A land use regression model (LUR model) is an algorithm often used for analyzing pollution, particularly in densely populated areas. The model is based on predictable pollution patterns to estimate concentrations in a particular area. This requires some linkage to the environmental characteristics of the area, especially characteristics that influence pollutant emission intensity and dispersion efficiency. LUR modeling is a useful approach for screening studies and can substitute for dispersion models given insufficient input data or dispersion models. Multiple regression equations are used to describe the relationship between sample locations and environmental variables, often relying on geographic information systems (GIS) to collect measurements. This results in an equation that can predict pollution concentrations at unmeasured locations based on data for the predictor variables in specific locations. A raster graphic image of the area is generated and intersected with area-level population data to formulate the exposure distribution. (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.
How to calculate Linear Regression using R. http://www.MyBookSucks.Com/R/Linear_Regression.R http://www.MyBookSucks.Com/R Playlist http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C
From playlist Linear Regression.
Logistic Regression Details Pt1: Coefficients
When you do logistic regression you have to make sense of the coefficients. These are based on the log(odds) and log(odds ratio), but, to be honest, the easiest way to make sense of these are through examples. In this StatQuest, I walk you though two Logistic Regression Examples, step-by-s
From playlist StatQuest
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity promoting techniques to select the nonlinear and partial derivative
From playlist Research Abstracts from Brunton Lab
In this video, I show you various methods for linear regression: finding a linear equation that models a given set of data points. First we work through a problem by hand, and then I show you how to find the best-fit line using Microsoft Excel and Google Sheets.
From playlist College Algebra
Linear regression is a cornerstone of data-driven modeling; here we show how the SVD can be used for linear regression. Book PDF: http://databookuw.com/databook.pdf Book Website: http://databookuw.com These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine L
From playlist Data-Driven Science and Engineering
Linear Regression in R | Linear Regression Model in R | R Programming Tutorial | Edureka
** Data Science Certification Course using R: https://www.edureka.co/data-science-r-programming-certification-course ** This R tutorial gives an introduction to Linear Regression in R tool. This R tutorial is specially designed to help beginners. View upcoming batches schedule: http://goo.
From playlist Data Analytics with R Tutorial Videos
Lecture 0205 Features and polynomial regression
Machine Learning by Andrew Ng [Coursera] 02-01 Linear Regression with multiple variables
From playlist Machine Learning by Professor Andrew Ng
Predictive Modelling Techniques | Data Science With R Tutorial
🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=PredictiveModeling-0gf5iLTbiQM&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bo
From playlist R Programming For Beginners [2022 Updated]
Lecture 17: Land Markets (Part 2)
MIT 14.771 Development Economics, Fall 2021 Instructor: Ben Olken View the complete course: https://ocw.mit.edu/courses/14-771-development-economics-fall-2021 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61kvh3caDts2R6LmkYbmzaG This video continues the discussion
From playlist MIT 14.771 Development Economics, Fall 2021
Statistical Rethinking - Lecture 07
Lecture 07, Model Comparison (1), from Statistical Rethinking: A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
Tradeoffs between Robustness and Accuracy - Percy Liang
More videos on http://video.ias.edu
From playlist Mathematics
Apply Logarithm Transformation in Linear Regression: Tutorial Box Cox in R | Rstudio Learning
How to improve my model? How to apply Box Cox and Why? How to implement logarithm transformation in the data? #rstudio #R #tutorial #model -. Learn how to fit a multiple linear regression -. How to create graphs with R -. How to check the model assumptions -. Apply Box Cox Transformation
From playlist Regression with R
Lecture 16: Land Markets (Part 1)
MIT 14.771 Development Economics, Fall 2021 Instructor: Ben Olken View the complete course: https://ocw.mit.edu/courses/14-771-development-economics-fall-2021 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61kvh3caDts2R6LmkYbmzaG Presents simple principal agent mode
From playlist MIT 14.771 Development Economics, Fall 2021
Digging into Data: Linear and Regularized Regression
Making predictions about real-valued data.
From playlist Digging into Data