diff --git a/notebook_必备数学基础/相关分析/.ipynb_checkpoints/相关分析-checkpoint.ipynb b/notebook_必备数学基础/相关分析/.ipynb_checkpoints/相关分析-checkpoint.ipynb
index d15e054..0f44ecc 100644
--- a/notebook_必备数学基础/相关分析/.ipynb_checkpoints/相关分析-checkpoint.ipynb
+++ b/notebook_必备数学基础/相关分析/.ipynb_checkpoints/相关分析-checkpoint.ipynb
@@ -35,12 +35,222 @@
     "左下图是线性相关一般的，右下图有一定的相关但不是线性相关；"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 皮尔逊相关系数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 连续变量的相关分析\n",
+    "<ul>\n",
+    "    <li>连续变量即数据变量，它的取值之间可以比较大小,可以用加减法计算出差异的大小。\n",
+    "    <li>如“年龄”、“收入”、“成绩\"等变量当两个变量都是正态连续变量，而且两者之间呈线性关系时，通常用 Pearson相关系数来衡量。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pearson.相关系数\n",
+    "**协方差:**\n",
+    "协方差是一个反映两个随机变量相关程度的指标，如果一个变量跟随着另一个变量同时变大或者变小，那么这两个变量的协方差就是正值\n",
+    "$$\n",
+    "cov(X, Y) = \\frac{\\sum_n^{i=1}(X_i-\\overline{X})(Y_i-\\overline{Y})}{n-1}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "虽然协方差能反映两个随机变量的相关程度(协方差大于0的时候表示两者正相关，小于0的时候表示两者负相关)，但是协方差值的大小并不能很好地度量两个随机变量的关联程度\n",
+    "<br><br>在二维空间中分布着一些数据，我们想知道数据点坐标X轴和Y轴的相关程度，如果X与Y的相关程度较小但是数据分布的比较离散，这样会导致求出的协方差值较大，用这个值来度量相关程度是不合理的\n",
+    "<img src=\"assets/20201120215604.png\" width=\"50%\">\n",
+    "为了更好的度量两个随机变量的相关程度， 引入Pearson相关系数，其在协方差的基础上除了两个随机变量的标准差"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pearson相关系数**\n",
+    "$$\n",
+    "P_{X,Y} = \\frac{cov(X,Y)}{σXσY} = \\frac{E[(X-μX)(Y-μY)]}{σXσY} \n",
+    "$$\n",
+    "pearson是一个介于-1和1之间的值，当两个变量的线性关系增强时，相关系数趋于1或-1；当一个变量增大，另一个变量也增大时，表明它们之间是正相关的，相关系数大于0；如果一个变量增大，另一个变量却减小，表明它们之间是负相关的，相关系数小于0；如果相关系数等于0，表明它们之间不存在线性相关关系\n",
+    "\n",
+    "<img src=\"assets/20201120220149.png\" width=\"50%\">\n",
+    "np.corrcoef(a)可结算行与行之间的相关系数，np.corrcoef(a,rowvar=0)用于计算各列之间的相关系数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 计算与检验"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10, 10,  8,  9,  7],\n",
+       "       [ 4,  5,  4,  3,  3],\n",
+       "       [ 3,  3,  1,  1,  1]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "matrix_t = np.array([[10,10,8,9,7],\n",
+    "               [4,5,4,3,3],\n",
+    "               [3,3,1,1,1]])\n",
+    "matrix_t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.        , 0.64168895, 0.84016805],\n",
+       "       [0.64168895, 1.        , 0.76376262],\n",
+       "       [0.84016805, 0.76376262, 1.        ]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.corrcoef(matrix_t)  # 计算行相关系数，对角线是自己与自己比较"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.        , 0.98898224, 0.9526832 , 0.9939441 , 0.97986371],\n",
+       "       [0.98898224, 1.        , 0.98718399, 0.99926008, 0.99862543],\n",
+       "       [0.9526832 , 0.98718399, 1.        , 0.98031562, 0.99419163],\n",
+       "       [0.9939441 , 0.99926008, 0.98031562, 1.        , 0.99587059],\n",
+       "       [0.97986371, 0.99862543, 0.99419163, 0.99587059, 1.        ]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.corrcoef(matrix_t, rowvar=0)  # 计算列相关系数，对角线是自己与自己比较"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "计算伦敦市月平均气温与降水量\n",
+    "<img src=\"assets/20201120221030.png\" width=\"50%\">\n",
+    "计算伦敦市月平均气温（t）与降水量（p）之间的相关系数\n",
+    "$$\n",
