C#实现的多种预测方法

using System;
using System.Collections.Generic;
using System.Text;
using System.Data;

namespace 铁路运量预测及改扩建辅助程序
{
    class Forecast
    {
        //data有二列，第一列表示x值，第二列表示y值
        
        //一元线性回归分析预测返回相关系数
        public static double LinearRegression（double[] dataout double Aout double B）
        {
            double[] derivedData = new double[data.GetLength（0） + 1 data.GetLength（1） + 3];
            for （int i = 0; i < data.GetLength（0）; i++）
            {
                derivedData[i 0] = data[i 0];   //X
                derivedData[i 1] = data[i 1];   //Y
                derivedData[i 2] = data[i 0] * data[i 1];  //XY
                derivedData[i 3] = data[i 0] * data[i 0];  //XX
                derivedData[i 4] = data[i 1] * data[i 1];  //YY
                derivedData[derivedData.GetLength（0） - 1 0] += derivedData[i 0]; //X的累加
                derivedData[derivedData.GetLength（0） - 1 1] += derivedData[i 1]; //Y的累加
                derivedData[derivedData.GetLength（0） - 1 2] += derivedData[i 2]; //XY的累加
                derivedData[derivedData.GetLength（0） - 1 3] += derivedData[i 3]; //XX的累加
                derivedData[derivedData.GetLength（0） - 1 4] += derivedData[i 4]; //YY的累加
            }

            double xba = derivedData[derivedData.GetLength（0） - 1 0] / data.GetLength（0）;
            double yba = derivedData[derivedData.GetLength（0） - 1 1] / data.GetLength（0）;
            double Lxx = derivedData[derivedData.GetLength（0） - 1 3] - Math.Pow（derivedData[derivedData.GetLength（0） - 1 0] 2） / data.GetLength（0）;
            double Lyy = derivedData[derivedData.GetLength（0） - 1 4] - Math.Pow（derivedData[derivedData.GetLength（0） - 1 1] 2） / data.GetLength（0）;
            double Lxy = derivedData[derivedData.GetLength（0） - 1 2] - derivedData[derivedData.GetLength（0） - 1 0] * derivedData[derivedData.GetLength（0） - 1 1] / data.GetLength（0）;
            double b = Lxy / Lxx;
            double a = yba - b * xba;
            A = a;
            B = b;
            return Lxy / Math.Sqrt（Lxx * Lyy）;
        }
        public static double LinearRegression（double[] data double x out double r）
        {
            double a;
            double b;
            r = LinearRegression（data out a out b）;
            return a + b * x;
        }
        //幂回归分析预测
        public static double PowerRegression（double[] data out double A out double B）
        {
            double[] derivedData = （double[]）data.Clone（）;
            for （int i = 0; i < derivedData.GetLength（0）; i++）
            {
                for （int j = 0; j < derivedData.GetLength（1）; j++）
                {
                    derivedData[i j] = Math.Log（derivedData[i j]）;
                }
            }
            double a;
            double b;
            double r;
            r = LinearRegression（derivedData out a out b）;
            A = Math.Exp（a）;
            B = b;