title: How to interpret R linear regression when there are multiple factor levels as the baseline?
body: <p>My data has 3 independent variables, all of which are categorical:</p>
  
  <pre><code>condition: cond1, cond2, cond3
  
  population: A,B,C
  
  task: 1,2,3,4,5
  </code></pre>
  
  <p>The dependent variable is the task completion time. I run <code>lm(time~condition+user+task,data)</code> in R and get the following results:</p>
  
  <p><img src="https://i.stack.imgur.com/Vkfow.jpg" alt="enter image description here"></p>
  
  <p>What confuses me is that cond1, groupA, and task1 are left out from the results. From the thread <a href="https://stackoverflow.com/questions/7337761/linear-regression-na-estimate-just-for-last-coefficient/7341074#7341074">linear regression &quot;NA&quot; estimate just for last coefficient</a>, I understand that one factor level is chosen as the "baseline" and shown in the (Intercept) row.</p>
  
  <p>But what if there are multiple factor levels used as the baseline, as in the above case? </p>
  
  <ul>
  <li>Does the (Intercept) row now indicates cond1+groupA+task1?</li>
  <li>What if I want to know the coefficient and significance for cond1, groupA, and task1 individually?</li>
  <li>For example, groupB has an estimated coefficient +9.3349, compared to groupA? Or compared to cond1+groupA+task1?</li>
  </ul>