Classic, yet contemporary. Theoretical, yet applied. McClave & Sincich’s Statistics: A First Course in Statistics gives you the best of both worlds. This text offers a trusted, comprehensive introduction to statistics that emphasizes inference and integrates real data throughout. The authors stress the development of statistical thinking, the assessment of credibility, and value of the inferences made from data.
The Eleventh Edition infuses a new focus on ethics, which is critically important when working with statistical data. Chapter Summaries have a new, study-oriented design, helping students stay focused when preparing for exams. Data, exercises, technology support, and Statistics in Action cases are updated throughout the book. In addition, MyStatLab will have increased exercise coverage and two new banks of questions to draw from: Getting Ready for Stats and Conceptual Question Library.
Ideal for one- or two-semester courses in introductory statistics, this text assumes a mathematical background of basic algebra. Flexibility is built in for instructors who teach a more advanced course, with optional footnotes about calculus and the underlying theory.
1. Statistics, Data, and Statistical Thinking
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
1.6 The Role of Statistics in Critical Thinking
2. Methods for Describing Sets of Data
2.1 Describing Qualitative Data
2.2 Graphical Methods for Describing Quantitative Data
2.3 Summation Notation
2.4 Numerical Measures of Central Tendency
2.5 Numerical Measures of Variability
2.6 Interpreting the Standard Deviation
2.7 Numerical Measures of Relative Standing
2.8 Methods for Detecting Outliers: Box Plots and z-Scores
2.9 Graphing Bivariate Relationships (Optional)
2.10 Distorting the Truth with Descriptive Techniques
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
3.7 Random Sampling
3.8 Some Additional Counting Rules (Optional)
3.9 Bayes’ Rule (Optional)
4. Random Variables and Probability Distributions
4.1 Two Types of Random Variables
4.2 Probability Distributions for Discrete Random Variables
4.3 Expected Values of Discrete Random Variables
4.4 The Binomial Random Variable
4.5 Continuous Probability Distributions
4.6 The Normal Distribution
4.7 Descriptive Methods for Assessing Normality
4.8 Approximating a Binomial Distribution with a Normal Distribution (Optional)
4.9 What is a Sampling Distribution?
4.10 The Sampling Distribution of (x-bar) and the Central Limit Theorem
5. Inferences Based on a Single Sample: Estimation with Confidence Intervals
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
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)
6. Inferences Based on a Single Sample: Tests of Hypothesis
6.1 The Elements of a Test of Hypothesis
6.2 Formulating Hypotheses and Setting Up the Rejection Region
6.3 Test of Hypothesis About a Population Mean: Normal (z) Statistic
6.4 Observed Significance Levels: p-Values
6.5 Test of Hypothesis About a Population Mean: Student's t-statistic
6.6 Large-Sample Test of Hypothesis About a Population Proportion
6.7 Calculating Type II Error Probabilities: More About β (Optional)
6.8 Test of Hypothesis About a Population Variance (Optional)
6.9 Single Population Inferences
7. Comparing Population Means
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 The Completely Randomized Design: Single Factor
7.6 Comparing Two Populations: Independent Samples
7.7 Comparing Two Populations: Paired Difference Experiment
8. Comparing Population Proportions
8.1 Categorical Data and the Multinomial Distribution
8.2 Testing Categorical Probabilities: One-Way Table
8.3 Testing Categorical Probabilities: Two-Way (Contingency) Table
8.4 A Word of Caution About Chi-Square Tests
8.5 Comparing Two Population Proportions: Independent Sampling
8.6 Determining the Sample Size
9. Simple Linear Regression
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 Rank Correlation
About this Text
McClave and Sincich provide support to students when they are learning to solve problems and when they are studying and reviewing the material.
“Where We’re Going” bullets begin each chapter, to offer learning objectives and to provide section numbers that correspond to where each concept is discussed in the chapter.
Examples foster problem-solving skills by taking a three-step approach: (1) "Problem", (2) "Solution", and (3) "Look Back" (or "Look Ahead"). This step-by-step process provides students with a defined structure by which to approach problems and enhances their problem-solving skills.
The "Look Back" feature often gives helpful hints to solving the problem and/or provides a further reflection or insight into the concept or procedure that is covered.
A “Now Work” exercise suggestion follows each Example, which provides a practice exercise that is similar in style and concept to the example. Students test and confirm their understanding immediately.
Redesigned! End-of-Chapter Summaries now serve as a more effective study aid for students. Important points are reinforced through flow graphs (which aid in selecting the appropriate statistical method) and boxed notes with key words, formulas, definitions, lists, and key concepts.
