query: OLS estimator for regression without intercept
positive_passages.docid: 20900
positive_passages.text: Regression without intercept: deriving $\hat{\beta}_1$ in least squares (no matrices)
negative_passages.docid: 120925
negative_passages.text: Linear regression with strongly non-normal response variable
negative_passages.docid: 73434
negative_passages.text: Assumptions to derive OLS estimator
negative_passages.docid: 31039
negative_passages.text: How can logistic regression have a factorial predictor and no intercept?
negative_passages.docid: 140241
negative_passages.text: Proving OLS unbiasedness without conditional zero error expectation?
negative_passages.docid: 26961
negative_passages.text: How do I calculate standard errors for sums of OLS coefficients?
negative_passages.docid: 73201
negative_passages.text: How to solve collinearity problems in OLS regression?
negative_passages.docid: 106194
negative_passages.text: What's wrong if I fit the auto-regression with OLS?
negative_passages.docid: 20853
negative_passages.text: Efficient online linear regression
negative_passages.docid: 148798
negative_passages.text: Closed form and gradient calculation for linear regression
negative_passages.docid: 132799
negative_passages.text: Constrained Regression in R: coefficients positive, sum to 1 and non-zero intercept
negative_passages.docid: 103272
negative_passages.text: Defining a univariate regression
negative_passages.docid: 109914
negative_passages.text: Generalized Least Squares vs Ordinary Least Squares under a special case
negative_passages.docid: 56923
negative_passages.text: Are estimates of regression coefficients uncorrelated?
negative_passages.docid: 79504
negative_passages.text: Analytical solution of a simple regression with fixed 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: 36807
negative_passages.text: Derivation of OLS Variance