py

Untitled-3.py

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+import numpy as np
+import scipy.stats as stats
+
+# 假设数据
+# 老龄化程度较高的群体数据
+high_aging_data = {
+    '老龄化给社会带来的主要问题': np.array([75.47, 85.85, 84.91, 53.77, 83.96]),
+    '政府应该采取哪些措施': np.array([62.26, 16.04, 91.51, 88.68, 51.89,0.94]),
+    'life_expectancy': np.array([75, 76, 74, 77, 75]),
+    # 其他可能的因素...
+}
+
+# 老龄化程度较低的群体数据
+low_aging_data = {
+    '老龄化给社会带来的主要问题': np.array([84.62, 69.23, 69.23, 15.38, 84.62]),
+    '政府应该采取哪些措施': np.array([46.15, 7.69, 69.23, 76.92, 30.77,7.69]),
+    'life_expectancy': np.array([70, 71, 69, 72, 70]),
+    # 其他可能的因素...
+}
+
+# 进行t检验并分析结果
+factors = ['老龄化给社会带来的主要问题', '政府应该采取哪些措施', 'life_expectancy']
+system_evaluation = {}
+
+for factor in factors:
+    high_group = high_aging_data[factor]
+    low_group = low_aging_data[factor]
+    t_stat, p_value = stats.ttest_ind(high_group, low_group)
+    system_evaluation[factor] = {
+        't_statistic': t_stat,
+        'p_value': p_value,
+        'effect_size': np.abs(t_stat) * np.sqrt((len(high_group) + len(low_group)) / (len(high_group) * len(low_group)))
+    }
+    print(f"{factor}的t统计量: {t_stat}, P值: {p_value}")
+    if p_value < 0.05:
+        print(f"{factor}在两个群体之间存在显著差异。\n")
+    else:
+        print(f"{factor}在两个群体之间没有显著差异。\n")
+
+# 打印系统评价
+print("系统评价：")
+for factor, evaluation in system_evaluation.items():
+    print(f"{factor}: t统计量={evaluation['t_statistic']}, P值={evaluation['p_value']}, 影响大小={evaluation['effect_size']}")