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Methods Exercises and Applications Exercises
The end-of-section exercises are split into two pails. Methods and Applications. The Methods exercises require students to use the formulas and make the necessary computations. The Applications exercises require students to use the chapter material in real-world situations. Thus, students first focus on the computational "nuts and bolts" and then move on to the subtleties of statistical application and interpretation.
Margin Annotations and Notes and Comments
Margin annotations that highlight key points and provide additional insights for the student are a key feature of this text. These annotations, which appear in the margins, are designed to provide emphasis and enhance understanding of the terms and concepts being presented in the text.
At the end of many sections, we provide Notes and Comments designed to give the
student additional insights about the statistical methodology and its application. Notes and Comments include warnings about or limitations of the methodology, recommendations for application, brief descriptions of additional technical considerations, and other matters.
Data Files Accompany the Text
Over 200 data files accompany this text. Data files are provided in Excel format and step-by-step instructions on how to open Excel files in JMP are provided in Appendix 1.1. Files for use with R are provided in comma-separated-value (CSV) format for easy loading into the R environment. Step-by-step instructions for importing CSV files into R are provided in MindTap Reader Appendix R 1.2.
The data files can be accessed from WebAssign within the resources section, directly within the MindTap Reader by clicking on the DATAfile icon, or online directly at www.cengage.com/decisionsciences/anderson/sbe/14e.
The end-of-section exercises are split into two pails. Methods and Applications. The Methods exercises require students to use the formulas and make the necessary computations. The Applications exercises require students to use the chapter material in real-world situations. Thus, students first focus on the computational "nuts and bolts" and then move on to the subtleties of statistical application and interpretation.
Margin Annotations and Notes and Comments
Margin annotations that highlight key points and provide additional insights for the student are a key feature of this text. These annotations, which appear in the margins, are designed to provide emphasis and enhance understanding of the terms and concepts being presented in the text.
At the end of many sections, we provide Notes and Comments designed to give the
student additional insights about the statistical methodology and its application. Notes and Comments include warnings about or limitations of the methodology, recommendations for application, brief descriptions of additional technical considerations, and other matters.
Data Files Accompany the Text
Over 200 data files accompany this text. Data files are provided in Excel format and step-by-step instructions on how to open Excel files in JMP are provided in Appendix 1.1. Files for use with R are provided in comma-separated-value (CSV) format for easy loading into the R environment. Step-by-step instructions for importing CSV files into R are provided in MindTap Reader Appendix R 1.2.
The data files can be accessed from WebAssign within the resources section, directly within the MindTap Reader by clicking on the DATAfile icon, or online directly at www.cengage.com/decisionsciences/anderson/sbe/14e.
作者簡介
Dr. David R. Anderson is a leading author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati.
Dr. Dennis J. Sweeney is a leading author, Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati.
Dr. Thomas A. Williams is both a prominent author and Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology.
Dr. Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business Analytics in the School of Business at Wake Forest University.
James J. Cochran is Associate Dean for Research, Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama.
Michael J. Fry is Professor and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati.
Jeffrey W. Ohlmann is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa.
Dr. Dennis J. Sweeney is a leading author, Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati.
Dr. Thomas A. Williams is both a prominent author and Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology.
Dr. Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business Analytics in the School of Business at Wake Forest University.
James J. Cochran is Associate Dean for Research, Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama.
Michael J. Fry is Professor and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati.
Jeffrey W. Ohlmann is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa.
序
●TRUSTED TEAM OF DISTINGUISHED AUTHORS ENSURES THE MOST ACCURATE, PROVEN PRESENTATION. Prominent leaders and active consultants in business and statistics, authors David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran, Michael J. Fry and Jeffrey W. Ohlmann deliver an accurate, real-world presentation of statistical concepts you can trust with every edition.
●SYSTEMATIC APPROACH EMPHASIZES PROVEN METHODS AND APPLICATIONS. Students first develop a computational foundation and thoroughly master the use of techniques before moving to statistical application and interpretation of the value of techniques. Methods Exercises at the end of each section stress computation and use of formulas, while Application Exercises require students to apply what they know about statistics to real-world problems
●SYSTEMATIC APPROACH EMPHASIZES PROVEN METHODS AND APPLICATIONS. Students first develop a computational foundation and thoroughly master the use of techniques before moving to statistical application and interpretation of the value of techniques. Methods Exercises at the end of each section stress computation and use of formulas, while Application Exercises require students to apply what they know about statistics to real-world problems
目次
1. Data and Statistics.
2. Descriptive Statistics: Tabular and Graphical Displays.
3. Descriptive Statistics: Numerical Measures.
4. Introduction to Probability.
5. Discrete Probability Distributions.
6. Continuous Probability Distributions.
7. Sampling and Sampling Distributions.
8. Interval Estimation.
9. Hypothesis Tests.
10. Inference about Means and Proportions with Two Populations.
11. Inferences about Population Variances.
12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit.
13. Experimental Design and Analysis of Variance.
14. Simple Linear Regression.
15. Multiple Regression.
Appendix A: References and Bibliography.
Appendix B: Tables.
Appendix C: Summation Notation.
Appendix D: Answers to Even-Numbered Exercises. (MindTap Reader)
Appendix E: Microsoft Excel 2016 and Tools for Statistical Analysis.
Appendix F: Computing p-Values with JMP and Excel.
2. Descriptive Statistics: Tabular and Graphical Displays.
3. Descriptive Statistics: Numerical Measures.
4. Introduction to Probability.
5. Discrete Probability Distributions.
6. Continuous Probability Distributions.
7. Sampling and Sampling Distributions.
8. Interval Estimation.
9. Hypothesis Tests.
10. Inference about Means and Proportions with Two Populations.
11. Inferences about Population Variances.
12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit.
13. Experimental Design and Analysis of Variance.
14. Simple Linear Regression.
15. Multiple Regression.
Appendix A: References and Bibliography.
Appendix B: Tables.
Appendix C: Summation Notation.
Appendix D: Answers to Even-Numbered Exercises. (MindTap Reader)
Appendix E: Microsoft Excel 2016 and Tools for Statistical Analysis.
Appendix F: Computing p-Values with JMP and Excel.
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