{"id":4823,"date":"2022-08-06T17:57:31","date_gmt":"2022-08-06T09:57:31","guid":{"rendered":"https:\/\/seit2019.xyz\/?p=4823"},"modified":"2022-11-14T14:47:45","modified_gmt":"2022-11-14T06:47:45","slug":"5-2-%e8%ae%a1%e9%87%8f%e5%88%86%e6%9e%902%ef%bc%9a%e4%b8%8d%e8%89%af%e8%b4%b7%e6%ac%be","status":"publish","type":"post","link":"https:\/\/seit2019.xyz\/?p=4823","title":{"rendered":"5.2 \u8ba1\u91cf\u5206\u67902\uff1a\u4e0d\u826f\u8d37\u6b3e"},"content":{"rendered":"<h3>5.2.0 \u8981\u70b9<\/h3>\n<ul>\n<li>\n<p>Python\u57fa\u672c\u64cd\u4f5c<\/p>\n<ul>\n<li>\n<p>\u81ea\u5b9a\u4e49<strong>\u7ed8\u56fe<\/strong>\u51fd\u6570\uff1aXY\u6563\u70b9\u56fe\uff0c\u8d8b\u52bf\u548c\u6563\u70b9\u56fe\u51fd\u6570<\/p>\n<\/li>\n<li>\n<p>\u81ea\u5b9a\u4e49\u5e8f\u5217<code>x<\/code>\u7684<strong>\u7edf\u8ba1\u6307\u6807<\/strong>\u51fd\u6570<\/p>\n<\/li>\n<\/ul>\n<pre><code class=\"language-python\">x.count()        # \u8ba1\u7b97\u603b\u6570\nx.min()         # \u6700\u5c0f\u503c\nx.quantile(.25)    # \u4e0b\u56db\u5206\u4f4d\u6570\nx.median()       # \u4e2d\u4f4d\u6570\nx.quantile(.75)    # \u4e0a\u56db\u5206\u4f4d\u6570\nx.max()         # \u6700\u5927\u503c\nx.mean()        # \u5e73\u5747\u6570\nx.var()         # \u65b9\u5dee\nx.std()         # \u6807\u51c6\u5dee\nx.skew()         # \u504f\u5ea6\nx.kurt()         # \u5cf0\u5ea6\nx.max()-x.min()    # \u6781\u5dee<\/code><\/pre>\n<ul>\n<li>\n<p>\u8c03\u7528\u81ea\u5b9a\u4e49\u51fd\u6570<\/p>\n<\/li>\n<li>\n<p>\u5b57\u7b26\u4e32\u62fc\u63a5\uff1a<code>OLS_equation = &quot;Y=&quot; + beta0 + &quot;+&quot; + beta1 + &quot;*X&quot;<\/code><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>Pandas\u7a0b\u5e8f\u5305<\/p>\n<ul>\n<li>\n<p>\u6570\u636e\u5e8f\u5217\u7684\u7b80\u8981\u7edf\u8ba1\u63cf\u8ff0\uff1a <code>.describe()<\/code><\/p>\n<\/li>\n<li>\n<p>\u6570\u636e\u5e8f\u5217\u7684\u534f\u65b9\u5dee\u77e9\u9635\uff1a<code>.cov()<\/code><\/p>\n<\/li>\n<li>\n<p>\u6570\u636e\u5e8f\u5217\u7684\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff1a<code>.corr()<\/code><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>Matplotlib\u7a0b\u5e8f\u5305<\/p>\n<ul>\n<li>\n<p>\u7ed8\u5236\u6563\u70b9\u56fe\uff1a<code>plt.scatter()<\/code><\/p>\n<\/li>\n<li>\n<p>\u7ed8\u5236\u6298\u7ebf\u56fe\uff1a<code>plt.plot()<\/code><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>Statsmodels\u7a0b\u5e8f\u5305<\/p>\n<ul>\n<li>\n<p>\u4e00\u5143\u7ebf\u6027\u56de\u5f52\uff1a<code>OLS_model=sm.OLS(y, X).fit()<\/code><\/p>\n<\/li>\n<li>\n<p>\u56de\u5f52\u7ed3\u679c\uff1a<code>OLS_model.summary()<\/code><\/p>\n<\/li>\n<li>\n<p>\u56de\u5f52\u53c2\u6570\uff1a<code>OLS_model.params<\/code><\/p>\n<\/li>\n<li>\n<p>\u62df\u5408\u503c\uff1a<code>OLS_model.predict()<\/code><\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<hr \/>\n<h3>5.2.1 \u9898\u76ee<\/h3>\n<p>\u67d0\u5927\u578b\u5546\u4e1a\u94f6\u884c\u5728\u591a\u4e2a\u5730\u533a\u8bbe\u6709\u5206\u884c\uff0c\u5176\u4e1a\u52a1\u4e3b\u8981\u662f\u8fdb\u884c\u57fa\u7840\u8bbe\u65bd\u5efa\u8bbe\u3001\u56fd\u5bb6\u91cd\u70b9\u9879\u76ee\u5efa\u8bbe\u3001\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u7b49\u9879\u76ee\u7684\u8d37\u6b3e\u3002\u8fd1\u5e74\u6765\uff0c\u8be5\u94f6\u884c\u7684\u8d37\u6b3e\u989d\u5e73\u7a33\u589e\u957f\uff0c\u4f46<strong>\u4e0d\u826f\u8d37\u6b3e<\/strong>\u4e5f\u6709\u8f83\u5927\u6bd4\u4f8b\u7684\u589e\u957f\uff0c\u8fd9\u7ed9\u94f6\u884c\u4e1a\u52a1\u7684\u53d1\u5c55\u5e26\u6765\u8f83\u5927\u538b\u529b\u3002\u7ba1\u7406\u8005\u5e0c\u671b\u77e5\u9053\uff1a\u4e0d\u826f\u8d37\u6b3e\u662f\u5426\u4e0e\u8d37\u6b3e\u4f59\u989d\u3001\u7d2f\u8ba1\u5e94\u6536\u8d37\u6b3e\u3001\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570\u3001\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u989d\u7b49\u56e0\u7d20\u6709\u5173\uff1f\u5173\u7cfb\u5f3a\u5ea6\u5982\u4f55\uff1f\u8d37\u6b3e\u4f59\u989d\u5bf9\u4e0d\u826f\u8d37\u6b3e\u7684\u5f71\u54cd\u7a0b\u5ea6\u5982\u4f55\uff1f<\/p>\n<p><strong>\u6570\u636e<\/strong>\uff1a<\/p>\n<div align=\"center\"><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_01.png?w=750&#038;ssl=1\" alt=\"\u4e0d\u826f\u8d37\u6b3e\u6570\u636e\"><\/div>\n<p>\u6570\u636e\u6765\u6e90\uff1a\u7edf\u8ba1\u5b66\u539f\u7406\uff08\u7b2c8\u7248\uff09\u7b2c216\u9875\uff0c\u8d3e\u4fca\u5e73\u7b49\u7f16\u8457\uff0c\u4e2d\u56fd\u4eba\u6c11\u5927\u5b66\u51fa\u7248\u793e\uff0c2021.