Discrete probability distribution

Lectures "Applied statistics for business - Chapter 5: Discrete probability distributions" provides students with the knowledge: Random variables, developing discrete probability distributions, expected value and variance, expected value and variance financial portfolios,... Invite you to refer to the disclosures.

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Lecture "Probability & statistics - Chapter 5: Discrete probability" has contents: Random variable, probability distribution, expected value, variance – standard deviation, bivariate probability, binomial distribution.

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After studying this chapter you will be able to: Introduction to statistics, methods for describing data, probability, discrete probability distributions, the normal probability distribution, confidence interval, hypothesis testing.

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The object (for students) in this course is: To learn how to interpret statistical summaries appearing in journals, newspaper reports, internet, television, etc; to learn about the concepts of probability and probabilistic reasoning; to understand variability and analyze sampling distribution; to learn how to interpret and analyze data arising in your own work (course work or research).

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Chapter 5 - Discrete random variables. After mastering the material in this chapter, you will be able to: Explain the difference between a discrete random variable and a continuous random variable, find a discrete probability distribution and compute its mean and standard deviation, use the binomial distribution to compute probabilities,...

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The lecture "Applied statistics for business" provides students with the knowledge: Introduction to the Course, Course Materials, requirements for Students, Main Contents. Invite you to refer to the disclosures.

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Let A be an n × n matrix, whose entries are independent copies of a centered random variable satisfying the subgaussian tail estimate. We prove that the operator norm of A−1 does not exceed Cn3/2 with probability close to 1. 1. Introduction Let A be an n × n matrix, whose entries are independent, identically distributed random variables. The spectral properties of such matrices, in particular invertibility, have been extensively studied (see, e.g. [M] and the survey [DS]).

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