update comment

ch03_linear/linear_stat.ipynb

@@ -344,7 +344,7 @@
     }
    ],
    "source": [
-    "# 用f test检测x对应的系数a是否显著\n",
+    "# 用f_test检测x对应的系数a是否显著\n",
     "print('检验假设x的系数等于0：')\n",
     "print(re.f_test('x=0'))"
    ]
@@ -364,7 +364,7 @@
     }
    ],
    "source": [
-    "# 用f test检测常量b是否显著\n",
+    "# 用f_test检测常量b是否显著\n",
     "print('检测假设const的系数等于0：')\n",
     "print(re.f_test('const=0'))"
    ]
@@ -384,7 +384,7 @@
     }
    ],
    "source": [
-    "# 用f test检测a=1, b=0同时成立的显著性\n",
+    "# 用f_test检测a=1, b=0同时成立的显著性\n",
     "print('检测假设x的系数等于1和const的系数等于0同时成立：')\n",
     "print(re.f_test(['x=1', 'const=0']))"
    ]

ch04_logit/logit_regression.ipynb

@@ -772,10 +772,10 @@
     }
    ],
    "source": [
-    "# 用f test检验education_num的系数是否显著\n",
+    "# 用f_test检验education_num的系数是否显著\n",
     "print('检验假设education_num的系数等于0：')\n",
     "print(re.f_test('education_num=0'))\n",
-    "# 用f test检验两个假设是否同时成立\n",
+    "# 用f_test检验两个假设是否同时成立\n",
     "print('检验假设education_num的系数等于0.32和hours_per_week的系数等于0.04同时成立：')\n",
     "print(re.f_test('education_num=0.32, hours_per_week=0.04'))"
    ]
@@ -804,7 +804,7 @@
     "pd.set_option('display.precision', 4)\n",
     "conf = re.conf_int()\n",
     "conf['OR'] = re.params\n",
-    "# 计算各个变量对事件发生比的影响\n",
+    "# 计算各个特征对事件发生比的影响\n",
     "# conf里的三列：置信区间的下界、上界和估计值\n",
     "conf.columns = ['2.5%', '97.5%', 'OR']\n",
     "# 各个变量对事件发生比的影响\n",
@@ -838,7 +838,7 @@
     }
    ],
    "source": [
-    "# 计算各个变量的边际效应\n",
+    "# 计算各个特征的边际效应\n",
     "print(re.get_margeff(at='overall').summary())"
    ]
   },