<\/p>\n<p>\u8bf7\u4ee5\u4e0a\u8ff0\u6570\u636e\u4e3a\u4f9d\u636e\uff0c\u8fdb\u884c\u5982\u4e0b\u5206\u6790\uff1a<\/p>\n<ul>\n<li>\u7ed8\u5236XY\u6563\u70b9\u56fe\uff1a\u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u4f59\u989d\uff0c\u4e0d\u826f\u8d37\u6b3e\u4e0e\u7d2f\u79ef\u5e94\u6536\u8d37\u6b3e\uff0c\u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570\uff0c\u4e0d\u826f\u8d37\u6b3e\u4e0e\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u3002<\/li>\n<li>\u8ba1\u7b97\u534f\u65b9\u5dee\u77e9\u9635\u4e0e\u76f8\u5173\u7cfb\u6570\u77e9\u9635\u3002<\/li>\n<li>\u5efa\u7acb\u4e00\u5143\u7ebf\u6027\u56de\u5f52\uff0c\u5206\u6790\uff1a\u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u4f59\u989d\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/li>\n<\/ul>\n<p><strong>\u53d8\u91cf\u8bf4\u660e<\/strong><\/p>\n<pre><code>NP_Loan           # \u4e0d\u826f\u8d37\u6b3e\nBalance           # \u8d37\u6b3e\u4f59\u989d\nCR_Loan           # \u7d2f\u79ef\u5e94\u6536\u8d37\u6b3e\nLoan_Num          # \u8d37\u6b3e\u9879\u76ee\u4e2a\u6570\nFix_Invest        # \u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44<\/code><\/pre>\n<hr \/>\n<h3>5.2.2 \u4ee3\u7801<\/h3>\n<pre><code class=\"language-python\"># \u5bfc\u5165\u7a0b\u5e8f\u5305\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\nimport ssl\n\n# \u7981\u7528SSL\u8bc1\u4e66\u6821\u9a8c\nssl._create_default_https_context = ssl._create_unverified_context\n\n# \u5bfc\u5165excel\u6570\u636e(\u5728\u7ebf\u6570\u636e)\ndf = pd.read_excel(r&quot;https:\/\/cdn.seit2019.xyz\/data\/econometrics\/exp11.6.xlsx&quot;, index_col=&quot;\u5206\u884c\u7f16\u53f7&quot;)\n\n# \u4fee\u6539\u5217\u7d22\u5f15\u4e3a\u82f1\u6587\ndf = df.rename(columns={&quot;\u4e0d\u826f\u8d37\u6b3e(\u4ebf\u5143\uff09&quot;: &quot;NP_Loan&quot;, &quot;\u8d37\u6b3e\u4f59\u989d(\u4ebf\u5143)&quot;: &quot;Balance&quot;, &quot;\u7d2f\u8ba1\u5e94\u6536\u8d37\u6b3e(\u4ebf\u5143)&quot;: &quot;CR_Loan&quot;, &quot;\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570(\u4e2a)&quot;: &quot;Loan_Num&quot;, &quot;\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u989d(\u4ebf\u5143)&quot;: &quot;Fix_Invest&quot;})\n\n# \u5c55\u793a\u6570\u636e\nprint(&quot;\u6570\u636e\u5c55\u793a\u5982\u4e0b\uff1a&quot;)\nprint(df, &quot;\\n&quot;)\n# \u5c55\u793a\u7b80\u8981\u7edf\u8ba1\u6307\u6807\nprint(&quot;\u6570\u636e\u7684\u7b80\u8981\u7edf\u8ba1\u6307\u6807\uff1a&quot;)\nprint(df.describe(), &quot;\\n&quot;)\n\n# \u7ed8\u5236XY\u6563\u70b9\u56fe\n# \u5b9a\u4e49XY\u6563\u70b9\u56fe\u51fd\u6570\ndef xy_scatter(x_var, y_var, x_label_str, y_label_str, x_unit_str, y_unit_str, color_str):    # \u51fd\u6570\u540d\u79f0\uff08\u53c2\u6570\uff09\n    plt.rcParams[&#039;font.sans-serif&#039;] = [&#039;SimHei&#039;]                  # \u5b9a\u4e49\u5b57\u4f53\uff0c\u663e\u793a\u4e2d\u6587\n    plt.rcParams[&#039;axes.unicode_minus&#039;] = False                    # \u5b9a\u4e49\u5b57\u4f53\uff0c\u663e\u793a\u4e2d\u6587\n    title_str = y_label_str + &quot;\u4e0e&quot; + x_label_str + &quot;\u6563\u70b9\u56fe&quot;         # \u5b9a\u4e49\u6807\u9898\u5b57\u7b26\u4e32\n    y_label_str = y_label_str + y_unit_str                        # y\u8f74\u6807\u7b7e+\u5355\u4f4d\n    x_label_str = x_label_str + x_unit_str                        # x\u8f74\u6807\u7b7e+\u5355\u4f4d\n    plt.scatter(x_var, y_var, c=color_str)                        # \u6563\u70b9\u56fe\n    plt.xlabel(x_label_str)                                       # \u663e\u793ax\u8f74\u6807\u7b7e\n    plt.ylabel(y_label_str)                                       # \u663e\u793ay\u8f74\u6807\u7b7e\n    