Available in MyStatLabTM for Your Introductory Statistics Courses
by Terry T. Sincich, James T. McClave
A First Course in Statistics, 12th Edition
- Available in MyStatLabTM for Your Introductory Statistics Courses
- Applet Correlation
- A First Course in Statistics
- A First Course in Statistics
- Contents
- Preface
- Applications Index
- 1 Statistics, Data, and Statistical Thinking
- Contents
- Where We’re Going
- 1.1 The Science of Statistics
- 1.2 Types of Statistical Applications
- 1.3 Fundamental Elements of Statistics
- 1.4 Types of Data
- 1.5 Collecting Data: Sampling and Related Issues
- 1.6 The Role of Statistics in Critical Thinking and Ethics
- Chapter Notes
- 2 Methods for Describing Sets of Data
- Contents
- Where We’ve Been
- Where We’re Going
- 2.1 Describing Qualitative Data
- 2.2 Graphical Methods for Describing Quantitative Data
- 2.3 Numerical Measures of Central Tendency
- 2.4 Numerical Measures of Variability
- 2.5 Using the Mean and Standard Deviation to Describe Data
- 2.6 Numerical Measures of Relative Standing
- 2.7 Methods for Detecting Outliers: Box Plots and z-Scores
- 2.8 Graphing Bivariate Relationships (Optional)
- 2.9 Distorting the Truth with Descriptive Statistics
- Chapter Notes
- Key Terms
- Key Symbols
- Key Ideas
- Supplementary Exercises 2.174–2.208
- References
- Using Technology MINITAB: Describing Data
- Graphing Data
- Numerical Descriptive Statistics
- TI-83/TI–84 Plus Graphing Calculator: Describing Data
- Histogram from Raw Data
- Histogram from a Frequency Table
- One-Variable Descriptive Statistics
- Sorting Data (to Find the Mode)
- Box Plot
- Scatterplots
- 3 Probability
- Contents
- Where We’ve Been
- Where We’re Going
- 3.1 Events, Sample Spaces, and Probability
- 3.2 Unions and Intersections
- 3.3 Complementary Events
- 3.4 The Additive Rule and Mutually Exclusive Events
- 3.5 Conditional Probability
- 3.6 The Multiplicative Rule and Independent Events
- Chapter Notes
- 4 Random Variables and Probability Distributions
- Contents
- Where We’ve Been
- Where We’re Going
- 4.1 Two Types of Random Variables
- 4.2 Probability Distributions for Discrete Random Variables
- 4.3 The Binomial Random Variable
- 4.4 Probability Distributions for Continuous Random Variables
- 4.5 The Normal Distribution
- 4.6 Descriptive Methods for Assessing Normality
- Problem
- Statistics in Action Revisited Assessing whether the Normal Distribution Is Appropriate for Modeling the Super Weapon Hit Data
- Exercises 4.103–4.124
- 4.7 Approximating a Binomial Distribution with a Normal Distribution (Optional)
- Exercises 4.125–4.142
- 4.8 Sampling Distributions
- Exercises 4.143–4.151
- 4.9 The Sampling Distribution of x¯ and the Central Limit Theorem
- Exercises 4.152–4.175
- Chapter Notes
- Key Terms
- Key Symbols
- Key Ideas
- Key Formulas
- Guide to Selecting a Probability Distribution
- Generating the Sampling Distribution of x ¯
- Supplementary Exercises 4.176–4.220
- References
- Using Technology MINITAB: Binomial Probabilities, Normal Probability, and Simulated Sampling Distribution
- Binomial Probabilites
- Normal Probabilities
- Normal Probability Plot
- TI-83/TI-84 Plus Graphing Calculator: Discrete Random Variables, Binomial, and Normal Probabilities
- Calculating the Mean and Standard Deviation of a Discrete Random Variable
- Calculating Binomial Probabilities
- III. P(x<k),P(x>k),P(x≥k)
- Graphing the Area under the Standard Normal Curve
- Finding Normal Probabilities without a Graph
- Finding Normal Probabilities with a Graph
- Example
- Graphing a Normal Probability Plot
- Simulating a Sampling Distribution
- 5 Inferences Based on a Single Sample Estimation with Confidence Intervals
- Contents
