title: N size for not being considerated leverage
body: <p>Suposse I have a dataset with two variables x and y, with the purpose to run a linear regression y ~ x.
  We have all x values equal and y varying between 1 and 10.
  For example (in R code):</p>
  <pre><code>x &lt;- rep(100, 50)
  y &lt;- runif(50, 1, 10)
  </code></pre>
  <p>If I add a new value, being x value 75, this new value will be considerated a leverage:</p>
  <pre><code>x &lt;- c(x, 75)
  y &lt;- c(y, runif(1, 1, 10))
  fit &lt;- lm(y ~ x)
  im &lt;- influence.measures(fit)
  tail(im$is.inf)
  </code></pre>
  <p>How many 75's I need to add to the dataset for not being considerated a leverage?</p>
  <p>Is there any R package that returns that critical N size?</p>
  <p>Edit after @RuiBarradas comments</p>
  <p>hatvalues with 51 observations (50 100's and 1 75) are:</p>
  <pre><code>&gt; im$infmat[, 6]
     1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
  0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 
    17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
  0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 
    33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48 
  0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 
    49   50   51 
  0.02 0.02 1.00 
  </code></pre>
  <p>What i want to know is how many 75's i have to add so that 75 are not considered leverages, because I'm deleting observations with high leverage from my analysis.</p>
  <p>This have to be done programatically for over 1000 cases.</p>