plt.title(title_str, fontsize=&quot;xx-large&quot;, fontweight=&quot;bold&quot;)  # \u663e\u793a\u6807\u9898\n    plt.show()                                                    # \u663e\u793a\u6574\u4e2a\u56fe\u5f62\n    return\n# \u5b9a\u4e49\u53d8\u91cf\ny = df[&quot;NP_Loan&quot;]\nx1 = df[&quot;Balance&quot;]\nx2 = df[&quot;CR_Loan&quot;]\nx3 = df[&quot;Loan_Num&quot;]\nx4 = df[&quot;Fix_Invest&quot;]\n# \u5b9a\u4e49\u6807\u7b7e\ny_label_str = &quot;\u4e0d\u826f\u8d37\u6b3e&quot;\nx1_label_str = &quot;\u8d37\u6b3e\u4f59\u989d&quot;\nx2_label_str = &quot;\u7d2f\u8ba1\u5e94\u6536\u8d37\u6b3e&quot;\nx3_label_str = &quot;\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570&quot;\nx4_label_str = &quot;\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u989d&quot;\n# \u7ed8\u56fe\n# \u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u4f59\u989d\u6563\u70b9\u56fe\nxy_scatter(x1, y, x1_label_str, y_label_str, &quot;(\u4ebf\u5143)&quot;, &quot;(\u4ebf\u5143)&quot;, &quot;red&quot;)\n# \u4e0d\u826f\u8d37\u6b3e\u4e0e\u7d2f\u8ba1\u5e94\u6536\u8d37\u6b3e\u6563\u70b9\u56fe\nxy_scatter(x2, y, x2_label_str, y_label_str, &quot;(\u4ebf\u5143)&quot;, &quot;(\u4ebf\u5143)&quot;, &quot;yellow&quot;)\n# \u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570\u6563\u70b9\u56fe\nxy_scatter(x3, y, x3_label_str, y_label_str, &quot;(\u4e2a)&quot;, &quot;(\u4ebf\u5143)&quot;, &quot;blue&quot;)\n# \u4e0d\u826f\u8d37\u6b3e\u4e0e\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u989d\u6563\u70b9\u56fe\nxy_scatter(x4, y, x4_label_str, y_label_str, &quot;(\u4ebf\u5143)&quot;, &quot;(\u4ebf\u5143)&quot;, &quot;black&quot;)\n\n# \u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\n# \u5b9a\u4e49\u7edf\u8ba1\u6307\u6807\u51fd\u6570\ndef stat(x):\n    return pd.Series(\n        [x.count(), x.min(), x.quantile(.25), x.median(), x.quantile(.75), x.max(), x.mean(), x.var(), x.std(),\n         x.skew(), x.kurt(), x.max() - x.min()],\n        index=[&#039;\u603b\u6570&#039;, &#039;\u6700\u5c0f\u503c&#039;, &#039;\u4e0b\u56db\u5206\u4f4d\u6570&#039;, &#039;\u4e2d\u4f4d\u6570&#039;, &#039;\u4e0a\u56db\u5206\u4f4d\u6570&#039;, &#039;\u6700\u5927\u503c&#039;, &#039;\u5e73\u5747\u6570&#039;, &#039;\u65b9\u5dee&#039;, &#039;\u6807\u51c6\u5dee&#039;, &#039;\u504f\u5ea6&#039;, &#039;\u5cf0\u5ea6&#039;, &#039;\u6781\u5dee&#039;])\n# \u8ba1\u7b97\u7edf\u8ba1\u6307\u6807\nstat_y = pd.DataFrame(stat(y))\nstat_x1 = pd.DataFrame(stat(x1))\nstat_x2 = pd.DataFrame(stat(x2))\nstat_x3 = pd.DataFrame(stat(x3))\nstat_x4 = pd.DataFrame(stat(x4))\n# \u5408\u5e76\u663e\u793a\u7edf\u8ba1\u6307\u6807\ndfs = pd.DataFrame(columns=[&#039;NP_Loan&#039;, &#039;Balance&#039;, &#039;CR_Loan&#039;, &#039;Loan_Num&#039;, &#039;Fix_Invest&#039;])\ndfs[&#039;NP_Loan&#039;] = stat_y\ndfs[&#039;Balance&#039;] = stat_x1\ndfs[&#039;CR_Loan&#039;] = stat_x2\ndfs[&#039;Loan_Num&#039;] = stat_x3\ndfs[&#039;Fix_Invest&#039;] = stat_x4\n# \u5c55\u793a\u8be6\u7ec6\u7684\u7edf\u8ba1\u63cf\u8ff0\nprint(&quot;\u6570\u636e\u7684\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\uff1a&quot;)\nprint(dfs, &quot;\\n&quot;)\n\n# \u5c55\u793a\u6570\u636e\u7684\u534f\u65b9\u5dee\u77e9\u9635\nprint(&quot;\u6570\u636e\u7684\u534f\u65b9\u5dee\u77e9\u9635\uff1a&quot;)\nprint(df.cov(), &quot;\\n&quot;)\n\n# \u5c55\u793a\u6570\u636e\u7684\u76f8\u5173\u7cfb\u6570\u77e9\u9635\nprint(&quot;\u6570\u636e\u7684\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff1a&quot;)\nprint(df.corr(), &quot;\\n&quot;)\n\n# \u4e00\u5143\u7ebf\u6027\u56de\u5f52\n# \u5b9a\u4e49\u53d8\u91cf\nX = sm.add_constant(x1)      # \u6dfb\u52a0\u5e38\u6570\u9879\n\n# OLS\u56de\u5f52\nOLS_model = sm.OLS(y, X).fit()\n\n# \u5c55\u793aOLS\u56de\u5f52\u7ed3\u679c\nprint(&quot;OLS\u56de\u5f52\u7ed3\u679c\uff1a&quot;)\nprint(OLS_model.summary(), &quot;\\n&quot;)\n\n# \u5c55\u793aOLS\u56de\u5f52\u65b9\u7a0b\ndfc = pd.DataFrame(OLS_model.params).round(4)       # \u83b7\u53d6\u56de\u5f52\u53c2\u6570,\u4fdd\u7559\u5c0f\u6570\u70b9\u540e4\u4f4d\uff0c\u8d4b\u503c\u7ed9dfc\ndfc = dfc.astype(&quot;string&quot;)                          # \u8f6c\u6362dfc\u7684\u683c\u5f0f\u4e3a\u5b57\u7b26\u4e32\nbeta0 = dfc.iloc[0, 0]                              # \u83b7\u53d6dfc\u7684\u7b2c1\u4e2a\u503c\uff0c\u8d4b\u503c\u7ed9beta0\nbeta1 = dfc.iloc[1, 0]                              # \u83b7\u53d6dfc\u7684\u7b2c2\u4e2a\u503c\uff0c\u8d4b\u503c\u7ed9\u5b9a\u4e49beta1\nOLS_equation = &quot;Y=&quot; + beta0 + &quot;+&quot; + beta1 + &quot;*X&quot;    # \u5b9a\u4e49\u56de\u5f52\u65b9\u7a0b\uff0c\u65b9\u5f0f\uff1a\u5b57\u7b26\u4e32\u62fc\u63a5\nprint(&quot;\u4e00\u5143\u7ebf\u6027\u56de\u5f52\u65b9\u7a0b\u4e3a\uff1a&quot;+OLS_equation, &quot;\\n&quot;)       # \u663e\u793a\u56de\u5f52\u65b9\u7a0b   \n\n# \u7ed8\u5236\u8d8b\u52bf\u7ebf\u4e0e\u6563\u70b9\u56fe\n# \u5b9a\u4e49\u8d8b\u52bf\u7ebf\u548c\u6563\u70b9\u56fe\u51fd\u6570\ndef fitted_scatter(x_var, y_var, y_fit_var, x_label_str, y_label_str, x_unit_str, y_unit_str, scatter_c, plot_c, equation_str): # \u51fd\u6570\u540d\u79f0\uff08\u53c2\u6570\uff09 \n    plt.rcParams[&#039;font.sans-serif&#039;] = [&#039;SimHei&#039;]                                  # \u5b9a\u4e49\u5b57\u4f53\uff0c\u663e\u793a\u4e2d\u6587\n    plt.rcParams[&#039;axes.unicode_minus&#039;] = False                                    # \u5b9a\u4e49\u5b57\u4f53\uff0c\u663e\u793a\u4e2d\u6587\n    title_str = y_label_str + &quot;\u4e0e&quot; + x_label_str + &quot;\u6563\u70b9\u56fe&quot;                        # \u5b9a\u4e49\u6807\u9898\u5b57\u7b26\u4e32\n    y_label_str = y_label_str + y_unit_str                                        # y\u8f74\u6807\u7b7e+\u5355\u4f4d\n    x_label_str = x_label_str + x_unit_str                                        # y\u8f74\u6807\u7b7e+\u5355\u4f4d\n    plt.scatter(x_var, y_var, color=scatter_c, label=&quot;\u5b9e\u9645\u503c&quot;)                     # \u6563\u70b9\u56fe\n    plt.plot(x_var, y_fit_var, color=plot_c, label=&quot;\u9884\u6d4b\u503c&quot;)                       # \u8d8b\u52bf\u7ebf\n    plt.title(title_str, fontsize=&quot;xx-large&quot;, fontweight=&quot;bold&quot;)                  # \u6807\u9898\n    plt.xlabel(x_label_str)                                                       # \u663e\u793ax\u8f74\u6807\u7b7e\n    plt.ylabel(y_label_str)                                                       # \u663e\u793ay\u8f74\u6807\u7b7e\n    plt.text(240, 7, s=equation_str, fontsize=&quot;x-large&quot;)                          # \u663e\u793a\u56de\u5f52\u65b9\u7a0b\uff0c\u4f4d\u7f6e\u81ea\u5b9a\u4e49\n    plt.legend()                                                                  # \u663e\u793a\u56fe\u4f8b\n    plt.show()                                                                    # \u663e\u793a\u6574\u4e2a\u56fe\u5f62\n    return\n# \u5b9a\u4e49\u53d8\u91cf\ny_fitted = OLS_model.predict()\n# \u5b9a\u4e49\u6807\u7b7e(\u5df2\u5b9a\u4e49)\n# \u7ed8\u56fe\nfitted_scatter(x1, y, y_fitted, x1_label_str, y_label_str, &quot;\u4ebf\u5143&quot;, &quot;\u4ebf\u5143&quot;, &quot;blue&quot;, &quot;black&quot;, OLS_equation)\n<\/code><\/pre>\n<hr \/>\n<h3>5.2.3 \u4ee3\u7801\u89e3\u6790<\/h3>\n<p><strong>\u5bfc\u5165\u7a0b\u5e8f\u5305<\/strong><\/p>\n<pre><code class=\"language-python\">import pandas as pd\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>pandas<\/code>\uff1aPython\u6570\u636e\u5904\u7406\u6700\u5e38\u7528\u7684\u7a0b\u5e8f\u5305<\/li>\n<li><code>matplotlib<\/code>\uff1aPython\u7ed8\u56fe\u5e38\u7528\u7a0b\u5e8f\u5305<\/li>\n<li><code>statsmodels<\/code>\uff1aPython\u56de\u5f52\u5206\u6790\u5e38\u7528\u7a0b\u5e8f\u5305<\/li>\n<li><code>import pandas as pd<\/code>\uff1a\u8868\u793a\u5c06pandas\u7a0b\u5e8f\u5305\u5bfc\u5165\uff0c\u5e76\u7b80\u5316\u547d\u540d\u4e3a\uff1a<code>pd<\/code><\/li>\n<li><code>import matplotlib.pyplot as plt<\/code>\uff1a\u8868\u793a\u5c06<code>matplotlib<\/code>\u7a0b\u5e8f\u5305\u4e2d\u7684<code>pyplot<\/code>\u6a21\u5757\u5bfc\u5165\uff0c\u5e76\u7b80\u5316\u547d\u540d\u4e3a\uff1a<code>plt<\/code><\/li>\n<li><code>import