For one semester algebra-based Introductory Statistics Courses.
This very popular text promotes student success while maintaining the statistical integrity of the course. Three objectives motivate this text: (1) to generate and maintain student interest, thereby promoting student success and confidence; (2) to provide extensive and effective opportunity for student practice; (3) to permit flexibility of teaching styles. This revision follows the Guidelines for Assessment and Instruction in Statistics Education (GAISE) for the college introductory statistics course.
Datasets and other resources (where applicable) for this book are available here.
• Guidelines for Assessment and Instruction in Statistics Education (GAISE) for the college introductory statistics course - Adheres closely to the guidelines endorsed recently by the American Statistical Association:
- 1. Emphasize statistical literacy and develop statistical thinking;
- 2. Use real data in teaching statistics;
- 3. Stress conceptual understanding;
- 4. Foster active learning;
- 5. Use technology for developing conceptual understanding; - 6. Use assessments to improve and evaluate student learning.
See www.amstat.org/education/gaise/ for more information.
• Emphasizes statistical literacy and develops statistical thinking in 5 ways:
I. Extensive discussion of data production - Sections 1.2 through 1.4 discuss sampling techniques and pitfalls of sampling. Section 1.5 has a thorough discussion of design and experiments.
II. Readingand interpretation of statistical graphs - The emphasis in Chapter 2 is on constructing and interpreting statistical graphs. In addition, in Section 2.3, the author illustrates the idea off misrepresenting data through graphs. The remainder of the text requires students to use graphs as a preliminary step in any statistical analysis.
III. Discussion making about appropriate techniques - When a research question is posed, the researcher must decide on the type of analysis to conduct using the data to answer the question. Different models may be used to answer the question. For example, in Section 1.5, students are asked to design an experiment to answer a research question. Different designs may all be legitimate choices. The student is asked to justify his/her design choice. New Putting It All Together sections in chapters 9-10 (Confidence Intervals and Hypothesis Testing) to help students in determining which technique to use.
IV. Decisions Projects - Each chapter opens with a Decisions Project that requires students to collect, summarize, and interpret data to help make decisions that occur in every day life.
V. Concepts and Vocabulary - Each section exercise set has a category of problems that require students to learn the language of statistics and explain statistical concepts in their own words.
• Use Real Data in Teaching Statistics - The examples and exercises use real, sourced data to illustrate statistical concepts and procedures. The data come from a wide variety of disciplines such as psychology, sociology, finance, economics, biology, environmental studies, business, and chemistry so that all students can see the importance of data analysis in their field of study.
• Stress Conceptual Understanding in the following ways:
I. Applying the Concepts Problems - Asks the student to analyze real data by drawing graphs or going through procedures, and help to develop a student's ability to think statistically.
II. Simulation and Applets - Throughout the text, exercises require students to perform simulations or use applets that will contribute to their conceptual understanding. Simulations and applets are powerful because they let students work actively with data. Further, many students who are visual learners find that applets give them the reinforcement that they need to master a concept.
· Students use simulation to enhance their understanding of sampling distributions (ex. #34-35, pg. 434)
· Students use an applet to enhance their understanding of confidence intervals (ex. XXX
III. The Presentation of Formulas - When formulas are presented, the author uses both a “computational” formula and “definition” formula.
o Pg. 127: Definition and formula for "Sample Variance"; Pg. 128: Example #4 - works the example BOTH ways.
• Foster Active Learning in the Classroom - to promote active learning, the author includes:
• Use Technology for Developing Concepts and Analyzing Data - Technology allows us to explore statistical ideas in new and exciting ways. Graphing calculators and statistical software packages can be used to explore “what if” scenarios. Doing so helps to develop the students' understanding of statistical concepts. Each example is presented using computation “by hand”. When appropriate, the results for the same example are obtained using a graphing calculator or software.
• Use Assessments to Improve and Evaluate Student Learning - The exercises at the end of each section are divided into three categories, all oriented towards helping student master concepts and gain a deeper statistical literacy.
