> Statistics:Informed Decisions Using Data

# Statistics:Informed Decisions Using Data

 Titel: Statistics:Informed Decisions Using Data Reihe: Addison-Wesley Autor: Michael Sullivan Verlag: Pearson Einband: Hardcover Auflage: 2 Sprache: Englisch Seiten: 928 CD Erschienen: März 2006 ISBN13: 9780131871496 ISBN10: 0-13-187149-8 Status: Der Titel ist leider nicht mehr lieferbar. Sorry, This title is no longer available. Malheureusement ce titre est épuisé.

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9781292023953 Statistics: Pearson New International Edition:Informed Decisions UsingData 4 Softcover 07.2013
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98.30

# Statistics:Informed Decisions Using Data

### Description

For algebra-based Introductory Statistics Courses.

This very popular text is written to promote student success while maintaining the statistical integrity of the course. The author draws on his teaching experience and background in statistics and mathematics to achieve this balance. Three fundamental 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) Allowing for flexibility of teaching styles.

Datasets and other resources (where applicable) for this book are available here.

## Features

The second edition of this very successful text adheres closely to the Guidelines for Assessment and Instruction in Statistics Education (GAISE) for the college introductory statistics course (www.amstat.org/education/gaise/) recently endorsed by the American Statistical Association (ASA).  The GAISE Report gives six recommendations for the college introductory statistics course:

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

Features:

1.         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 2.4, 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.

2.         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.

• Among the many interesting data sets are data sets from the authors' students so that students understand that thinking statistically applies to ordinary situations.
• See the Applications Index on pg. xxiv for a full listing.

3.         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.

·        Exercise 14, pg. 109

·        Exercise 25, pg. 132

·        Exercises 21-22, pg. 149

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. #50-52, pg. 465)

III.      The Presentation of Formulas - When formulas are presented, the author uses both a “computational” formula and “definition” formula.

• Pg. 141: Definition and formula for "Sample Variance"; Pg. 142: Example #4 - works the example BOTH ways.

4.         Foster Active Learning in the Classroom - to promote active learning, the author includes:

• Numerous in-class activities that encourage students to collect, summarize and analyze data.  Doing these activities promotes conceptual understanding of statistical con concepts.
• Approximately 2-3 Activities Per chapter.
• Sample Standard Deviation Activity, pg. 143
• Correlation Activity, pg. 202
• Expected Value Activity, pg. 322
• Applets that can be used in the classroom or as a group project Thes are on the text CD. Full listing of applets on pg. xxiii.
• Subset of the applets include, among others: Sampling, Sampling Distribution, long run probability, Central Limit Theorem, Binomial Distribution.
• 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. #50-52, pg. 465)
• End of Chapter Case Study that can be assigned as a small-group project.
• Chapter 5 (pg. 312): The Case of the Body Bag

5.         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.

• Exercise 24 (Section 4.1) asks the student to determine the impact of removing an observation from a data set.  This problem foreshadows the idea of influential observations and is done easily with the help of technology.
• Steps for obtaining output using the graphing calculator, MINITAB and Excel are included at the ends of section: Technology Step-by-Step.
• Output is included, where appropriate, so students get used to "eyeballing" different statistical packages.
• Pg. 128: MINITAB
• Pg. 129: TI-Calculator

6.         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.

• The Concepts and Vocabulary exercises are fill-in-the-blank, and open-ended questions that assess students' understanding of vocabulary and statistical concepts.
• The SkillBuilding exercises can be thought of as “drill and practice” problems that allow students to develop a level of comfort with the formulas and procedures in the section.
• The Applying the Concepts exercises are based in real data that ask a variety of questions to help develop statistical thinking.  While these exercises  do ask the standard questions, such as finding the mean, they also ask the student to explain the results or to respond to a variety of “what if” questions that broaden the scope of the concept and the analysis.

