Analysis of Wordle's Data Based on a Stepwise Regression Iterative Prediction Model

Abstract

Wordle is currently a popular puzzle game featured daily in the New York Times. Players are required to guess a five-letter word in up to six attempts to solve the puzzle. This paper considers 30 word attributes that affect the percentage. It assigns values to the attributes by means of dummy variables and other methods in order to study the percentage of the number of players who succeed in solving the puzzle at different number of attempts. A stepwise regression model is established to determine the equation of the attributes affecting each percentage. It is found that the number of repeated letters in a word has the greatest impact on the difficulty of guessing the word. Finally, the word EERIE is used as an example for prediction analysis, which is predicted as a difficult puzzle.