• CD Contents:
· Case Studies
· Additional Topics
· Formula Cards
· Data Sets
· Graphing Calculator Links
Part I: Getting the Information You Need
Chapter 1 Data Collection
1.1 Introduction to the Practice of Statistics
1.2 Observational Studies, Experiments, and Simple Random
1.3 Other Effective Sampling Methods
1.4 Sources of Error in Sampling
1.5 The Design of Experiments
Case Study: Chrysalises for Cash (on CD)
Part II: Descriptive Statistics
Chapter 2 Organizing and Summarizing Data
2.1 Organizing Qualitative Data
2.2 Organizing Quantitative Data
2.3 Graphical Misrepresentations of Data
Case Study: The Day the Sky Roared (on CD)
Chapter 3 Numerically Summarizing Data
3.1 Measures of Central Tendency
3.2 Measures of Dispersion
3.3 Measures of Central Tendency and Dispersion from Grouped
3.4 Measures of Position
3.5 The Five-Number Summary and Boxplots
Case Study:Who Was 'A Mourner'? (on CD)
Chapter 4 Describing the Relation between Two Variables
4.1 Scatter Diagrams and Correlation
4.2 Least-Squares Regression
4.3 The Coefficient of Determination
Case Study: Thomas Malthus, Population, and Subsistence (on CD)
Part III: Probability and Probability Distributions
Chapter 5 Probability
5.1 Probability Rules
5.2 The Addition Rule and Complements
5.3 Independence and the Multiplication Rule
5.4 Conditional Probability and the General Multiplication Rule
5.5 Counting Techniques
Case Study: The Case of the Body in the Bag (on CD)
Chapter 6 Discrete Probability Distributions
6.1 Discrete Random Variables
6.2 The Binomial Probability Distribution
Case Study: The Voyage of the St.Andrew (on CD)
Chapter 7 The Normal Probability Distribution
7.1 Properties of the Normal Distribution
7.2 The Standard Normal Distribution
7.3 Applications of the Normal Distribution
7.4 Assessing Normality
7.5 The Normal Approximation to the Binomial Probability Distribution
Case Study: A Tale of Blood, Chemistry, and Health (on CD)
Putting It All Together: Probability
Part IV: Inference: From Samples to Population
Chapter 8 Sampling Distributions
8.1 Distribution of the Sample Mean
8.2 Distribution of the Sample Proportion
Case Study: Sampling Distribution of the Median (on CD)
Chapter 9 Estimating the Value of a Parameter Using Confidence Intervals
9.1 The Logic in Constructing Confidence Intervals about a Population Mean Where the Population Standard Deviation is Known
9.2 Confidence Intervals about a Population Mean in Practice Where the Population Standard Deviation is Unknown
9.3 Confidence Intervals about a Population Proportion
9.4 Putting It All Together:Which Method Do I Use?
Case Study: The Search for a Fire-Safe Cigarette (on CD)
Chapter 10 Testing Claims Regarding a Parameter
10.1 The Language of Hypothesis Testing
10.2 Testing Claims about a Population Mean Assuming the Population Standard Deviation Is Known
10.3 Testing Claims about a Population Mean in Practice
10.4 Testing Claims about a Population Proportion
10.5 Putting It All Together:Which Method Do I Use?
Case Study: How Old Is Stonehenge? (on CD)
Chapter 11 Inference on Two Samples
11.1 Inference about Two Means: Dependent Samples
11.2 Inference about Two Means: Independent Samples
11.3 Inference about Two Population Proportions
11.4 Putting It All Together: Which Method Do I Use?
Case Study: Control in the Design of an Experiment (on CD)
Chapter 12 Additional Inferential Procedures
12.1 Goodness of Fit Test
12.2 Tests for Independence and the Homogeneity of Proportions
12.3 Testing the Significance of the Least-Squares Regression Model
12.4 Confidence and Prediction Intervals
Case Study: Feeling Lucky? Well, Are You? (on CD)
Additional Topics on CD
C.1 Review of Lines
C.2 Confidence Intervals about a Population Standard Deviation
C.3 Testing Claims about the Population Standard Deviation
C.4 One-Way Analysis of Variance
Appendix A Tables
Michael Sullivan III is a Professor of mathematics at Joliet Junior College. He holds graduate degrees from DePaul University in both mathematics and economics. Mike is a very successful textbook author, having co-authored the following texts
His time in the classroom and extensive authoring experience has given him an excellent foundation to write a successful introductory Statistics text: Statistics: Informed Decisions Using Data 2e (2007) and a briefer version of this text, Fundamentals of Statistics, 1/e (2005).
Because Mike's passion is making math more exciting and accessible to students, he is coauthoring a developmental math series for Prentice Hall that will be published in 2007. These titles will include:
Mike is the proud father of three children and when he isn't teaching or writing, he can be found coaching his children's baseball and soccer teams.