Distance sampling is a widely used group of closely related methods for estimating the density and/or abundance of populations. The main methods are based on line transects or point transects. In this method of sampling, the data collected are the distances of the objects being surveyed from these randomly placed lines or points, and the objective is to estimate the average density of the objects within a region. (Wikipedia).
What is quota sampling? Advantages and disadvantages. General steps and an example of how to find a quote sample. 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.creator-spring.
From playlist Sampling
Statistics - Types of sampling
This video will show you the many ways that you could sample. Remember to look for those small differences such as if you are breaking things into groups first. For more videos visit http://www.mysecretmathtutor.com
From playlist Statistics
Statistics Lesson #1: Sampling
This video is for my College Algebra and Statistics students (and anyone else who may find it helpful). It includes defining and looking at examples of five sampling methods: simple random sampling, convenience sampling, systematic sampling, stratified sampling, cluster sampling. We also l
From playlist Statistics
An overview of the most popular sampling methods used in statistics. 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.creator-spring.com/listing/sampling-in-statistics
From playlist Sampling
What is multistage sampling? Examples, including real life examples. Advantages and disadvantages. 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.creator-spring.com/listing/sam
From playlist Sampling
What is purposive (deliberate) sampling? Types of purposive sampling, advantages and disadvantages. 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.creator-spring.com/listing/sam
From playlist Sampling
Statistics: Introduction (12 of 13) Sampling: Definitions and Terms
Visit http://ilectureonline.com for more math and science lectures! We will review a sampling of definitions and terms of statistics: census, sampling frame, sampling plan, judgment sample, probability samples, random samples, systematic sample, stratified sample, and cluster sample. To
From playlist STATISTICS CH 1 INTRODUCTION
Using parallax / triangulation to measure large distances in astronomy: from fizzics.org
Notes to support this lesson are here: https://www.fizzics.org/measuring-large-distances-in-astronomy-by-parallax-triangulation/ The measurement of large distances in astronomy is often imprecise. It is better termed the estimation of distance and it is one of the hardest problems facing a
From playlist My Top Videos
Sampling (4 of 5: Introductory Examples of Stratified Random Sampling)
More resources available at www.misterwootube.com
From playlist Data Analysis
Ximena Fernández - Intrinsic persistent homology via density-based metric learning
38th Annual Geometric Topology Workshop (Online), June 15-17, 2021 Ximena Fernández, Swansea University Title: Intrinsic persistent homology via density-based metric learning Abstract: Typically, persistence diagrams computed from a sample depend strongly on the distance associated to th
From playlist 38th Annual Geometric Topology Workshop (Online), June 15-17, 2021
Intrinsic Persistent Homology [Ximena Fernández]
In this tutorial we study the usage of persistence diagrams in data that lie on a submanifold of the Euclidean space. We highlight the importance of the appropriate choice of distance in the input data, and we explore some estimators of geodesic distances computed from (possibly noisy) sam
From playlist Tutorial-a-thon 2021 Spring
The Mathematics of Signal Processing | The z-transform, discrete signals, and more
Sign up with Dashlane and get 10% off your subscription: https://www.dashlane.com/majorprep STEMerch Store: https://stemerch.com/ This video goes through an overview of what you would learn in a discrete time signals (or digital signal processing) course. Sampling, digital filters, the z-
From playlist Fourier
Hierarchical Clustering - Unsupervised Learning and Clustering
This video is about Hierarchical Clustering - Unsupervised Learning and Clustering
From playlist Machine Learning
Recent advances in high dimensional robust statistics - Daniel Kane
Computer Science/Discrete Mathematics Seminar I Topic: Recent advances in high dimensional robust statistics Speaker: Daniel Kane, University of California, San Diego Date: December 11, 2017 For more videos, please visit http://video.ias.edu
From playlist Mathematics
Interpreting the Sample Mean, Variance and Standard Deviation and their units
Interpreting the Sample Mean, Variance and Standard Deviation and their units
From playlist Exam 1 material
KNN (K Nearest Neighbors) in Python - Machine Learning From Scratch 01 - Python Tutorial
Get my Free NumPy Handbook: https://www.python-engineer.com/numpybook In this Machine Learning from Scratch Tutorial, we are going to implement the K Nearest Neighbors (KNN) algorithm, using only built-in Python modules and numpy. We will also learn about the concept and the math behind t
From playlist Machine Learning from Scratch - Python Tutorials
Iterative Optimisation - Unsupervised Learning and Clustering
This video is about Iterative Optimisation - Unsupervised Learning and Clustering
From playlist Machine Learning
Research Methods 1: Sampling Techniques
In this video, I discuss several types of sampling: random sampling, stratified random sampling, cluster sampling, systematic sampling, and convenience sampling. The figures presented are adopted/adapted from: https://www.pngkey.com/detail/u2y3q8q8e6o0u2t4_population-and-sample-graphic-de
From playlist Research Methods
