def summarize_one_branch()

in src/mozanalysis/frequentist_stats/linear_models/functions.py [0:0]

• 12 lines of code
• 2 McCabe index (conditional complexity)

def summarize_one_branch(branch_data: pd.Series, alphas: list[float]) -> Estimates:
    """Inferences (point estimate and confidence intervals) for
    the mean of a single branch's data. Constructs confidence
    intervals from central limit theory (uses the t-distribution)

    Parameters:
    - branch_data (pd.Series): the vector of observations from a
    single branch.
    - alphas (list[float]): the desired confidence levels

    Returns:
    - result (pd.Series): the resulting inferences on the mean. Has
    the following elements in its index (assuming `alphas=[0.01,0.05]`):
      - 'mean': the point estimate of the mean
      - '0.5': also the point estimate, included for backwards compatibility
    with prior bootstrap implementations
      - '0.005': the lower bound of the 99% confidence interval
      - '0.025': the lower bound of the 95% confidence interval
      - '0.975': the upper bound of the 95% confidence interval
      - '0.995': the upper bound of the 99% confidence interval
    """
    res = _make_univariate_output(alphas)
    dsw = DescrStatsW(branch_data)
    mean = dsw.mean
    res["0.5"] = mean  # backwards compatibility
    res["mean"] = mean
    for alpha in alphas:
        low, high = dsw.tconfint_mean(alpha)
        low_str, high_str = _stringify_alpha(alpha)
        res[low_str] = low
        res[high_str] = high
    return res