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Visualize the Bootstrap

Continuing where we left off earlier in this lesson, let's visualize the bootstrap distribution of speeds estimated using bootstrap resampling, where we computed a least-squares fit to the slope for every sample to test the variation or uncertainty in our slope estimation.

To get you started, we've preloaded a function compute_resample_speeds(distances, times) to do the computation of generate the speed sample distribution.

Deze oefening maakt deel uit van de cursus

Introduction to Linear Modeling in Python

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Oefeninstructies

  • Use the pre-defined compute_resample_speeds(distances, times) to compute the resample_speeds.
  • Use np.mean() to compute the speed_estimate from the resample_speeds.
  • Use np.percentile() with [5, 95] to compute the percentiles of resample_speeds, which define the confidence interval boundaries.
  • Use axis.hist() to plot the resample_speeds, specifying the bins with hist_bin_edges.
  • Using axis.axvline, specify the correct two indices of percentiles to mark the confidence interval boundaries on the chart.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create the bootstrap distribution of speeds
resample_speeds = compute_resample_speeds(____, ____)
speed_estimate = np.mean(____)
percentiles = np.percentile(____, [5, 95])

# Plot the histogram with the estimate and confidence interval
fig, axis = plt.subplots()
hist_bin_edges = np.linspace(0.0, 4.0, 21)
axis.hist(____, ____, color='green', alpha=0.35, rwidth=0.8)
axis.axvline(speed_estimate, label='Estimate', color='black')
axis.axvline(percentiles[____], label=' 5th', color='blue')
axis.axvline(percentiles[____], label='95th', color='blue')
axis.legend()
plt.show()
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