Add hw1, q7 explanation of weights (#6)

Author: frederick-douglas-pearce

_questions/machine-learning-zoomcamp/module-1-homework/008_y97mg42O7d_homework-q7-what-do-the-weights-represent.md

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+---
+id: y97mg42O7d
+question: 'Homework Q7: What do the weights represent?'
+sort_order: 8
+---
+
+The weight vector, `w`, contains the coefficients for a linear model fit between the target variable, `y`, and the input features in `X`, with the model estimate of `y`, `y_est` defined as follows:
+$$ y_{est} = w[0]*X[0] + w[1]*X[1] $$
+where the values in brackets refer to each column of the feature matrix, `X`, and the corresponding row of the weight vector, `w`. Each value in `w` describes the slope of the trend line that fits `y` the best for each feature. As we'll learn in Module 2, least squares yields a "best" fit that minimizes the squared difference between `y` and `y_est`. The weights, `w`, can be checked to see if they're reasonable by multiplying `X` by the weight vector, `w`:
+$$ y_{est} = X.dot(w) $$
+This should produce a vector, `y_est` that is similar, plus or minus some error, to the original target variable, `y`.