statsmodels.api as sm<\/code>\uff1a\u8868\u793a\u5c06<code>statsmodels<\/code>\u7a0b\u5e8f\u5305\u4e2d\u7684<code>api<\/code>\u6a21\u5757\u5bfc\u5165\uff0c\u5e76\u7b80\u5316\u547d\u540d\u4e3a\uff1a<code>sm<\/code><\/li>\n<li>\u4e0a\u8ff0\u7a0b\u5e8f\u5305\u5bfc\u5165\u548c\u7b80\u5316\u547d\u540d\u65b9\u5f0f\uff0c\u662fPython\u7f16\u7a0b\u901a\u7528\u7684\u65b9\u5f0f\u3002\u4e3a\u4e86\u4fbf\u4e8e\u4ee3\u7801\u9605\u8bfb\u548c\u5206\u4eab\uff0c\u5efa\u8bae\u4fdd\u6301\u4e0d\u53d8\u3002<\/li>\n<\/ul>\n<p><strong>\u5bfc\u5165\u6570\u636e<\/strong><\/p>\n<pre><code class=\"language-python\">df = pd.read_excel(r&quot;https:\/\/cdn.seit2019.xyz\/data\/econometrics\/exp11.6.xlsx&quot;, index_col=&quot;\u5206\u884c\u7f16\u53f7&quot;)<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u8c03\u7528<code>pandas<\/code>\u7a0b\u5e8f\u5305\u4e2d\u7684<code>read_excel()<\/code>\u51fd\u6570\uff0c\u5c06\u94fe\u63a5\u4e2d\u6570\u636e\u5bfc\u5165\uff0c\u5e76\u547d\u540d\u4e3a\u53d8\u91cf<code>df01<\/code>\uff0c\u540c\u65f6\u8bbe\u5b9a\u540d\u79f0\u4e3a<code>&quot;\u5206\u884c\u7f16\u53f7&quot;<\/code>\u7684\u5217\u4e3a\u884c\u7d22\u5f15\u3002<\/li>\n<li>\u53d8\u91cf<code>df<\/code>\u4e2d\uff0c\u6570\u636e\u4ee5DataFrame\u683c\u5f0f\u5b58\u50a8\u6570\u636e\u3002<\/li>\n<li>DataFrame\u662fpandas\u652f\u6301\u7684\u8868\u683c\u578b\u6570\u636e\u683c\u5f0f\uff0c\u7c7b\u4f3cexcel\u3002<\/li>\n<li>\u5173\u4e8eDataFrame\uff0c\u8be6\u60c5\u53c2\u8003\uff1a<a href=\"https:\/\/www.runoob.com\/pandas\/pandas-dataframe.html\">Pandas \u6570\u636e\u7ed3\u6784 &#8211; DataFrame<\/a><\/li>\n<li>\u4e3a\u4e86\u4fbf\u4e8e\u6559\u7a0b\u5206\u4eab\uff0c\u8fd9\u91cc\u91c7\u7528\u5728\u7ebf\u6570\u636e\u3002<\/li>\n<li><code>index_col=&quot;\u5206\u884c\u7f16\u53f7&quot;<\/code>\uff1a\u8868\u793a\u5c06\u540d\u79f0\u4e3a<code>&quot;\u5206\u884c\u7f16\u53f7&quot;<\/code>\u7684\u5217\u8bbe\u5b9a\u4e3a\u884c\u7d22\u5f15\u3002<\/li>\n<li>\u8be6\u7ec6\u7684\u6570\u636e\u5bfc\u5165\u4e0e\u5bfc\u51fa\uff0c\u8bf7\u53c2\u8003\uff1a<a href=\"https:\/\/seit2019.xyz\/?p=4778\">2.2 Python\u6570\u636e\u5bfc\u5165\u4e0e\u5bfc\u51fa<\/a><\/li>\n<\/ul>\n<p><strong>\u4fee\u6539\u6570\u636e\u5217\u7d22\u5f15<\/strong><\/p>\n<pre><code class=\"language-python\">df = df.rename(columns={&quot;\u4e0d\u826f\u8d37\u6b3e(\u4ebf\u5143\uff09&quot;: &quot;NP_Loan&quot;, &quot;\u8d37\u6b3e\u4f59\u989d(\u4ebf\u5143)&quot;: &quot;Balance&quot;, &quot;\u7d2f\u8ba1\u5e94\u6536\u8d37\u6b3e(\u4ebf\u5143)&quot;: &quot;CR_Loan&quot;, &quot;\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570(\u4e2a)&quot;: &quot;Loan_Num&quot;, &quot;\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u989d(\u4ebf\u5143)&quot;: &quot;Fix_Invest&quot;})<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u4fee\u6539\u53d8\u91cf<code>df<\/code>\u4e2d\u6570\u636e\u7684\u5217\u540d\u79f0\u3002<\/li>\n<li><code>.rename()<\/code>\uff1a\u4fee\u6539\u884c\u6216\u5217\u6807\u7b7e\u7684\u51fd\u6570<\/li>\n<li><code>columns={}<\/code>\uff1a\u8868\u793a\u4ee5\u5b57\u5178\u7684\u65b9\u5f0f\u4fee\u6539\u5217\u540d\u79f0\uff0c\u5373\u7528<code>:<\/code>\u540e\u9762\u7684<code>value<\/code>(\u65b0\u5217\u540d)\u66ff\u6362<code>:<\/code>\u524d\u9762\u7684<code>key<\/code>(\u539f\u5217\u540d)<\/li>\n<li>\u5b57\u5178\u662fPython\u4e2d\u91cd\u8981\u7684\u6570\u636e\u7ed3\u6784\uff0c\u901a\u5e38\u4ee5<code>{}<\/code>\u6807\u8bb0\uff1b\u5b57\u5178\u7684\u6bcf\u4e2a\u952e\u503c\u7531<code>:<\/code>\u5206\u5f00\uff0c\u8868\u793a\u4e00\u4e00\u5bf9\u5e94\u5173\u7cfb\uff0c\u683c\u5f0f\uff1a<code>key:value<\/code>\uff1b\u591a\u4e2a\u952e\u503c\u4e4b\u95f4\u7528<code>,<\/code>\u5206\u9694\uff0c\u683c\u5f0f\uff1a{key1: value, key2: value}\u3002<\/li>\n<li>\u5173\u4e8e\u5b57\u5178\uff0c\u8be6\u60c5\u53c2\u8003\uff1a<a href=\"https:\/\/www.runoob.com\/python\/python-dictionary.html\">\u83dc\u9e1f\u6559\u7a0b\uff1aPython \u5b57\u5178(Dictionary)<\/a><\/li>\n<\/ul>\n<p><strong>\u5c55\u793a\u6570\u636e<\/strong><\/p>\n<pre><code