Beginning Statistics, 3rd Edition
1.1 Getting Started
1.2 Data Classification
1.3 The Process of a Statistical Study
- Examples
- Example 1.3.1: Identifying Population and Variables
- Example 1.3.2: Identifying Observational Studies and Experiments
- Example 1.3.4: Classifying Studies as Cross-Sectional or Longitudinal
- Example 1.3.5: Classifying Studies as Meta-Analysis or Case Study
- Example 1.3.6: Identifying Parts of an Experiment
1.4 How to Critique a Published Study
5.1 Discrete Random Variables
5.2 Binomial Distribution
- Examples
- Example 5.2.1: Finding the Mean and Standard Deviation of a Binomial Distribution
- Example 5.2.2: Calculating a Binomial Probability Using the Formula
- Example 5.2.3: Finding a Binomial Probability Using a Table
- Example 5.2.4: Calculating a Binomial Probability,
- Example 5.2.5: Calculating Binomial Probabilities,
5.3 Poisson Distribution
5.4 Hypergeometric Distribution
6.1 Introduction to the Normal Distribution
6.2 The Standard Normal Distribution
- Examples
- Example 6.2.1: Calculating and Graphing z-values
- Example 6.2.2: Finding Area to the Left of a Positive z-value Using a Cumulative Normal Table
- Example 6.2.3: Finding Area to the Left of a Negative z-value Using a Table
- Example 6.2.4: Finding Area to the Right of a Positive z-value Using a Cumulative Normal Table
- Example 6.2.5: Finding Area to the Right of a Negative z-value Using a Table
6.3 Finding Probability Using a Normal Distribution
- Examples
- Example 6.3.1: Finding the Probability that a Normally Distributed Random Variable Will Be Less Than a Given Value
- Example 6.3.2: Finding the Probability that a Normal Distributed Random Variable Will Be Greater Than a Given Value
- Example 6.3.3: Finding the Probability that a Normally Distributed Random Variable Will Be between Two Given Values Or in the Tails Defined by Two Given Values
6.4 Finding Values of a Normally Distributed Random Variable
6.5 Approximating a Binomial Distribution Using a Normal Distribution
- Examples
- Example 6.5.1: Using the Continuity Correction Factor with a Normal Distribution to Approximate a Binomial Probability
- Example 6.5.2: Using the Continuity Correction Factor with a Normal Distribution to Approximate a Binomial Probability
- Example 6.5.3: Using a Normal Distribution to Approximate a Binomial Probability of the Form P(X > x)
- Example 6.5.4: Using a Normal Distribution to Approximate a Binomial Probability of the Form P(X ≤ x)
- Example 6.5.5: Using a Normal Distribution to Approximate a Binomial Probability of the Form P(X = x)
7.1 Sampling Distributions and the Central Limit Theorem
7.2 Central Limit Theorem with Means
- Examples
- Example 7.2.1: Finding the Probability that a Sample Mean Will Be Less Than a Given Value
- Example 7.2.2: Finding the Probability that a Sample Mean Will Be Greater Than a Given Value
- Example 7.2.3: Finding the Probability that a Sample Mean Will Differ from the Population Mean by Less Than a Given Amount
7.3 Central Limit Theorem with Proportions
8.1 Estimating Population Means (Sigma Known)
- Examples
- Example 8.1.1: Finding a Point Estimate for a Population Mean
- Example 8.1.2: Constructing a Confidence Interval with a Given Margin of Error
- Example 8.1.3: Finding the Margin of Error of a Confidence Interval for a Population Mean (σ Known)
- Example 8.1.7: Interpreting a Confidence Interval
- Example 8.1.8: Finding the Minimum Sample Size Needed for a Confidence Interval for a Population Mean
8.2 Student's t-Distribution
- Examples
- Example 8.2.1: Finding the Value of tα Using a Table
- Example 8.2.2: Finding the Value of t Given the Area to the Left
- Example 8.2.3: Finding the Value of t Given the Area to the Right
