Beginning Statistics plus Integrated Review, 2nd Edition

1.R.1 Problem Solving with Whole Numbers

• Examples
• Example 1: Application: Adding Whole Numbers
• Example 2: Application: Multiplying Whole Numbers
• Example 3: Application: Dividing Whole Numbers
• Example 4: Application: Calculating Loan Amounts
• Example 5: Application: Balancing a Checking Account
• Example 6: Application: Finding the Area of Rectangles
• Example 7: Calculating an Average
• Example 8: Application: Calculating an Average
• Example 9: Application: Calculating an Average
• Example 10: Application: Calculating an Average

1.R.2 Introduction to Decimal Numbers

• Examples
• Example 1: Reading and Writing Decimal Numbers
• Example 2: Reading and Writing Decimal Numbers
• Example 3: Reading and Writing Decimal Numbers
• Example 4: Comparing Decimal Numbers
• Example 5: Comparing Decimal Numbers
• Example 6: Comparing Decimal Numbers
• Example 7: Rounding Decimal Numbers
• Example 8: Rounding Decimal Numbers

1.1 Getting Started

• Examples
• Example 1.1: Identifying Population and Sample
• Example 1.2: Identifying Population, Sample, Parameters, and Statistics
• Example 1.3: Identifying Descriptive and Inferential Statistics

1.2 Data Classification

• Examples
• Example 1.4: Classifying Data as Qualitative or Quantitative
• Example 1.5: Classifying Data as Continuous or Discrete
• Example 1.6: Understanding the Nominal Level of Measurement
• Example 1.7: Classifying Data as Nominal or Ordinal
• Example 1.8: Classifying Data by the Level of Measurement
• Example 1.9: Classifying Data by the Level of Measurement
• Example 1.10: Classifying Data

1.3 The Process of a Statistical Study

• Examples
• Example 1.11: Identifying Population and Variables
• Example 1.12: Identifying Observational Studies and Experiments
• Example 1.13: Identifying Sampling Methods
• Example 1.14: Classifying Studies as Cross-Sectional or Longitudinal
• Example 1.15: Classifying Studies as Meta-Analysis or Case Study
• Example 1.16: Analyzing an Experiment

1.4 How to Critique a Published Study

• Examples
• Example 1.17: Considering the Variables

2.R.1 Introduction to Fractions and Mixed Numbers

• Examples
• Example 1: Understanding Fractions
• Example 2: Understanding Fractions
• Example 3: Understanding Proper Fractions
• Example 4: Understanding Improper Fractions
• Example 5: Evaluating Fractions Involving 0
• Example 6: Graphing Proper Fractions
• Example 7: Graphing Improper Fractions
• Example 8: Identifying Types of Fractions and Mixed Numbers
• Example 9: Application: Understanding Mixed Numbers
• Example 10: Application: Understanding Mixed Numbers
• Example 11: Graphing Mixed Numbers
• Example 12: Changing Mixed Numbers to Improper Fractions
• Example 14: Changing Improper Fractions to Mixed Numbers
• Example 15: Changing Improper Fractions to Mixed Numbers

2.R.2 Decimal Numbers and Fractions

• Examples
• Example 1: Changing Decimal Numbers to Fractions
• Example 2: Changing Decimal Numbers to Fractions
• Example 3: Changing Decimal Numbers to Fractions
• Example 4: Changing Fractions to Decimal Numbers
• Example 5: Changing Fractions to Decimal Numbers
• Example 6: Changing Fractions to Decimal Numbers
• Example 7: Changing Fractions to Decimal Numbers
• Example 8: Simplifying Expressions with Decimals and Fractions
• Example 9: Comparing Decimal Numbers and Fractions
• Example 10: Application: Decimal and Fraction Expressions

2.R.3 Decimals and Percents

• Examples
• Example 1: Changing Fractions with Denominators of $100$ to Percents
• Example 2: Changing Decimal Numbers to Percents
• Example 3: Changing Percents to Decimal Numbers

2.R.4 Reading Graphs

• Examples
• Example 1: Reading a Bar Graph
• Example 2: Reading a Circle Graph
• Example 3: Reading a Line Graph
• Example 4: Reading a Histogram

2.R.5 Constructing Graphs from Databases

• Examples
• Example 1: Constructing a Bar Graph
• Example 2: Constructing a Circle Graph

2.R.6 The Real Number Line and Inequalities

• Examples
• Example 1: Finding the Opposite of an Integer
• Example 2: Graphing Integers on a Number Line
• Example 3: Identifying Types of Numbers
• Example 4: Graphing Sets of Numbers
• Example 5: Graphing Sets of Numbers
• Example 6: Verifying Inequalities

