Arbitrary Variables and Probability Models 7. The Normal and different Continuous Distributions 8. Overviews and Sampling 9. Testing Distributions and Confidence Intervals for Proportions Testing Hypotheses about Proportions Certainty Intervals and Hypothesis Tests for Means Progressively About Tests and Intervals Looking at Two Means Derivation for Counts: Chi-Square Tests Derivation for Regression Understanding Residuals Various Regression Building Multiple Regression Models Time Series Analysis Plan and Analysis of Experiments and Observational Studies Quality Control Nonparametric Methods No registration required.
Sharpe, Richard D. De Veaux and Paul F. Business Statistics, Third Edition , by Sharpe, De Veaux, and Velleman , narrows the gap between theory and practice—relevant statistical methods empower business students to make effective, data-informed decisions. Students tell us to their amazement that they actually enjoy the book. More than any other textbook, Business Statistics emphasizes the need to verify assumptions when using statistical pro-cedures.
We reiterate this focus throughout the examples and exercises. We make every effort to provide templates that reinforce the practice of checking these assumptions and conditions, rather than rushing through the computa-tions.
Business decisions have consequences. Blind calculations open the door to errors that could easily be avoided by taking the time to graph the data, check assumptions and conditions, and then check again that the results and residuals make sense. Our consistent emphasis on the importance of displaying data is evident from the first chapters on understand-ing data to the sophisticated model-building chapters at the end.
Examples often illustrate the value of examining data graphically, and the exercises rein-force this. Good graphics reveal structures, patterns, and occasional anomalies that could otherwise go unnoticed. These patterns often raise new questions and inform both the path of a resulting statistical analysis and the business decisions. Hundreds of new graphics found throughout the book demonstrate that the simple structures that underlie even the most sophisticated statistical inferences are the same ones we look for in the simplest examples.
This helps tie the concepts of the book together to tell a coherent story. Having taught the importance of plotting data and checking assump-tions and conditions, we are careful to model that behavior throughout the book. This consistency helps reinforce these fundamental principles and provides a familiar foundation for the more sophisticated topics. In this book, important concepts, definitions, and sample solutions are not always set aside in boxes.
It never did work as a way to learn about and understand statistics. But the order of these topics and the relative emphasis given to each is not well established. Busi-ness Statistics presents some topics sooner or later than other texts. Although many chapters can be taught in a different order, we urge you to consider the order we have chosen. Each new topic should fit into the growing structure of understanding that students develop throughout the course. For example, we teach inference concepts with proportions first and then with means.
Most people have a wider experience with proportions, seeing them in polls and advertising. We introduce the concepts of association, correlation, and regression early in Business Statistics.
Our experience in the classroom shows that introducing these fundamental ideas early makes statistics useful and relevant even at the beginning of the course. By Chapter 4, students can discuss relationships among variables in a meaningful way. Later in the semester, when we discuss inference, it is natural and relatively easy to build on the fundamental concepts learned earlier and enhance them with inferential methods.
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