Choice modelling | Dimension reduction
Preference regression is a statistical technique used by marketers to determine consumers’ preferred core benefits. It usually supplements product positioning techniques like multi dimensional scaling or factor analysis and is used to create ideal vectors on perceptual maps. (Wikipedia).
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
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.
(ML 9.2) Linear regression - Definition & Motivation
Linear regression arises naturally from a sequence of simple choices: discriminative model, Gaussian distributions, and linear functions. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
An introduction to Regression Analysis
Regression Analysis, R squared, statistics class, GCSE Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos Playlist on Linear Regression http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C Using SPSS for Multiple Linear Regression http://www.youtube.com/playlist?li
From playlist Linear Regression.
RELATIONSHIPS Between Variables: Standardized Covariance (7-1)
Correlation is a way of measuring the extent to which two variables are related. The term correlation is synonymous with “relationship.” Variables are related when changes in one variable are consistently associated with changes in another variable. Dr. Daniel reviews Variance, Covariance,
From playlist Correlation And Regression in Statistics (WK 07 - QBA 237)
(ML 9.3) Choosing f under linear regression
Deriving the optimal prediction function f(x)=y under square loss. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
QRM 6-2: TS for RM 1 (detrending)
Welcome to Quantitative Risk Management (QRM). How to detrend a time series? Why is it important? Better to use linear regression or to rely on first differences? Let us see together. The R Notebook is available here: https://www.dropbox.com/s/xmjbt6qlb9f9j67/Lesson6.Rmd And here the pd
From playlist Quantitative Risk Management
Singular Learning Theory - Seminar 3 - Neural networks and the Bayesian posterior
This seminar series is an introduction to Watanabe's Singular Learning Theory, a theory about algebraic geometry and statistical learning theory. In this seminar Liam Carroll explains free energy, feedforward neural networks and the role of the Bayesian posterior, and shows some plots of p
From playlist Metauni
Machine learning - Gaussian processes
Regression with Gaussian processes Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de Freitas
From playlist Machine Learning 2013
Introduction to Decision Trees | Decision Trees for Machine Learning | Part 1
The decision tree algorithm belongs to the family of supervised learning algorithms. Just like other supervised learning algorithms, decision trees model relationships, and dependencies between the predictive outputs and the input features. As the name suggests, the decision tree algorit
From playlist Introduction to Machine Learning 101
SPSS Tutorial for data analysis | SPSS for Beginners | Part 2
SPSS Statistics is a software package used for interactive, or batched, statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009. The current versions are named IBM SPSS Statistics. In this course you will how to use SPSS for data analysis. This #SPSS course is begi
From playlist SPSS data Analysis
Digging into Data: Linear and Regularized Regression
Making predictions about real-valued data.
From playlist Digging into Data
STAT501: Information Criteria & PRESS
From playlist STAT 501
Robust Principal Component Analysis (RPCA)
Robust statistics is essential for handling data with corruption or missing entries. This robust variant of principal component analysis (PCA) is now a workhorse algorithm in several fields, including fluid mechanics, the Netflix prize, and image processing. Book Website: http://databoo
From playlist Data-Driven Science and Engineering
Statistics Tutorial : Root Mean Squared Deviation & Chi Square Test
New Data Science / Machine Learning Video Everyday at 1 PM EST!!! [ Click Notification Bell ] I cover the Root Mean Squared Deviation which is the measure of the differences between sample points and the regression line. Then I'll cover Chi Square Tests which allows you to compare the pro
From playlist Statistics Tutorial
Linear regression: Sample Regression Function (SRF, FRM T2-14)
[my xls is here http://trtl.bz/2G8CSN3] In theory, there is one population (and one population regression function). Each sample varies and generates its own sample regression function (SRF). Therefore, the regression coefficients generated by the SRF are random variables; e.g., their stan
From playlist Quantitative Analysis (FRM Topic 2)
Linear classifiers (2): Learning parameters
Perceptron algorithm, logistic regression, and surrogate loss functions
From playlist cs273a
This lesson reviews sources of bias when conducting a survey or poll. Site: http://mathispower4u.com
From playlist Introduction to Statistics
