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This book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, Large Sample Techniques for Statistics begins with fundamental techniques, and connects theory and applications in engaging ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. The book's case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science. This text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites.

Statistical science --- Operational research. Game theory --- Probability theory --- waarschijnlijkheidstheorie --- stochastische analyse --- statistiek --- kansrekening --- statistisch onderzoek --- Mathematical statistics. --- Sampling (Statistics) --- Mostreig (Estadística)

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This highly accessible and innovative text with supporting web site uses Excel (R) to teach the core concepts of econometrics without advanced mathematics. It enables students to use Monte Carlo simulations in order to understand the data generating process and sampling distribution. Intelligent repetition of concrete examples effectively conveys the properties of the ordinary least squares (OLS) estimator and the nature of heteroskedasticity and autocorrelation. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software. The accompanying web site with text support can be found at www.wabash.edu/econometrics.

Programming --- Quantitative methods (economics) --- Econometrics --- Monte Carlo method --- Microsoft Excel (Computer file) --- Data processing --- 330.115 --- dataverwerking --- econometrie --- regressie-analyse --- softwarepakketten --- spreadsheets --- -330.01518282 --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Stochastic processes --- Economics, Mathematical --- Statistics --- Econometrie --- Microsoft Excel for the Macintosh --- Microsoft Excel for Windows --- Excel (Computer file) --- Excel for Windows --- Microsoft Excel for Windows 95 --- Excel 97 --- Microsoft Excel 97 for Windows --- Excel 2000 --- Excel 2000 for Windows 95 --- Microsoft Excel 2002 --- Microsoft Office Excel 2003 --- Excel 2003 --- Microsoft Excel 2007 --- Excel 2007 --- Excel 2010 --- Microsoft Excel 2013 --- Excel 2013 --- 330.115 Econometrie --- 330.01518282 --- Microsoft Excel 2016 --- Excel 2016 --- Econometrics. --- Data processing. --- Business, Economy and Management --- Economics --- Monte Carlo method - Data processing