## New to this Edition

New to This Edition

Content Changes

• Over 45% new and updated exercises, with an emphasis on adding even greater variety, to give instructors an even larger range of exercises to choose from in selecting homework. The array of exercises includes many intriguing and meaningful topics, from M&Ms (always popular in statistics books) to the meaning of cholesterol counts, from identity theft to gas mileage, and from election results to cosmetic surgery. An interesting new exercise in chapter 3 based on an experiment by Trina McNamara of Joliet Junior College compares a name-brand chocolate-chip cookie with a store-brand chocolate-chip cookie-one of the many interesting data sets contributed by students to the textbook.
• Over 60% new examples, most of which contain real world data, acting to further illustrate and reinforce concepts and techniques. Like the exercises, the choice of topics is practical, ranges widely, and will hold students' attention.
• All sourced data have been updated and carefully selected to engage the students.
• Each chapters open with a scenario, “Making an Informed Decision, that poses a question to the student and then, within the chapter, presents the content that is necessary to use statistics to make the Decision. This feature engages the student in the statistical-thinking process and highlights the practicality of statistics.
• [See pg. 120 where the "Decision" is introduced, and pg. 180 where it is analyzed in detail.]
• Using Technology examples show the way that software and calculators assist an analysis, once students have mastered “by hand” calculations. The Using Technology examples also give the students a feeling for statistics in practice, demonstrate when to rely on different devices and software, and show the output or screens to expect.
• See Example 5 (pg. 143), Obtaining the Standard Deviation [by Hand] and Example 6 (pg. 144), Determining the Variance and Standard Deviation Using Technology.
• Chapter 5 on probability has been revamped to ease the introduction of topics in probability. The early sections take greater care in delineating disjoint events and the complement rule. The concept of independence gets fuller coverage in this new edition, too. At the request of reviewers, a new and optional topic has been added to the CD, Bayes's rule.
• To cater to different teaching styles, as much or as little (just section 5.1) probability can be covered.
• Updated and rewritten Chapter 6 on discrete probability models addresses reviewers' concerns. Mike Sullivan focused especially on enhancing coverage of the binomial probability distribution. At the request of reviewers, the binomial tables have been added to the appendixes.  Also at the request of reviewers, the Hypergeometric Probability Distribution has been added to the CD.
• New Chapter 8 on sampling distributions was added to strengthen the coverage of this important topic-which tends to cause students to stumble. The decision to give more play to sampling distributions by placing them in their own chapter was heartily endorsed by the reviewers of this new edition.
• Chapters 9 and 10 continue to discuss confidence intervals and hypothesis testing about a mean by presenting the “s known use z, s unknown use t” approach. The author finds that students benefit from some discussion of confidence intervals and hypothesis testing when the population standard deviation is known, even if such situations seldom arise in practice. In both of these chapters, Mike Sullivan presents the sigma-known case as a model or template, followed by sections on tests more likely to be used in practice.
• Furthering comprehension of Confidence Intervals and Hypothesis Testing through new Putting It All Together sections in chapters 9 and 10.  These concepts are typically challenging for students and they have difficulty determining WHICH technique to apply.  These new sections require that students first determine the appropriate confidence interval or hypothesis test to conduct. This forces students to think about when each technique should be used, which is typically a stumbling block on exams.
• Section 9.5 (Confidence Intervals)
• Sectiono 10.6 (Hypothesis Testing)
• New Chapter 13 on comparing three or more means reflects adopters' and reviewers' need for a separate chapter on analysis of variance. This chapter now incorporates the reliable Tukey test. The last section gives the instructor the option of delving into two-way ANOVA, too.
• New Chapter 14 offers more complete coverage of regression analysis. Section 14.3 is a new introduction to multiple linear regression for courses that include this topic.

Pedagogical Changes

• New side-by-side presentation of hypothesis testing (chapter 10), which allows instructors the flexibility for presenting the classical approach, P-value approach, or both approaches. Recognizing that there are advocates of each of these two approaches, this format allows an instructor to choose the method that suits the philosophy of the course. [ See Example #1, pg. 533-534]
• Activities have been added to each chapter, giving instructors the flexibility to engage the class in an activity or assign them for homework.  There are approximately 2-3 activities per chapter. Use of activities promotes participation in the course by the students, makes concepts more vivid, and underscores the value of learning and using statistical techniques. [see Activity on Sample. Standard Deviation, pg. 143; Activity on Correlation, pg. 202; Activity on Expected Value, pg. 322).
• More emphasis on statistical thinking and interpretation of results appears throughout the chapters. Mike Sullivan added more follow-up questions to exercises to require consideration and interpretation of what the students have calculated. Further, examples and exercises encourage the use of boxplots and normal probability plots so that students can use descriptive statistics throughout the text to interpret data and understand the workings of techniques. The author also redoubled his effort to include screen captures and computer printouts so that students will learn to “eyeball” and interpret results when using technology.
• A division into parts makes the structure of the book more transparent to instructors and to the students. The parts show the student that the order of topics is logical and practical by following the statistical process.
• MyStatLab, offering course management, online homework and tutorial and assessment, is fully integrated with the text.  Since 2001, over one million students have done better in math and statistics with  MyMathLab and MyStatLab!

