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Index
Preface Note 01: It's A Game! Note 02: Some Aspects Of Study Design Note 03: Intention-to-Treat Analysis Note 04: Meta Analysis Note 05: Random Samples / Randomization Note 06: Look At The Data!!! Note 07: Logarithms Note 08: Summary Statistics -- Location & Spread Note 09: Correlation Coefficients Note 10: Probability Theory Note 11: Probability, Histograms, Distributions Note 12: The Normal Distribution Note 13: Outliers Note 14: The Behavior of the Sample Mean (or Why Confidence Intervals Always Seem to be Based On the Normal Distribution?) Note 15: Confidence Intervals Note 16: Probability & Statistics / A Boy & His Dog Note 17: Confidence Intervals Involving Data to Which a Logarithmic Transformation Has Been Applied Note 18: LARGE SAMPLE Formulas for Confidence Intervals Involving Population Means Note 19: Other Intervals Note 20: Paired Data Note 21: What does pairing Note 22: Units of Analysis Note 23: The Ubiquitous Sample Mean! Note 24: What Student Did Note 25: What Did Student Note 26: Significance Tests: An Introduction Note 27: Significance Tests Simplified Note 28: Student's t Test for Independent Samples Note 29: The Robustness of Student's t Test Note 30: P values Note 31: Why P=0.05? Note 32: A Valuable Lesson Note 33: One-Sided Tests Note 34: Contingency Tables Note 35: Proportions Note 36: Odds Note 37: The Disease Odds Ratio and Exposure Odds Ratio Are Equal Note 38: Paired Counts Note 39: Sample Size Calculations: Underlying Theory and Practical Advice Note 40: Sample Size Calculations Simplified: Controlled Trials Note 41: Sample Size Calculations Simplified(?): Surveys Note 42: Sample Size for Group Randomized, Multi-level, & Hierarchical Trials Note 43: Sample Size Calculations for Gels & Plates Note 44: An Underappreciated Consequence of Sample Size Calculations As They Are Usually Performed Note 45: Simple Linear Regression: An Introduction Note 46: How to Read the Output From Simple Linear Regression Analyses Note 47: Correlation & Regression Note 48: Frank Anscombe's Regression Examples Note 49: Transformations In Linear Regression Note 50: The Regression Effect / The Regression Fallacy Note 51: Sample Size Estimates for Linear Regression Note 52: Comparing Two Measuring Devices, Part I Note 53: Comparing Two Measuring Devices, Part II Note 54: Terminology: Regression, ANOVA, ANCOVA Note 55: Student's t Test for Independent Samples Is A Special Case of Simple Linear Regression Note 56: Introduction to Multiple Linear Regression Note 57: The Most Important Lesson You'll Ever Learn About Multiple Linear Regression Analysis Note 58: How to Read the Output From Multiple Linear Regression Analyses Note 59: What do the Coefficients in a Multiple Linear Regression Mean? Note 60: What Does Multiple Regression Look Like, Part 1? Note 61: What Does Multiple Regression Look Like, Part 2? Note 62: Why Is A Simple Linear Regression Line Straight? Note 63: Partial Correlation Coefficients Note 64: Which Predictors Are More Important? Note 65: The Extra Sum of Squares Principle Note 66: Simplifying a Multiple Regression Equation Note 67: The BootstrapNote 58a: The Bootstrap Note 68: Using the Bootstrap to Simplify a Multiple Regression Equation Note 69: Which variables go into a multiple regression equation? Note 70: Multiple Linear Regression: Categorical Variables With More Than Two Categories Note 71: Interactions In Multiple Linear Regression Models Note 72: Multiple Linear Regression: Centering Note 73: Multiple Linear Regression: Collinearity Note 74: Regression Diagnostics Note 75: Single Factor Analysis of Variance (ANOVA) Note 76: How to Read the Output From One-Way ANOVA Analyses Note 77: Multiple Comparison Procedures Note 78: Obtaining Superscripts to Affix to Means Whose Differences Are Not Statistically Significant Note 79: Adjusted Means (Least Squares Means) Note 80: Adjusted Means: Adjusting For Numerical Variables Note 81: Adjusted Means: Adjusting For Categorical Variables Note 82: Which Variables Should We Adjust For? Note 83: Multi-Factor Analysis of Variance Note 84: The Model For Two-Factor Analysis of Variance Note 85: Two Factor ANOVA--an Example Note 85.1: Sample Size Calculations for a 2x2 Factorial Design Note 86: Why The Way A Model Is Parametrized Matters Note 87: Pooling Effects Note 88: Fixed and Random Factors Note 89: Randomized (Complete) Block Designs Note 90: Crossed and Nested Factors Note 91: Repeated Measures Analysis Of Variance Part I: Before SAS's Mixed Procedure Note 92: Repeated Measures Analysis Of Variance Part II: After SAS's Mixed Procedure Note 93: Serial Measurements Note 94: The Analysis of Pre-test/Post-test Experiments Note 95: Crossover Studies Note 96: Logistic Regression Note 97: Poisson Regression Note 98: Nonparametric Statistics Note 99: Degrees of Freedom
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