title: Clarifying the aim of linear regression with multiple predictor variables and how to plot using ggplot2
body: <p>I'm trying to learn the intricacies of linear regression for prediction, and I'd like to ask two questions:</p>
  <ol>
  <li><p>I've got one dependent variable (call it X) and, let's say, ten independent variables. I can use <code>lm()</code> to generate a model. But my question is this: is the aim of generating a model (or, more likely, multiple models) to identify the <em>single</em> best predictor of X, or is the aim to discover the best <em>combination of</em> predictors of X? I assumed the latter, but after several hours of reading online I am now unsure.</p>
  </li>
  <li><p>If the aim is to discover the best <em>combination of</em> predictors of X, then (once I've identified that combination) how is a combination plotted properly? Plotting one line is easy, but for a combination would it be proper to (a) plot ten distinct regression lines (one per independent variable) or (b) plot a single line that somehow represents the combination? I've provided the <code>summary()</code> I'm working with in case it facilitates answering this question. <a href="https://i.stack.imgur.com/qyDAN.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/qyDAN.png" alt="enter image description here" /></a></p>
  </li>
  </ol>