class=\"language-python\">print(&quot;\u6570\u636e\u5c55\u793a\u5982\u4e0b\uff1a&quot;)\nprint(df, &quot;\\n&quot;)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_02.png?w=750&#038;ssl=1\" alt=\"\u6570\u636e\u5c55\u793a\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>print(&quot;\u6570\u636e\u5c55\u793a\u5982\u4e0b\uff1a&quot;)<\/code>\u8868\u793a\u5c06<code>&quot;&quot;<\/code>\u4e2d\u7684\u6587\u5b57\uff08<a href=\"https:\/\/www.runoob.com\/python\/python-strings.html\">\u5b57\u7b26\u4e32<\/a>\uff09\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>print(df, &quot;\\n&quot;)<\/code>\uff1a\u663e\u793a\u540d\u79f0\u4e3a<code>df<\/code>\u7684\u53d8\u91cf\uff0c\u7136\u540e\u56de\u8f66\uff0c\u6362\u884c\u3002<\/li>\n<li><code>print()<\/code>\u662fpython\u663e\u793a\u8f93\u51fa\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06\u53d8\u91cf\uff0c\u6570\u636e\uff0c\u8fd0\u884c\u7ed3\u679c\u7b49\u4ee5\u6587\u672c\u7684\u65b9\u5f0f\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>\u201d\\n&quot;<\/code>\uff1a\u8868\u793a\u56de\u8f66\u3001\u6362\u884c\uff0c\u5176\u529f\u80fd\u662f\uff1a\u5c06\u524d\u540e\u4e24\u6b21\u663e\u793a\u7684\u7ed3\u679c\u4ee5\u7a7a\u884c\u9694\u5f00\u3002<\/li>\n<\/ul>\n<p><strong>\u5c55\u793a\u7b80\u8981\u7edf\u8ba1\u6307\u6807<\/strong><\/p>\n<pre><code class=\"language-python\">print(&quot;\u6570\u636e\u7684\u7b80\u8981\u7edf\u8ba1\u6307\u6807\uff1a&quot;)\nprint(df.describe(), &quot;\\n&quot;)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_03.png?w=750&#038;ssl=1\" alt=\"\u7b80\u8981\u7edf\u8ba1\u7ed3\u679c\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>print(&quot;\u6570\u636e\u7684\u7b80\u8981\u7edf\u8ba1\u6307\u6807\uff1a&quot;)<\/code>\u8868\u793a\u5c06<code>&quot;&quot;<\/code>\u4e2d\u7684\u6587\u5b57\uff08<a href=\"https:\/\/www.runoob.com\/python\/python-strings.html\">\u5b57\u7b26\u4e32<\/a>\uff09\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>print(df.describe(), &quot;\\n&quot;)<\/code>\uff1a\u663e\u793a\u540d\u79f0\u4e3a<code>df<\/code>\u53d8\u91cf\u7684\u7b80\u8981\u7edf\u8ba1\u7ed3\u679c\uff0c\u7136\u540e\u56de\u8f66\uff0c\u6362\u884c\u3002<\/li>\n<li><code>print()<\/code>\u662fpython\u663e\u793a\u8f93\u51fa\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06\u53d8\u91cf\uff0c\u6570\u636e\uff0c\u8fd0\u884c\u7ed3\u679c\u7b49\u4ee5\u6587\u672c\u7684\u65b9\u5f0f\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>\u201d\\n&quot;<\/code>\uff1a\u8868\u793a\u56de\u8f66\u3001\u6362\u884c\uff0c\u5176\u529f\u80fd\u662f\uff1a\u5c06\u524d\u540e\u4e24\u6b21\u663e\u793a\u7684\u7ed3\u679c\u4ee5\u7a7a\u884c\u9694\u5f00\u3002<\/li>\n<\/ul>\n<p><strong>\u7ed8\u5236XY\u6563\u70b9\u56fe\uff1a\u5b9a\u4e49\u51fd\u6570<\/strong><\/p>\n<pre><code class=\"language-python\">def xy_scatter(x_var, y_var, x_label_str, y_label_str, x_unit_str, y_unit_str, color_str):    # \u51fd\u6570\u540d\u79f0\uff08\u53c2\u6570\uff09\n    plt.rcParams[&#039;font.sans-serif&#039;] = [&#039;SimHei&#039;]                  # \u5b9a\u4e49\u5b57\u4f53\uff0c\u663e\u793a\u4e2d\u6587\n    plt.rcParams[&#039;axes.unicode_minus&#039;] = False                    # \u5b9a\u4e49\u5b57\u4f53\uff0c\u663e\u793a\u4e2d\u6587\n    title_str = y_label_str + &quot;\u4e0e&quot; + x_label_str + &quot;\u6563\u70b9\u56fe&quot;         # \u5b9a\u4e49\u6807\u9898\u5b57\u7b26\u4e32\n    y_label_str = y_label_str + y_unit_str                        # y\u8f74\u6807\u7b7e+\u5355\u4f4d\n    x_label_str = x_label_str + x_unit_str                        # x\u8f74\u6807\u7b7e+\u5355\u4f4d\n    plt.scatter(x_var, y_var, c=color_str)                        # \u6563\u70b9\u56fe\n    plt.xlabel(x_label_str)                                       # \u663e\u793ax\u8f74\u6807\u7b7e\n    plt.ylabel(y_label_str)                                       # \u663e\u793ay\u8f74\u6807\u7b7e\n    plt.title(title_str, fontsize=&quot;xx-large&quot;, fontweight=&quot;bold&quot;)  # \u663e\u793a\u6807\u9898\n    plt.show()                                                    # \u663e\u793a\u6574\u4e2a\u56fe\u5f62\n    return<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u5b9a\u4e49\u4e00\u4e2a\u7ed8\u5236XY\u6563\u70b9\u56fe\u7684\u51fd\u6570<code>xy_scatter()<\/code>\u3002<\/li>\n<li>\u51fd\u6570\u5b9a\u4e49\uff1a\u51fd\u6570\u662f\u6307\u4e00\u6bb5\u529f\u80fd\u5b8c\u6574\uff0c\u53ef\u4ee5\u91cd\u590d\u4f7f\u7528\u7684\u4ee3\u7801\u7247\u6bb5\u3002<\/li>\n<li>\u51fd\u6570\u8bf4\u660e\uff1apython\u4e2d\uff0c\u51fd\u6570\u5b9a\u4e49\u4ee5<code>def<\/code>\u5f00\u59cb\uff0c<code>return<\/code>\u7ed3\u675f\u3002<\/li>\n<li>\u51fd\u6570\u4f7f\u7528\u573a\u666f\uff1a\u5f53\u67d0\u4e00\u7279\u5b9a\u529f\u80fd\u9700\u8981\u591a\u6b21\u5b9e\u73b0\uff0c\u5efa\u8bae\u5b9a\u4e49\u4e3a\u51fd\u6570\uff0c\u91cd\u590d\u8c03\u7528\u3002<\/li>\n<li>\u5173\u4e8e\u51fd\u6570\uff0c\u8be6\u60c5\u53c2\u8003\uff1a<a href=\"https:\/\/www.runoob.com\/python\/python-functions.html\">Python \u51fd\u6570<\/a><\/li>\n<\/ul>\n<p><strong>\u7ed8\u5236XY\u6563\u70b9\u56fe\uff1a\u5b9a\u4e49\u53d8\u91cf<\/strong><\/p>\n<pre><code class=\"language-python\">y = df[&quot;NP_Loan&quot;]\nx1 = df[&quot;Balance&quot;]\nx2 = df[&quot;CR_Loan&quot;]\nx3 = df[&quot;Loan_Num&quot;]\nx4 = df[&quot;Fix_Invest&quot;]<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u5c06\u53d8\u91cf<code>df<\/code>\u4e2d\u540d\u79f0\u4e3a<code>&quot;NP_Loan&quot;<\/code>\u7684\u5217\uff0c\u8d4b\u503c\u7ed9\u53d8\u91cf<code>y<\/code>\uff1b\u5176\u4ed6\u53d8\u91cf\u8d4b\u503c\uff0c\u4f9d\u6b21\u7c7b\u63a8\u3002<\/li>\n<\/ul>\n<p><strong>\u7ed8\u5236XY\u6563\u70b9\u56fe\uff1a\u5b9a\u4e49\u6807\u7b7e<\/strong><\/p>\n<pre><code class=\"language-python\">y_label_str = &quot;\u4e0d\u826f\u8d37\u6b3e&quot;\nx1_label_str = &quot;\u8d37\u6b3e\u4f59\u989d&quot;\nx2_label_str = &quot;\u7d2f\u8ba1\u5e94\u6536\u8d37\u6b3e&quot;\nx3_label_str = &quot;\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570&quot;\nx4_label_str = &quot;\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u989d&quot;<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u5c06\u5b57\u7b26\u4e32<code>&quot;\u4e0d\u826f\u8d37\u6b3e&quot;<\/code>\u8d4b\u503c\u7ed9\u53d8\u91cf<code>y_label_str<\/code>\uff1b\u5176\u4ed6\u53d8\u91cf\u8d4b\u503c\uff0c\u4f9d\u6b21\u7c7b\u63a8\u3002<\/li>\n<\/ul>\n<p><strong>\u7ed8\u5236XY\u6563\u70b9\u56fe\uff1a\u7ed8\u56fe<\/strong><\/p>\n<pre><code class=\"language-python\">xy_scatter(x1, y, x1_label_str, y_label_str, &quot;(\u4ebf\u5143)&quot;, &quot;(\u4ebf\u5143)&quot;, &quot;red&quot;)      # \u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u4f59\u989d\u6563\u70b9\u56fe\nxy_scatter(x2, y, x2_label_str, y_label_str, &quot;(\u4ebf\u5143)&quot;, &quot;(\u4ebf\u5143)&quot;, &quot;yellow&quot;)   # \u4e0d\u826f\u8d37\u6b3e\u4e0e\u7d2f\u8ba1\u5e94\u6536\u8d37\u6b3e\u6563\u70b9\u56fe\nxy_scatter(x3, y, x3_label_str, y_label_str, &quot;(\u4e2a)&quot;, &quot;(\u4ebf\u5143)&quot;, &quot;blue&quot;)       # \u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570\u6563\u70b9\u56fe\nxy_scatter(x4, y, x4_label_str, y_label_str, &quot;(\u4ebf\u5143)&quot;, &quot;(\u4ebf\u5143)&quot;, &quot;black&quot;)    # \u4e0d\u826f\u8d37\u6b3e\u4e0e\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u989d\u6563\u70b9\u56fe<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_04.png?w=750&#038;ssl=1\" alt=\"\u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u4f59\u989d\u6563\u70b9\u56fe\" \/><\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_05.png?w=750&#038;ssl=1\" alt=\"\u4e0d\u826f\u8d37\u6b3e\u4e0e\u7d2f\u8ba1\u5e94\u6536\u8d37\u6b3e\u6563\u70b9\u56fe\" \/><\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_06.png?w=750&#038;ssl=1\" alt=\"\u4e0d\u826f\u8d37\u6b3e\u4e0e\u8d37\u6b3e\u9879\u76ee\u4e2a\u6570\u6563\u70b9\u56fe\" \/><\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_07.png?w=750&#038;ssl=1\" alt=\"\u4e0d\u826f\u8d37\u6b3e\u4e0e\u56fa\u5b9a\u8d44\u4ea7\u6295\u8d44\u989d\u6563\u70b9\u56fe\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