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Though I'm a while in this field I recognized that I can't say for sure that I understood this basic, very simple equation $ \hat{y} = xw + w_0 $

I know $ w_0 $ denotes the bias term (mostly given as $ b $) which is basically the offset of the function. But how do I understand $ xw $? Does $ x $ represent the feature(s) like speed and acceleration so it would have two dimensions with $ x_i , i=0,1 $ Or does it stand for all the data points of a feature with $ x_i, i=0,..,n $ ? And $ w $ is then exactly, what?

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  • $\begingroup$ Does this answer your question? How to tell the difference between linear and non-linear regression models? $\endgroup$
    – mhdadk
    Commented Jun 5, 2021 at 19:07
  • $\begingroup$ I'm trying to grasp it, it's quite formal. But for X is "X is a vector of numbers" given. So this is not the feature, then but the datapoints itself? $\endgroup$
    – Ben
    Commented Jun 5, 2021 at 22:04
  • $\begingroup$ Answers to this are in evidence in several thousand posts here on CV. Good search terms are normal equations and design matrix. Alternatively, search for multiple regression interpretation. $\endgroup$
    – whuber ♦
    Commented Jun 6, 2021 at 13:54
  • $\begingroup$ Thanks, but I couldn't see a post. Yes, there are a lot of posts which include this but this would mean investing hours of trying to understand all these totally different questions with varying notations and background motivations. I will try finding something on google, instead. $\endgroup$
    – Ben
    Commented Jun 6, 2021 at 16:19
  • $\begingroup$ In case someone is stumbling over this question: towardsdatascience.com/weighted-linear-regression-2ef23b12a6d7 $\endgroup$
    – Ben
    Commented Jun 6, 2021 at 16:52

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