Efnisyfirlit

• Cover
• Title
• Copyright
• About the Author
• Contents
• Preface
• Acknowledgments
• A Step by Step Approach
• Supplements
• Index of Applications
• Chapter 1 The Nature of Probability and Statistics
  • Introduction
  • 1–1 Descriptive and Inferential Statistics
  • 1–2 Variables and Types of Data
  • 1–3 Data Collection and Sampling Techniques
    • Random Sampling
    • Systematic Sampling
    • Stratified Sampling
    • Cluster Sampling
    • Other Sampling Methods
  • 1–4 Experimental Design
    • Observational and Experimental Studies
    • Uses and Misuses of Statistics
  • 1–5 Computers and Calculators
  • Summary
• Chapter 2 Frequency Distributions and Graphs
  • Introduction
  • 2–1 Organizing Data
    • Categorical Frequency Distributions
    • Grouped Frequency Distributions
  • 2–2 Histograms, Frequency Polygons, and Ogives
    • The Histogram
    • The Frequency Polygon
    • The Ogive
    • Relative Frequency Graphs
    • Distribution Shapes
  • 2–3 Other Types of Graphs
    • Bar Graphs
    • Pareto Charts
    • Time Series Graphs
    • Pie Graphs
    • Dotplots
    • Stem and Leaf Plots
    • Misleading Graphs
  • 2–4 Paired Data and Scatter Plots
    • Analyzing the Scatter Plot
  • Summary
• Chapter 3 Data Description
  • Introduction
  • 3–1 Measures of Central Tendency
    • The Mean
    • The Median
    • The Mode
    • The Midrange
    • The Weighted Mean
    • Distribution Shapes
  • 3–2 Measures of Variation
    • Range
    • Population Variance and Standard Deviation
    • Sample Variance and Standard Deviation
    • Variance and Standard Deviation for Grouped Data
    • Coefficient of Variation
    • Range Rule of Thumb
    • Chebyshev’s Theorem
    • The Empirical (Normal) Rule
    • Linear Transformation of Data
  • 3–3 Measures of Position
    • Standard Scores
    • Percentiles
    • Quartiles and Deciles
    • Outliers
  • 3–4 Exploratory Data Analysis
  • The Five-Number Summary and Boxplots
  • Summary
• Chapter 4 Probability and Counting Rules
  • Introduction
  • 4–1 Sample Spaces and Probability
    • Basic Concepts
    • Classical Probability
    • Complementary Events
    • Empirical Probability
    • Law of Large Numbers
    • Subjective Probability
    • Probability and Risk Taking
  • 4–2 The Addition Rules for Probability
  • 4–3 The Multiplication Rules and Conditional Probability
    • The Multiplication Rules
    • Conditional Probability
    • Probabilities for “At Least”
  • 4–4 Counting Rules
    • The Fundamental Counting Rule
    • Factorial Notation
    • Permutations
    • Combinations
  • 4–5 Probability and Counting Rules
  • Summary
• Chapter 5 Discrete Probability Distributions
  • Introduction
  • 5–1 Probability Distributions
  • 5–2 Mean, Variance, Standard Deviation, and Expectation
    • Mean
    • Variance and Standard Deviation
    • Expectation
  • 5–3 The Binomial Distribution
  • Summary
• Chapter 6 The Normal Distribution
  • Introduction
  • 6–1 Normal Distributions
    • The Standard Normal Distribution
    • Finding Areas Under the Standard Normal Distribution Curve
    • A Normal Distribution Curve as a Probability Distribution Curve
  • 6–2 Applications of the Normal Distribution
    • Finding Data Values Given Specific Probabilities
    • Determining Normality
  • 6–3 The Central Limit Theorem
    • Distribution of Sample Means
    • Finite Population Correction Factor (Optional)
  • 6–4 The Normal Approximation to the Binomial Distribution
  • Summary
• Chapter 7 Confidence Intervals and Sample Size
  • Introduction
  • 7–1 Confidence Intervals
  • 7–2 Confidence Intervals for the Mean When σ Is Known
    • Sample Size
  • 7–3 Confidence Intervals for the Mean When σ Is Unknown
  • 7–4 Confidence Intervals and Sample Size for Proportions
    • Confidence Intervals
    • Sample Size for Proportions
  • 7–5 Confidence Intervals for Variances and Standard Deviations
  • Summary
• Chapter 8 Hypothesis Testing
  • Introduction
  • 8–1 Steps in Hypothesis Testing—Traditional Method
    • P-Value Method for Hypothesis Testing
  • 8–2 z Test for a Mean
  • 8–3 t Test for a Mean
  • 8–4 z Test for a Proportion
  • 8–5 x 2 Test for a Variance or Standard Deviation
  • 8–6 Additional Topics Regarding Hypothesis Testing
    • Confidence Intervals and Hypothesis Testing
    • Type II Error and the Power of a Test
  • Summary
• Chapter 9 Testing the Difference Between Two Means, Two Proportions, and Two Variances
  • Introduction
  • 9–1 Testing the Difference Between Two Parameters
  • 9–2 Testing the Difference Between Two Means: Using the z Test
  • 9–3 Testing the Difference Between Two Means of Independent Samples: Using the t Test
  • 9–4 Testing the Difference Between Two Means: Dependent Samples
  • 9–5 Testing the Difference Between Proportions
  • 9–6 Testing the Difference Between Two Variances
  • Summary
• Chapter 10 Correlation and Regression
  • Introduction
  • 10–1 Correlation
  • 10–2 Regression
    • Line of Best Fit
    • Determination of the Regression Line Equation
  • 10–3 Coefficient of Determination and Standard Error of the Estimate
    • Types of Variation for the Regression Model
    • Residual Plots
    • Coefficient of Determination
    • Standard Error of the Estimate
    • Prediction Interval
  • Summary
• Chapter 11 Chi-Square and Analysis of Variance (ANOVA)
  • Introduction
  • 11–1 Test for Goodness of Fit
    • Test of Normality (Optional)
  • 11–2 Tests Using Contingency Tables
    • Test for Independence
    • Test for Homogeneity of Proportions
  • 11–3 Analysis of Variance (ANOVA)
  • Summary
• Appendices
  • A Tables
  • B Data Bank
  • C Glossary
  • D Selected Answers
• Index

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