The Dagum distribution (or Mielke Beta-Kappa distribution) is a continuous probability distribution defined over positive real numbers. It is named after Camilo Dagum, who proposed it in a series of papers in the 1970s. The Dagum distribution arose from several variants of a new model on the size distribution of personal income and is mostly associated with the study of income distribution. There is both a three-parameter specification (Type I) and a four-parameter specification (Type II) of the Dagum distribution; a summary of the genesis of this distribution can be found in "A Guide to the Dagum Distributions". A general source on statistical size distributions often cited in work using the Dagum distribution is Statistical Size Distributions in Economics and Actuarial Sciences. (Wikipedia).
The Normal Distribution (1 of 3: Introductory definition)
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From playlist The Normal Distribution
What is the t-distribution? An extensive guide!
See all my videos at http://www.zstatistics.com/videos/ 0:00 Introduction 2:17 Overview 6:06 Sampling RECAP 12:27 Visualising the t distribution 14:24 Calculating values from the t distribution (EXCEL and t-tables!)
From playlist Distributions (10 videos)
What is a Sampling Distribution?
Intro to sampling distributions. What is a sampling distribution? What is the mean of the sampling distribution of the mean? Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creat
From playlist Probability Distributions
(ML 7.7.A1) Dirichlet distribution
Definition of the Dirichlet distribution, what it looks like, intuition for what the parameters control, and some statistics: mean, mode, and variance.
From playlist Machine Learning
Statistics Lecture 6.3: The Standard Normal Distribution. Using z-score, Standard Score
https://www.patreon.com/ProfessorLeonard Statistics Lecture 6.3: Applications of the Standard Normal Distribution. Using z-score, Standard Score
From playlist Statistics (Full Length Videos)
Continuous Probability Distributions - Basic Introduction
This statistics video tutorial provides a basic introduction into continuous probability distributions. It discusses the normal distribution, uniform distribution, and the exponential distribution. The probability is equal to the area under the curve and the total area under the curve is
From playlist Statistics
My #MegaFavNumber - The Bremner-Macleod Numbers
Much better video here: https://youtu.be/Ct3lCfgJV_A
From playlist MegaFavNumbers
Normal Distribution - Harder Questions (1 of 3: Population within given z-scores)
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From playlist The Normal Distribution
We use the Normal Distribution app on ArtofSat.com to show how to find probabilities and percentiles under the normal distribution. We also use the app to explain how the two parameters mu (the mean) and sigma (the standard deviation) determine the shape of the distribution.
From playlist Chapter 6: Distributions
05 Data Analytics: Parametric Distributions
Lecture on parametric distributions, examples and applications. Follow along with the demonstration workflows in Python: o. Interactive visualization of parametric distributions: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Interactive_ParametricDistributions.ipynb o.
From playlist Data Analytics and Geostatistics
Continuous Distributions: Beta and Dirichlet Distributions
Video Lecture from the course INST 414: Advanced Data Science at UMD's iSchool. Full course information here: http://www.umiacs.umd.edu/~jbg/teaching/INST_414/
From playlist Advanced Data Science
Lecture 10 - Statistical Distributions
This is Lecture 10 of the CSE519 (Data Science) course taught by Professor Steven Skiena [http://www.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: http://www.cs.stonybrook.edu/~skiena/519 More information may be found here: http://www
From playlist CSE519 - Data Science Fall 2016
Central Limit Theorem - Sampling Distribution of Sample Means - Stats & Probability
This statistics video tutorial provides a basic introduction into the central limit theorem. It explains that a sampling distribution of sample means will form the shape of a normal distribution regardless of the shape of the population distribution if a large enough sample is taken from
From playlist Statistics
From playlist STAT 200 Video Lectures
QRM 4-2: The Fisher-Tippett and the Pickands-Balkema-de Haan Theorems
Welcome to Quantitative Risk Management (QRM). It is time to discuss the two fundamental theorems of EVT. We will give the necessary information, for their interpretation and use, but we will skip the proofs. Most of all, we will try to connect the two theorems, which give us extremely st
From playlist Quantitative Risk Management
Python for Data Analysis: Probability Distributions
This video covers the basics of working with probability distributions in Python, including the uniform, normal, binomial, geometric, exponential and Poisson distributions. It also includes a discussion of random number generation and setting the random seed. Subscribe: ► https://www.yout
From playlist Python for Data Analysis
ETH Lec 02. Data and Empirics II: Distributions (01/03/2012)
Course: ETH - Collective Dynamics of Firms (Spring 2012) From: ETH Zürich Source: http://www.video.ethz.ch/lectures/d-mtec/2012/spring/363-0543-00L/b0cfc537-1b86-4d4c-88c3-ce932c1156c1.html
From playlist ETH Zürich: Collective Dynamics of Firms (Spring 2012) | CosmoLearning.org Finance
Анализ Социальных Сетей. Лекция 2.Степенные законы распределения
Слайды: http://www.leonidzhukov.net/hse/2014/socialnetworks/lectures/lecture2.pdf Степенное распределение. Масштабно-инвариантные сети (scale-free networks). Распределение Парето, нормализация, моменты. Закон Ципфа.Граф ранк-частота. Power laws. Power law distribution. Scale-free networ
From playlist Анализ Социальных Сетей. Курс НИУ ВШЭ
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions