安裝中文字典英文字典辭典工具!
安裝中文字典英文字典辭典工具!
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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 - Whats the difference between multiple R and R squared . . .
$\begingroup$ So if in a multiple regression R^2 is 76, then we can say the model explains 76% of the variance in the dependent variable, whereas if r^2 is 86, we can say that the model explains 86% of the variance in the dependent variable?
- correlation - What is the difference between linear regression on y . . .
The insight that since Pearson's correlation is the same whether we do a regression of x against y, or y against x is a good one, we should get the same linear regression is a good one It is only slightly incorrect, and we can use it to understand what is actually occurring
- 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 - When is R squared negative? - Cross Validated
Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values Hence, it must be non-negative For simple OLS regression with one predictor, this is equivalent to the squared correlation between the predictor and the dependent variable -- again, this must be non-negative $\endgroup$ –
- Whats the difference between correlation and simple linear regression . . .
Regression is a much more useful method, with results which are clearly related to the measurement obtained The strength of the relation is explicit, and uncertainty can be seen clearly from confidence intervals or prediction intervals"
- Regression with multiple dependent variables? - Cross Validated
Here, the suggestion is to do two discrete steps in sequence (i e , find weighted linear composite variables then regress them); multivariate regression performs the two steps simultaneously Multivariate regression will be more powerful, as the WLCV's are formed so as to maximize the regression
- 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
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