2.1 Frequency Distributions

• Examples
• Example 2.1: Constructing a Frequency Distribution
• Example 2.2: Calculating Class Boundaries
• Example 2.3: Calculating Class Midpoints
• Example 2.4: Calculating Relative Frequencies
• Example 2.5: Calculating Cumulative Frequencies
• Example 2.6: Characteristics of a Frequency Distribution

2.2a Graphical Displays of Data: Pie Charts and Bar Graphs

• Examples
• Example 2.7: Creating a Pie Chart
• Example 2.8: Creating a Bar Graph
• Example 2.9: Creating a Pareto Chart
• Example 2.10: Creating a Side-by-Side Bar Graph
• Example 2.11: Creating a Stacked Bar Graph

2.2b Graphical Displays of Data: Histograms, Polygons, Stem and Leaf Plots

• Examples
• Example 2.12: Constructing a Histogram
• Example 2.13: Constructing a Relative Frequency Histogram
• Example 2.14: Creating an Ogive
• Example 2.15: Creating a Stem-and-Leaf Plot
• Example 2.16: Creating and Interpreting a Stem-and-Leaf Plot
• Example 2.17: Constructing a Line Graph

2.3 Analyzing Graphs

• Examples
• Example 2.18: Scaling of Graphs
• Example 2.19: Shapes of Graphs

3.R.1 Addition with Real Numbers

• Examples
• Example 1: Adding Real Numbers with Like Signs
• Example 2: Adding Real Numbers with Unlike Signs
• Example 3: Adding More Than Two Real Numbers
• Example 4: Adding Three or More Real Numbers

3.R.2 Subtraction with Real Numbers

• Examples
• Example 1: Finding Additive Inverses
• Example 2: Subtracting Real Numbers
• Example 3: Application: Calculating Change in Value
• Example 4: Application: Calculating Change in Value
• Example 5: Application: Calculating Net Change
• Example 6: Application: Calculating Net Change

3.R.3 Multiplication and Division with Real Numbers

• Examples
• Example 1: Multiplying Positive and Negative Real Numbers
• Example 2: Multiplying Two Negative Real Numbers
• Example 3: Multiplication by $0$
• Example 4: Dividing Real Numbers
• Example 5: Dividing Fractions and Decimals
• Example 6: Application: Calculating an Average
• Example 7: Application: Calculating an Average
• Example 8: Application: Calculating an Average

3.R.4 Exponents and Order of Operations

• Examples
• Example 1: Identifying the Base and Exponent
• Example 3: Evaluating Exponential Expressions
• Example 4: Evaluating Exponential Expressions
• Example 5: Using the Order of Operations with Whole Numbers
• Example 6: Using the Order of Operations with Whole Numbers
• Example 7: Using the Order of Operations with Whole Numbers
• Example 8: Using the Order of Operations with Whole Numbers
• Example 9: Using the Order of Operations with Whole Numbers

3.R.5 Simplifying and Evaluating Algebraic Expressions

• Examples
• Example 1: Identifying Like Terms
• Example 2: Combining Like Terms
• Example 3: Evaluating Algebraic Expressions
• Example 4: Simplifying and Evaluating Algebraic Expressions
• Example 5: Simplifying and Evaluating Algebraic Expressions
• Example 6: Simplifying and Evaluating Algebraic Expressions

3.R.6 Evaluating Radicals

• Examples
• Example 1: Evaluating Square Roots
• Example 2: Evaluating Square Roots
• Example 3: Estimating Square Roots
• Example 4: Evaluating Cube Roots

3.1 Measures of Center

• Examples
• Example 3.1: Calculating the Sample Mean
• Example 3.2: Using the Mean to Find a Data Value
• Example 3.3: Calculating a Weighted Mean
• Example 3.4: Calculating a Weighted Mean
• Example 3.5: Finding the Median
• Example 3.6: Finding the Mode
• Example 3.7: Calculating Measures of Center—Mean, Median, and Mode
• Example 3.8: Choosing the Most Appropriate Measure of Center
• Example 3.9: Determining Mean, Median, and Mode from a Graph

3.2a Measures of Dispersion

• Examples
• Example 3.10: Calculating the Range
• Example 3.11: Calculating Standard Deviation
• Example 3.13: Calculating Variance

3.2b Applying the Standard Deviation

• Examples
• Example 3.14: Interpreting Standard Deviations
• Example 3.15: Calculating and Interpreting Coefficient of Variation
• Example 3.16: Applying the Empirical Rule for Bell-Shaped Distributions
• Example 3.17: Applying Chebyshev's Theorem
• Example 3.18: Standard Deviation of Grouped Data
• Example 3.19: Standard Deviation of Grouped Data

