我的线性回归statsmodel看起来有什么问题？

Question

我有一个这样的问题：Question screenshot。我一直在使用Python，但结果似乎很可疑。你能否检查代码有什么问题？

import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf

sig = 1  # standard deviation of the distributions
nsim = 100  # number of simulations
tvec = [10, 25, 50, 100, 250, 1000]  # how many different inputs are used? store the number in the vector.
nvec = [1, 2, 3, 5, 10]  # how many observations for each sample?

holding_place = np.zeros((nsim, len(tvec), len(nvec)))  # stores the rsquared number of the regression.

for isim in range(nsim):
    for tdx, t in enumerate(tvec):
        y = np.zeros((t, 1))
        for i in range(t):  # y[0] = 0 + np.random.normal(0, sig)
            y[i] = y[i-1] + np.random.normal(0, sig)  # get new numbers
        for ndx, n in enumerate(nvec):
            x = np.zeros((t, n))
            for i in range(t):
                x[i, ndx] = x[i-1, ndx] + np.random.normal(0, sig)
            x = sm.add_constant(x)
            model = smf.OLS(y, x).fit()
            holding_place[isim, tdx, ndx] = model.rsquared

print(holding_place.mean(0))  # the average rsquared for the (n,t) combination