William M Mendenhall, Terry L Sincich

Statistics for Engineering and the Sciences

• Gebundenes Buch

Produktbeschreibung

Designed for a two-semester introductory course, 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. Along with updated and reorganized material, this sixth edition includes many new and updated exercises based on contemporary engineering and scientific-related studies and real data. It also offers more statistical software printouts and corresponding instructions for use that reflect the latest versions of the SAS, SPSS, and MINITAB software.

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Produktdetails

• Verlag: CRC Press
• 6th edition
• Seitenzahl: 1166
• Erscheinungstermin: 16. Dezember 2015
• Englisch
• Abmessung: 257mm x 206mm x 48mm
• Gewicht: 2359g
• ISBN-13: 9781498728850
• ISBN-10: 1498728855
• Artikelnr.: 42801513

Autorenporträt

William Mendenhall was a professor emeritus in the Statistics Department and the first chairman of the department at the University of Florida. Dr. Mendenhall published articles in top statistics journals and was a prolific author of statistics textbooks. Terry L. Sincich is an associate professor in the Information Systems Decision Sciences Department at the University of South Florida, where he teaches introductory statistics at the undergraduate level and advanced statistics courses at the doctoral level. He has won numerous teaching awards, including the Kahn Teaching Award and Outstanding Teacher Award. Dr. Sincich is a member of the American Statistical Association and the Decision Sciences Institute. His research interests include applied statistical analysis and statistical modeling.

Inhaltsangabe

Introduction. Descriptive Statistics. Probability. Discrete Random
Variables. Continuous Random Variables. Bivariate Probability Distributions
and Sampling Distributions. Estimation Using Confidence Intervals. Tests of
Hypotheses. Categorical Data Analysis. Simple Linear Regression. Multiple
Regression Analysis. Model Building. Principles of Experimental Design. The
Analysis of Variance for Designed Experiments. Nonparametric Statistics.
Statistical Process and Quality Control. Product and System Reliability.