2024年12月20日
摘要: `import numpy as np import statsmodels.formula.api as smf import pylab as plt 创建x值序列 x = np.arange(17, 30, 2) 加载数据 a = np.loadtxt('data10_3.txt') 设置图形 阅读全文
posted @ 2024-12-20 10:01 VVV1 阅读(13) 评论(0) 推荐(0)
摘要: `import numpy as np import statsmodels.api as sm import pylab as plt 加载数据 a = np.loadtxt('data10_2.txt') plt.rc('text', usetex=True) plt.rc('font', si 阅读全文
posted @ 2024-12-20 09:55 VVV1 阅读(12) 评论(0) 推荐(0)
摘要: `import numpy as np import statsmodels.api as sm import pylab as plt def check(d): x0 = d[:, 0] y0 = d[:, 1] data = {'x': x0, 'y': y0} model = sm.form 阅读全文
posted @ 2024-12-20 09:47 VVV1 阅读(19) 评论(0) 推荐(0)
摘要: `import pandas as pd from statsmodels.formula.api import ols from statsmodels.stats.anova import anova_lm 列名列表 column_names = ["城市1", "城市2", "城市3", "城 阅读全文
posted @ 2024-12-20 09:31 VVV1 阅读(13) 评论(0) 推荐(0)
摘要: `import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy import stats file_path = '9.3.xlsx' data = pd.read_excel(file_pa 阅读全文
posted @ 2024-12-20 09:05 VVV1 阅读(23) 评论(0) 推荐(0)
  2024年12月13日
摘要: `import numpy as np from scipy.stats import shapiro data = [15.0, 15.8, 15.2, 15.1, 15.9, 14.7, 14.8, 15.5, 15.6, 15.3, 15.1, 15.3, 15.0, 15.6, 15.7, 阅读全文
posted @ 2024-12-13 09:25 VVV1 阅读(12) 评论(0) 推荐(0)
摘要: `def calculate_monthly_payment(P, annual_interest_rate, n_years): monthly_interest_rate = annual_interest_rate / 12 / 100 total_months = n_years * 12 阅读全文
posted @ 2024-12-13 09:16 VVV1 阅读(30) 评论(0) 推荐(0)
摘要: `import numpy as np import matplotlib.pyplot as plt a = 1 - 0.2 * (1/12) m = 1.109 * 105 W3 = 17.86 w4 = 22.99 X = [] Z = [] for k in np.arange(0, 0.8 阅读全文
posted @ 2024-12-13 09:11 VVV1 阅读(29) 评论(0) 推荐(0)
  2024年11月17日
摘要: `import numpy as np import matplotlib.pyplot as plt from scipy.integrate import solve_ivp 定义微分方程模型 def model(t, y): f, df_dm, d2f_dm2, T, dT_dm = y d3 阅读全文
posted @ 2024-11-17 15:08 VVV1 阅读(11) 评论(0) 推荐(0)
摘要: `import numpy as np import matplotlib.pyplot as plt from scipy.integrate import solve_ivp 定义微分方程系统 def system(t, state): x, y = state dxdt = -x - y dy 阅读全文
posted @ 2024-11-17 15:03 VVV1 阅读(21) 评论(0) 推荐(0)