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| Introduction | |
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| What is Statistics? | |
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| Introduction | |
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| Why Study Statistics? | |
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| Some Current Applications of Statistics | |
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| What Do Statisticians Do? | |
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| Quality and Process Improvement | |
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| A Note to the Student | |
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| Summary | |
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| Supplementary Exercises | |
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| Collecting the Data | |
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| Using Surveys and Scientific Studies to Collect Data | |
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| Introduction | |
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| Surveys | |
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| Scientific Studies | |
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| Observational Studies | |
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| Data Management: Preparing Data for Summarization and Analysis | |
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| Summary | |
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| Summarizing Data | |
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| Data Description | |
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| Introduction | |
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| Describing Data on a Single Variable: Graphical Methods | |
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| Describing Data on a Single Variable: Measures of Central Tendency | |
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| Describing Data on a Single Variable: Measures of Variability | |
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| The Box Plot | |
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| Summarizing Data from More than One Variable | |
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| Calculators, Computers, and Software Systems | |
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| Summary | |
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| Key Formulas | |
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| Supplementary Exercises | |
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| Tools and Concepts | |
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| Probability and Probability Distributions | |
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| How Probability Can Be Used in Making Inferences | |
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| Finding the Probability of an Event | |
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| Basic Event Relations and Probability Laws | |
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| Conditional Probability and Independence | |
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| Bayes's Formula | |
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| Variables: Discrete and Continuous | |
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| Probability Distributions for Discrete Random Variables | |
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| A Useful Discrete Random Variable: The Binomial | |
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| Probability Distributions for Continuous Random Variables | |
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| A Useful Continuous Random Variable: The Normal Distribution | |
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| Random Sampling | |
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| Sampling Distributions | |
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| Normal Approximation to the Binomial | |
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| Summary | |
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| Key Formulas | |
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| Supplementary Exercises | |
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| Analyzing Data: Central Values, Variances, and Proportions | |
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| Inferences on a Population Central Value | |
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| Introduction and Case Study | |
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| Estimation of ? | |
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| Choosing the Sample Size for Estimating ? | |
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| A Statistical Test for ? | |
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| Choosing the Sample Size for Testing ? | |
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| The Level of Significance of a Statistical Test | |
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| Inferences about ? for Normal Population, s Unknown | |
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| Inferences About the Population Median | |
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| Summary | |
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| Key Formulas | |
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| Supplementary Exercises | |
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| Comparing Two Population Central Values | |
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| Introduction and Case Study | |
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| Inferences about ?1 - ?2: Independent Samples | |
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| A Nonparametric Alternative: The Wilcoxon Rank Sum Test | |
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| Inferences About ?1 - ?2: Paired Data | |
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| A Nonparametric Alternative: The Wilcoxon Signed-Rank Test | |
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| Choosing Sample Sizes for Inferences About ?1 - ?2 | |
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| Summary | |
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| Key Formulas | |
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| Supplementary Exercises | |
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| Inferences about Population Variances | |
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| Introduction and Case Study | |
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| Estimation and Tests for a Population Variance | |
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| Estimation and Tests for Comparing Two Population Variances | |
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| Tests for Comparing k>2 Population Variances | |
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| Summary | |
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| Key Formulas | |
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| Supplementary Exercises | |
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| Inferences About More Than Two Population Central Values | |
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| Introduction and Case Study | |
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| A Statistical Test About More Than Two Population Means | |
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| Checking on the Assumptions | |
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| Alternative When Assumptions are Violated: Transformations | |
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| A Nonparametric Alternative: The Kruskal-Wallis test | |
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| Summary | |
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| Key Formulas | |
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| Supplementary Exercises | |
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| Mulitple Comparisons | |
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| Introduction and Case Study | |
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| Planned Comparisons Among Treatments: Linear Contrasts | |
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| Which Error Rate is Controlled | |
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| Mulitple Comparisons with the Best Treatment | |
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| Comparison of Treatments to a Control | |
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| Pairwise Comparison on All Treatments | |
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| Summary | |
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| Key Formulas | |
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| Supplementary Exercises | |
