我想对一个尺寸为96×100的数据集进行回归分析。列表示天数（100天）的数值，而独立变量是时间。我的目标变量是多列的情况下，如何进行线性回归？样本数据集如下：
time day1 day2 day3 day4 day5 day6 day7 day8 day9 day10 day11 day12 day13 day14 day15 day16 day17 day18 day19 day20 day21 day22 day23 day24 day25 day26 day27 day28 day29 day30 day31 day32 day33 day34 day35 day36 day37 day38 day39 day40 day41 day42 day43 day44 day45 day46 day47 day48 day49 day50 day51 day52 day53 day54 day55 day56 day57 day58 day59 day60 day61 day62 day63 day64 day65 day66 day57 day68 day69 day70 day71 day72 day73 day74 day75 day76 day77 day78 day79 day80 day81 day82 day83 day84 day85 day86 day87 day88 day89 day90 day91 day92 day93 day94 day95 day96 day97 day98 day99 day100500 6.07588E-10 6.13664E-10 5.89361E-10 5.95437E-10 6.31892E-10 6.37968E-10 5.83285E-10 6.01512E-10 5.83285E-10 6.1974E-10 3.03794E-09 -6.07588E-10 -2.43035E-09 1.21518E-09 2.43035E-09 6.07588E-10 6.07588E-10 -1.21518E-09 -1.21518E-09 0 3.03794E-09 1.82276E-09 -1.82276E-09 1.82276E-09 -2.43035E-09 -1.21518E-09 -1.21518E-09 -1.82276E-09 -1.21518E-09 2.43035E-09 1.82276E-09 -2.43035E-09 1.21518E-09 -6.07588E-10 -1.21518E-09 0 -1.21518E-09 1.21518E-09 -2.43035E-09 -2.43035E-09 3.03794E-09 -1.82276E-09 6.07588E-10 -1.82276E-09 3.03794E-09 -2.43035E-09 1.82276E-09 -1.82276E-09 0 0 1.82276E-09 -3.03794E-09 0 3.03794E-09 -1.21518E-09 -1.21518E-09 0 3.03794E-09 1.21518E-09 6.07588E-10 -3.03794E-09 1.21518E-09 3.03794E-09 0 6.07588E-10 -6.07588E-10 -6.07588E-10 1.82276E-09 -3.03794E-09 -1.21518E-09 1.21518E-09 1.82276E-09 1.82276E-09 2.43035E-09 3.03794E-09 1.21518E-09 1.21518E-09 -2.43035E-09 3.03794E-09 0 -1.21518E-09 -1.82276E-09 -1.82276E-09 1.82276E-09 -3.03794E-09 1.82276E-09 0 2.43035E-09 3.03794E-09 -2.43035E-09 -1.21518E-09 6.07588E-10 -1.21518E-09 6.07588E-10 3.03794E-09 0 -2.43035E-09 -1.21518E-09 -1.82276E-09 0515 6.07588E-10 5.89361E-10 6.07588E-10 6.01512E-10 6.25816E-10 6.07588E-10 6.1974E-10 6.37968E-10 5.77209E-10 5.95437E-10 1.82276E-09 -3.03794E-09 0 2.43035E-09 1.21518E-09 -3.03794E-09 -3.03794E-09 -1.82276E-09 2.43035E-09 0 1.82276E-09 3.03794E-09 2.43035E-09 6.07588E-10 1.21518E-09 -2.43035E-09 -6.07588E-10 -1.82276E-09 -1.21518E-09 -2.43035E-09 1.82276E-09 -1.21518E-09 6.07588E-10 6.07588E-10 0 6.07588E-10 3.03794E-09 -3.03794E-09 -1.21518E-09 -1.82276E-09 0 -3.03794E-09 1.21518E-09 -2.43035E-09 -2.43035E-09 -2.43035E-09 1.82276E-09 -1.82276E-09 6.07588E-10 -3.03794E-09 -6.07588E-10 -1.21518E-09 3.03794E-09 -1.82276E-09 -6.07588E-10 -1.21518E-09 1.82276E-09 3.03794E-09 -1.21518E-09 -6.07588E-10 -1.82276E-09 -2.43035E-09 -1.21518E-09 1.82276E-09 3.03794E-09 1.21518E-09 6.07588E-10 -1.82276E-09 2.43035E-09 -3.03794E-09 0 -2.43035E-09 -1.82276E-09 -3.03794E-09 3.03794E-09 3.03794E-09 3.03794E-09 -6.07588E-10 -6.07588E-10 -6.07588E-10 -2.43035E-09 -2.43035E-09 -1.82276E-09 -3.03794E-09 -1.21518E-09 -6.07588E-10 6.07588E-10 -3.03794E-09 -1.82276E-09 6.07588E-10 2.43035E-09 1.82276E-09 1.21518E-09 0 0 1.21518E-09 3.03794E-09 2.43035E-09 6.07588E-10 3.03794E-09530 6.07588E-10 6.01512E-10 6.1974E-10 6.13664E-10 5.95437E-10 6.31892E-10 6.01512E-10 5.77209E-10 6.13664E-10 6.25816E-10 1.82276E-09 2.43035E-09 1.82276E-09 -1.21518E-09 1.82276E-09 2.43035E-09 3.03794E-09 3.03794E-09 2.43035E-09 6.07588E-10 6.07588E-10 -6.07588E-10 2.43035E-09 0 1.82276E-09 6.07588E-10 0 3.03794E-09 -1.82276E-09 3.03794E-09 0 1.82276E-09 1.21518E-09 -2.43035E-09 -2.43035E-09 -3.03794E-09 1.21518E-09 -6.07588E-10 -1.82276E-09 2.43035E-09 3.03794E-09 -1.21518E-09 -6.07588E-10 6.07588E-10 2.43035E-09 0 -6.07588E-10 3.03794E-09 3.03794E-09 -1.82276E-09 3.03794E-09 1.82276E-09 6.07588E-10 0 -2.43035E-09 -3.03794E-09 -6.07588E-10 -2.43035E-09 -3.03794E-09 -1.21518E-09 1.82276E-09 6.07588E-10 3.03794E-09 6.07588E-10 0 3.03794E-09 2.43035E-09 0 -3.03794E-09 -3.03794E-09 1.21518E-09 -1.82276E-09 -3.03794E-09 0 -6.07588E-10 3.03794E-09 6.07588E-10 -2.43035E-09 -1.21518E-09 -2.43035E-09 -3.03794E-09 0 1.21518E-09 3.03794E-09 2.43035E-09 -1.82276E-09 -6.07588E-10 1.82276E-09 -2.43035E-09 1.21518E-09 1.21518E-09 -6.07588E-10 1.21518E-09 -3.03794E-09 -6.07588E-10 -2.43035E-09 -1.82276E-09 3.03794E-09 -2.43035E-09 3.03794E-09545 6.07588E-10 6.25816E-10 6.07588E-10 6.13664E-10 6.07588E-10 6.01512E-10 5.95437E-10 6.07588E-10 5.95437E-10 6.01512E-10 3.03794E-09 1.21518E-09 -1.82276E-09 -3.03794E-09 3.03794E-09 1.82276E-09 1.21518E-09 6.07588E-10 6.07588E-10 -1.82276E-09 -1.21518E-09 3.03794E-09 1.82276E-09 2.43035E-09 1.21518E-09 2.43035E-09 -1.82276E-09 2.43035E-09 -3.03794E-09 1.82276E-09 -2.43035E-09 -6.07588E-10 3.03794E-09 2.43035E-09 1.21518E-09 3.03794E-09 -3.03794E-09 0 -1.82276E-09 2.43035E-09 -1.21518E-09 6.07588E-10 1.82276E-09 1.21518E-09 1.21518E-09 -6.07588E-10 -1.21518E-09 -6.07588E-10 3.03794E-09 1.21518E-09 2.43035E-09 -1.21518E-09 0 1.82276E-09 -1.82276E-09 1.21518E-09 1.21518E-09 3.03794E-09 -6.07588E-10 -1.21518E-09 6.07588E-10 -6.07588E-10 6.07588E-10 1.82276E-09 -6.07588E-10 3.03794E-09 -1.82276E-09 1.21518E-09 -6.07588E-10 1.21518E-09 1.82276E-09 -2.43035E-09 -2.43035E-09 -6.07588E-10 -6.07588E-10 6.07588E-10 6.07588E-10 3.03794E-09 -6.07588E-10 1.21518E-09 -6.07588E-10 2.43035E-09 -2.43035E-09 -2.43035E-09 -2.43035E-09 -1.82276E-09 0 -1.82276E-09 -3.03794E-09 1.21518E-09 3.03794E-09 1.21518E-09 3.03794E-09 -1.21518E-09 -3.03794E-09 -6.07588E-10 -1.21518E-09 1.21518E-09 -2.43035E-09 -6.07588E-10plt.scatter(reg.predict(X), reg.predict(X) - y, color = "green", s = 10, label = 'Train data') 回答：
你可以将时间设为因变量（标签），将不同日期的值设为模型的特征（独立变量）。你可以使用sklearn轻松地进行多层次回归：
from sklearn.linear_model import LinearRegressionX=np.array(df.drop("time",1))y=np.array(df["time"])clf=LinearRegression()clf.fit(X,y)