Elementary Statistics (ISE)

Höfundur: Allan Bluman (Útgáfa: 11)
Elementary Statistics (ISE)

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ISE eBook Online Access for Elementary Statistics: A Step By Step Approach

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Útgefandi
McGraw-Hill Higher Education (International)
ISBN
9781265622978
Print ISBN
9781265248123
Format
ePub
Útgáfa
11
Höfundar
Allan Bluman
Tungumál
English
Útgefið
2022-07-27
Prent takmörkun á líftíma
100

Kaflar

  • Cover
  • Title Page
  • Copyright Page
  • About the Author
  • Contents
  • Preface
  • 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
  • The Time Series Graph
  • The Pie Graph
  • Dotplots
  • Stem and Leaf Plots
  • Misleading Graphs
  • 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
  • 5–4: Other Types of Distributions
  • The Multinomial Distribution
  • The Poisson Distribution
  • The Hypergeometric Distribution
  • The Geometric 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: χ2 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: Scatter Plots and Correlation
  • 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
  • 10–4: Multiple Regression (Optional)
  • The Multiple Regression Equation
  • Testing the Significance of R
  • Adjusted R2
  • Summary
  • Chapter 11: Other Chi-Square Tests
  • 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
  • Summary
  • Chapter 12: Analysis of Variance
  • Introduction
  • 12–1: One-Way Analysis of Variance
  • 12–2: The Scheffé Test, Tukey Test, and Bonferroni Test
  • Scheffé Test
  • Tukey Test
  • Bonferroni Test
  • 12–3: Two-Way Analysis of Variance
  • Summary
  • Chapter 13: Nonparametric Statistics
  • Introduction
  • 13–1: Advantages and Disadvantages of Nonparametric Methods
  • Advantages
  • Disadvantages
  • Ranking
  • 13–2: The Sign Test
  • Single-Sample Sign Test
  • Paired-Sample Sign Test
  • 13–3: The Wilcoxon Rank Sum Test
  • 13–4: The Wilcoxon Signed-Rank Test
  • 13–5: The Kruskal-Wallis Test
  • 13–6: The Spearman Rank Correlation Coefficient and the Runs Test
  • Rank Correlation Coefficient
  • The Runs Test
  • Summary
  • Chapter 14: Sampling and Simulation
  • Introduction
  • 14–1: Common Sampling Techniques
  • Random Sampling
  • Systematic Sampling
  • Stratified Sampling
  • Cluster Sampling
  • Other Types of Sampling Techniques
  • 14–2: Surveys and Questionnaire Design
  • 14–3: Simulation Techniques and the Monte Carlo Method
  • The Monte Carlo Method
  • 14–4: Big Data
  • Summary
  • Appendix A: Tables
  • Appendix B: Data Bank
  • Appendix C: Glossary
  • Appendix D: Selected Answers
  • Appendix E: Important Formulas
  • Index
  • Algebra Review
  • Writing the Research Report
  • Bayes’ Theorem
  • Alternate Approach to the Standard Normal Distribution
  • Bibliography