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Informationen zum Autor Ann Cannon; Daren Starnes; Josh Tabor Klappentext Introductory Statistics: A Student-Centered Approach provides the key to understanding statistics in the real world for today's students. The authors started first with the needs of students, how they learn and digest new material, what's relevant to them, and how they practice their skills. They then built out an all-in-one offering designed to provide a pathway to understanding for all students and show why statistics matters in the modern world. Digital resources in Achieve connect the mission of the text to immersive learning experiences and multimedia with a variety of assessment types to fit any teaching style. Inhaltsverzeichnis Chapter 1 Collecting DataSection 1.1 Introduction to Data CollectionSection 1.2 Sampling: Good and BadSection 1.3 Simple Random SamplingSection 1.4 Other Sampling MethodsSection 1.5 Observational Studies and ExperimentsSection 1.6 Completely Randomized DesignsSection 1.7 BlockingSection 1.8 Data Ethics and the Scope of InferenceChapter 2 Displaying Data with GraphsSection 2.1 Displaying Categorical DataSection 2.2 Displaying Relationships Between Two Categorical VariablesSection 2.3 Displaying Quantitative Data: DotplotsSection 2.4 Displaying Quantitative Data: StemplotsSection 2.5 Displaying Quantitative Data: HistogramsSection 2.6 Displaying Relationships Between Two Quantitative VariablesChapter 3 Numerical Summaries for Quantitative DataSection 3.1 Measuring CenterSection 3.2 Measuring VariabilitySection 3.3 Boxplots and OutliersSection 3.4 Measuring Location in a DistributionSection 3.5 Relationships Between Two Variables: CorrelationSection 3.6 More About CorrelationChapter 4 ProbabilitySection 4.1 Randomness, Probability, and SimulationSection 4.2 Basic Probability RulesSection 4.3 Two-Way Tables and Venn DiagramsSection 4.4 Conditional Probability and IndependenceSection 4.5 The General Multiplication Rule and Bayes' TheoremSection 4.6 The Multiplication Rule for Independent EventsSection 4.7 The Multiplication Counting Principle and PermutationsSection 4.8 Combinations and ProbabilityChapter 5 Discrete Random VariablesSection 5.1 Introduction to Random VariablesSection 5.2 Analyzing Discrete Random VariablesSection 5.3 Binomial Random VariablesSection 5.4 Analyzing Binomial Random VariablesSection 5.5 Poisson Random VariablesChapter 6 Normal Distributions and Sampling DistributionsSection 6.1 Continuous Random VariablesSection 6.2 Normal Distributions: Finding Areas from ValuesSection 6.3 Normal Distributions: Finding Values from AreasSection 6.4 Normal Approximation to the Binomial Distribution and Assessing NormalitySection 6.5 Sampling DistributionsSection 6.6 Sampling Distributions: Bias and VariabilitySection 6.7 Sampling Distribution of the Sample ProportionSection 6.8 Sampling Distribution of the Sample Mean and the Central Limit TheoremChapter 7 Estimating a ParameterSection 7.1 The Idea of a Confidence IntervalSection 7.2 Factors That Affect the Margin of ErrorSection 7.3 Estimating a Population ProportionSection 7.4 Confidence Intervals for a Population ProportionSection 7.5 Estimating a Population MeanSection 7.6 Confidence Intervals for a Population MeanSection 7.7 Estimating a Population Standard Deviation or VarianceSection 7.8 Confidence Intervals for a Population Standard Deviation or VarianceChapter 8 Testing a ClaimSection 8.1 The Idea of a Significance TestSection 8.2 Significance Tests and Decision MakingSection 8.3 Testing a Claim About a Population ProportionSection 8.4 Significance Tests for a Population ProportionSection 8.5 Testing a Claim About a Population MeanSection 8.6 Significance Tests for a Population MeanSection 8.7 Power of a TestSection 8.8 Significance Tests for a Population Standard Deviation or VarianceChapter 9 Comparing Two Populations or TreatmentsSection 9.1 Confidence Intervals for a Difference Between Two Population ProportionsSection 9...
