query: Computation of R-squared with lm() in R
positive_passages.docid: 13616
positive_passages.text: Removal of statistically significant intercept term increases $R^2$ in linear model
negative_passages.docid: 79397
negative_passages.text: How to do a generalized linear model with multiple dependent variables in R?
negative_passages.docid: 82864
negative_passages.text: Curve fitting in R. L-shaped?
negative_passages.docid: 42773
negative_passages.text: How to minimize residual sum of squares of an exponential fit?
negative_passages.docid: 147094
negative_passages.text: What is the "$R^2$" value given in the summary of a coxph model in R
negative_passages.docid: 68257
negative_passages.text: Computing p-value using bootstrap with R
negative_passages.docid: 84693
negative_passages.text: Interpreting linear regression with NA for estimates
negative_passages.docid: 105482
negative_passages.text: Interpreting summary function for lm model in R
negative_passages.docid: 136457
negative_passages.text: Difference in scaling of Linear Discriminant Analysis coefficients between manual calculation and R
negative_passages.docid: 152881
negative_passages.text: Using R's lm on a dataframe with a list of predictors
negative_passages.docid: 53854
negative_passages.text: Within, between or overall R-square for random effects in Stata
negative_passages.docid: 92621
negative_passages.text: Software package to solve L-infinity norm linear regression
negative_passages.docid: 116463
negative_passages.text: R linear regression issue : lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...)
negative_passages.docid: 116581
negative_passages.text: Quadratic models with R. The use of poly(..) and I(..) functions (R-language)
negative_passages.docid: 20900
negative_passages.text: Regression without intercept: deriving $\hat{\beta}_1$ in least squares (no matrices)
negative_passages.docid: 148798
negative_passages.text: Closed form and gradient calculation for linear regression
negative_passages.docid: 130496
negative_passages.text: How do I fit a constrained regression in R so that coefficients total = 1?