3.3 Measures of Relative Position

• Examples
• Example 3.20: Finding Data Values Given the Percentiles
• Example 3.21: Finding the Percentile of a Given Data Value
• Example 3.22: Finding the Quartiles of a Given Data Set
• Example 3.23: Finding the Quartiles of a Given Data Set
• Example 3.24: Writing the Five-Number Summary of a Given Data Set
• Example 3.25: Creating a Box Plot
• Example 3.26: Interpreting Box Plots
• Example 3.27: Calculating a Standard Score
• Example 3.28: Comparing Standard Scores

4.R.1 Multiplication and Division with Fractions and Mixed Numbers

• Examples
• Example 1: Multiplying Fractions
• Example 2: Application: Multiplying Fractions
• Example 3: Reducing Fractions to Lowest Terms
• Example 5: Application: Reducing Fractions to Lowest Terms
• Example 6: Multiplying and Reducing Using Prime Factors
• Example 7: Multiplying and Reducing Using Prime Factors
• Example 8: Multiplying and Reducing Using Prime Factors
• Example 9: Application: Multiplying and Reducing Fractions
• Example 10: Multiplying and Reducing Using the Division Method
• Example 11: Multiplying and Reducing Using the Division Method
• Example 12: Multiplying and Reducing Using the Division Method
• Example 13: Finding Reciprocals
• Example 14: Finding Reciprocals
• Example 15: Dividing Fractions
• Example 16: Dividing and Reducing Fractions
• Example 18: Finding a Missing Number
• Example 19: Application: Dividing Fractions
• Example 20: Application: Multiplying and Dividing Fractions
• Example 21: Multiplying Mixed Numbers
• Example 22: Multiplying and Reducing Mixed Numbers
• Example 24: Finding Fractional Parts of Mixed Numbers
• Example 25: Dividing and Reducing Mixed Numbers
• Example 27: Application: Dividing Mixed Numbers

4.R.2 Least Common Multiple (LCM)

• Examples
• Example 1: Finding the Least Common Multiple (LCM)
• Example 2: Finding the Least Common Multiple (LCM)
• Example 4: Finding the Least Common Multiple (LCM)
• Example 5: Finding the Least Common Multiple (LCM)
• Example 6: Finding the Least Common Multiple (LCM)
• Example 7: Finding the Least Common Multiple (LCM)
• Example 9: Application: Finding the LCM
• Example 10: Finding Equivalent Fractions
• Example 11: Finding Equivalent Fractions

4.R.3 Addition and Subtraction with Fractions

• Examples
• Example 1: Adding Fractions with the Same Denominator
• Example 2: Finding the Least Common Denominator (LCD)
• Example 3: Adding Fractions with Different Denominators
• Example 4: Adding Fractions with Different Denominators
• Example 5: Adding Three Fractions with Different Denominators
• Example 6: Application: Adding Fractions
• Example 7: Application: Adding Fractions
• Example 8: Subtracting Fractions with the Same Denominator
• Example 9: Subtracting Fractions with Different Denominators
• Example 10: Subtracting Fractions with Different Denominators
• Example 11: Subtracting Fractions with Different Denominators
• Example 12: Application: Subtracting Fractions with Different Denominators

4.R.4 Fractions and Percents

• Examples
• Example 1: Changing Fractions to Percents
• Example 2: Changing Fractions to Percents
• Example 3: Changing Mixed Numbers to Percents
• Example 5: Changing Fractions to Percents
• Example 5: Application: Changing Fractions to Percents
• Example 7: Changing Percents to Fractions
• Example 8: Changing Percents to Mixed Numbers

4.1 Introduction to Probability

• Examples
• Example 4.1: Identifying Outcomes in a Sample Space or Event
• Example 4.2: Using a Pattern to List All Outcomes in a Sample Space
• Example 4.3: Using a Tree Diagram to List All Outcomes in a Sample Space
• Example 4.4: Identifying Types of Probability
• Example 4.5: Calculating Classical Probability
• Example 4.6: Calculating Classical Probability
• Example 4.7: Calculating Classical Probability
• Example 4.8: Calculating Classical Probability

4.2 Addition Rules for Probability

• Examples
• Example 4.9: Describing the Complement of an Event
• Example 4.10: Using the Complement Rule for Probability
• Example 4.11: Using the Complement Rule for Probability
• Example 4.12: Using the Addition Rule for Probability
• Example 4.13: Using the Addition Rule for Probability
• Example 4.14: Using the Addition Rule for Probability
• Example 4.15: Using the Addition Rule for Probability of Mutually Exclusive Events
• Example 4.16: Using the Extended Addition Rule for Probability of Mutually Exclusive Events

4.3 Multiplication Rules for Probability

• Examples
• Example 4.17: Using the Multiplication Rule for Probability of Independent Events
• Example 4.18: Using the Extended Multiplication Rule for Probability of Independent Events
• Example 4.19: Calculating Probability of Dependent Events
• Example 4.20: Calculating Conditional Probability
• Example 4.21: Using the Multiplication Rule for Probability of Dependent Events
• Example 4.22: Using the Multiplication Rule for Probability of Dependent Events
• Example 4.23: Using the Rule for Conditional Probability
• Example 4.24: Using the Fundamental Counting Principle
• Example 4.25: Using the Fundamental Counting Principle (Without Replacement)
• Example 4.26: Using the Fundamental Counting Principle to Calculate Probability