+    "r_tp = \\frac{\\sum_{i=1}^{12}(t_i-\\overline{t})(p_i-\\overline{p})}\n",
+    "{\\sqrt{\\sum^{12}_{t=1}(t_i-\\overline{t})^2}\\sqrt{\\sum^{12}_{i=1}(p_i-\\overline{p})}}\n",
+    "=\n",
+    "\\frac{-300.91}{\\sqrt{250.55}\\sqrt{1508.34}}\n",
+    "$$\n",
+    "$$\n",
+    "= \\frac{-300.91}{15.83*38.84} = -0.4895\n",
+    "$$\n",
+    "计算结果表明，伦敦市的月平均气温（t）与降水量（p）呈负相关，即异向相关"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 相关系数的显著性检验\n",
+    "假设\n",
+    "<ul>\n",
+    "    <li>H0：p=0\n",
+    "    <li>H1：p≠0\n",
+    "</ul>\n",
+    "统计量\n",
+    "$$\n",
+    "t = \\frac{r\\sqrt{n-2}}{\\sqrt{1-r^2}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "10个学生初一数学分数与初二数学分数的相关系数为087，问从总体上来说，初一与初二数学分数是否存在相关?\n",
+    "<img src=\"assets/20201120222349.png\" width=\"50%\">\n",
+    "计算检验统计量：\n",
+    "$$\n",
+    "t = \\frac{r\\sqrt{n-2}}{\\sqrt{1-r^2}} \n",
+    "= \\frac{0.78\\sqrt{10-2}}{\\sqrt{1-0.78^2}}\n",
+    "= 3.524\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "t = 3.524 > 3.355 = t_{(8)0.01}\n",
+    "$$\n",
+    "所以，总体来说初一和初二的成绩存在正相关。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "correlation: 0.9891763198690562\n",
+      "pvalue: 5.926875946481138e-08\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import stats\n",
+    "x = [10.35, 6.24,3.18,8.46,3.21,7.65,4.32,8.66,9.12,10.31]\n",
+    "y = [5.1, 3.15,1.67,4.33,1.76,4.11,2.11,4.88,4.99,5.12]\n",
+    "correlation, pvalue = stats.stats.pearsonr(x,y)\n",
+    "print('correlation:', correlation)\n",
+    "print('pvalue:', pvalue)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 等级变量的相关分析\n",
+    "当测量得到的数据不是等距或等比数据，而是具有等级顺序的数据；或者得到的数据是等距或等比数据，但其所来自的总体分布不是正态的，不满足求皮尔森相关系数(积差相关)的要求。这时就要运用等级相关系数。"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebook_必备数学基础/相关分析/assets/20201120221030.png b/notebook_必备数学基础/相关分析/assets/20201120221030.png
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diff --git a/notebook_必备数学基础/相关分析/assets/20201120222349.png b/notebook_必备数学基础/相关分析/assets/20201120222349.png
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diff --git a/notebook_必备数学基础/相关分析/相关分析.ipynb b/notebook_必备数学基础/相关分析/相关分析.ipynb
index 0f5984e..0f44ecc 100644
--- a/notebook_必备数学基础/相关分析/相关分析.ipynb
+++ b/notebook_必备数学基础/相关分析/相关分析.ipynb
@@ -60,7 +60,7 @@
     "**协方差:**\n",
     "协方差是一个反映两个随机变量相关程度的指标，如果一个变量跟随着另一个变量同时变大或者变小，那么这两个变量的协方差就是正值\n",
     "$$\n",
-    "cov(X, Y) = \\frac{\\sum_n^i=1(X_i-\\overline{X})(Y_i-\\overline{Y})}{n-1}\n",
+    "cov(X, Y) = \\frac{\\sum_n^{i=1}(X_i-\\overline{X})(Y_i-\\overline{Y})}{n-1}\n",
     "$$"
    ]
   },
@@ -80,18 +80,177 @@
    "source": [
     "**Pearson相关系数**\n",
     "$$\n",
-    "PX,Y = \\frac{cov(X,Y)}{σXσY} = \\frac{E[(X-μX)(Y-μY)]}{σXσY} \n",
+    "P_{X,Y} = \\frac{cov(X,Y)}{σXσY} = \\frac{E[(X-μX)(Y-μY)]}{σXσY} \n",
     "$$\n",
     "pearson是一个介于-1和1之间的值，当两个变量的线性关系增强时，相关系数趋于1或-1；当一个变量增大，另一个变量也增大时，表明它们之间是正相关的，相关系数大于0；如果一个变量增大，另一个变量却减小，表明它们之间是负相关的，相关系数小于0；如果相关系数等于0，表明它们之间不存在线性相关关系\n",
     "\n",
     "<img src=\"assets/20201120220149.png\" width=\"50%\">\n",
-    "np.corrcoef(a)可结算行与行之间的相关系数，np.corrcoe"
