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  4. Correlation and Regression Lines - A Quick Recap #2
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Correlation and Regression Lines - A Quick Recap #2

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  • LucyCraft11
     + 0 comments

    Artificial Intelligence is truly transforming the way we live and work every day. Excited to see how it continues to shape the future with smarter solutions and innovations! betinexchange 247 login

  • vfacuu
     + 0 comments

    my solution was 0.208333 but it was "wrong" as it was not rounded, judging by the leaderboard I should've submitted 0.208 instead and that was correct :(

  • NataliaLincoln
     + 0 comments

    The regression line suggests exam score increases by ~4.85 points for each additional study hour.

    The fit looks good visually, and the correlation confirms strong linear association.

    Would you like to try this on a specific dataset or should I explain how residuals or R² come into play? Betguru Login

  • frankeast5
     + 0 comments

    Interesting problem! It's a great way to practice applying regression analysis with real data. Calculating the slope by hand gives good insight into how relationships between variables are analyzed. Thanks for sharing this! Cricbet99

  • ericstricker73
     + 1 comment
    import numpy as np
from sklearn.linear_model import LinearRegression

Physics = [  15,  12,  8,   8,   7,   7,   7,   6,   5,   3]
History = [ 10,  25,  17,  11,  13,  17,  20,  13,  9,   15]

#Sample data
X = np.array(Physics).reshape((-1, 1))
y = np.array(History)

#Create and train the model
model = LinearRegression()
model.fit(X, y)

#Get the slope (coefficient)
slope = model.coef_[0]
slope