import type { array } from "../types";
/**
 * Linear regression of Y on X.
 *
 * Performs linear regression and returns an object with regression coefficients,
 * R-squared value, and a prediction function.
 *
 * @param y array of dependent variable values
 * @param x array of independent variable values
 * @return Object with beta (slope), alpha (intercept), rsq (R-squared), and prediction function
 *
 * @example Basic linear regression
 * ```ts
 * import { assertEquals } from "jsr:@std/assert";
 *
 * const x = [0.003,0.026,0.015,-0.009,0.014,0.024,0.015,0.066,-0.014,0.039];
 * const y = [-0.005,0.081,0.04,-0.037,-0.061,0.058,-0.049,-0.021,0.062,0.058];
 * const result = linearreg(y,x);
 * assertEquals(Math.round(result.beta * 1000) / 1000, 0.119);
 * assertEquals(Math.round(result.alpha * 1000) / 1000, 0.01);
 *
 * ```
 *
 * @example Linear regression with stronger correlation
 * ```ts
 * import { assertEquals } from "jsr:@std/assert";
 *
 * const result2 = linearreg([100,101,99,102,105],[1,2,3,4,5]);
 * assertEquals(result2.beta, 1.1);
 * assertEquals(result2.alpha, 98.1);
 *
 * ```
 *
 * @example Using the prediction function
 * ```ts
 * import { assertEquals } from "jsr:@std/assert";
 *
 * const result2 = linearreg([100,101,99,102,105],[1,2,3,4,5]);
 *
 * assertEquals(Math.round(result2.fun(6) * 10) / 10, 104.7);
 * ```
 */
export default function linearreg(y: array, x: array): {
    beta: number;
    alpha: number;
    rsq: number;
    fun: (x: number) => number;
};
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