Why not approach classification through regression?
86 "..approach classification problem through regression.." by "regression" I will assume you mean linear regression, and I will compare this approach to the "classification" approach of fitting a logistic …
DFBETA in regression model diagnostics of influential points
Feb 4, 2025 · Belsley (1980) mentioned how DFBETA are calculated for linear regression models "DFBETA values are usually calculated via equations that relate the least-squares fit of a …
In linear regression, when is it appropriate to use the log of an ...
Aug 24, 2021 · This is because any regression coefficients involving the original variable - whether it is the dependent or the independent variable - will have a percentage point change interpretation.
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 for all points combined can't be "correct" if the four …
Regression with multiple dependent variables? - Cross Validated
Nov 14, 2010 · Is it possible to have a (multiple) regression equation with two or more dependent variables? Sure, you could run two separate regression equations, one for each DV, but that doesn't …
When is it ok to remove the intercept in a linear regression model ...
Hence, if the sum of squared errors is to be minimized, the constant must be chosen such that the mean of the errors is zero.) In a simple regression model, the constant represents the Y-intercept of the …
How should outliers be dealt with in linear regression analysis ...
What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?
regression - What does it mean to regress a variable against another ...
Dec 4, 2014 · When we say, to regress Y Y against X X, do we mean that X X is the independent variable and Y the dependent variable? i.e. Y = aX + b Y = a X + b.
What is the difference between 'regular' linear regression and deep ...
Dec 27, 2016 · 19 I want to know the difference between linear regression in a regular machine learning analysis and linear regression in "deep learning" setting. What algorithms are used for linear …
What's the difference between logistic regression and perceptron?
Jul 20, 2015 · 4 You can use logistic regression to build a perceptron. The logistic regression uses logistic function to build the output from a given inputs. Logistic function produces a smooth output …