# Questions tagged [least-squares]

Refers to a general estimation technique that selects the parameter value to minimize the squared difference between two quantities, such as the observed value of a variable, and the expected value of that observation conditioned on the parameter value. Gaussian linear models are fit by least squares and least squares is the idea underlying the use of mean-squared-error (MSE) as a way of evaluating an estimator.

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### LS classifier that is based on the sum of error squares criterion

Is it possible given a set of points from two classes to determine the LS classifier that is based on the sum of error squares criterion? I don't ask if it is efficient if it is possible and if yes ...
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### Does standard error affected by the coefficient?

I make a comparison on ridge regression and OLS using simulation. As i set my correlation as 0.9, which is high, i expect the standard error of ridge regression to be low. However, it is not. ...
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### Intuition behind White's Estimators/ Heteroscedasticity-consistent Standard Errors

For a medical study I am trying to understand the intuition behind heteroscedasticity-consistent standard errors. I know that it can be used, when in OLS regression residuals are heteroscedastic. By ...
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### Obtain global linear regression estimate from subsamples

I want to estimate $\widehat\beta$ in a simple linear regression with scikit. $$y = X \beta + \varepsilon$$ The problem is that the dimension of the complete $X$ is too large to fit into memory. Is ...
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### Comparing the mean of predicted values for a misspecified model against the mean of the observed values

I have a regression that I have run on average ratings for some products (dependent variable) and their characteristics (Model 1). I have reason to believe there is a prejudice against a specific set ...
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### How to verify the “random sampling” Gauss-Markov Assumption with Stata (or anything else)?

According to the book I am using, Introductory Econometrics by J.M. Wooldridge, there are 5 Gauss-Markov assumptions necessary to obtain BLUE. However, by looking in other literature, there is one ...
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### linear regression predicts lower than expected

I am trying to predict first term GPA for college students based on a number of incoming factors (high school gpa, placement test, year). This isn't the overall model just a simpler one. The first ...
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### Minimize Logged Sum of Squares?

When numerically maximizing the likelihood function it is standard practice to do this indirectly by minimizing the negative log-likelihood. When numerically minimizing the residual sum of squares (...
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### Endogeneity - Omitted variable bias in OLS

If a have a true model $y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_3 + \epsilon$ but $x_3$ is unobservable. What are the consequences of having a unobservable variable which correlates ...
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### Linear least squares algorithms

I have stumbled across these two questions and accepted answers: (1) Do we need gradient descent to find the coefficients of a linear regression model? (2) Why use gradient descent for linear ...
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### Diff-in-Diff special type of OLS?

Is difference-in-differences just a special type of OLS? Can I add fixed effects in my diff-in-diff model?
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### Lagged Dependent variable in OLS

I have a question about one of my models. I am sorry if I am using Terms wrongly, as I am part of the management research field and this quite often leads to different terminologies. I try to model ...
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### Errors and residuals in linear regression

I think in common literature about statstics the authors are often very imprecise when it comes to residuals and errors. So far, I could not work that difference out completely and therefore have ...
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