02729 2200421 4500999001700000008004100017020001800058082001500076100002100091245002800112250001200140260003900152300004600191365002300237504005100260520142800311650002801739650001501767650001601782650002101798650002701819650002701846650003301873650002001906650002301926650002201949650002501971650003601996650001202032650001902044650002302063650001902086650001902105650001102124650002102135942001202156952013902168 c30263d30263210616b xxu||||| |||| 00| 0 eng d a9780128043172 a519.5bROS aRoss, Sheldon M. aIntroductory Statistics a4th ed. bAcademic Press,c2017aAmsterdam : axxvii, 796 p. ;bill. (some col.),c24 cm b125.00cUSDd77.00 aIncludes bibliographical references and index. aIntroductory Statistics, Fourth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures developed, but also how to understand why these procedures should be used. The text's main merits are the clarity of presentation, contemporary examples and applications from diverse areas, an explanation of intuition, and the ideas behind the statistical methods. Concepts are motivated, illustrated, and explained in a way that attempts to increase one's intuition. To quote from the preface, it is only when a student develops a feel or intuition for statistics that she or he is really on the path toward making sense of data. Ross achieves this goal through a coherent mix of mathematical analysis, intuitive discussions, and examples. Applications and examples refer to real-world issues, such as gun control, stock price models, health issues, driving age limits, school admission ages, use of helmets, sports, scientific fraud, and many others. Examples relating to data mining techniques using the number of Google queries or Twitter tweets are also considered. For this fourth edition, new topical coverage includes sections on Pareto distribution and the 80-20 rule, Benford's law, added material on odds and joint distributions and correlation, logistic regression, A-B testing, and more modern (big data) examples and exercises. aMathematical statistics aStatistics aANOVA tests a; Bayes' theorem aBinomial distributions a Central limit theorem aChi-squared random variables a Control Charts a Stem - Leaf Plots a F - distribution aLeast squares method a Nonparametric hypotheses tests aP value aPlacebo effect aSigned - rank test aRandom Samples a Limit theorem aZ test a Quality Control 2ddccBK 00102ddc406519_500000000000000_ROS70940405aDAIICTbDAIICTd2021-06-16g9625.00l4o519.5 ROSp032574r2024-09-06s2024-09-03yBK