Introduction: Variables and Processes in Statistics | |

Types of Variables: Categorical or Quantitative | |

Students Talk Stats: Identifying Types of Variables | |

Handling | |

Data for Two Types of Variables | |

Roles of Variables: Explanatory or Response | |

Statistics as a Four-Stage Process | |

Data Production | |

Sampling: Which Individuals Are Studied | |

Sources of Bias in Sampling: When Selected Individuals Are Not Representative | |

Probability Sampling Plans: Relying on Randomness | |

Role of Sample Size: Bigger Is Better if the Sample Is Representative | |

From Sample to Population: To What Extent Can We Generalize? | |

Students Talk Stats: Seeking a Representative Sample | |

Design: How Individuals Are Studied | |

Various Designs for Studying Variables | |

Sample Surveys: When Individuals Report Their Own Values | |

Observational Studies: When Nature Takes Its Course | |

Experiments: When Researchers Take Control | |

Students Talk Stats: Does TV Cause ADHD? | |

Considering Study Design | |

Displaying and Summarizing Data | |

Displaying and Summarizing Data for a Single Variable | |

Single Categorical Variable | |

Students Talk Stats: Biased Sample, Biased Assessment | |

Single Quantitative Variables and the Shape of a Distribution | |

Center and Spread: What's Typical for Quantitative Values, and How They Vary | |

Normal Distributions: The Shape of Things to Come | |

Displaying and Summarizing Relationships | |

Relationship Between One Categorical and One Quantitative Variable | |

Students Talk Stats: Displaying and Summarizing Paired Data | |

Relationship Between Two Categorical Variables | |

Relationships Between Two Quantitative Variables | |

Students Talk Stats: How Outliers and Influential Observations Affect a Relationship | |

Students Talk Stats: Confounding in a Relationship Between Two Quantitative Variables | |

Probability | |

Finding Probabilities | |

The Meaning of "Probability" and Basic Rules | |

More General Probability Rules and Conditional Probability | |

Students Talk Stats: Probability as a Weighted Average of Conditional Probabilities | |

Random Variables | |

Discrete Random Variables | |

Binomial Random Variables | |

Students Talk Stats: Calculating and Interpreting the Mean and Standard Deviation of Count or Proportion | |

Continuous Random Variables and the Normal Distribution | |

Students Talk Stats: Means, Standard Deviations, and Below-Average Heights | |

Sampling Distributions | |

The Behavior of Sample Proportion in Repeated Random Samples | |

The Behavior of Sample Mean in Repeated Random Samples | |

Students Talk Stats: When Normal Approximations Are Appropriate | |

Statistical Inference | |

Inference for a Single Categorical Variable | |

Point Estimate and Confidence Interval: A Best Guess and a Range of Plausible Values for Population Proportion | |

Students Talk Stats: Interpreting a Confidence Interval | |

Test: Is a Proposed Population Proportion Plausible? | |

Students Talk Stats: Interpreting a P-value | |

Students Talk Stats: What Type of Error Was Made? | |

Students Talk Stats: The Correct Interpretation of a Small P-value | |

Students Talk Stats: The Correct Interpretation When a P-value Is Not Small | |

Inference for a Single Quantitative Variable | |

Inference for a Mean when Population Standard Deviation Is Known or Sample Size Is Large | |

Students Talk Stats: Confidence Interval for a Mean | |

Students Talk Stats: Interpreting a Confidence Interval for the Mean Correctly | |

Inference for a Mean When the Population Standard Deviation Is Unknown and the Sample Size Is Small | |

Students Talk Stats: Practical Application of a t Test | |

A Closer Look at Inference for Means | |

Inference for Relationships Between Categorical and Quantitative Variables | |

Inference for a Paired Design with t | |

Inference for a Two-Sample Design with t | |

Students Talk Stats: Ordinary vs. Pooled Two-Sample t | |

Inference for a Several-sample Design with F: Analysis of Variance | |

Students Talk Stats: Reviewing Relationships between Categorical and Quantitative Variables | |

Inference for Relationships Between Two Categorical Variables | |

Comparing Proportions with a z Test | |

Comparing Counts with a Chi-Square Test | |

Inference for Relationships Between Two Quantitative Variables | |

Inference for Regression: Focus on the Slope of the Regression Line | |

Students Talk Stats: No Evidence of a Relationship | |

Interval Estimates for an Individual or Mean Response | |

How Statistics Problems Fit into the Big Picture | |

The Big Picture in Problem-Solving | |

Students Talk Stats: Choosing the Appropriate Statistical Tools | |

Non-Parametric Methods (Online) | |

The Sign Test as an Alternative to the Paired t Test | |

The Rank-Sum Test as an Alternative to the Two-Sample t Test | |

Summary of Non-Parametrics | |

Two-Way ANOVA (Online) | |

Multiple Regression (Online) | |

Solutions to Selected Exercises | |

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