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regression - What does it mean to regress a variable against another ...
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.
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 me how to "explain" a multiple linear regression model and how to visually show it.
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."
Can I merge multiple linear regressions into one regression?
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 individual models are correct (unless in reality they are all equal), because the combined model then can't be a single linear regression but would be a ...
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 regression model. Before we do this, it is important to clarify the distinction between regression and classification models.
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?
Newest 'regression' Questions - Cross Validated
Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization
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 regression line, in unstandardized form.
How to determine which variables are statistically significant in ...
How to determine which variables are statistically significant in multiple regression? Ask Question Asked 13 years, 4 months ago Modified 3 years, 4 months ago
DFBETA in regression model diagnostics of influential points
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 model calculated with...
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