Statistics for Engineering and the Sciences

Statistics for Engineering and the Sciences

<P><EM>Prepare Your Students for Statistical Work in the Real World</EM></P> <P><STRONG>Statistics for Engineering and the Sciences, Sixth Edition</STRONG> is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences. This popular text continues to teach students the basic concepts of data description and statistical inference as well as the statistical methods necessary for real-world applications. Students will understand how to collect and analyze data and think critically about the results.</P> <P><STRONG>New to the Sixth Edition</STRONG></P> <UL> <LI>Many new and updated exercises based on contemporary engineering and scientific-related studies and real data</LI> <LI>More statistical software printouts and corresponding instructions for use that reflect the latest versions of the SAS, SPSS, and MINITAB software</LI> <LI>Introduction of the case studies at the beginning of each chapter</LI> <LI>Streamlined material on all basic sampling concepts, such as random sampling and sample survey designs, which gives students an earlier introduction to key sampling issues</LI> <LI>New examples on comparing matched pairs versus independent samples, selecting the sample size for a designed experiment, and analyzing a two-factor experiment with quantitative factors</LI> <LI>New section on using regression residuals to check the assumptions required in a simple linear regression analysis</LI></UL> <P></P> <P>The first several chapters of the book identify the objectives of statistics, explain how to describe data, and present the basic concepts of probability. The text then introduces the two methods for making inferences about population parameters: estimation with confidence intervals and hypothesis testing. The remaining chapters extend these concepts to cover other topics useful in analyzing engineering and scientific data, including the analysis of categorical data, regression analysis, model building, analysis of variance for designed experiments, nonparametric statistics, statistical quality control, and product and system reliability.</P>