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Using a successfully class-tested approach that gives coherence to a broad range of introductory topics, this innovative text provides students with a real-world, big picture view of statistics as well as problem-solving strategies that can be applied to the statistical questions, real data, and examples that they will encounter. Author Nancy Pfenning organizes content around four basic processes of statistics: producing data, displaying and summarizing data, understanding probability, and using probability to perform statistical inference. Within this framework, the book progresses systematically through five basic problem situations involving values of variables (quantitative, categorical, or a blend). As a result, students learn to identify which situation applies and how to choose the correct display, summary, or inference tool or technique. As students gain proficiency in specific statistical techniques, the author also points out connections among topics and techniques. More than 1,000 real-life examples and categorized exercises support the approach, engaging students in practicing and developing a variety of skills.
- Big picture problem-solving approach--An innovative approach organizes content around four processes of statistics and the basic situations that apply to any statistical problem. Students learn specific techniques while gaining a broad perspective on statistics along with strategies for choosing the appropriate technique for any research situation.
- Flexible content and technology integration--The presentation of each topic is self-contained enough to allow for more advanced topics such as ANOVA or chi-square to be skipped if desired with no loss in course coherence. A "Using Software" section near the end of each chapter makes it easy for instructors to emphasize the use of software in the course, or choose not to do so.
- Examples using real data--Hundreds of well-chosen examples, based on current data from a wide variety of statistical applications, clarify the presentation of ideas. Each example follows a consistent format: Background, Question, Response, and Practice Exercise. Data sets feature values that allow easy entering by hand into a computer or calculator; they are also available at the Book Companion Website.
- Plentiful categorized exercises, computational and conceptual--A set of problems after each major section assures that students practice basic concepts as they are introduced. A more comprehensive set at the end of each chapter ("Warming Up," "Exploring the Big Picture," "Using Software," "Discovering Research," and "Reporting on Research") encourages students to integrate individual topics. Like the examples, exercises cover a variety of topics and use real data.
- Emphasis on connections among topics--Strategically placed marginal notes show how each new topic fits into the larger framework of statistics, relating it to material covered earlier as well as to content that follows. These connections can be digested by students at their own pace, without disrupting the flow of main ideas.
Types of Variables: Categorical or Quantitative. Students Talk Stats: Identifying Types of Variables. Handling. Data for Two Types of Variables. Roles of Variables: Explanatory or Response. Statistics as a Four-Stage Process.
PART I: DATA PRODUCTION.
2. Sampling: Which Individuals Are Studied.
Sources of Bias in Sampling: When Selected Individuals Are Not Representative.
Probability Sampling Plans: Relying on Randomness. Role of Sample Size: Bigger Is Better if the Sample Is Representative. From Sample to Population: To What Extent Can We Generalize? Students Talk Stats: Seeking a Representative Sample.
3. Design: How Individuals Are Studied.
Various Designs for Studying Variables. Sample Surveys: When Individuals Report Their Own Values. Observational Studies: When Nature Takes Its Course. Experiments: When Researchers Take Control. Students Talk Stats: Does TV Cause ADHD? Considering Study Design.
PART II: DISPLAYING AND SUMMARIZING DATA.
4. Displaying and Summarizing Data for a Single Variable.
Single Categorical Variable. Students Talk Stats: Biased Sample, Biased Assessment. Single Quantitative Variables and the Shape of a Distribution. Center and Spread: What''s Typical for Quantitative Values, and How They Vary. Normal Distributions: The Shape of Things to Come.
5. Displaying and Summarizing Relationships.
Relationship Between One Categorical and One Quantitative Variable. Students Talk Stats: Displaying and Summarizing Paired Data. Relationship Between Two Categorical Variables. Relationships Between Two Quantitative Variables. Students Talk Stats: How Outliers and Influential Observations Affect a Relationship. Students Talk Stats: Confounding in a Relationship Between Two Quantitative Variables.
PART III: PROBABILITY.
6. Finding Probabilities.
The Meaning of "Probability" and Basic Rules. More General Probability Rules and Conditional Probability. Students Talk Stats: Probability as a Weighted Average of Conditional Probabilities.
7. Random Variables.
Discrete Random Variables. Binomial Random Variables. Students Talk Stats: Calculating and Interpreting the Mean and Standard Deviation of Count or Proportion. Continuous Random Variables and the Normal Distribution. Students Talk Stats: Means, Standard Deviations, and Below-Average Heights.
8. Sampling Distributions.
The Behavior of Sample Proportion in Repeated Random Samples. The Behavior of Sample Mean in Repeated Random Samples. Students Talk Stats: When Normal Approximations Are Appropriate.
PART IV: STATISTICAL INFERENCE.
9. Inference for a Single Categorical Variable.
Point Estimate and Confidence Interval: A Best Guess and a Range of Plausible Values for Population Proportion. Students Talk Stats: Interpreting a Confidence Interval. Test: Is a Proposed Population Proportion Plausible? Students Talk Stats: Interpreting a P-value. Students Talk Stats: What Type of Error Was Made? Students Talk Stats: The Correct Interpretation of a Small P-value. Students Talk Stats: The Correct Interpretation When a P-value Is Not Small.
10. Inference for a Single Quantitative Variable.
Inference for a Mean when Population Standard Deviation Is Known or Sample Size Is Large. Students Talk Stats: Confidence Interval for a Mean. Students Talk Stats: Interpreting a Confidence Interval for the Mean Correctly. Inference for a Mean When the Population Standard Deviation Is Unknown and the Sample Size Is Small. Students Talk Stats: Practical Application of a t Test. A Closer Look at Inference for Means.
11. Inference for Relationships Between Categorical and Quantitative Variables.
Inference for a Paired Design with t. Inference for a Two-Sample Design with t. Students Talk Stats: Ordinary Vs. Pooled Two-Sample t. Inference for a Several-sample Design with F: Analysis of Variance. Students Talk Stats: Reviewing Relationships between Categorical and Quantitative Variables.
12. Inference for Relationships Between Two Categorical Variables.
Comparing Proportions with a z Test. Comparing Counts with a Chi-Square Test.
13. Inference for Relationships Between Two Quantitative Variables.
Inference for Regression: Focus on the Slope of the Regression Line. Students Talk Stats: No Evidence of a Relationship. Interval Estimates for an Individual or Mean Response.
14. How Statistics Problems Fit into the Big Picture.
The Big Picture in Problem-Solving. Students Talk Stats: Choosing the Appropriate Statistical Tools.
15. Non-Parametric Methods (Online).
The Sign Test as an Alternative to the Paired t Test. The Rank-Sum Test as an Alternative to the Two-Sample t Test. Summary of Non-Parametrics.
16. Two-Way ANOVA (Online).
17. Multiple Regression (Online).
PART V: SOLUTIONS TO SELECTED EXERCISES.
Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.
Student Solutions Manual
Includes worked-out solutions to the odd-numbered problems.
Student Solutions Manual
Homework help! Worked-out solutions to the odd-numbered problems.