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    <title>prediction on Casual Inference</title>
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    <description>Recent content in prediction on Casual Inference</description>
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      <title>woRdle Play</title>
      <link>https://www.casualinf.com/post/2022-08-23-wordle-play/</link>
      <pubDate>Tue, 23 Aug 2022 00:00:00 +0000</pubDate>
      
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      <description>Intro After watching 3Blue1Brown’s video on solving Wordle using information theory, I’ve decided to try my own method using a similar method using probability. His take on using word frequency and combining this with expected information gain quantified by bits for finding the solution was interesting. This is a great approach, especially when playing against a person, who may chose to play a word that’s not in the predefined list of the official Wordle webiste.</description>
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      <title>My First Post</title>
      <link>https://www.casualinf.com/post/first-post/</link>
      <pubDate>Sun, 17 Jun 2018 00:00:00 +0000</pubDate>
      
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      <description>This is the first blog post of my life! I will be using this blog to post about anything that I want to share in statistics. For starter, I will run a linear regression with the iris dataset.
names(iris) ## [1] &amp;quot;Sepal.Length&amp;quot; &amp;quot;Sepal.Width&amp;quot; &amp;quot;Petal.Length&amp;quot; &amp;quot;Petal.Width&amp;quot; &amp;quot;Species&amp;quot; Let’s predict Sepal.Length with Petal.Length and Petal.Width.
#separate into training and testing sets set.seed(1234) train_ind &amp;lt;- sample(nrow(iris), floor(0.8 * nrow(iris))) iris_train &amp;lt;- iris[train_ind,] iris_test &amp;lt;- iris[-train_ind,] #run linear regression iris_lm &amp;lt;- lm(Sepal.</description>
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