query: Deliberately fitting a model without intercept
positive_passages.docid: 11880
positive_passages.text: When is it ok to remove the intercept in a linear regression model?
negative_passages.docid: 25971
negative_passages.text: Fitting piecewise functions
negative_passages.docid: 15444
negative_passages.text: Linear Regression with individual constraints in R
negative_passages.docid: 14701
negative_passages.text: Ridge & LASSO norms
negative_passages.docid: 100267
negative_passages.text: Fit individual-level model on aggregate-level target-data
negative_passages.docid: 62345
negative_passages.text: Linear regression: Intercept isn't significant
negative_passages.docid: 23648
negative_passages.text: How to approximate the student-t CDF at a point without the hypergeometric function?
negative_passages.docid: 33823
negative_passages.text: Forcing smoothness of regression coefficients
negative_passages.docid: 51207
negative_passages.text: How exactly to compute the ridge regression penalty parameter given the constraint?
negative_passages.docid: 38471
negative_passages.text: Is it possible to interpret regression output omitting the intercept?
negative_passages.docid: 134446
negative_passages.text: Convert standardized coefficients to unstandardized (metric) coefficients for linear regression of a standardized independent variable
negative_passages.docid: 33521
negative_passages.text: Including the interaction but not the main effects in a model
negative_passages.docid: 91200
negative_passages.text: Fitting polynomial model to data in R
negative_passages.docid: 85976
negative_passages.text: Implementing linear regression with standardization
negative_passages.docid: 71082
negative_passages.text: How to find a good fit for semi-sinusoidal model in R?
negative_passages.docid: 7779
negative_passages.text: How does centering the data get rid of the intercept in regression and PCA?
negative_passages.docid: 134716
negative_passages.text: Simultaneous fit to three datasets with measurement errors using different models but same parameters