prediction

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. [Read More]

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] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" Let’s predict Sepal.Length with Petal.Length and Petal.Width. #separate into training and testing sets set.seed(1234) train_ind <- sample(nrow(iris), floor(0.8 * nrow(iris))) iris_train <- iris[train_ind,] iris_test <- iris[-train_ind,] #run linear regression iris_lm <- lm(Sepal. [Read More]