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Manually predicting house prices

You can manually calculate the predictions from the model coefficients. When making predictions in real life, it is better to use .predict(), but doing this manually is helpful to reassure yourself that predictions aren't magic - they are simply arithmetic.

In fact, for a simple linear regression, the predicted value is just the intercept plus the slope times the explanatory variable.

$$\text{response} = \text{intercept} + \text{slope} * \text{explanatory}$$

mdl_price_vs_conv and explanatory_data are available.

This exercise is part of the course

Introduction to Regression with statsmodels in Python

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Exercise instructions

  • Get the coefficients/parameters of mdl_price_vs_conv, assigning to coeffs.
  • Get the intercept, which is the first element of coeffs, assigning to intercept.
  • Get the slope, which is the second element of coeffs, assigning to slope.
  • Manually predict price_twd_msq using the formula, specifying the intercept, slope, and explanatory_data.
  • Run the code to compare your manually calculated predictions to the results from .predict().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Get the coefficients of mdl_price_vs_conv
coeffs = ____

# Get the intercept
intercept = ____

# Get the slope
slope = ____

# Manually calculate the predictions
price_twd_msq = ____
print(price_twd_msq)

# Compare to the results from .predict()
print(price_twd_msq.assign(predictions_auto=mdl_price_vs_conv.predict(explanatory_data)))
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