How to describe or visualize a multiple linear regression model
Then this simplified version can be visually shown as a simple regression as this: I'm confused on this in spite of going through appropriate material on this topic. Can someone please explain to …
regression - What does it mean to regress a variable against …
Dec 21, 2016 · When we say, to regress $Y$ against $X$, do we mean that $X$ is the independent variable and Y the dependent variable? i.e. $Y =aX + b$.
Why are regression problems called "regression" problems?
I was just wondering why regression problems are called "regression" problems. What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state."
What's the difference between correlation and simple linear …
Aug 1, 2013 · Note that one perspective on the relationship between regression & correlation can be discerned from my answer here: What is the difference between doing linear regression on …
Can I merge multiple linear regressions into one regression?
Oct 3, 2021 · Although one can compute a single regression for all data points, if you include model assumptions such as i.i.d. normal errors, the model for all points combined can't be …
Derive Variance of regression coefficient in simple linear regression
Derive Variance of regression coefficient in simple linear regression Ask Question Asked 11 years, 9 months ago Modified 2 years, 6 months ago
What is the relationship between R-squared and p-value in a …
Context - I'm performing OLS regression on a range of variables and am trying to develop the best explanatory functional form by producing a table containing the R-squared values …
distributions - What are the myths associated with linear …
Feb 5, 2022 · I have been encountering many assumptions associated with linear regression (especially ordinary least squares regression) which are untrue or unnecessary. For example: …
machine learning - How to determine the accuracy of regression?
Mar 22, 2015 · I have problem with defining the unit of accuracy in a regression task. In classification tasks is easy to calculate sensitivity or specificity of classifier because output is …
Deciding between a linear regression model or non-linear …
There are four principal assumptions which justify the use of linear regression models for purposes of inference or prediction: (i) linearity and additivity of the relationship between …