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Exercise

Calculating confidence intervals

You have learned about two methods for calculating confidence intervals: the quantile method and the standard error method. The standard error method involves using the inverse cumulative distribution function (inverse CDF) of the normal distribution to calculate confidence intervals. In this exercise, you'll perform these two methods on the Spotify data.

spotify_population, spotify_sample, and bootstrap_distribution are available; pandas and numpy are loaded with their usual aliases, and norm has been loaded from scipy.stats.

Instructions 1/2
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  • 1
    • Generate a 95% confidence interval using the quantile method on the bootstrap distribution, setting the 0.025 quantile as lower_quant and the 0.975 quantile as upper_quant.
  • 2

    Generate a 95% confidence interval using the standard error method from the bootstrap distribution.

    • Calculate point_estimate as the mean of bootstrap_distribution, and standard_error as the standard deviation of bootstrap_distribution.
    • Calculate lower_se as the 0.025 quantile of an inv. CDF from a normal distribution with mean point_estimate and standard deviation standard_error.
    • Calculate upper_se as the 0.975 quantile of that same inv. CDF.
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