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- Why are regression problems called regression problems?
Origin of 'regression' The term "regression" was coined by Francis Galton in the 19th century to describe a biological phenomenon The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon also known as regression toward the mean)(Galton, reprinted 1989)
- regression - What does it mean to regress a variable against another . . .
As an example, the data is X = 1, ,100 The value of Y is plotted on the Y axis The red line is the linear regression surface Personally, I don't find the independent dependent variable language to be that helpful Those words connote causality, but regression can work the other way round too (use Y to predict X)
- regression - Trying to understand the fitted vs residual plot? - Cross . . .
In this example, variances for the first quarter of the data, up to about a fitted value of 40 are smaller than variances for fitted values larger than 40 The middle portion of the fitted values has substantially larger variances than the outer values This indicates that the regression model may have failed to account for heteroscedasticity
- regression - What is the correct formula to compute R-squared? - Cross . . .
I'm completely confused about how to calculate R-squared for given lists of predicted and actual values As an example, assume that my predicted values are: [3, 8, 10, 17, 24, 27] and my actual va
- regression - Why do we say the outcome variable is regressed on the . . .
The word "regressed" is used instead of "dependent" because we want to emphasise that we are using a regression technique to represent this dependency between x and y So, this sentence "y is regressed on x" is the short format of: Every predicted y shall "be dependent on" a value of x through a regression technique
- regression - Using MLE vs. OLS - Cross Validated
In nonlinear regression, when is MLE equivalent to least squares regression? Hot Network Questions
- regression - How to Perform Cross-Validation for LASSO in GAMLSS to . . .
I am working with a Generalized Additive Model for Location, Scale, and Shape (GAMLSS) and trying to determine the optimal $\lambda$ values for LASSO-penalized regression using cross-validation However, I am struggling to understand how to properly set up the cross-validation procedure in this context
- regression - how to interpret the interaction term in lm formula in R . . .
It is easiest to think about interactions in terms of discrete variables Perhaps you might have studied two-way ANOVAs, where we have two grouping variables (e g gender and age category, with three levels for age) and are looking at how they pertain to some continuous measure (our dependent variable, e g IQ)
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