Probability & StatisticsA developed, complete treatment of undergraduate probability and statistics by a very well known author. The approach develops a unified theory presented with clarity and economy. Included many examples and applications. Appropriate for an introductory undergraduate course in probability and statistics for students in engineering, math, the physical sciences, and computer science.(vs. Walpole/Myers, Miller/Freund, Devore, Scheaffer/McClave, Milton/Arnold) |
Common terms and phrases
A₁ approximation assume assumption binomial distribution c₁ c₂ coin conclude conditional probability confidence interval consists constant critical region denote density f(x determine discrete type distribution F(x elementary events elements entropy equals equation error Example expected values Figure Find the 95 Find the probability follows fq(q function f(x fy(y H₁ hence integral interval estimate joint density k₁ k₂ likelihood function masses ML estimates normal RV null hypothesis observe obtain outcomes p₁ p₂ parameter partition percentile points Poisson Poisson distribution problem Proof random ratio repeated trials RN sequence RVS x sample mean specified staircase function sufficient statistic Suppose takes the values test statistic test the hypothesis theorem tion toss typical sequences unknown variable variance wish to test x₁ x²(n y₁ yields σ² στ