Function welch_alt_hyp_ci 

pub fn welch_alt_hyp_ci(
    moments_x: &SampleMoments,
    moments_y: &SampleMoments,
    alt_hyp: AltHyp,
    alpha: f64,
) -> Result<Ci, StatsError>

Welch’s confidence interval for the difference of means (μ(X) - μ(Y)) of two distributions.

Arguments:

• moments_x: first sample’s moments struct.
• moments_y: second sample’s moments struct.
• alt_hyp: alternative hypothesis.
• alpha: confidence level = 1 - alpha.

Errors

Returns an error in any of the following conditions:

• moments_x.n() <= 1.
• moments_y.n() <= 1.
• moments_x.stdev() == 0 AND moments_y.stdev() == 0.
• alpha not in interval (0, 1).

Examples found in repository

examples/aok.rs (line 29)

fn main() {
    let x = [14., 15., 15., 15., 16., 18., 22., 23., 24., 25., 25.];
    let y = [
        10., 12., 14., 15., 18., 22., 24., 27., 31., 33., 34., 34., 34.,
    ];

    let moments_x = SampleMoments::from_slice(&x);
    let moments_y = SampleMoments::from_slice(&y);
    let alt_hyp = AltHyp::Gt;

    {
        println!("*** Ok scenario:");

        let alpha = 0.05;

        // Welch function calls below return Ok prior to invocation of aok().

        let p = welch_p(&moments_x, &moments_y, alt_hyp).aok();
        println!("p={p}");
        let ci = welch_alt_hyp_ci(&moments_x, &moments_y, alt_hyp, alpha).aok();
        println!("ci={ci:?}");
        let test_res = welch_test(&moments_x, &moments_y, alt_hyp, alpha).aok();
        println!("test_res={test_res:?}");
    }

    {
        println!("*** Err scenario:");

        let alpha = 1.0;

        // Welch function calls below return Err prior to invocation of aok().

        let p = welch_p(&moments_x, &SampleMoments::default(), alt_hyp).aok();
        println!("p={p}");
        let ci = welch_alt_hyp_ci(&moments_x, &moments_y, alt_hyp, alpha).aok();
        println!("ci={ci:?}");
        let test_res = welch_test(&moments_x, &moments_y, alt_hyp, alpha).aok();
        println!("test_res={test_res:?}");
    }
}