# Linked Questions

14 questions linked to/from What algorithm is used in linear regression?
0answers
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### How does $\vec{\beta}=(H^TH)^{-1}H^T\vec{y}$ equivalent to least squares criteria for evaluating splines? [duplicate]

I'm learning about splines and the equation for a spline trying to predict the true function given data points is expressed as $$f(x)=\sum^k_{m=1}\beta_mh_m(x)$$ Where $\beta_m$ is some linear ...
0answers
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### How does Polynomial Regression work? [duplicate]

My input data ...
0answers
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### OLS estimators for a linear log function [duplicate]

So I am doing some linear log functions of the form $Y_t=\beta_0 + \beta_1*log(t) + X_t$ where $X_t$ is white noise I have been using R to do the least squares estimations and that easy enough to ...
7answers
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### Why use gradient descent for linear regression, when a closed-form math solution is available?

I am taking the Machine Learning courses online and learnt about Gradient Descent for calculating the optimal values in the hypothesis. h(x) = B0 + B1X why we ...
5answers
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### How to derive the least square estimator for multiple linear regression?

In the simple linear regression case $y=\beta_0+\beta_1x$, you can derive the least square estimator $\hat\beta_1=\frac{\sum(x_i-\bar x)(y_i-\bar y)}{\sum(x_i-\bar x)^2}$ such that you don't have to ...
2answers
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### Least Squares Regression Step-By-Step Linear Algebra Computation

As a prequel to a question about linear-mixed models in R, and to share as a reference for beginner/intermediate statistics aficionados, I decided to post as an independent "Q&A-style" the steps ...
2answers
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### Full-Rank design matrix from overdetermined linear model

I'm trying to create a full-rank design matrix X for a randomized block design model starting from something like the example from page 3/8 of this paper . It's been suggested that I can go about ...
2answers
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### sklearn Linear Regression vs Batch Gradient Descent

tldr: Why would sklearn LinearRegression give a different result than gradient descent? My understanding is that LinearRegression is computing the closed form solution for linear regression (...
2answers
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### How is the linear regression optimize in R and Python?

I am currently working a lot with R and Python. I am not able to access the C code the the R function lm_fit. I am wondering how is the linear regression optimize in R and python ? I am pretty sure ...
1answer
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### Linear algebra use case [closed]

I learning some machine learning course, and I would like to know in which case we use linea algebra and Matrix Algebra? Thank you Kind regards
1answer
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### Computing overhead of statistical models for training?

Could someone provide overhead of the following model for training (With respect to input size or if there are any relevant parameters). Overhead I mean somewhat like asymptotic time complexity form. ...
1answer
78 views

### Algorithm for simple linear regression that is efficient and numerically stable

I'm developing an application that is fed with continuous data while older data is discarded. I'm using some algorithms to compute simple linear regression on these data with Perl. Basically that ...
1answer
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### The best line fit can be found analytically by the least squares method. So can we say that linear regression (least squares) has an optimizer?

The best line fit can be found analytically by the least squares method. So can we say that linear regression (least squares) has an optimizer? For example, for logistic regression I can use an ...
1answer
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### Computing β in multiple regression (the coefficients)

In my book I have here that $\hat\beta=(X'X)^{-1}X'Y$, and that's fine and dandy, but I have a maybe dumb question regarding this. So these $β$s are the coefficients that we must obtain from our ...