4.4 Combinations and Permutations

• Examples
• Example 4.27: Calculating Factorial Expressions
• Example 4.28: Calculating Numbers of Combinations and Permutations by Hand and by Using Formulas
• Example 4.29: Calculating the Number of Permutations
• Example 4.30: Calculating the Number of Combinations
• Example 4.31: Calculating Probability Using Permutations
• Example 4.32: Calculating Probability Using Combinations
• Example 4.33: Calculating the Number of Special Permutations

4.5 Combining Probability and Counting Techniques

• Examples
• Example 4.34: Calculating Probability Using Combinations
• Example 4.35: Calculating Probability Using Permutations
• Example 4.36: Using Numbers of Combinations with the Fundamental Counting Principle
• Example 4.37: Using Numbers of Combinations with the Fundamental Counting Principle

5.R.1 Order of Operations with Real Numbers

• Examples
• Example 1: Using the Order of Operations with Real Numbers
• Example 2: Using the Order of Operations with Real Numbers
• Example 3: Using the Order of Operations with Real Numbers
• Example 4: Using the Order of Operations with Real Numbers
• Example 6: Using the Order of Operations with Real Numbers
• Example 7: Using the Order of Operations with Real Numbers

5.R.2 Linear Inequalities and Compound Inequalities in One Variable

• Examples
• Example 5: Solving an Inequality and Graphing the Solution Set
• Example 6: Solving an Inequality and Graphing the Solution Set
• Example 7: Solving an Inequality and Graphing the Solution Set
• Example 8: Solving an Inequality and Graphing the Solution Set
• Example 9: Solving Linear Inequalities
• Example 10: Solving Linear Inequalities
• Example 11: Solving Linear Inequalities
• Example 13: Application: Using Inequalities
• Example 14: Application: Using Inequalities
• Example 15: Finding the Union and Intersection of Two Sets
• Example 16: Finding the Union and Intersection of Two Sets
• Example 17: Finding the Union and Intersection of Two Sets
• Example 18: Graphing Compound Inequalities
• Example 19: Graphing Compound Inequalities
• Example 20: Solving Compound Inequalities Containing AND
• Example 21: Solving Compound Inequalities Containing AND
• Example 22: Solving Compound Inequalities Containing AND
• Example 23: Solving Compound Inequalities Containing OR
• Example 24: Solving Compound Inequalities Containing OR

5.1 Discrete Random Variables

• Examples
• Example 5.1: Creating a Discrete Probability Distribution
• Example 5.2: Calculating Expected Values
• Example 5.3: Calculating Expected Values
• Example 5.4: Calculating the Variances and Standard Deviations for Discrete Probability Distributions

5.2 Binomial Distribution

• Examples
• Example 5.5: Calculating a Binomial Probability Using the Formula
• Example 5.6: Calculating a Binomial Probability Using the Formula or a TI-83/84 Plus Calculator
• Example 5.7: Calculating Binomial Probabilities Using the Formula or a TI-83/84 Plus Calculator
• Example 5.10: Finding a Binomial Probability Using a Table

5.3 Poisson Distribution

• Examples
• Example 5.11: Calculating a Poisson Probability Using the Formula
• Example 5.12: Calculating a Poisson Probability Using the Formula or a TI-83/84 Plus Calculator
• Example 5.13: Calculating a Poisson Probability Using the Formula or a TI-83/84 Plus Calculator
• Example 5.14: Calculating Poisson Probabilities Using the Formula or a TI-83/84 Plus Calculator
• Example 5.17: Finding a Poisson Probability Using a Table

5.4 Hypergeometric Distribution

• Examples
• Example 5.18: Calculating a Hypergeometric Probability
• Example 5.19: Calculating a Hypergeometric Probability
• Example 5.20: Calculating a Cumulative Hypergeometric Probability
• Example 5.21: Calculating Hypergeometric Probabilities
• Example 5.22: Determining Which Discrete Probability Distribution to Use

6.R.1 Area

• Examples
• Example 1: Calculating the Area of a Triangle Using a Formula
• Example 2: Calculating the Area of a Trapezoid Using a Formula
• Example 3: Calculating the Area of a Composite Figure
• Example 4: Calculating Area
• Example 5: Calculating Area
• Example 6: Application: Calculating Perimeter and Area

