In statistics, a scan statistic or window statistic is a problem relating to the clustering of randomly positioned points. An example of a typical problem is the maximum size of a cluster of points on a line or the longest series of successes recorded by a moving window of fixed length. first published on the problem in the 1960s, and has been called the "father of the scan statistic" in honour of his early contributions. The results can be applied in epidemiology, public health and astronomy to find unusual clusters of events. It was extended by Martin Kulldorff to settings and varying window sizes in a 1997 paper, which is (as of 11 October 2015) the most cited article in its journal, Communications in Statistics – Theory and Methods. Recent results have shown that using scale-dependent critical values for the scan statistic allows to attain asymptotically optimal detection simultaneously for all signal lengths, thereby improving on the traditional scan, but this procedure has been criticized for losing too much power for short signals. Guenther and Perry (2022) considered the problem of detecting an elevated mean on an interval with unknown location and length in the univariate Gaussian sequence model. They explain this discrepancy by showing that these asymptotic optimality results will necessarily be too imprecise to discern the performance of scan statistics in a practically relevant way, even in a large sample context. Instead, they propose to assess the performance with a new finite sample criterion. They presented three new calibration techniques for scan statistics that perform well across a range of relevant signal lengths to optimally increase performance of short signals. The scan-statistic-based methods have been specifically developed to detect rare variant associations in the noncoding genome, especially for the intergenic region. Compared with fixed-size sliding window analysis, scan-statistic-based methods use data-adaptive size dynamic window to scan the genome continuously, and increase the analysis power by flexibly selecting the locations and sizes of the signal regions. Some examples of these methods are Q-SCAN, SCANG, WGScan. (Wikipedia).
This video explains how to determine mean, median and mode. It also provided examples. http://mathispower4u.yolasite.com/
From playlist Statistics: Describing Data
Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
From playlist Statistics (Full Length Videos)
Find x given the z-score, sample mean, and sample standard deviation
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Find x given the z-score, sample mean, and sample standard deviation
From playlist Statistics
Data that are collected for statistical analysis can be classified according to their type. It is important to know what data type we are dealing with as this determines the type of statistical test to use.
From playlist Learning medical statistics with python and Jupyter notebooks
Statistic vs Parameter & Population vs Sample
This stats video tutorial explains the difference between a statistic and a parameter. It also discusses the difference between the population and sample. It includes examples such as the sample mean, population mean, sample standard deviation, population standard deviation, sample propo
From playlist Statistics
In this video I show you how to conduct a t-test, analysis of variance, and linear regression in SPSS.
From playlist Healthcare statistics with SPSS
Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
https://www.patreon.com/ProfessorLeonard Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
From playlist Statistics (Full Length Videos)
Determine if the Given Value is from a Discrete or Continuous Data Set MyMathlab Statistics
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Determine if the Given Value is from a Discrete or Continuous Data Set MyMathlab Statistics
From playlist Statistics
Statistics 5_1 Confidence Intervals
In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.
From playlist Medical Statistics
RailsConf 2019 - Optimizing your app by understanding your PostgreSQL database by Samay Sharma
RailsConf 2019 - Optimizing your app by understanding your PostgreSQL database by Samay Sharma _______________________________________________________________________________________________ Cloud 66 - Pain Free Rails Deployments Cloud 66 for Rails acts like your in-house DevOps team to bu
From playlist RailsConf 2019
PGConf NYC 2021 - PostgreSQL Query Performance Insights by Hamid Quddus Akhtar
PostgreSQL Query Performance Insights by Hamid Quddus Akhtar Understanding query performance patterns are essentially the foundation for query performance tuning. It, in many ways, dictates how a database cluster evolves. And then there are obviously direct and indirect cost connotations
From playlist PGConf NYC 2021
Claudia Kirch: Scan statistics for the detection of anomalies in random fields
CONFERENCE Recording during the thematic meeting : "Adaptive and High-Dimensional Spatio-Temporal Methods for Forecasting " the September 29, 2022 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks
From playlist Probability and Statistics
Understanding the initial steps of eukaryotic protein synthesis and its... by Tanweer Hussain
PROGRAM : STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL (ONLINE) ORGANIZERS : Debashish Chowdhury (IIT Kanpur), Ambarish Kunwar (IIT Bombay) and Prabal K Maiti (IISc, Bengaluru) DATE : 07 December 2020 to 18 December 2020 VENUE :Online 'Fluctuation-and-noise' are themes th
From playlist Statistical Biological Physics: From Single Molecule to Cell (Online)
Richard J. Samworth: High-dimensional, multiscale onlinechangepoint detection
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 05, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
10c Data Analytics: Variogram Introduction
Lecture on the variogram as a measure to quantify spatial continuity.
From playlist Data Analytics and Geostatistics
Sohini Ramachandran: "Genomic Reconstructions of Deep Human History"
Computational Genomics Summer Institute 2016 "Genomic Reconstructions of Deep Human History" Sohini Ramachandran, Brown University Institute for Pure and Applied Mathematics, UCLA July 19, 2016 For more information: http://computationalgenomics.bioinformatics.ucla.edu/
From playlist Computational Genomics Summer Institute 2016
RailsConf 2022 - Puny to Powerful PostgreSQL Rails Apps by Andrew Atkinson
This talk covers 5 challenging areas when scaling Rails applications on PostgreSQL databases. From identifying symptoms to applying solutions and understanding trade-offs, this talk will equip you with practical working knowledge you can apply immediately. This talk covers topics like safe
From playlist RailsConf 2022
#21. Finding the Sample Size Needed to Estimate a Population Proportion using StatCrunch
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys #21. Finding the Sample Size Needed to Estimate a Population Proportion using StatCrunch
From playlist Statistics Final Exam
Conditions for a z test about a proportion | AP Statistics | Khan Academy
Examples showing how to check whether or not the conditions have been met for doing a z test about a proportion. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/tests-significance-ap/one-sample-z-test-proportion/v/conditions-for-a-z-test-about-a
From playlist Significance tests (hypothesis testing) | AP Statistics | Khan Academy