title: Interpretation of .L, .Q., .C, .4… for logistic regression
body: <p>I've done a good amount of googling and the explanations either don't make any sense or they say just use factors instead of ordinal data. I understand that the ``.L<code>is linear,</code>.Q` is quadratic, ... etc. But I don't know how to actually say what it means. So for example let's say</p>
  
  <pre><code>Primary.L     7.73502       0.984
  Primary.Q     6.81674       0.400
  Primary.C     -4.07055      0.450
  Primary^4     1.48845       0.600
  </code></pre>
  
  <p>where the first column is the variable, second is the estimate, and the third is the p-value. What would I be saying about the variables as they increase in order? Is this basically saying what model I would use so this would be <code>7.73502x + 6.81674x^2 - 4.07055x^3</code> is how the model is? Or would it just include quadratic? All of this is so confusing. If anyone could shine a light into how to interpret these <code>.L</code>, <code>.Q</code>, <code>.C</code>, etc., that would be fantastic. </p>
  
  <h2>example</h2>
  
  <pre><code>&gt; summary(glm(DEPENDENT ~ Year, data = HAVE, family = "binomial"))
  
  Call:
  glm(formula = DEPENDENT ~ Year, family = "binomial", data = HAVE)
  
  Deviance Residuals: 
      Min       1Q   Median       3Q      Max  
  -0.3376  -0.2490  -0.2155  -0.1635   3.1802  
  
  Coefficients:
               Estimate Std. Error  z value Pr(&gt;|z|)    
  (Intercept) -3.572966   0.028179 -126.798  &lt; 2e-16 ***
  Year.L      -2.212443   0.150295  -14.721  &lt; 2e-16 ***
  Year.Q      -0.932844   0.162011   -5.758 8.52e-09 ***
  Year.C       0.187344   0.156462    1.197   0.2312    
  Year^4      -0.595352   0.147113   -4.047 5.19e-05 ***
  Year^5      -0.027306   0.135214   -0.202   0.8400    
  Year^6      -0.023756   0.120969   -0.196   0.8443    
  Year^7       0.079723   0.111786    0.713   0.4757    
  Year^8      -0.080749   0.103615   -0.779   0.4358    
  Year^9      -0.117472   0.098423   -1.194   0.2327    
  Year^10     -0.134956   0.095098   -1.419   0.1559    
  Year^11     -0.106700   0.089791   -1.188   0.2347    
  Year^12      0.102289   0.088613    1.154   0.2484    
  Year^13      0.125736   0.084283    1.492   0.1357    
  Year^14     -0.009941   0.084058   -0.118   0.9059    
  Year^15     -0.173013   0.088781   -1.949   0.0513 .  
  Year^16     -0.146597   0.090398   -1.622   0.1049    
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  (Dispersion parameter for binomial family taken to be 1)
  
      Null deviance: 18687  on 80083  degrees of freedom
  Residual deviance: 18120  on 80067  degrees of freedom
  AIC: 18154
  
  Number of Fisher Scoring iterations: 7
  </code></pre>