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The Role of Statistics and the Data Analysis Process | |
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Three Reasons to Study Statistics | |
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The Nature and Role of Variability | |
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Statistics and the Data Analysis Process | |
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Types of Data and Some Simple Graphical Displays | |
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Collecting Data Sensibly | |
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Statistical Studies: Observation and Experimentation | |
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Sampling | |
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Simple Comparative Experiments | |
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More Experimental Design | |
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More on Observational Studies: Designing Surveys | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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Graphical Methods for Describing Data | |
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Displaying Categorical Data: Comparative Bar Charts and Pie Charts | |
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Displaying Numerical Data: Stem-and-Leaf Displays | |
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Displaying Numerical Data: Frequency Distributions and Histograms | |
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Displaying Bivariate Numerical Data | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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Numerical Methods for Describing Data | |
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Describing the Center of a Data Set | |
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Describing the Variability in a Data Set | |
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Summarizing a Data Set: Boxplots | |
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Interpreting Center and Variability: Chebyshev's Rule, the Empirical Rule, and z Scores | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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Summarizing Bivariate Data | |
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Correlation | |
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Linear Regression: Fitting a Line to Bivariate Data | |
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Assessing the Fit of a Line | |
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Nonlinear Relationship and Transformations | |
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Logistic Regression | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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Probability | |
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Interpreting Probabilities and Basic Probability Rules | |
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Probability as a Basis for Making Decisions | |
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Estimating Probabilities Empirically and by Using Simulation | |
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Population Distributions | |
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Describing the Distribution of Values in a Population | |
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Population Models for Continuous Numerical Variables | |
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Normal Distributions | |
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Checking for Normality and Normalizing Transformations | |
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Sampling Variability and Sampling Distributions | |
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Statistics and Sampling Variability | |
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The Sampling Distribution of a Sample Mean | |
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The Sampling Distribution of a Sample Proportion | |
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Estimation Using a Single Sample | |
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Point Estimation | |
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Large-Sample Confidence Interval for a Population Proportion | |
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Confidence Interval for a Population Mean | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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Hypotheses Testing Using a Single Sample | |
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Hypotheses and Test Procedures | |
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Errors in Hypothesis Testing | |
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Large-Sample Hypothesis Tests for a Population Proportion | |
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Hypothesis Test for a Population Mean | |
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Power and Probability of Type II Error | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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Comparing Two Populations or Treatments | |
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Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples | |
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Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples | |
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Large Sample Inferences Concerning a Difference Between Two Population or Treatment Proportions | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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The Analysis of Categorical Data and Doogness-of-Fit Tests | |
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Chi-Square Tests for Univariate Data | |
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Tests for Homogeneity and Independence in a Two-way Table | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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Simple Linear Regression and Correlation Inferential Methods | |
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Simple Linear Regression Model | |
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Inferences About the Slope of the Population Regression Line | |
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Checking Model Adequacy | |
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Inferences Based on the Estimated Regression Line | |
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Inferences About the Population Correlation Coefficient | |
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Interpreting and Communicating the Results of Statistical Analyses | |
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Multiple Regression Analysis | |
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Multiple Regression Models | |
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Fitting a Model and Assessing Its Utility | |
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Inferences Based on an Estimated Model | |
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Other Issues in Multiple Regression | |
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Interpreting and C | |