- Where We’ve Been
- Where We’re Going
- 5.1 Identifying and Estimating the Target Parameter
- 5.2 Confidence Interval for a Population Mean: Normal (z) Statistic
- 5.3 Confidence Interval for a Population Mean: Student’s t-Statistic Confidence Interval for a Population Mean: Student’s t-Statistic
- 5.4 Large-Sample Confidence Interval for a Population Proportion
- 5.5 Determining the Sample Size
- 5.6 Confidence Interval for a Population Variance (Optional)
- Chapter Notes
- 6 Inferences Based on a Single Sample Tests of Hypothesis
- Contents
- Where We’ve Been
- Where We’re Going
- 6.1 The Elements of a Test of Hypothesis
- 6.2 Formulating Hypotheses and Setting Up the Rejection Region
- 6.3 Observed Significance Levels: p-Values
- 6.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic
- 6.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic Test of Hypothesis about a Population Mean: Student’s t-Statistic
- 6.6 Large-Sample Test of Hypothesis about a Population Proportion
- 6.7 Test of Hypothesis about a Population Variance (Optional)
- 6.8 A Nonparametric Test about a Population Median (Optional)
- Chapter Notes
- 7 Comparing Population Means
- Contents
- Where We’ve Been
- Where We’re Going
- 7.1 Identifying the Target Parameter
- 7.2 Comparing Two Population Means: Independent Sampling
- 7.3 Comparing Two Population Means: Paired Difference Experiments
- 7.4 Determining the Sample Size
- 7.5 A Nonparametric Test for Comparing Two Populations: Independent Samples (Optional)
- 7.6 A Nonparametric Test for Comparing Two Populations: Paired Difference Experiment (Optional)
- 7.7 Comparing Three or More Population Means: Analysis of Variance (Optional)
- Chapter Notes
- Key Terms
- Key Symbols
- Key Ideas
- Key Words for Identifying the Target Parameter
- Determining the Sample Size
- Conditions Required for Inferences about μ1−μ2
- *Conditions Required for ANOVA
- Conditions Required for Inferences about μd
- Using a Confidence Interval for (μ1−μ2) to Determine whether a Difference Exists
- Guide to Comparing Population Means
- Supplementary Exercises 7.122–7.147
- References
- 8 Comparing Population Proportions
- Contents
- Where We’ve Been
- Where We’re Going
- 8.1 Comparing Two Population Proportions: Independent Sampling
- 8.2 Determining the Sample Size
- 8.3 Testing Category Probabilities: Multinomial Experiment
- 8.4 Testing Categorical Probabilities: Two-Way (Contingency) Table
- Chapter Notes
- 9 Simple Linear Regression
- Contents
- Where We’ve Been
- Where We’re Going
- 9.1 Probabilistic Models
- 9.2 Fitting the Model: The Least Squares Approach
- 9.3 Model Assumptions
- 9.4 Assessing the Utility of the Model: Making Inferences about the Slope β1
- 9.5 The Coefficients of Correlation and Determination
- 9.6 Using the Model for Estimation and Prediction
- 9.7 A Complete Example
- 9.8 A Nonparametric Test for Correlation (Optional)
- Chapter Notes
- Key Terms
- Key Symbols/Notation
- Key Ideas
- Simple Linear Regression Variables
- Method of least squares properties
- First-order (straight-line) model
- Practical interpretation of y-intercept
- Practical interpretation of slope
- Coefficient of correlation, r
- Coefficient of determination, r2
- Practical interpretation of model standard deviation s
- Comparing Intervals in Step 5
- Key Formulas
- Guide to Simple Linear Regression
- Supplementary Exercises 9.140–9.163
- References
- Appendix A: Summation Notation
- Appendix B: Tables
- Appendix C: Calculation Formulas for Analysis of Variance (Independent Sampling)
- Short Answers to Selected Odd Exercises
- Index
- Photo Credits
- Chapter 9
- Selected Formulas
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