Part I: Getting the Information You Need

Chapter 1   Data Collection

1.1 Introduction to the Practice of Statistics

1. Define statistics

2. Understand the process of statistics

3. Distinguish between qualitative and quantitative variables

4. Distinguish between discrete and continuous variables

1.2 Observational Studies, Experiments, and Simple Random Sampling

1. Distinguish between an observational study and an experiment

2. Obtain a simple random sample

1.3 Other Effective Sampling Methods

1. Obtain a stratified sample

2. Obtain a systematic sample

3. Obtain a cluster sample

1.4 Sources of Error in Sampling

1. Understand how error can be introduced during sampling

1.5 The Design of Experiments

1. Define designed experiment

2. Understand the steps in designing an experiment

3. Understand the completely randomized design

4. Understand the randomized block design

5. Understand the matched-pairs design

Chapter Review

Case Study:

Part II: Descriptive Statistics

Chapter 2   Organizing and Summarizing Data from One Variable

2.1 Organizing Qualitative Data

1. Organize qualitative data in tables

2. Construct bar graphs

3. Construct pie charts

2.2 Organizing Quantitative Data - The Popular Displays

1. Organize discrete data in tables

2. Construct histograms of discrete data

3. Organize continuous data in tables

4. Construct histograms of continuous data

5. Draw stem-and-leaf plots

6. Identify the shape of a distribution

2.3 Additional Displays of Quantitative Data

1. Construct frequency polygons

2. Create cumulative frequency and relative frequency tables

3. Construct frequency and relative frequency ogives

4. Draw time series graphs

2.4 Graphical Misrepresentations of Data

1. Describe what can make a graph misleading or deceptive

Chapter Review

Case Study

Chapter 3   Numerically Describing Data from One Variable

3.1 Measures of Central Tendency

1. Determine the arithmetic mean of a variable from raw data

2. Determine the median of a variable from raw data

3. Determine the mode of the variable from raw data

4. Use the mean and the median to help identify the shape of a distribution

3.2 Measures of Dispersion

1. Compute the range of a variable from raw data

2. Compute the variance of a variable from raw data

3. Compute the standard deviation of a variable from raw data

4. Use the Empirical Rule to describe data that are bell-shaped

5. Use Chebyshev's inequality to describe any set of data

3.3 Measures of Central Tendency and Dispersion from Grouped Data

1. Approximate the mean of a variable from grouped data

2. Compute the weighted mean

3. Approximate the standard deviation of a variable from grouped data

3.4 Measures of Position

1. Determine and interpret z-scores

2. Determine and interpret percentiles

3. Determine and interpret quartiles

4. Check a set of data for outliers

3.5 The Five-Number Summary and Boxplots

1. Compute the five-number summary

2. Draw and interpret boxplots

Chapter Review

Case Study

Chapter 4   Describing the Relation between Two Variables

4.1 Scatter Diagrams and Correlation

1. Draw scatter diagrams

2. Interpret scatter diagrams

3. Understand the properties of the linear correlation coefficient

4. Compute and interpret the linear correlation coefficient

4.2 Least-Squares Regression

1. Find the least-squares regression line

2. Interpret the slope and y-intercept of the least-squares regression line

3. Predict the value of the response variable using the least-squares regression line

4. Determine the values of the residuals

5. Compute the sum of squared residuals

4.3 Diagnostics on the Least-Squares Regression Line

1. Compute and interpret the coefficient of determination

2. Perform residual analysis on a regression model

3. Identify influential observations

4.4 Nonlinear Regression (On CD)

1. Change exponential expressions to logarithmic expressions and logarithmic expressions to exponential expressions

2. Simplify expressions containing logarithms

3. Use logarithmic transformations to linearize exponential relations

4. Use logarithmic transformations to linearize power relations

Chapter Review

Case Study

Part III: Probability and Probability Distributions

Chapter 5   Probability

5.1 Probability Rules

