Statistics for Engineers

BASIC IDEAS Introduction Basic rules of uncertainty and variability BASIC TOOLS Representing data variability Representing belief uncertainty Graphical Summaries THE BAYESIAN APPROACH The learning process Implementing the learning process STANDARD PROBLEMS WITH ONE UNKNOWN Learning from count data Learning from measurement data STANDARD PROBLEMS WITH MORE THAN ONE UNKNOWN Many sample problems Simple Regression Simple contingency tables SPECIAL TOPICS Business applications of multiple regression Business applications of time series models Quality Decision trees MATHEMATICAL APPENDICES