6.R.2 Solving Linear Equations: ax + b = c

• Examples
• Example 1: Solving Linear Equations of the Form ax + b = c
• Example 2: Solving Linear Equations of the Form ax + b = c
• Example 3: Solving Linear Equations Involving Decimals
• Example 4: Solving Linear Equations Involving Decimals
• Example 5: Solving Linear Equations Involving Fractions
• Example 6: Solving Linear Equations Involving Fractions

6.R.3 Working with Formulas

• Examples
• Example 1: Application: Evaluating Formulas
• Example 2: Evaluating Formulas
• Example 3: Application: Evaluating Formulas
• Example 4: Evaluating Formulas
• Example 5: Solving for Different Variables
• Example 6: Solving for Different Variables
• Example 7: Solving for Different Variables
• Example 8: Solving for Different Variables

6.1 Introduction to the Normal Distribution

• Examples
• Example 6.1: Calculating and Graphing z-values

6.2 Finding Area Under a Normal Distribution

• Examples
• Example 6.2: Finding Area to the Left of a Positive z-value Using a Cumulative Normal Table
• Example 6.3: Finding Area to the Left of a Negative z-value Using a Table or a TI-83/84 Plus Calculator
• Example 6.4: Finding Area to the Right of a Positive z-value Using a Cumulative Normal Table
• Example 6.5: Finding Area to the Right of a Negative z-value Using a Table or a TI-83/84 Plus Calculator
• Example 6.6: Finding Area between Two z-values Using Tables or a TI-83/84 Plus Calculator
• Example 6.10: Interpreting Probability for the Standard Normal Distribution as an Area under the Curve

6.3 Finding Probability Using a Normal Distribution

• Examples
• Example 6.12: Finding the Probability that a Normally Distributed Random Variable Will Be Less Than a Given Value
• Example 6.13: Finding the Probability that a Normal Distributed Random Variable Will Be Greater Than a Given Value
• Example 6.14: Finding the Probability that a Normally Distributed Random Variable Will Be between Two Given Values
• Example 6.15: Finding the Probability that a Normally Distributed Random Variable Will Be in the Tails Defined by Two Given Values
• Example 6.16: Finding the Probability that a Normally Distributed Random Variable Will Differ from the Mean by More Than a Given Value

6.4 Finding Values of a Normally Distributed Random Variable

• Examples
• Example 6.17: Finding the z-value with a Given Area to Its Left
• Example 6.18: Finding the z-value with a Given Area to Its Left
• Example 6.19: Finding the z-value That Represents a Given Percentile
• Example 6.20: Finding the z-value with a Given Area to Its Right
• Example 6.21: Finding the z-value with a Given Area between −z and z
• Example 6.22: Finding the z-value with a Given Area in the Tails to the Left of −z and to the Right of z
• Example 6.23: Finding the Value of a Normally Distributed Random Variable with a Given Area to Its Right

6.5 Approximating a Binomial Distribution Using a Normal Distribution

• Examples
• Example 6.26: Using the Continuity Correction Factor with a Normal Distribution to Approximate a Binomial Probability
• Example 6.27: Using the Continuity Correction Factor with a Normal Distribution to Approximate a Binomial Probability
• Example 6.28: Using a Normal Distribution to Approximate a Binomial Probability of the Form P(X > x)
• Example 6.29: Using a Normal Distribution to Approximate a Binomial Probability of the Form P(X ≤ x)
• Example 6.30: Using a Normal Distribution to Approximate a Binomial Probability of the Form P(X = x)

7.R.1 Ratios, Unit Rates, and Proportions

• Examples
• Example 1: Application: Writing Ratios
• Example 2: Writing Ratios that Compare Mixed Numbers
• Example 3: Writing Ratios that Compare Decimal Numbers
• Example 4: Application: Writing Ratios from Graphs
• Example 5: Writing Ratios in Geometry
• Example 6: Writing Ratios that Compare Measurements
• Example 7: Writing a Rate
• Example 8: Application: Batting Average
• Example 9: Application: Writing a Unit Rate
• Example 10: Application: Writing a Unit Rate
• Example 11: Application: Writing a Unit Rate
• Example 12: Application: Comparing Unit Prices
• Example 13: Application: Comparing Unit Prices
• Example 14: Verifying Proportions
• Example 16: Solving Proportions
• Example 17: Solving Proportions
• Example 18: Solving Proportions
• Example 19: Solving Proportions
• Example 21: Application: Solving Proportions
• Example 22: Application: Solving Proportions
• Example 23: Application: Solving Proportions
• Example 26: Application: Solving Proportions Written in Medical Notation

7.1 Introduction to the Central Limit Theorem

• Examples
• Example 7.1: Calculating the Standard Deviation for a Sampling Distribution of Sample Means Using the Central Limit Theorem
• Example 7.2: Applying the Central Limit Theorem
• Example 7.3: Applying the Central Limit Theorem