1. Understand the properties of probabilities

2. Compute and interpret probabilities using the empirical method

3. Compute and interpret probabilities using the classical method

4. Use simulation to obtain probabilities

5. Understand subjective probabilities

5.2 The Addition Rule and Complements

1. Use the Addition Rule for Disjoint Events

2. Use the General Addition Rule

3. Compute the Probability of an Event Using the Complement Rule

5.3 Independence and the Multiplication Rule

1. Understand Independence

2. Use the Multiplication Rule for Independent Events

3. Compute “at least” probabilities

5.4 Conditional Probability

1. Compute Conditional Probabilities

2. Compute Probabilities Using the General Multiplication Rule

5.5 Counting Techniques

1. Solve counting problems using the Multiplication Principle

2. Solve counting problems using permutations

3. Solve counting problems using combinations

4. Solve counting problems using permutations with repetition

5. Compute probabilities involving permutations and combinations

5.6 Bayes's Rule (ON CD)

1. Use the Theorem of Total Probability

2. Use Bayes' Theorem to compute probabilities

Chapter Review

Case Study

Chapter 6   Discrete Probability Distributions

6.1 Discrete Random Variables

1. Distinguish between discrete and continuous random variables

2. Identify discrete probability distributions

3. Construct probability histograms

4. Compute and interpret the mean of a discrete random variable

5. Interpret the mean of a discrete random variable as an expected value

6. Compute the variance and standard deviation of a discrete random variable

6.2 The Binomial Probability Distribution

1. Determine whether a probability experiment is a binomial experiment

2. Compute probabilities of binomial experiments

3. Compute the mean and standard deviation of a binomial random variable

4. Construct binomial probability histograms

6.3 The Poisson Probability Distribution

1. Understand when a probability experiment follows a Poisson process

2. Compute probabilities of a Poisson random variable

3. Find the mean and standard deviation of a Poisson random variable

6.4 The Hypergeometric Probability Distribution (ON CD)

1. Determine whether a probability experiment is a hypergeometric experiment

2. Compute probabilities of hypergeometric experiments

3. Compute the mean and standard deviation of a hypergeometric random variable

Chapter Review

Case Study

Chapter 7   The Normal Probability Distribution

7.1 Properties of the Normal Distribution

1. Understand the uniform probability distribution

2. Graph a normal density curve

3. State the properties of the normal curve

4. Understand the role of area in the normal distribution

5. Understand the relation between a normal random variable and a standard normal random variable

7.2 The Standard Normal Distribution

1. Find the area under the standard normal curve

2. Find z-scores for a given area

3. Interpret the area under a standard normal curve as a probability

7.3 Applications of the Normal Distribution

1. Find and interpret area under a normal curve

2. Find the value of a normal random variable

7.4 Assessing Normality

1. Draw a normal probability plot to assess normality

7.5 The Normal Approximation to the Binomial Probability Distribution

1. Approximate binomial probabilities using the normal model

Chapter Review

Case Study

Part IV: Inference - From Samples to Population

Chapter 8   Sampling Distributions

8.1 Distribution of the Sample Mean

1. Understand the concept of a sampling distribution

2. Compute the mean and standard deviation of a sampling distribution of the mean

3. Compute probabilities of a sample mean for samples obtained from a normal population

4. Compute probabilities of a sample mean for samples obtained from a population that is not normal

8.2 Distribution of the Sample Proportion

1. Compute the mean and standard deviation of a sampling distribution of a proportion

2. Compute probabilities of a sample proportion

Chapter Review

Case Study

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

1. Compute the point estimate of the population mean

2. Construct and interpret a confidence interval about the population mean (assuming the population standard deviation is known)

