Statistics-related lists | Outlines of mathematics and logic | Regression analysis

Outline of regression analysis

The following outline is provided as an overview of and topical guide to regression analysis: Regression analysis – use of statistical techniques for learning about the relationship between one or more dependent variables (Y) and one or more independent variables (X). (Wikipedia).

Video thumbnail

Introduction to Regression Analysis

This video introduced analysis and discusses how to determine if a given regression equation is a good model using r and r^2.

From playlist Performing Linear Regression and Correlation

Video thumbnail

What is a Regression Equation?

Link to next video: https://youtu.be/p_fu7gIikxY

From playlist Regression Analysis

Video thumbnail

Intro to Regression Analysis

Overview of regression analysis, linear and multiple regression, and the coefficient of determination.

From playlist Regression Analysis

Video thumbnail

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.

Video thumbnail

Simple Linear Regression Formula, Visualized | Ch.1

In this video, I will guide you through a really beautiful way to visualize the formula for the slope, beta, in simple linear regression. In the next few chapters, I will explain the regression problem in the context of linear algebra, and visualize linear algebra concepts like least squa

From playlist From Linear Regression to Linear Algebra

Video thumbnail

Least squares method for simple linear regression

In this video I show you how to derive the equations for the coefficients of the simple linear regression line. The least squares method for the simple linear regression line, requires the calculation of the intercept and the slope, commonly written as beta-sub-zero and beta-sub-one. Deriv

From playlist Machine learning

Video thumbnail

What are Residuals in Regression?

Brief intro to residuals in regression. What they are and what they look like in relation to a line of best fit. Sum and mean of residuals.

From playlist Regression Analysis

Video thumbnail

Everything Data Science

In this video I will give you the resources you need to learn data science from zero knowledge. We will discuss several programming books and math books that are perfect for beginners who want to acquire the skills to become a data scientist. In particular we will look at books on R, Pytho

From playlist Book Reviews

Video thumbnail

Robust and accurate inference via a mixture of Gaussian and terrors by Hyungsuk Tak

20 March 2017 to 25 March 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru This joint program is co-sponsored by ICTS and SAMSI (as part of the SAMSI yearlong program on Astronomy; ASTRO). The primary goal of this program is to further enrich the international collaboration in the area

From playlist Time Series Analysis for Synoptic Surveys and Gravitational Wave Astronomy

Video thumbnail

Data Science Fundamentals: Linear Regression

In this video, I walk you through a simple linear regression and a multiple linear regression model using the ordinary least squares method. I build on the previous data science fundamental videos. #DataScience #DataScienceFundamentals #LinearRegression #Python Github: https://github.com

From playlist Data Science Fundamentals

Video thumbnail

How to Build a Test Automation Strategy? | Software Testing Training | Edureka

** Test Automation Engineer Masters Program : https://www.edureka.co/masters-program/automation-testing-engineer-training ** In this ‘Test Automation Strategy’ video by Edureka, you will learn about how to make test automation successful with a test automation strategy. Below topics are co

From playlist Software Testing Training Videos | Edureka

Video thumbnail

System Identification: Regression Models

This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and the sparse identification of nonlinear dynamics (SINDy). https://www.eigensteve.com/

From playlist Data-Driven Control with Machine Learning

Video thumbnail

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.

Video thumbnail

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

Video thumbnail

Statistics and Machine Learning

This is an invited presentation delivered at the UK Association for Language Testing and Assessment (UKALTA). #machine learning #statistics #GLM #structural_equation_modeling To support the channel, I would like to invite you to join this channel to get access to perks: https://www.youtu

From playlist Language Assessment & Technology

Video thumbnail

Simplified Machine Learning Workflows with Anton Antonov, Session #3: Quantile Regression (Part 3)

Anton Antonov presents the first session on quantile regression workflows in Wolfram Language.

From playlist Simplified Machine Learning Workflows with Anton Antonov

Video thumbnail

Simplified Machine Learning Workflows with Anton Antonov, Session #2: Quantile Regression (Part 2)

Anton Antonov presents the second session on quantile regression workflows in Wolfram Language.

From playlist Simplified Machine Learning Workflows with Anton Antonov

Video thumbnail

Time Series Forecasting with Machine Learning

INVESTING [1] Webull (You can get 3 free stocks setting up a webull account today): https://a.webull.com/8XVa1znjYxio6ESdff TIMESTAMPS 0:00 Introduction 1:51 Defining Problem 2:50 Understanding the Data 3:18 Analyzing Data (Trend, Seasonality) 4:40 Traditional Timeseries Forecasting (ARIM

From playlist Time Series Forecasting

Video thumbnail

Intro to Linear Regression

Brief intro the the linear regression formula and errors.

From playlist Regression Analysis

Related pages

Polynomial regression | General linear model | Ordered probit | Generalized additive model | Generalized least squares | Semiparametric regression | Outline (list) | Model selection | Condition number | Linear model | Cross-sectional data | Least squares | Cook's distance | Bayesian information criterion | Random effects model | Ordered logit | Ridge regression | Coefficient of determination | Time series | Cross-sectional study | Logistic regression | Poisson regression | Segmented regression | Least absolute deviations | Residual sum of squares | Cointegration | Simple linear regression | Leverage (statistics) | Multinomial probit | Durbin–Watson statistic | Deming regression | Total least squares | Correlation | Autoregressive integrated moving average | Isotonic regression | Cross-validation (statistics) | Numerical methods for linear least squares | Data transformation (statistics) | Total sum of squares | Causality | Statistics | Normality test | Mallows's Cp | Partial regression plot | Trend-stationary process | Mixed model | Partial residual plot | Probit model | Quantile regression | Variance inflation factor | Hannan–Quinn information criterion | DFFITS | Multicollinearity | Cochrane–Orcutt estimation | Smoothing | Regression analysis | Local regression | Autoregressive model | Design of experiments | Outlier | Scheffé's method | Autocorrelation | Explained sum of squares | Akaike information criterion | Nonparametric regression | F-test | Non-linear least squares | Lack-of-fit sum of squares | Autoregressive conditional heteroskedasticity | Goodness of fit | Ordinary least squares | Linear regression | Robust regression | Nonlinear regression | Dependent and independent variables | Studentized residual | Power transform | Conditional expectation | Curve fitting