Elementary Statistics: A Step By Step Approach ISE (International Student Edition) Statistics: A Step By Step Approach ISE [Team-IRA]

✍ Scribed by Allan G. Bluman

• Publisher: Mcgraw Hill Education
• Year: 2023
• Tongue: English
• Leaves: 945
• Edition: 11
• Category: Library

✦ Synopsis

ISBN: 9781265248123 is an International Student Edition of Elementary Statistics: A Step By Step Approach 11th Edition by Allan G. Bluman*Student textbook only**No Connect Access Code Included*** Elementary Statistics: A Step by Step Approach was written as an aid in the beginning statistics course to students whose mathematical background is limited to basic algebra. The book follows a non-theoretical approach without formal proofs, explaining concepts intuitively and supporting them with abundant examples. The applications span a range of topics, including problems in business, sports, health, architecture, education, entertainment, political science, psychology, history, criminal justice, the environment, transportation, physical sciences, demographics, and travel. The text is strengthened by its offering in ALEKS, now featuring Custom Question Authoring, Video Assignments, interactive tools, and more! ALEKS is a course assistant that helps math instructors forge Constructive Learning Paths for their students – blending personalized modules with instructor-driven assignments to ensure every student always has another block to build on their knowledge base.

✦ Table of Contents

Cover
Title Page
Copyright Page
About The Author
Contents
Preface
Acknowledgments
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
APPENDICES
A Tables
B Data Bank
C Glossary
D Selected Answers
E Important Formulas
Index