7.2 Central Limit Theorem with Means

• Examples
• Example 7.4: Finding the Probability that a Sample Mean Will Be Less Than a Given Value
• Example 7.5: Finding the Probability that a Sample Mean Will Be Greater Than a Given Value
• Example 7.6: Finding the Probability that a Sample Mean Will Differ from the Population Mean by Less Than a Given Amount
• Example 7.7: Finding the Probability that a Sample Mean Will Differ from the Population Mean by More Than a Given Amount

7.3 Central Limit Theorem with Proportions

• Examples
• Example 7.8: Finding the Probability that a Sample Proportion Will Be At Least a Given Value
• Example 7.9: Finding the Probability that a Sample Proportion Will Be No More Than a Given Value
• Example 7.10: Finding the Probability that a Sample Proportion Will Differ from the Population Proportion by Less Than a Given Amount
• Example 7.11: Finding the Probability that a Sample Proportion Will Differ from the Population Proportion by More Than a Given Amount

8.1 Estimating Population Means (Sigma Known)

• Examples
• Example 8.1: Finding a Point Estimate for a Population Mean
• Example 8.2: Constructing a Confidence Interval with a Given Margin of Error
• Example 8.3: Finding the Margin of Error of a Confidence Interval for a Population Mean (σ Known)
• Example 8.4: Constructing a Confidence Interval for a Population Mean (σ Known)
• Example 8.5: Constructing a Confidence Interval for a Population Mean (σ Known)
• Example 8.7: Interpreting a Confidence Interval
• Example 8.8: Finding the Minimum Sample Size Needed for a Confidence Interval for a Population Mean

8.2 Student's t-Distribution

• Examples
• Example 8.9: Finding the Value of t_α
• Example 8.10: Finding the Value of t Given the Area to the Right
• Example 8.11: Finding the Value of t Given the Area to the Left
• Example 8.12: Finding the Value of t Given the Area in Two Tails
• Example 8.13: Finding the Value of t Given Area between −t and t
• Example 8.14: Finding the Critical t-value for a Confidence Interval

8.3 Estimating Population Means (Sigma Unknown)

• Examples
• Example 8.15: Finding the Margin of Error of a Confidence Interval for a Population Mean (σ Unknown)
• Example 8.16: Constructing a Confidence Interval for a Population Mean (σ Unknown)
• Example 8.17: Constructing a Confidence Interval for a Population Mean (σ Unknown)

8.4 Estimating Population Proportions

• Examples
• Example 8.19: Finding a Point Estimate for a Population Proportion
• Example 8.20: Constructing a Confidence Interval for a Population Proportion
• Example 8.22: Finding the Minimum Sample Size Needed for a Confidence Interval for a Population Proportion
• Example 8.23: Finding the Minimum Sample Size, Point Estimate, and Confidence Interval for a Population Proportion

8.5 Estimating Population Variances

• Examples
• Example 8.24: Finding Point Estimates for the Population Standard Deviation and Variance
• Example 8.25: Constructing a Confidence Interval for a Population Variance
• Example 8.26: Constructing a Confidence Interval for a Population Standard Deviation
• Example 8.27: Constructing a Confidence Interval for a Population Variance
• Example 8.28: Constructing a Confidence Interval for a Population Standard Deviation
• Example 8.29: Finding the Minimum Sample Size Needed for a Confidence Interval for a Population Standard Deviation

9.1 Comparing Two Population Means (Sigma Known)

• Examples
• Example 9.1: Finding the Point Estimate and Margin of Error of a Confidence Interval for the Difference between Two Population Means (σ Known)
• Example 9.2: Constructing a Confidence Interval for the Difference between Two Population Means (σ Known)

9.2 Comparing Two Population Means (Sigma Unknown)

• Examples
• Example 9.4: Finding the Margin of Error of a Confidence Interval for the Difference between Two Population Means (σ Unknown, Unequal Variances)
• Example 9.5: Constructing a Confidence Interval for the Difference between Two Population Means (σ Unknown, Unequal Variances)
• Example 9.6: Constructing a Confidence Interval for the Difference between Two Population Means (σ Unknown, Equal Variances)

9.3 Comparing Two Population Means (Sigma Unknown, Dependent Samples)

• Examples
• Example 9.8: Calculating Paired Differences
• Example 9.9: Finding a Point Estimate for the Mean of the Paired Differences for Two Populations (σ Unknown, Dependent Samples)
• Example 9.10: Constructing a Confidence Interval for the Mean of the Paired Differences for Two Populations (σ Unknown, Dependent Samples)

9.4 Comparing Two Population Proportions

• Examples
• Example 9.12: Calculating Sample Proportions and Verifying Sample Size Conditions
• Example 9.13: Constructing a Confidence Interval for the Difference between Two Population Proportions
• Example 9.14: Constructing a Confidence Interval for the Difference between Two Population Proportions

