Practical Statistics by Example Using Microsoft Excel and Minitab

by Sincich, Terry L.; Levine, David M.; Stephan, David F.

• ISBN13: 9780130415219
• ISBN10: 0130415219
• Edition: 2nd
• Format: Paperback
• Copyright: 2001-07-24
• Publisher: Pearson

Summary

This book integrates technology into the practical introduction of statistics both Microsoft Excel and MINITAB are incorporated as tools for data analysis. These Excel and MINITAB tutorials give users access to step-by-step instructions and screen shots for using the software to perform the statistical techniques presented in the chapter. Real-world applications and critical thinking skills are emphasized throughout that will allow readers to realize greater success in the workplace.Reorganized content Rank tests are integrated throughout, dot plots added in Chapter 2, cumulative binomial tables added to appendix, section on the normal approximation to the binomial distribution added to Chapter 6, and goodness-of-fit test of multinomial category probabilities added to Chapter 8.For use as an introduction to statistics reference with a background in college algebra.

Table of Contents

Microsoft Excel Primer.
MINITAB Primer.
1. Introduction: Statistics and Data.

What Is Statistics? Types of Data. Descriptive vs. Inferential Statistics. Collecting Data. Random Sampling. Other Types of Samples. Ethical Issues and Other Concerns in Statistical Applications. What Readers Need to Know about Microsoft Excel, the PHStat Add-In, and Minitab for this textbook.

2. Exploring Data with Graphs and Tables.

The Objective of Data Description. Describing a Single Qualitative Variable: Frequency Tables, Bar Graphs and Pie Charts. Describing a Single Qualitative Variable: Frequency Tables, Dot Plots, Stem-and-Leaf Displays and Histograms. Exploring the Relationship between Two Qualitative Variables: Cross-Classification Tables and Side-by-Side Bar Charts. Exploring the Relationship between Two Quantitative Variables: Scatterplots. Proper Graphical Presentation.

3. Exploring Quantitative Data with Numerical Descriptive Measures.

Objectives of Numerical Descriptive Measures. Summation Notation. Measures of Central Tendency: Mean, Median, and Mode. Measures of Variation: Range, Variance, and Standard Deviation. Interpreting the Standard Deviation. Measures of Relative Standing: Percentiles and z-scores. Methods for Detecting Outliers. A Measure of Association: Correlation. Numerical Descriptive Measures for Populations.

4. Probability: Basic Concepts.

The Role of Probability in Statistics. Experiments, Events, and The Probability of an Event. Probability Rules for Mutually Exclusive Events. The Combinatorial Rule for Counting Simple Events. Conditional Probability and Independence. The Additive and Multiplicative Laws of Probability.

5. Discrete Probability Distributions.

Random Variables. Probability Models for Discrete Random Variables. The Binomial Probability Distribution. The Poisson Probability Distribution. The Hypergeometric Probability Distribution.

6. Normal Probability Distributions.

Probability Models for Continuous Random Variables. The Normal Probability Distribution. Descriptive Methods for Assessing Normality. The Normal Approximation to the Binomial Distribution. Sampling Distributions. The Sampling Distribution of the Sample Mean and the Central Limit Theorem.

7. Estimation of Population Parameters Using Confidence Intervals: One Sample.

Point Estimators. Estimation of a Population Mean: Normal (z) Statistic. Estimation of a Population Mean: Student's (t) Statistic. Estimation of a Population Proportion. Choosing the Sample Size. Estimation of a Population Variance.

8. Testing Hypotheses about Population Parameters: One Sample.

The Relationship between Hypothesis Tests and Confidence Intervals. Hypothesis-Testing Methodology: Forming Hypotheses. Hypothesis-Testing Methodology: Test Statistics and Rejection Regions. Guidelines for Determining the Target Parameter. Testing a Population Mean. Reporting Test Results: p-Values. Testing a Population Proportion. Testing a Population Variance. Testing Category Probabilities for a Qualitative Variable. Potential Hypothesis-Testing Pitfalls and Ethical Issues.

9. Inferences about Population Parameters: Two Samples.

Determining the Target Parameter. Comparing Two Population Means: Independent Samples. Comparing Two Population Means: Matched Pairs. Comparing Two Population Proportions: Independent Samples. Comparing Two Population Proportions: Contingency Tables. Comparing Two Population Variances. A Nonparametric Test for Comparing Two Populations: Independent Samples. A Nonparametric Test for Comparing Two Populations: Matched Pairs.

10. Regression Analysis.

Introduction to Regression Models. The Straight-Line Model Simple Linear Regression. Estimating and Interpreting the Model Parameters. Model Assumptions. Measuring Variability around the Least Squares. Inferences about the Slope. Inferences about the Correlation Coefficient. The Coefficient of Determination. Using the Model for Estimation and Prediction. Computations in Simple Linear Regression. Residual Analysis: Checking the Assumptions. Multiple Regression Models. A Nonparametric Test for Rank Correlation. Pitfalls in Regression and Ethical Issues.

11. Analysis of Variance.

Experimental Design. ANOVA Fundamentals. Completely Randomized Designs: One-Way ANOVA. Follow-Up Analysis: Multiple Comparisons of Means. Factorial Designs: Two-Way ANOVA. Checking ANOVA Assumptions. A Nonparametric Test for Comparing Populations: Independent Samples.

Appendix A: Review of Arithmetic and Algebra.
Appendix B: Statistical Tables.
Appendix C: Documentation for CD-ROM Data Files.
Appendix D: Microsoft Excel Configuration and Customization.
Appendix E: More about Phstat2.

