The Role of Statistics and the Data Analysis Process | |

Three Reasons to Study Statistics | |

The Nature and Role of Variability | |

Statistics and the Data Analysis Process | |

Types of Data and Some Simple Graphical Displays | |

Collecting Data Sensibly | |

Statistical Studies: Observation and Experimentation | |

Sampling | |

Simple Comparative Experiments | |

More Experimental Design | |

More on Observational Studies: Designing Surveys | |

Interpreting and Communicating the Results of Statistical Analyses | |

Graphical Methods for Describing Data | |

Displaying Categorical Data: Comparative Bar Charts and Pie Charts | |

Displaying Numerical Data: Stem-and-Leaf Displays | |

Displaying Numerical Data: Frequency Distributions and Histograms | |

Displaying Bivariate Numerical Data | |

Interpreting and Communicating the Results of Statistical Analyses | |

Numerical Methods for Describing Data | |

Describing the Center of a Data Set | |

Describing the Variability in a Data Set | |

Summarizing a Data Set: Boxplots | |

Interpreting Center and Variability: Chebyshev's Rule, the Empirical Rule, and z Scores | |

Interpreting and Communicating the Results of Statistical Analyses | |

Summarizing Bivariate Data | |

Correlation | |

Linear Regression: Fitting a Line to Bivariate Data | |

Assessing the Fit of a Line | |

Nonlinear Relationship and Transformations | |

Logistic Regression | |

Interpreting and Communicating the Results of Statistical Analyses | |

Probability | |

Interpreting Probabilities and Basic Probability Rules | |

Probability as a Basis for Making Decisions | |

Estimating Probabilities Empirically and by Using Simulation | |

Population Distributions | |

Describing the Distribution of Values in a Population | |

Population Models for Continuous Numerical Variables | |

Normal Distributions | |

Checking for Normality and Normalizing Transformations | |

Sampling Variability and Sampling Distributions | |

Statistics and Sampling Variability | |

The Sampling Distribution of a Sample Mean | |

The Sampling Distribution of a Sample Proportion | |

Estimation Using a Single Sample | |

Point Estimation | |

Large-Sample Confidence Interval for a Population Proportion | |

Confidence Interval for a Population Mean | |

Interpreting and Communicating the Results of Statistical Analyses | |

Hypotheses Testing Using a Single Sample | |

Hypotheses and Test Procedures | |

Errors in Hypothesis Testing | |

Large-Sample Hypothesis Tests for a Population Proportion | |

Hypothesis Test for a Population Mean | |

Power and Probability of Type II Error | |

Interpreting and Communicating the Results of Statistical Analyses | |

Comparing Two Populations or Treatments | |

Inferences Concerning the Difference Between Two Population or Treatment Means Using Independent Samples | |

Inferences Concerning the Difference Between Two Population or Treatment Means Using Paired Samples | |

Large Sample Inferences Concerning a Difference Between Two Population or Treatment Proportions | |

Interpreting and Communicating the Results of Statistical Analyses | |

The Analysis of Categorical Data and Doogness-of-Fit Tests | |

Chi-Square Tests for Univariate Data | |

Tests for Homogeneity and Independence in a Two-way Table | |

Interpreting and Communicating the Results of Statistical Analyses | |

Simple Linear Regression and Correlation Inferential Methods | |

Simple Linear Regression Model | |

Inferences About the Slope of the Population Regression Line | |

Checking Model Adequacy | |

Inferences Based on the Estimated Regression Line | |

Inferences About the Population Correlation Coefficient | |

Interpreting and Communicating the Results of Statistical Analyses | |

Multiple Regression Analysis | |

Multiple Regression Models | |

Fitting a Model and Assessing Its Utility | |

Inferences Based on an Estimated Model | |

Other Issues in Multiple Regression | |

Interpreting and C | |

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