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Probabilities
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This sequence is ideal for students or early data science professionals who want to strengthen their knowledge of fundamental probability and statistics concepts. Mastery of Mathematical Fundamentals is a prerequisite.
- Course related:
- AMA1110 Basic Mathematics I – Calculus and Probability & Statistics
- Subjects:
- Finance and Mathematics and Statistics
- Keywords:
- Probabilities Business mathematics Mathematical statistics
- Resource Type:
- Others
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Courseware
Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you the tools needed to understand data, science, philosophy, engineering, economics, and finance. You will learn not only how to solve challenging technical problems, but also how you can apply those solutions in everyday life.With examples ranging from medical testing to sports prediction, you will gain a strong foundation for the study of statistical inference, stochastic processes, randomized algorithms, and other subjects where probability is needed.
- Course related:
- AMA1501 Introduction to Statistics in Business
- Subjects:
- Mathematics and Statistics
- Keywords:
- Probabilities
- Resource Type:
- Courseware
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e-book
This is a "first course" in the sense that it presumes no previous course in probability. The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. Appendix B provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired. In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which we refer to as m-programs, or simply programs) to solve many important problems in basic probability. This should make the work useful as a stand-alone exposition as well as a supplement to any of several current textbooks. Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternate implementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox, if they desire its aid in evaluating integrals). Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called minterm analysis. This material is not only important for computational purposes, but is also useful in displaying some of the structure of the relationships among events.
- Subjects:
- Mathematics and Statistics
- Keywords:
- MATLAB Textbooks Probabilities
- Resource Type:
- e-book
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Courseware
Second introductory course covering basic principles of probability and statistical inference. Topics: Point estimation, interval estimating, and testing hypotheses, Bayesian approaches to inference.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Probabilities Mathematical statistics
- Resource Type:
- Courseware
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Courseware
Introductory course covering basic principles of probability and statistical inference. Topics covered in this course: Axiomatic definition of probability, random variables, probability distributions, expectation.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Probabilities Mathematical statistics
- Resource Type:
- Courseware