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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.
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."
How to describe or visualize a multiple linear regression model
I'm trying to fit a multiple linear regression model to my data with couple of input parameters, say 3.
regression - When is R squared negative? - Cross Validated
With linear regression with no constraints, R2 R 2 must be positive (or zero) and equals the square of the correlation coefficient, r r. A negative R2 R 2 is only possible with linear regression when either the intercept or the slope are constrained so that the "best-fit" line (given the constraint) fits worse than a horizontal line.
What is the lasso in regression analysis? - Cross Validated
LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value of coefficients of regression.
correlation - What is the difference between linear regression on y ...
The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). This suggests that doing a linear regression of y given x or x given y should be the ...
Newest 'regression' Questions - Cross Validated
Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization
Sample size for logistic regression? - Cross Validated
Sample size calculation for logistic regression is a complex problem, but based on the work of Peduzzi et al. (1996) the following guideline for a minimum number of cases to include in your study can be suggested.
Transforming variables for multiple regression in R
I am trying to perform a multiple regression in R. However, my dependent variable has the following plot: Here is a scatterplot matrix with all my variables (WAR is the dependent variable): I know ...
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.
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