3. Understand the role of margin of error in constructing a confidence interval

4. Determine the sample size necessary for estimating the population mean within a specified margin of error

9.2 Confidence Intervals about a Population Mean in Practice Where the Population Standard Deviation is Unknown

1. Know the properties of Student's t-distribution

2. Determine t-values

3. Construct and interpret a confidence interval about a population mean

9.3 Confidence Intervals about a Population Proportion

1. Obtain a point estimate for the population proportion

2. Construct and interpret a confidence interval for the population proportion

3. Determine the sample size necessary for estimating a population proportion within a specified margin of error

9.4 Confidence Intervals about a Population Standard Deviation

1. Find critical values for the chi-square distribution

2. Construct and interpret confidence intervals about s2 and s

9.5 Putting It All Together: Which Method Do I Use?

1. Determine the appropriate confidence interval to construct

Chapter Review

Case Study

Chapter 10  Testing Claims Regarding a Parameter

10.1 The Language of Hypothesis Testing

1. Determine the null and alternative hypothesis from a claim

2. Understand Type I and Type II errors

3. Understand the probability of making a Type I or Type II error

4. State conclusions to hypothesis tests

10.2 Testing Claims about a Population Mean Where the Population Standard Deviation Is Known

1. Understand the logic of hypothesis testing

2. Test a claim about a population mean with s known using P-values

3. Test a claim about a population mean with s known using the classical method

4. Test a claim about a population mean with s known using confidence intervals

5. Understand the difference between practical significance and statistical significance

10.3 Testing Claims about a Population Mean in Practice

1. Test a claim about a population mean with s unknown

10.4 Testing Claims about a Population Proportion

1. Test a claim about a population proportion using the normal model

2. Test a claim about a population proportion using the binomial distribution function

10.5 Testing Claims about a Population Standard Deviation

1. Test a claim about a population standard deviation

10.6 Putting It All Together: Which Method Do I Use?

1. Determine the appropriate hypothesis test to perform

10.7 The Probability of a Type II Error and the Power of the Test

1. Determine the probability of making a Type II error

2. Compute the power of the test

Chapter Review

Case Study

Chapter 11  Inference on Two Samples

11.1 Inference about Two Means: Dependent Samples

1. Distinguish between independent and dependent samples

2. Test claims made regarding matched-pairs data

3. Construct and interpret confidence intervals about the population mean difference of matched-pairs data

11.2 Inference about Two Means: Independent Samples

1. Test claims regarding the difference of two means

2. Construct and interpret confidence intervals regarding the difference of two means

11.3 Inference about Two Population Proportions

1. Test claims regarding the difference between two population proportions

2. Construct and interpret confidence intervals for the difference between two population proportions

3. Determine the sample size necessary for estimating the difference between two population proportions within a specified margin of error

11.4 Inference about Two Population Standard Deviations

1. Find critical values of the F-distribution

2. Test claims regarding two population standard deviations

Chapter Review

Case Study

Chapter 12  Inference on Categorical Data

12.1 Goodness of Fit Test

1. Perform a chi-square goodness of fit test

12.2 Contingency Tables and Association

2. Compute the marginal distribution of a variable

3. Use the conditional distribution to identify association among categorical data

12.3 Tests for Independence and the Homogeneity of Proportions

1. Perform chi-square test for independence

2. Perform chi-square test for homogeneity of proportions

Chapter Review

Case Study

Chapter 13  Comparing Three or More Means

13.1 Inference on the Completely Randomized Design (One -way Analysis of Variance)

1. Verify the requirements to perform one-way ANOVA

2. Test a claim regarding three or more means using one-way ANOVA

13.2 Post-hoc Tests on One-way Analysis of Variance

1. Perform the Tukey Test

13.3 Inference on the Randomized Complete Block Design

1. Perform an analysis of variance on the randomized block design

2. Perform the Tukey Test

13.4 Two-way Analysis of Variance

1. Analyze a Two-way Analysis of Variance design

2. Draw interaction plots

3. Perform the Tukey Test

Chapter Review

Case Study

Chapter 14  Inference on the Least-squares Regression Model and Multiple Regression

14.1 Testing the Significance of the Least-squares Regression Model

1. Understand the requirements of the least-squares regression model

2. Compute the standard error of the estimate

3. Verify that residuals are normally distributed

4. Test the claim that a linear relation exists between two variables

5. Construct and interpret a confidence interval about the slope of the least-squares regression model

14.2 Confidence and Prediction Intervals

1. Construct and interpret confidence intervals for a mean response about a predicted value

2. Construct and interpret prediction intervals for an individual observation about a predicted value

14.3 Multiple Linear Regression

1. Obtain the correlation matrix

2. Use technology to find the multiple regression model

3. Interpret the coefficients of the multiple regression model

4. Determine R2 and adjusted R2

5. Perform an F-test for lack of fit using the P-value approach

6. Test individual regression coefficients for significance

7. Construct and interpret confidence and prediction intervals

8. Build a regression model

Chapter Review

Case Study

Chapter 15  Nonparametrics (ON CD)

15.1 An Overview of Nonparametric Statistics

1. Understand the difference between parametric statistical procedures and nonparametric statistical procedures

15.2 Runs Test for Randomness

1. Perform a runs test for randomness

15.3 Inferences about Measures of Central Tendency

1. Conduct a one-sample sign test

15.4 Inferences about the Difference between Two Measures of Central Tendency: Dependent Samples

1. Test a claim regarding the difference between two medians of dependent samples

15.5 Inferences about the Difference between Two Measures of Central Tendency: Independent Samples

1. Test a claim regarding the difference between two medians of independent samples

15.6 Spearman's Rank-Correlation Test

1. Perform Spearman's rank-correlation test

15.7 Kruskal-Wallis Test of One-way Analysis of Variance

1. Test a claim regarding three or more populations using the Kruskal-Wallis test

Chapter Review

Case Study

Appendix A   Tables

Appendix B   Lines (On CD)

