自变量和因变量都是nd.array类型，给出dw检验和逐步回归的python代码

下面是一个示例代码，其中X和y是自变量和因变量的nd.array类型：

import numpy as np
import statsmodels.api as sm

# DW检验
def dw_test(resid):
    dw = np.sum(np.diff(resid)**2) / np.sum(resid**2)
    return dw

# 逐步回归
def stepwise_regression(X, y):
    results = []
    p_values = np.ones(X.shape[1])
    included = np.zeros(X.shape[1], dtype=bool)
    for i in range(X.shape[1]):
        best_p = np.inf
        best_j = -1
        for j in range(X.shape[1]):
            if not included[j]:
                model = sm.OLS(y, sm.add_constant(X[:, included + (j,)]))
                result = model.fit()
                if result.pvalues[-1] < best_p:
                    best_p = result.pvalues[-1]
                    best_j = j
        if best_j >= 0:
            model = sm.OLS(y, sm.add_constant(X[:, included + (best_j,)]))
            result = model.fit()
            included[best_j] = True
            p_values[best_j] = best_p
            results.append(result)
    return results, p_values, included

# 测试代码
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([1, 2, 3])
results, p_values, included = stepwise_regression(X, y)
print('DW检验结果：', dw_test(results[-1].resid))
print('逐步回归结果：', included)

请注意，这只是一个示例代码，具体实现可能因数据类型、数据大小等因素而有所不同。