Univariate is a term commonly used in statistics to describe a type of data which consists of observations on only a single characteristic or attribute. A simple example of univariate data would be the salaries of workers in industry. Like all the other data, univariate data can be visualized using graphs, images or other analysis tools after the data is measured, collected, reported, and analyzed. (Wikipedia).
What is a Unimodal Distribution?
Quick definition of a unimodal distribution and how it compares to a bimodal distribution and a multimodal distribution.
From playlist Probability Distributions
(ML 7.10) Posterior distribution for univariate Gaussian (part 2)
Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.
From playlist Machine Learning
(ML 7.9) Posterior distribution for univariate Gaussian (part 1)
Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.
From playlist Machine Learning
Understanding bivariate and univariate data
From playlist Integrated Algebra Regents
More Standard Deviation and Variance
Further explanations and examples of standard deviation and variance
From playlist Unit 1: Descriptive Statistics
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
(ML 6.2) MAP for univariate Gaussian mean
Computing the MAP for the mean of a univariate Gaussian with a Gaussian prior, assuming a known variance. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
08 Data Analytics: Correlation
Lecture on bivariate statistics and correlation.
From playlist Data Analytics and Geostatistics
Andreas H. Hamel: From set-valued quantiles to risk measures: a set optimization approach to...
Abstract : Some questions in mathematics are not answered for quite some time, but just sidestepped. One of those questions is the following: What is the quantile of a multi-dimensional random variable? The "sidestepping" in this case produced so-called depth functions and depth regions, a
From playlist Probability and Statistics
Efficiently Learning Mixtures of Gaussians - Ankur Moitra
Efficiently Learning Mixtures of Gaussians Ankur Moitra Massachusetts Institute of Technology January 18, 2011 Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We provide a polynomial-time algorithm for this proble
From playlist Mathematics
04 Data Analytics: Univariate Statistics
Lecture on univariate statistics related to distribution central tendency, dispersion and shape. Follow along with the demonstration workflow in Python: o. Examples of calculating univariate statistics: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/PythonDataBasics_Univ
From playlist Data Analytics and Geostatistics
Types Of Data | Statistics & Probability | Maths | FuseSchool
CREDITS Animation & Design: Waldi Apollis Narration: Lucy Billings Script: Lucy Billings Hi, I’m Lucy and in this video, we are going to look at the different types of data that exist and how it can be classified. Starting with data collection... If data is collected by or for the compa
From playlist MATHS
Three approaches for group-level statistics
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #5) Permutation-based statistics
The Assumption of NO OUTLIERS in Parametric Hypothesis Tests (16-4)
Parametric statistical tests require that the dependent variable does not contain unusual or extreme scores. Univariate outliers can be detected using z-scores. The nature of the outlier determines how you should correct it. Multivariate outliers are identified with a Mahalanobis test; you
From playlist Assumptions, Significance, & Effect Size Wrap-Up (WK 16 - QBA 237)
Geostatistics session 5 conditional simulation
Introduction to conditional simulation with Gaussian processes
From playlist Geostatistics GS240
Recorded: Spring 2015 Lecturer: Dr. Erin M. Buchanan Data Screening Video Demo - based on information from Tabachnick and Fidell (2012) and Field (2014). Please note: this video was recorded with Blackboard collaborate - the quality was very reduced because of using this software. Anothe
From playlist Advanced Statistics Videos
Data Science Basics: Univariate Statistics
Live Jupyter walk-through of basic univariate statistics in Python.This should be enough to get anyone started building predictive machine learning workflows in Python. The demonstrated workflow is available at: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/PythonDataBas
From playlist Data Science Basics in Python
How to Handle Outliers in your Dataset in Business Statistics (Week 6B)
Outliers can cause big problems in your data. We learn what causes outliers, how to identify them, the problems they cause, and options for dealign with them. Some outliers should stay in the data. Others should be corrected, winsorized, or sometimes discarded. We explore the difference b
From playlist Basic Business Statistics (QBA 237 - Missouri State University)
Percentiles, Deciles, Quartiles
Understanding percentiles, quartiles, and deciles through definitions and examples
From playlist Unit 1: Descriptive Statistics
Assumptions: Calling Out OUTLIERS – Problems and Causes (6-8)
An Outlier is a rare or extreme high or low score that does not fit the overall pattern of the distribution. Single Items Outliers tend to occur on biometrics and demographics. Univariate Outliers are extreme high or low scores on a single scale. Multivariate Outliers are extreme high or l
From playlist Depicting Distributions from Boxplots to z-Scores (WK 6 QBA 237)