- Example 8.2.5: Finding the Value of t Given Area between and
- Example 8.2.6: Finding the Critical t-value for a Confidence Interval
8.3 Estimating Population Means (Sigma Unknown)
8.4 Estimating Population Proportions
8.5 Estimating Population Variances
- Examples
- Example 8.5.1: Finding Point Estimates for the Population Standard Deviation and Variance
- Example 8.5.2: Constructing a Confidence Interval for a Population Variance
- Example 8.5.2: Constructing a Confidence Interval for a Population Variance
- Example 8.5.3: Constructing a Confidence Interval for a Population Standard Deviation
- Example 8.5.4: Constructing a Confidence Interval for a Population Variance
- Example 8.5.4: Constructing a Confidence Interval for a Population Variance
- Example 8.5.5: Constructing a Confidence Interval for a Population Standard Deviation
- Example 8.5.5: Constructing a Confidence Interval for a Population Standard Deviation
- Example 8.5.6: 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 9.1.1: Finding the Point Estimate and Margin of Error of a Confidence Interval for the Difference between Two Population Means (σ Known, Independent Samples)
- Example 9.1.2: Constructing a Confidence Interval for the Difference between Two Population Means (σ Known, Independent Samples)
- Example 9.1.2: Constructing a Confidence Interval for the Difference between Two Population Means (σ Known, Independent Samples)
- Example 9.1.3: Constructing a Confidence Interval for the Difference between Two Population Means (σ Known, Independent Samples)
9.2 Comparing Two Population Means (Sigma Unknown)
- Examples
- Example 9.2.1: Finding the Margin of Error of a Confidence Interval for the Difference between Two Population Means with Unequal Variances (σ Unknown, Independent Samples)
- Example 9.2.2: Constructing a Confidence Interval for the Difference between Two Population Means with Unequal Variances (σ Unknown, Independent Samples)
- Example 9.2.3: Constructing a Confidence Interval for the Difference between Two Population Means with Equal Variances (σ Unknown, Independent Samples)
9.3 Comparing Two Population Means (Sigma Unknown, Dependent Samples)
9.4 Comparing Two Population Proportions
9.5 Comparing Two Population Variances
10.1 Fundamentals of Hypothesis Testing
10.2 Hypothesis Testing for Population Means (Sigma Known)
10.3 Hypothesis Testing for Population Means (Sigma Unknown)
10.4 Hypothesis Testing for Population Proportions
10.5 Hypothesis Testing for Population Variances
10.6 Chi-Square Test for Goodness of Fit
10.7 Chi-Square Test for Association
11.1 Hypothesis Testing: Two Population Means (Sigma Known, Independent Samples)
11.2 Hypothesis Testing: Two Population Means (Sigma Unknown, Independent Samples)
11.3 Hypothesis Testing: Two Population Means (Sigma Unknown, Dependent Samples)
11.4 Hypothesis Testing: Two Population Proportions
11.5 Hypothesis Testing: Two Population Variances
11.6 ANOVA (Analysis of Variance)
12.1 Scatter Plots and Correlation
- Examples
- Example 12.1.1: Creating a Scatter Plot to Identify Trends in Data
- Example 12.1.2: Determining Whether a Scatter Plot Would Have a Positive Slope, Negative Slope, or Not Follow a Straight-Line Pattern
- Example 12.1.4: Describing the Linear Relationship Between Two Variables, Graphically and Numerically
12.2 Linear Regression
12.3 Regression Analysis
12.4 Multiple Regression
A.1 Constructing Samples
- Examples
- Example 1: Example A.1.1: Construct a Sample
- Example 2: Example A.1.2: Construct a Sample
- Example 3: Example A.1.3: Construct a Bimodal Sample
- Example 4: Example A.1.4: Adding a Sample Value
- Example 5: Example A.1.5: Adding a Sample Value
- Example 6: Example A.1.6: Adding a Sample Value
- Example 7: Example A.1.7: Construct a Sample