More than 1,800 exercises are included, based on a wide variety of applications in various disciplines and research areas, and more than 20% have been updated for the new edition. Some students have difficulty learning the mechanics of statistical techniques while applying the techniques to real applications. For this reason, exercise sections are divided into four parts:
Learning the Mechanics: These exercises allow students to test their ability to comprehend a mathematical concept or a definition.
Applying the Concepts—Basic: Based on applications taken from a wide variety of journals, newspapers, and other sources, these short exercises help students begin developing the skills necessary to diagnose and analyze real-world problems.
Applying the Concepts—Intermediate: Based on more detailed real-world applications, these exercises require students to apply their knowledge of the technique presented in the section.
Applying the Concepts—Advanced: These more difficult real-data exercises require students to use critical thinking skills.
Critical Thinking Challenges: Students apply critical thinking skills to solve one or two challenging real-life problems. These expose students to real-world problems with solutions that are derived from careful, logical thought and use of the appropriate statistical analysis tool.
Case studies, applications, and biographies keep students motivated and show the relevance of statistics.
NEW! Ethics Boxes have been added where appropriate to highlight the importance of ethical behavior when collecting, analyzing, and interpreting statistical data.
Statistics in Action begins each chapter with a case study based on an actual contemporary, controversial, or high-profile issue. Relevant research questions and data from the study are presented and the proper analysis demonstrated in short "Statistics in Action Revisited" sections throughout the chapter.
Brief Biographies of famous statisticians and their achievements are presented within the main chapter, as well as in marginal boxes. Students develop an appreciation for the statistician's efforts and the discipline of statistics as a whole.
Support for statistical software is integrated throughout the text and online, so instructors can focus less time on teaching the software and more time teaching statistics.
Each statistical analysis method presented is demonstrated using output from SAS, SPSS, and MINITAB. These outputs appear in examples and exercises, exposing students to the output they will encounter in their future careers.
Using Technology boxes at the end of each chapter offer statistical software tutorials, with step-by-step instructions and screen shots for MINITAB and, where appropriate, the TI-83/84 Plus Graphing Calculator.
To complement the text, support for the statistical software is available in MyStatLab’s Technology Instruction Videos and the three-hole punched, tri-fold Technology Study Cards. Student discounts on select statistical software packages are also available. Ask your Pearson sales representative for details.
A Resource CD-ROM accompanies the text, with files for text examples, exercises, Statistics in Action and Real-World case data sets marked with a CD icon. All data files are available as .csv, .txt, and TI files. The CD also contains Chapter 14, Nonparametric Statistics, and a set of appletsthat allowstudents to run simulations that visually demonstrate some of the difficult statistical concepts (e.g., sampling distributions and confidence intervals.)
Flexibility in Coverage
- Probability and Counting Rules:
- Probability poses a challenge for instructors because they must decide on the level of presentation, and students find it a difficult subject to comprehend.
- Unlike other texts that combine probability and counting rules, McClave/Sincich includes the counting rules (with examples) in an appendix rather than in the body of the chapter on probability; the instructor can control the level of coverage of probability covered.
- Multiple Regression and Model Building:
- Two full chapters are devoted to discussing the major types of inferences that can be derived from a regression analysis, showing how these results appear in the output from statistical software, and, most important, selecting multiple regression models to be used in an analysis.
- The instructor has the choice of a one-chapter coverage of simple linear regression (Chapter 11), a two-chapter treatment of simple and multiple regression (excluding the sections on model building in Chapter 12), or complete coverage of regression analysis, including model building and regression diagnostics.
- This extensive coverage of such useful statistical tools will provide added evidence to the student of the relevance of statistics to real-world problems.
Role of calculus:
Although the text is designed for students without a calculus background, footnotes explain the role of calculus in various derivations.
Footnotes are also used to inform the student about some of the theory underlying certain methods of analysis. They provide additional flexibility in the mathematical and theoretical level at which the material is presented.
Dr. Jim McClave is currently President and CEO of Info Tech, Inc., a statistical consulting and software development firm with an international clientele. He is also currently an Adjunct Professor of Statistics at the University of Florida, where he was a full-time member of the faculty for twenty years.
Dr. Terry Sincich
obtained his PhD in Statistics from the University of Florida in 1980. He is an Associate Professor in the Information Systems & Decision Sciences Department at the University of South Florida in Tampa. Dr. Sincich is responsible for teaching basic statistics to all undergraduates, as well as advanced statistics to all doctoral candidates, in the College of Business Administration. He has published articles in such journals as the Journal of the American Statistical Association, International Journal of Forecasting, Academy of Management Journal, and the Auditing: A Journal of Practice & Theory. Dr. Sincich is a co-author of the texts Statistics, Statistics for Business & Economics, Statistics for Engineering & the Sciences, and A Second Course in Statistics: Regression Analysis.