9.5 Comparing Two Population Variances

• Examples
• Example 9.15: Calculating the Point Estimate for Comparing Two Population Variances and Finding Critical F-Values
• Example 9.16: Constructing a Confidence Interval for the Ratio of Two Population Variances
• Example 9.17: Constructing a Confidence Interval for the Ratio of Two Population Standard Deviations

10.R.1 Translating English Phrases and Algebraic Expressions

• Examples
• Example 1: Translating English Phrases into Algebraic Expressions
• Example 2: Application: Translating English Phrases
• Example 3: Translating Algebraic Expressions to English Phrases
• Example 4: Translating Equations into Word Problems

10.R.2 Comparisons and Order of Operations with Fractions

• Examples
• Example 1: Comparing Fractions
• Example 2: Comparing Fractions
• Example 3: Comparing Fractions
• Example 4: Using the Order of Operations with Fractions
• Example 5: Using the Order of Operations with Fractions
• Example 6: Using the Order of Operations with Fractions
• Example 8: Finding the Average

10.1 Fundamentals of Hypothesis Testing

• Examples
• Example 10.1: Determining the Null and Alternative Hypotheses
• Example 10.2: Determining the Null and Alternative Hypotheses
• Example 10.3: Determining the Null and Alternative Hypotheses
• Example 10.4: Determining the Null and Alternative Hypotheses
• Example 10.5: Interpreting the Conclusion to a Hypothesis Test
• Example 10.6: Interpreting the Conclusion to a Hypothesis Test
• Example 10.7: Determining the Type of Error
• Example 10.8: Determining the Type of Error
• Example 10.9: Determining the Type of Error

10.2 Hypothesis Testing for Population Means (Sigma Known)

• Examples
• Example 10.10: Using a Rejection Region in a Hypothesis Test for a Population Mean (Right-Tailed, σ Known)
• Example 10.11: Calculating the p-Value for a z-test Statistic for a Left-Tailed Test
• Example 10.12: Calculating the p-Value for a z-test Statistic for a Right-Tailed Test
• Example 10.13: Calculating the p-Value for a z-test Statistic for a Two-Tailed Test
• Example 10.15: Determining the Conclusion to a Hypothesis Test Using the p-Value
• Example 10.16: Performing a Hypothesis Test for a Population Mean (Right-Tailed, σ Known)
• Example 10.17: Performing a Hypothesis Test for a Population Mean (Two-Tailed, σ Known)

10.3 Hypothesis Testing for Population Means (Sigma Unknown)

• Examples
• Example 10.19: Finding the Critical t-value for a Right-Tailed Hypothesis Test
• Example 10.20: Using a Rejection Region in a Hypothesis Test for a Population Mean (Left-Tailed, σ Unknown)

10.4 Hypothesis Testing for Population Proportions

• Examples
• Example 10.23: Determining Which Distribution to Use for the Test Statistic in a Hypothesis Test
• Example 10.24: Performing a Hypothesis Test for a Population Proportion (Left-Tailed)
• Example 10.25: Performing a Hypothesis Test for a Population Proportion (Two-Tailed)

10.5 Hypothesis Testing for Population Variances

• Examples
• Example 10.27: Performing a Hypothesis Test for a Population Variance (Left-Tailed)
• Example 10.28: Performing a Hypothesis Test for a Population Standard Deviation (Right-Tailed)
• Example 10.29: Performing a Hypothesis Test for a Population Variance (Two-Tailed)

10.6 Chi-Square Test for Goodness of Fit

• Examples
• Example 10.30: Performing a Chi-Square Test for Goodness of Fit

10.7 Chi-Square Test for Association

• Examples
• Example 10.32: Performing a Chi-Square Test for Association

11.1 Hypothesis Testing: Two Population Means (Sigma Known)

• Examples
• Example 11.1: Determining the Null and Alternative Hypotheses for a Left-Tailed Test
• Example 11.2: Determining the Null and Alternative Hypotheses for a Right-Tailed Test
• Example 11.3: Determining the Null and Alternative Hypotheses for a Two-Tailed Test
• Example 11.4: Determining the Null and Alternative Hypotheses
• Example 11.5: Performing a Hypothesis Test for Two Population Means (Right-Tailed, σ Known)
• Example 11.6: Performing a Hypothesis Test for Two Population Means (Two-Tailed, σ Known)
• Example 11.7: Performing a Hypothesis Test for Two Population Means (Right-Tailed, σ Known)

11.2 Hypothesis Testing: Two Population Means (Sigma Unknown)

• Examples
• Example 11.8: Performing a Hypothesis Test for Two Population Means (Right-Tailed, σ Unknown, Unequal Variances)
• Example 11.9: Performing a Hypothesis Test for Two Population Means (Left-Tailed, σ Unknown, Equal Variances)