+    "np.corrcoef(a)可结算行与行之间的相关系数，np.corrcoef(a,rowvar=0)用于计算各列之间的相关系数"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "## 计算与检验"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[10, 10,  8,  9,  7],\n",
+       "       [ 4,  5,  4,  3,  3],\n",
+       "       [ 3,  3,  1,  1,  1]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "matrix_t = np.array([[10,10,8,9,7],\n",
+    "               [4,5,4,3,3],\n",
+    "               [3,3,1,1,1]])\n",
+    "matrix_t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.        , 0.64168895, 0.84016805],\n",
+       "       [0.64168895, 1.        , 0.76376262],\n",
+       "       [0.84016805, 0.76376262, 1.        ]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.corrcoef(matrix_t)  # 计算行相关系数，对角线是自己与自己比较"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.        , 0.98898224, 0.9526832 , 0.9939441 , 0.97986371],\n",
+       "       [0.98898224, 1.        , 0.98718399, 0.99926008, 0.99862543],\n",
+       "       [0.9526832 , 0.98718399, 1.        , 0.98031562, 0.99419163],\n",
+       "       [0.9939441 , 0.99926008, 0.98031562, 1.        , 0.99587059],\n",
+       "       [0.97986371, 0.99862543, 0.99419163, 0.99587059, 1.        ]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.corrcoef(matrix_t, rowvar=0)  # 计算列相关系数，对角线是自己与自己比较"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "计算伦敦市月平均气温与降水量\n",
+    "<img src=\"assets/20201120221030.png\" width=\"50%\">\n",
+    "计算伦敦市月平均气温（t）与降水量（p）之间的相关系数\n",
+    "$$\n",
+    "r_tp = \\frac{\\sum_{i=1}^{12}(t_i-\\overline{t})(p_i-\\overline{p})}\n",
+    "{\\sqrt{\\sum^{12}_{t=1}(t_i-\\overline{t})^2}\\sqrt{\\sum^{12}_{i=1}(p_i-\\overline{p})}}\n",
+    "=\n",
+    "\\frac{-300.91}{\\sqrt{250.55}\\sqrt{1508.34}}\n",
+    "$$\n",
+    "$$\n",
+    "= \\frac{-300.91}{15.83*38.84} = -0.4895\n",
+    "$$\n",
+    "计算结果表明，伦敦市的月平均气温（t）与降水量（p）呈负相关，即异向相关"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 相关系数的显著性检验\n",
+    "假设\n",
+    "<ul>\n",
+    "    <li>H0：p=0\n",
+    "    <li>H1：p≠0\n",
+    "</ul>\n",
+    "统计量\n",
+    "$$\n",
+    "t = \\frac{r\\sqrt{n-2}}{\\sqrt{1-r^2}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "10个学生初一数学分数与初二数学分数的相关系数为087，问从总体上来说，初一与初二数学分数是否存在相关?\n",
+    "<img src=\"assets/20201120222349.png\" width=\"50%\">\n",
+    "计算检验统计量：\n",
+    "$$\n",
+    "t = \\frac{r\\sqrt{n-2}}{\\sqrt{1-r^2}} \n",
+    "= \\frac{0.78\\sqrt{10-2}}{\\sqrt{1-0.78^2}}\n",
+    "= 3.524\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "t = 3.524 > 3.355 = t_{(8)0.01}\n",
+    "$$\n",
+    "所以，总体来说初一和初二的成绩存在正相关。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "correlation: 0.9891763198690562\n",
+      "pvalue: 5.926875946481138e-08\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy import stats\n",
+    "x = [10.35, 6.24,3.18,8.46,3.21,7.65,4.32,8.66,9.12,10.31]\n",
+    "y = [5.1, 3.15,1.67,4.33,1.76,4.11,2.11,4.88,4.99,5.12]\n",
+    "correlation, pvalue = stats.stats.pearsonr(x,y)\n",
+    "print('correlation:', correlation)\n",
+    "print('pvalue:', pvalue)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 等级变量的相关分析\n",
+    "当测量得到的数据不是等距或等比数据，而是具有等级顺序的数据；或者得到的数据是等距或等比数据，但其所来自的总体分布不是正态的，不满足求皮尔森相关系数(积差相关)的要求。这时就要运用等级相关系数。"
+   ]
   }
  ],
  "metadata": {