranging the Deviations | |
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| Sums of Squared Deviations | |
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| Deviation Tables | |
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| Influences as Sources of Variance | |
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| Source Tables | |
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| Checking Your Calculations | |
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| Summary: Calculate ANOVA in Two Easy Tables | |
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| Meaning of F | |
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| F Distributions | |
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| Degrees of Freedom and the F Table | |
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| Reporting F-Tests in Articles | |
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| F's and t's | |
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| Effects Size and Power | |
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| Effects of Confounds | |
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| Within-Participant Designs | |
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| Final Summary: Calculating ANOVA | |
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| How to Calculate F for a Between-Participant Design | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Using More Than Two Groups | |
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| The Example Experiment | |
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| Doing the Multilevel Analysis of Variance | |
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| Completed Tables | |
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| Interpretation F in the Multilevel Case | |
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| Follow-Up Tests | |
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| A Priori Hypotheses: Planned Comparisons | |
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| After the Experiment: Post-Hoc Tests Given a Significant F | |
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| The Pitfalls of Data Fishing | |
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| A Post-Hoc Test: The Scheffe Test | |
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| Computational Summary: Multilevel ANOVA for Between-Participant Designs | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Using More Than One Independent Variable | |
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| An Example Study | |
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| Most Basic Levels: Cells | |
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| There Are Four Cell Means | |
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| The Entire Design | |
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| Next Level: Main Effects and Four Main Effect Means | |
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| Highest Level: One Grand Mean | |
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| The Example Made Concrete with Numbers | |
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| Interpreting the Main Effects | |
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| The Completed Table | |
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| Three IV Effects in the 2 [times] 2 Factorial | |
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| Main Effect of IV A | |
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| Main Effect of IV B | |
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| Interaction of A and B | |
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| Where We Are Going | |
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| Five Sums of Squares in the Factorial Design | |
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| Error Deviations | |
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| Total | |
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| IV A Effect | |
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| IV B Effect | |
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| Interaction of A [times] B | |
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| Putting It Together: The Entire Deviation Formula | |
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| Time Out for a Review | |
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| Doing the ANOVA | |
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| The Sums of Squares | |
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| Source Table for a 2 [times] 2 Factorial Design | |
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| Compare the Obtained and Critical F's | |
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| Degrees of Freedom | |
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| Interpreting 2 [times] 2 Graphs | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Relations Between Variables: Linear Regression and Correlation | |
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| Illustrating Relations between Variables: Graphs of Means | |
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| Illustrating Relations for all Scores: The Scatterplot | |
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| Regression Example | |
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| Correlation: The Strength of a Relation | |
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| Regression Lines | |
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| Slope: The Regression Coefficient | |
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| Intercept | |
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| Error | |
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| Using Regression for Prediction | |
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| Regression Equation | |
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| Summary Example | |
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| Letting the Computer "Fit" Your Regression Line | |
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| Measuring the Strength of a Relation: Correlation | |
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| z Scores and Correlation | |
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| z Score Correlation Formula | |
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| Correlation, Regression, and Variance: The Coefficient of Determination | |
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| Testing the Significance of Regression and Correlation Values | |
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| Two Limitations of Linear Regression and Correlation | |
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| Only Linear Relations are Measured | |
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| A Relation Does Not Imply Causality | |
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| The Third Variable Problem | |
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| Regression and Correlation versus t-Tests and ANOVA | |
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| Advanced Methods of Correlation and Regression | |
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| Correlation Matrices | |
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| Multiple Regression | |
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| Reference: Calculating Regression and Correlation Values | |
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| Step-by-Step Method for Regression and Correlation Values | |
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| Completed Step-by-Step Methods for Calculating Correlation | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Analyzing Categorical Data | |
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| The Basic Idea of Chi-Square ([Chi superscript 2]) | |
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| Testing the Significance of a Chi-Square Value | |
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| Expected Frequencies with More than Two Categories | |
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| Extending the Basic Idea to Contingencies | |
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| Contingency Tables | |
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| This is Chi-Square | |
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| Review | |
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| Exercises | |
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| Answers | |
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| Perspective: Looking Back at Your Journey | |
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| Descriptive Statistics | |
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| t-Test: Are These Two Samples the Same or Different? | |
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| ANOVA: One-Factor and Multifactor | |
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| Regression and Correlation: Relations Between Variables | |
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| Relations between Statistics | |
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| Chi-Square for Categories | |
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| Have We Missed Something? | |
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| The Creative Nature of Statistics | |
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| Exercises | |
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| Answers | |
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| Coping with Math and Test Anxiety | |
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| Index | |