754 3754:22-06-26 2026:22-06-26 20:57:44.000 3754:22-06-26 utf8 0 Aax Text txt ohne Hilfsmittel zu benutzen n Band nc 1973530996 OCoLC 1597485060 9783031844812 Kartoniert, Paperback : EUR 106,99 MPIB_drd-67215621 KXP 1973530996 eng rda 2026 https://www.dietmardreier.de/annot/564C427C7C62663433663134343662643334633439623164373866313935313937303632337C7C434F50.jpg?sq=2 H Cover 93 3 Beyond the T-Test Scott A. Pardo Scott A. Pardo VerfasserIn aut Cham Springer International Publishing 298 Seiten bisacsh s COMPUTERS / Artificial Intelligence Dreier bisacsh s COMPUTERS / Mathematical & Statistical Software Dreier bisacsh s MATHEMATICS / Probability & Statistics / General Dreier thema s Maschinelles Lernen Dreier bisacsh s SOCIAL SCIENCE / Statistics Dreier thema s Sozialforschung und -statistik Dreier thema s Wahrscheinlichkeitsrechnung und Statistik Dreier u Bayesian methods Dreier u Hypothesis tests Dreier u Intermediate 2nd Course Dreier u Linear Models Dreier u MCMC Dreier u Machine learning Dreier u Model Building Dreier u Resampling methods Dreier u Sampling Plans Dreier u statistical methods Dreier Chapter 1. Populations, Samples, Parameters, and Statistics.- Chapter 2. Some Probability Concepts.- Chapter 3. Estimation, Hypothesis Testing and the Scientific Method.- Chapter 4. Binary Random Variables and Acceptance Sampling Plans.- Chapter 5. Continuous Variables, the Normal Distribution, and the Central Limit Theorem.- Chapter 6. Continuous Variables and Acceptance Sampling Plans.- Chapter 7. Confidence.- Chapter 8. Some Confidence Interval Computations, Including Bootstrapping.- Chapter 9. Linear Regression, Correlation, and Least Squares.- Chapter 10. Analysis of Variance.- Chapter 11. Poisson and Exponential Variables, Rate, and Time-to-Event.- Chapter 12. 2k Factorial Experiments.- Chapter 13. Nonparametric Methods - Rank-Based Tests, Permutation Tests and Resampling Methods.- Chapter 14. Nonlinear and Logistic Regression.- Chapter 15. Model Building.- Chapter 16. Multivariate Analysis.- Chapter 17. Bayesian Methods - Markov Chain Montel Carlo Approach.- Chapter 18. Machine Learning and Data-Intensive Methods.- Chapter 19. Time Series and Dynamic Systems.- Index. PDA-Print-Projekt ELN 3754. Bitte zu diesem Datensatz keine Mailboxen an ELN 3754 schreiben. Bearbeitung im Rahmen des Handbuchs Nutzung von K10plus-Datensätzen erlaubt. This book was inspired by years of questions asked by non-statistical professionals, from social scientists, public policy analysts, regulatory affairs specialists, engineers, and physical scientists. It provides them with both an intuitive explanation of many common statistical methods and enough mathematical background to help them justify those methods to others, such as regulatory agencies. It provides an introduction to commonly used methods that are not covered in a first elementary statistics course, such as partial least squares, MCMC, and neural networks. It also provides R code for making all the computations described in the text. As a textbook, it could be used as a second course instatistics for non-statisticians, in fields such as social sciences, public policy, engineering, chemistry, and physics. Many first-year graduate students have had an elementary statistics course, but were not exposed to enough of the mathematics to justify the application of those methods. Furthermore, they often encounter methods and concepts not touched upon in their first statistics course. This book provides the tools required to give a deeper understanding of statistical methods without being all about theorems and proofs 754 PICA 22-06-26 22-06-26 20:09:27.000 utf8 22-06-26 csf_mm 1999 4980249825 22-06-26 a Dreier_printPDA_2026 00 PDA 22-06-2026 Artificial Intelligence - Human-Machine Interaction / Kuenstliche Intelligenz - Mensch-Maschine Interaktion pda