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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
- 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
- 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
- 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 model 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
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