Probability and Statistics for Engineers and ScientistsThis introduction to probability and statistics for engineering and science students focuses on the fundamental concepts of statistical analysis, not on mathematical details or obscure techniques. The sequence of topics will fit almost all one-semester applied probability and statistics courses. The clear, thorough presentation of basic concepts is balanced by a wealth of applied examples and problems. Numerous in-text examples, problems, and real-life applications and illustrations demonstrate how a variety of computer-based statistical software packages (including Minitab) may be used in statistical analysis. |
Contents
CHAPTER | 10 |
Random Variables | 73 |
Discrete Probability Distributions | 159 |
Copyright | |
18 other sections not shown
Common terms and phrases
accepts the null average balls beta distribution binomial distribution boxplots breaking strength calculated cards conditional probability confidence interval confidence level Construct continuous random variable critical point cumulative distribution function data observations data set defective chips degrees of freedom density function f(x distribution with parameters don't-vote equal event EXAMPLE expectation and variance expected number expected value experimenter exponential distribution gamma distribution given in Figure histogram hypothesis testing hypothesis testing problem illustrated in Figure joint probability larger machine breakdowns median milk containers Minitab Notice null hypothesis obtained outcomes p-value P(ANB plant plausible point estimate Poisson distribution population mean probability density function probability distribution probability mass function probability values proportion quartile sample mean sample space sample standard deviation score shown in Figure simulated standard error standard normal distribution statistic Suppose t-distribution t-statistic tosses two-sided confidence interval underweight Var(X variance o² versus X₁ μο