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| Categorical Data | |
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| Introduction and Case Study | |
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| Inferences About a Population Proportion p | |
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| Comparing Two Population Proportions p1 - p2 | |
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| Probability Distributions for Discrete Random Variables | |
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| The Multinomial Experiment and Chi-Square Goodness-of-Fit Test | |
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| The Chi-Square Test of Homogeneity of Proportions | |
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| The Chi-Square of Independence of Two Nominal Level Variables | |
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| Fisher's Exact Test, a Permutation Test | |
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| Measures of Association | |
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| Combining Sets of Contingency Tables | |
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| Summary | |
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| Key Formulas | |
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| Supplementary Exercises | |
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| Analyzing Data: Regression Methods, Model Building | |
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| Simple Linear Regression and Correlation | |
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| Linear Regression and the Method of Least Squares | |
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| Transformations to Linearize Data | |
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| Correlation | |
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| A Look Ahead: Multiple Regression | |
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| Summary of Key Formulas | |
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| Supplementary Exercises | |
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| Inferences Related to Linear Regression and Correlation | |
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| Introduction and Case Study | |
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| Diagnostics for Detecting Violations of Model Conditions | |
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| Inferences About the Intercept and Slope of the Regression Line | |
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| Inferences About the Population Mean for a Specified Value of the Explanatory Variable | |
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| Predications and Prediction Intervals | |
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| Examining Lack of Fit in the Model | |
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| The Inverse Regression Problem (Calibration): Predicting Values for x for a Specified Value of y | |
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| Multiple Regression and the General Linear Model | |
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| The General Linear Model | |
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| Least Squares Estimates of Parameters in the General Linear Model | |
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| Inferences About the Parameters in the General Linear Model | |
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| Inferences About the Population Mean and Predictions from the General Linear Model | |
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| Comparing the Slope of Several Regression Lines | |
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| Logistic Regression | |
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| Matrix Formulation of the General Linear Model | |
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| Building Regression Models with Diagnostics | |
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| Selecting the Variables (Step 1) | |
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| Model Formulation (Step 2) | |
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| Checking Model Conditions (Step 3) | |
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| Analyzing Data: Design of Experiments and Anova | |
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| Design Concepts for Experiments and Studies | |
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| Experiments, Treatments, Experimental Units, Blocking, Randomization, and Measurement Units | |
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| How Many Replications | |
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| Studies for Comparing Means Versus Studies for Comparing Variances | |
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| Analysis of Variance for Standard Designs | |
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| Completely Randomized Design with Single Factor | |
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| Randomized Block Design | |
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| Latin Square Design | |
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| Factorial Experiments in a Completely Randomized Design | |
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| The Estimation of Treatment Differences and Planned Comparisons in the Treatment Means | |
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| Checking Model Conditions | |
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| Alternative Analyses: Transformation and Friedman's Rank Based Test | |
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| Analysis of Covariance | |
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| A Completely Randomized Design with One Covariate | |
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| The Extrapolation Problem | |
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| Multiple Covariates and More Complicated Designs | |
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| Analysis of Variance for Some Unbalanced Designs | |
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| A Randomized Block Design with One or More Mission Observations | |
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| A Latin Square Design with Missing Data | |
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| Incomplete Block Designs | |
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| A Factorial Experiment with Missing Factors | |
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| Analysis of Variance for Some Fixed Effects, Random Effects and Mixed Effects Models | |
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| A One-Factor Experiment with Random Treatment Effects | |
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| Extensions of Random-Effects Models | |
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| A Mixed Model: Experiments with Both Fixed and Random Treatment Effects | |
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| Models with Nested Factors | |
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| Rules for Obtaining Expected Mean Squares | |
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| Split-Plot Designs and Experiments with Repeated Measures | |
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| Split-Plot Designs | |
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| Single-Factor Experiments with Repeated Measures | |
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| Two-Factor Experiments with Repeated Measures on One of the Factors | |
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| Crossover Design | |
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| Communicating and Documenting the Results of a Study or Experiment | |
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| Communicating and Documenting the Results of a Study or Experiment | |
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| Introduction | |
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| The Difficulty of Good Communication | |
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| Communication Hurdles: Graphical Distortions | |
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| Communication Hurdles: Biased Samples | |
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| Communication Hurdles | |
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| Sample Size | |
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| The Statistical Report | |
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| Documentation and Storage of Results | |
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| Summary | |
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| Supplementary Exercises | |