11.3 Hypothesis Testing: Two Population Means (Sigma Unknown, Dependent Samples)

• Examples
• Example 11.10: Determining the Null and Alternative Hypotheses for a Left-Tailed Test Using Dependent Samples
• Example 11.11: Using Rejection Regions in a Hypothesis Test for the Mean of the Paired Differences for Two Populations (Right-Tailed, σ Unknown, Dependent Samples)

11.4 Hypothesis Testing: Two Population Proportions

• Examples
• Example 11.13: Performing a Hypothesis Test for Two Population Proportions (Left-Tailed)

11.5 Hypothesis Testing: Two Population Variances

• Examples
• Example 11.15: Performing a Hypothesis Test for Two Population Variances (Left-Tailed)
• Example 11.17: Performing a Hypothesis Test for Two Population Variances (Two-Tailed)

11.6 ANOVA (Analysis of Variance)

• Examples
• Example 11.18: Completing an ANOVA Table and Performing an ANOVA Test

12.R.1 The Cartesian Coordinate System

• Examples
• Example 1: Plotting Ordered Pairs
• Example 2: Plotting Ordered Pairs
• Example 3: Finding Ordered Pairs
• Example 4: Finding Ordered Pairs
• Example 6: Locating Points on the Graph of a Line

12.R.2 Graphing Linear Equations in Two Variables

• Examples
• Example 1: Graphing a Linear Equation in Two Variables
• Example 2: Graphing a Linear Equation in Two Variables
• Example 3: Graphing a Linear Equation in Two Variables
• Example 4: Using Intercepts to Graph Linear Equations
• Example 5: Using Intercepts to Graph Linear Equations
• Example 7: Graphing Horizontal Lines
• Example 8: Graphing Vertical Lines

12.R.3 Slope-Intercept Form

• Examples
• Example 1: Finding the Slope of a Line
• Example 2: Finding the Slope of a Line
• Example 3: Finding the Slope of a Horizontal Line
• Example 4: Finding the Slope of a Vertical Line
• Example 5: Using Slope and the y‑Intercept to Graph a Line
• Example 6: Using Slope and the y-Intercept to Graph a Line
• Example 7: Finding Equations Given the Slope and the y‑Intercept

12.1 Scatter Plots and Correlation

• Examples
• Example 12.1: Creating a Scatter Plot to Identify Trends in Data
• Example 12.2: Creating a Scatter Plot to Identify Trends in Data
• Example 12.3: Determining Whether a Scatter Plot Would Have a Positive Slope, Negative Slope, or Not Follow a Straight-Line Pattern
• Example 12.6: Using a Table of Critical Values to Determine Significance of a Linear Relationship
• Example 12.9: Calculating and Interpreting the Coefficient of Determination

12.2 Linear Regression

• Examples
• Example 12.11: Making Predictions Using a Least-Squares Regression Line
• Example 12.13: Making Predictions Using a Linear Regression Model

12.3 Regression Analysis

• Examples
• Example 12.16: Calculating the Sum of Squared Errors
• Example 12.18: Constructing a Prediction Interval for an Individual y-Value

12.4 Multiple Regression Equations

A.1 Constructing Samples

• Examples
• Example A1.1: Construct a Sample
• Example A1.2: Construct a Sample
• Example A1.3: Construct a Bimodal Sample
• Example A1.4: Adding a Sample Value
• Example A1.5: Adding a Sample Value
• Example A1.6: Adding a Sample Value
• Example A1.7: Construct a Sample

A.2 Games of Chance

• Examples
• Example A2.1: Expected Value
• Example A2.2: Expected Value
• Example A2.3: Expected Value

A.3 Name that Distribution

A.4 Type II Errors

A.5 Direct Mail

A.6 Hypothesis Testing Means (z Value)

• Examples
• Example A6.1: Hypothesis of a Manufacturer
• Example A6.2: Hypothesis of the Life of a Microchip
• Example A6.3: Hypothesis of Tensile Strength
• Example A6.4: Hypothesis of Students' Reading Levels

A.7 Hypothesis Testing Proportions (z Value)

• Examples
• Example A7.1: Hypothesis of Students
• Example A7.2: Hypothesis of a Computer Chip

A.8 ANOVA Regression

A.9 R-Charts

• Examples
• Example A9.1: R-Charts

A.10 p-Charts

• Examples
• Example A10.1: p-Charts

A.11 c-Charts

• Examples
• Example A11.1: c-Charts

A.12 Mean Charts Using Range

• Examples
• Example A12.1: Mean Charts Using Range

A.13 Mean Charts Using s

• Examples
• Example A13.1: Mean Charts Using s

A.14 Introduction to Estimating Population Means

• Examples
• Example A14.1: Best Point Estimate
• Example A14.2: Confidence Intervals
• Example A14.3: Determining the Appropriate Method
• Example A14.4: Determining the Appropriate Method