1. Calculate and interpret the slope of a line

2. Graph lines given a point and the slope

3. Use the point-slope form of a line; identify horizontal lines

4. Find the equation of a line given two points

5. Write the equation of a line in slope-intercept form

6. Identify the slope and y-intercept of a line from its equation

Index

## Back Cover

“…Sullivan will help my students be more successful.  The writing style and some of the pedagogy seems to go further towards putting the students at ease and alleviating their fears.” Kevin Bodden, Lewis & ClarkCommunity College

"...[Sullivan] finds examples and problems that are current and relevant.  As an instructor that motivates me and in turn the students tend to be more motivated." Rita Kolb, The Community College of BaltimoreCounty

Comprehensive and Valuable Resources

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Using MyMathLab Instructors can:

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• Access the multi-media textbook and all instructor and student supplements.

Using MyMathLab Students can:

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www.mymathlab.com

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Available Fall 2006.

## Author

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

• Precalculus Enhanced with Graphing Utilities 4e (CR 2006)
• College Algebra Enhanced with Graphing Utilities 4e (CR2006)
• Algebra and Trigonometry Enhanced with Graphing Utilities 4e (CR2006)

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:

• Elementary Algebra (CR2007)
• Intermediate Algebra (CR2007)
• Elementary and Intermediate Algebra (CR2007)

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.

Mezbahur Rahman, Ph.D.: Minnesota State University:

“Now-a-days it is difficult to have a distinct pedagogy. But I would say Sullivan is one of among the elites.”

Rita Kolb, Ph.D. Community College of Baltimore County

“. . . the examples and exercises are excellent: relevant and interesting.”

“Sullivan is very readable. . . . He writes more like a statistics instructor, not a math instructor.

[re: real world data] “Sullivan does very well in the use of real data.”

[re: examples] “Sullivan has a style of writing interesting to students and instructors; he finds examples and problems that are current and relevant. As an instructor that motivates me and in turn the students tend to be more motivated.”

Thomas S. Pomykalski, Madison Area Technical College

[re: overall text] “Clear, concise, well written and supported by excellent examples.”

Virginia Parks, Ph.D., Georgia Perimeter College

[re: overall text] “Sullivan has current, topical data and examples-students notice that.”

William Applebaugh, Ph.D., University of Wisconsin - Eau Claire

[re: exercises] “We look for the exercises to graduate from simple to realistic. That is one of the qualities that we like best about Sullivan.”

“. . . it is as modern, if not more modern, in its approach as any other text on the market.”

Daniel C. Weiner, Ph.D., Boston University

“Sullivan is ultra-organized; his treatment is honest - he has the integrity to offer students a truthful version of statistical reality (ideas, attitudes, calculations, abstract definitions as required) without attempting to dumb it down for them.”

“. . . the Sullivan text has given my method of teaching statistics new meaning.”

“Now, my goals were to (1) ensure my students enjoyed the class while they were learning, (2) foster their desire to learning , and (3) reveal to my students that anyone can learn statistics given the right instructor and statistics text. All of my goals were met with the aid of the Sullivan text.”

Arjun Gupta, Ph.D., Bowling Green State University

“. . . Exercises are one of the strengths of this book. Sullivan has lots of exercises and they are good.”

Susan Lenker, Ph.D., Central Michigan University

[re: real world data] “Sullivan does a good job with this and is especially good at asking what the data means . . .”

[re: exercises] “I look to see that the questions progress with levels of difficulty and understanding ending with problems that combine multiple concepts, are based on real problems and that are relevant to students and incorporate the use of available technology. Sullivan does very well in all these areas . . .”

Carol Curtis, Fresno City College

[re: overall assessment] “{Sullivan's} main strengths are its pedagogy, exercise sets, and abundant instructor resources.”

Said E. Said, Ph.D. East Carolina University

“[Sullivan's] writing style & presentation make this textbook very enjoyable to read & easy to follow.”

“All I can say is that Sullivan is among one of my favorites.”

Kevin Bodden, Lewis & Clark Community College

“. . . Sullivan will help my students be more successful. The writing style and some of the pedagogy seems to go further towards putting the students at ease and alleviating their fears.”

“. . . I think that Sullivan makes more of an effort to help students understand 'why' and to appreciate the usefulness of statistics in their everyday life.”

Jill Smith, University of Georgia at Athens

“This is one of the best textbooks I've ever used as far as readability from the students' point of view.”