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Mathematical statistics. --- Mathematical statistics --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Estadística matemàtica --- Estadística descriptiva --- Inferència estadística --- Matemàtica estadística --- Mètodes estadístics --- Estadística --- Anàlisi d'error (Matemàtica) --- Anàlisi de regressió --- Anàlisi de sèries temporals --- Anàlisi de variància --- Anàlisi multivariable --- Anàlisi seqüencial --- Astronomia estadística --- Correlació (Estadística) --- Dependència (Estadística) --- Estadística no paramètrica --- Estadística robusta --- Física estadística --- Mètode dels moments (Estadística) --- Models lineals (Estadística) --- Models no lineals (Estadística) --- Teoria de l'estimació --- Teoria de la predicció --- Tests d'hipòtesi (Estadística) --- Biometria --- Mostreig (Estadística)

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Mathematical statistics --- Probabilities

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Many mathematical statistics texts are heavily oriented toward a rigorous mathematical development of probability and statistics, without much attention paid to how statistics is actually used.. In contrast, Modern Mathematical Statistics with Applications, Second Edition strikes a balance between mathematical foundations and statistical practice. In keeping with the recommendation that every math student should study statistics and probability with an emphasis on data analysis, accomplished authors Jay Devore and Kenneth Berk make statistical concepts and methods clear and relevant through careful explanations and a broad range of applications involving real data. The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The next six chapters develop the probability material that bridges the gap between descriptive and inferential statistics. Point estimation, inferences based on statistical intervals, and hypothesis testing are then introduced in the next three chapters. The remainder of the book explores the use of this methodology in a variety of more complex settings. This edition includes a plethora of new exercises, a number of which are similar to what would be encountered on the actuarial exams that cover probability and statistics. Representative applications include investigating whether the average tip percentage in a particular restaurant exceeds the standard 15%, considering whether the flavor and aroma of Champagne are affected by bottle temperature or type of pour, modeling the relationship between college graduation rate and average SAT score, and assessing the likelihood of O-ring failure in space shuttle launches as related to launch temperature. Other features include: - An extensive range of applications that will appeal to a wide audience, including mathematics and statistics majors, prospective engineers and scientists, and business, economics, and quantitative social science students. - Nearly 1,500 exercises to help students master the material and better understand sophisticated concepts and arguments. - An emphasis on the importance of statistical software, including output from the statistical software packages Minitab, R, and SAS.

Mathematical statistics -- Problems, exercises, etc. --- Mathematical statistics. --- Mathematical statistics --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Statistical inference --- Statistics, Mathematical --- Statistical methods --- Statistics. --- Statistics, general. --- Statistical Theory and Methods. --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Statistics .

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Estadística matemática. --- Mathematical statistics.

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This book provides a contemporary and lively postcalculus introduction to the subject of probability.  The exposition reflects a desirable balance between fundamental theory and many applications involving a broad range of real problem scenarios.   It is intended to appeal to a wide audience, including mathematics and statistics majors, prospective engineers and scientists, and those business and social science majors interested in the quantitative aspects of their disciplines.  A one-term course would cover material in the core chapters (1-4), hopefully supplemented by selections from one or more of the remaining chapters on statistical inference (Ch. 5), Markov chains (Ch. 6), stochastic processes (Ch. 7), and signal processing (Ch. 8). The last chapter is specifically designed for electrical and computer engineers, making the book suitable for a one-term class on random signals and noise. Alternatively, there is certainly enough material for those lucky enough to be teaching or taking a year-long course.  Most of the core will be accessible to those who have taken a year of univariate differential and integral calculus; matrix algebra, multivariate calculus, and engineering mathematics are needed for the later, more advanced chapters. One unique feature of this book is the inclusion of sections that illustrate the importance of software for carrying out simulations when answers to questions cannot be obtained analytically; R and Matlab code are provided so that students can create their own simulations.  Another feature that sets this book apart is the Introduction, which addresses the question “Why study probability?” by surveying selected examples from recent journal articles and discussing some classic problems whose solutions run counter to intuition.  The book contains about 1100 exercises, ranging from straightforward to reasonably challenging; roughly 700 of these appear in the first four chapters.  The book’s preface provides more information about our purpose, content, mathematical level, and suggestions for what can be covered in courses of varying duration.

Statistical science --- Operational research. Game theory --- Mathematical statistics --- Probability theory --- Matlab (informatica) --- waarschijnlijkheidstheorie --- stochastische analyse --- statistiek --- kansrekening --- statistisch onderzoek