/** Options for `ols`. */
export interface OlsOptions {
    /** Prepend a column of ones (default true). */
    intercept?: boolean;
}
/** Result of `ols`: the coefficients and their inference statistics. */
export interface OlsResult {
    /** Fitted coefficients (intercept first, if included). */
    coefficients: number[];
    /** Standard error of each coefficient. */
    stderr: number[];
    /** t-statistic for each coefficient (H0: coefficient = 0). */
    tValues: number[];
    /** Two-sided p-value for each coefficient's t-statistic. */
    pValues: number[];
    /** Coefficient of determination. */
    r2: number;
    /** R² adjusted for the number of predictors. */
    adjR2: number;
    /** Overall model F-statistic (H0: all slope coefficients = 0). */
    fStat: number;
    /** Residuals y - Xβ. */
    residuals: number[];
}
/**
 * Multiple linear regression `y ≈ Xβ` by ordinary least squares (normal equations),
 * with full inference: standard errors, t/p-values per coefficient, R²/adjusted-R²,
 * the overall model F-statistic, and residuals.
 *
 * @param X - Design matrix (rows = observations, cols = predictors)
 * @param y - Response vector (length = number of observations)
 * @param opts - `intercept` (default true) prepends a column of ones to X
 *
 * @example
 * ols([[1, 1], [2, 0], [3, 1], [4, 0]], [6, 5, 10, 9])
 * // => coefficients ~= [1, 2, 3] (y = 1 + 2*x1 + 3*x2), r2 = 1
 */
export declare function ols(X: number[][], y: number[], opts?: OlsOptions): OlsResult;
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