Excerpts

Educational Philosophy In our many years of teaching introductory statistics courses at the University of South Florida and Baruch College, we have continually searched for ways to improve the teaching of these courses. Our vision for teaching these introductory statistics courses has been shaped by active participation in a series of professional conferences as well as the reality of serving a diverse group of students at a large university. Over the years, our vision has come to include these principles: Students need a frame of reference when learning about a subject, especially one that is not their major. That frame of reference for introductory statistics students should be the various areas in which statistics can be applied, including business, biology, education, engineering, mathematics, political science, psychology, and sociology. Each statistical topic needs to be related to at least one of these areas of application. Virtually all the students taking introductory statistics courses are majoring in areas other than statistics. Introductory courses should focus on underlying principles that are important for non-statistics majors. The use of spreadsheet and/or statistical software should be integrated into all aspects of the introductory statistics course. The reality that exists in the workplace is that spreadsheet software (and sometimes statistical software) is most typically available on the desktop. Our teaching approach needs to recognize this reality and make our courses more consistent with the workplace environment. Textbooks that use software must provide instructions at a depth that maximizes the student''s ability to use the software with a minimum risk of failure. The focus in teaching each topic should be on (1) the application of the topic to a specific problem, (2) the interpretation of results, (3) the presentation of assumptions, (4) the evaluation of the assumptions, and (5) the discussion of what should be done if the assumptions are violated. These points are particularly important in regression and forecasting and in hypothesis testing. Although the illustration of some computations is inevitable, the focus on computations should be minimized. Both classroom examples and homework exercises should relate to actual or realistic data as much as possible. Students should be encouraged to look beyond the statistical analysis of data to the interpretation of results in an applied context, preferably through the use of case studies. This philosophy led us to develop Practical Statistics by Example Using Microsoft Excel and MINITAB. Designed as an introductory text in statistics for students with a background in college algebra, our text contains the following features that distinguish it from the many other statistics texts available. "By Example" Introduction of Concepts Each new idea is introduced and illustrated by real data-based examples taken from a wide variety of disciplines and sources. These examples demonstrate how to solve various types of statistical problems encountered in the real world. We believe that students better understand definitions, generalizations, and concepts afterseeing a real application. Each example is set off for easy identification and contains a full, detailed solution to the problem. H3>Microsoft Excel and MINITAB as Tools for Statistical Analysis The spreadsheet application Microsoft Excel and the statistical software MINITAB are integrated throughout the entire text. Many texts published and revised in the past twenty years have incorporated the use of popular statistical software packages such as SAS, SPSS, and MINITAB. Few, however, have successfully integrated Excel. With the increasing functionality and power of worksheet applications, virtually all kinds of statistical analyses taught in the introductory course can now be supported by Excel and the statistics add-in provided with this text (PHStat2). In addition to its possible use in a statistics course, students Emphasis on Critical Thinking and Interpretation of Computer Output Both Excel- and MINITAB-generated graphs and output accompany every statistical technique presented, allowing instructors to focus on the statistical analysis of data and the interpretation of the results rather than the calculations required to obtain the results. Free from memorizing formulas and performing hand calculations, students are encouraged to develop critical thinking skills that will allow them to realize greater success in the workplace. Examples on hand calculations are provided for those instructors who desire flexibility in teaching the course. Tutorials on Using Microsoft Excel and MINITAB For the novice, The Excel Primerprovides basic instruction on using Windows and Microsoft Excel and The MINITAB Primerprovides basic instruction on using MINITAB. Exceland MINITAB Tutorialsappear at the end of pertinent chapters and give step-by-step instructions and screen shots for using the applications to perform the statistical techniques presented in the chapter. All data sets that are stored in the Excel and MINITAB directories on the CD that accompanies the text are identified with a CD-ROM icon and the name is provided. Statistics Add-In for Microsoft Excel: PHStat The CD-ROM that accompanies the text also includes PHStat2, the latest version of PHStat, Prentice Hall''s statistical add-in for Microsoft Excel for Windows. PHStat2 minimizes the work associated with setting up statistical solutions in Microsoft Excel by automating the creation of worksheets and charts. PHStat2, in combination with Excels Data Analysis Too1PAK add-in and table and chart wizards, allows users to perform statistical analyses on virtually all topics covered in an introductory statistics course. (Compared to its predecessor, PHStat2 contains a number of new or enhanced procedures and now includes a full help system for easy reference. For more information about PHStat2, see Appendix E.) "Statistics in the Real World" Application in Each Chapter Each chapter opens with a real-world application and data set to motivate the material presented in the chapter and to provide a real-life context for learning statistics. The "Statistics in the Real World" problem is revisited throughout the chapter in relevant sections. At the end of each of these sections, the data set is analyzed using the method presented in the section and relevant conclusions are drawn from the analysis. Built-In Study Guide The following features are incorporated throughout the text to help students learn and retain new ideas: Self-test questionsappear immediately after important ideas have been introduced to test the student''s comprehension of the concept and to help develop good study habits. Answers given in the Appendix allow students to check their work. Summary boxesare set off to provide step-by-step instructions for the statistical techniques presented. Side notesprovide additional explanations of key ideas adjacent to where the concept is first referenced. Each chapter ends with a list of Key Terms, Formulas,and Symbolswith page references that guide the student back to the text in order to review the element in context. Each chapter begins with a set of Objectives.Students can determine if the objectives have been met by answering a series of Checking Your Understandingquestions at the end of the chapter. Topical Coverage at the Introductory Level This text includes all the topics covered in a basic introductory statistics course, including data collection (Chapter 1), descriptive statistics (Chapters 2 and 3), probability and proba