u8c03\u7528\u4e0a\u8ff0\u81ea\u5b9a\u4e49\u51fd\u6570<code>xy_scatter()<\/code>\uff0c\u7ed8\u5236XY\u6563\u70b9\u56fe\uff1b<\/li>\n<li><code>()<\/code>\u5185\u4e3a\u51fd\u6570\u53c2\u6570\uff0c\u53c2\u6570\u6b21\u5e8f\u4e0e\u51fd\u6570\u5b9a\u4e49\u4fdd\u6301\u4e00\u81f4\u3002<\/li>\n<li>\u53c2\u6570\u542b\u4e49\uff08\u4f9d\u6b21\u4e3a\uff09\uff1ax\u53d8\u91cf\uff0cy\u53d8\u91cf\uff0cx\u53d8\u91cf\u6807\u7b7e\uff0cy\u53d8\u91cf\u6807\u7b7e\uff0cx\u53d8\u91cf\u5355\u4f4d\uff0cy\u53d8\u91cf\u5355\u4f4d\uff0c\u6563\u70b9\u7684\u989c\u8272<\/li>\n<\/ul>\n<p><strong>\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\uff1a\u5b9a\u4e49\u51fd\u6570<\/strong><\/p>\n<pre><code class=\"language-python\">def stat(x):\n    return pd.Series(\n        [x.count(), x.min(), x.quantile(.25), x.median(), x.quantile(.75), x.max(), x.mean(), x.var(), x.std(),\n         x.skew(), x.kurt(), x.max() - x.min()],\n        index=[&#039;\u603b\u6570&#039;, &#039;\u6700\u5c0f\u503c&#039;, &#039;\u4e0b\u56db\u5206\u4f4d\u6570&#039;, &#039;\u4e2d\u4f4d\u6570&#039;, &#039;\u4e0a\u56db\u5206\u4f4d\u6570&#039;, &#039;\u6700\u5927\u503c&#039;, &#039;\u5e73\u5747\u6570&#039;, &#039;\u65b9\u5dee&#039;, &#039;\u6807\u51c6\u5dee&#039;, &#039;\u504f\u5ea6&#039;, &#039;\u5cf0\u5ea6&#039;, &#039;\u6781\u5dee&#039;])<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u5b9a\u4e49\u4e00\u4e2a\u8ba1\u7b97\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\u7684\u51fd\u6570<code>stat()<\/code>\u3002<\/li>\n<li>\u51fd\u6570\u5b9a\u4e49\uff1a\u51fd\u6570\u662f\u6307\u4e00\u6bb5\u529f\u80fd\u5b8c\u6574\uff0c\u53ef\u4ee5\u91cd\u590d\u4f7f\u7528\u7684\u4ee3\u7801\u7247\u6bb5\u3002<\/li>\n<li>\u51fd\u6570\u8bf4\u660e\uff1apython\u4e2d\uff0c\u51fd\u6570\u5b9a\u4e49\u4ee5<code>def<\/code>\u5f00\u59cb\uff0c<code>return<\/code>\u7ed3\u675f\u3002<\/li>\n<li>\u51fd\u6570\u4f7f\u7528\u573a\u666f\uff1a\u5f53\u67d0\u4e00\u7279\u5b9a\u529f\u80fd\u9700\u8981\u591a\u6b21\u5b9e\u73b0\uff0c\u5efa\u8bae\u5b9a\u4e49\u4e3a\u51fd\u6570\uff0c\u91cd\u590d\u8c03\u7528\u3002<\/li>\n<li>\u5173\u4e8e\u51fd\u6570\uff0c\u8be6\u60c5\u53c2\u8003\uff1a<a href=\"https:\/\/www.runoob.com\/python\/python-functions.html\">Python \u51fd\u6570<\/a><\/li>\n<\/ul>\n<p><strong>\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\uff1a\u8ba1\u7b97\u7edf\u8ba1\u6307\u6807<\/strong><\/p>\n<pre><code class=\"language-python\">stat_y = pd.DataFrame(stat(y))\nstat_x1 = pd.DataFrame(stat(x1))\nstat_x2 = pd.DataFrame(stat(x2))\nstat_x3 = pd.DataFrame(stat(x3))\nstat_x4 = pd.DataFrame(stat(x4))<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u8c03\u7528\u4e0a\u8ff0\u81ea\u5b9a\u4e49\u51fd\u6570<code>stat()<\/code>,\u8ba1\u7b97\u53d8\u91cf<code>y<\/code>\u7684\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\u53d6\u503c\uff0c\u5e76\u5c06\u7edf\u8ba1\u6307\u6807\u53d6\u503c\u8d4b\u7ed9\u53d8\u91cf<code>stat_y<\/code>;\u5176\u4ed6\u53d8\u91cf\uff0c\u4f9d\u6b21\u7c7b\u63a8\u3002<\/li>\n<\/ul>\n<p><strong>\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\uff1a\u5408\u5e76\u663e\u793a\u7edf\u8ba1\u6307\u6807<\/strong><\/p>\n<pre><code class=\"language-python\">dfs = pd.DataFrame(columns=[&#039;NP_Loan&#039;, &#039;Balance&#039;, &#039;CR_Loan&#039;, &#039;Loan_Num&#039;, &#039;Fix_Invest&#039;])\ndfs[&#039;NP_Loan&#039;] = stat_y\ndfs[&#039;Balance&#039;] = stat_x1\ndfs[&#039;CR_Loan&#039;] = stat_x2\ndfs[&#039;Loan_Num&#039;] = stat_x3\ndfs[&#039;Fix_Invest&#039;] = stat_x4<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>dfs = pd.DataFrame()<\/code>\uff1a\u5b9a\u4e49\u4e00\u4e2a\u683c\u5f0f\u4e3aDataFrame\u7684\u53d8\u91cf<code>dfs<\/code>\u3002<\/li>\n<li><code>columns=[]<\/code>\uff1a\u8868\u793a\u4ee5\u5217\u8868\u65b9\u5f0f\u5b9a\u4e49\u53d8\u91cf<code>dfs<\/code>\u7684\u5217\u6807\u7b7e\u3002<\/li>\n<li>\u5217\u8868\u662fpython\u4e2d\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\uff0c\u901a\u5e38\u4ee5<code>[]<\/code>\u6807\u8bb0\uff0c\u5217\u8868\u5185\u7684\u5143\u7d20\u53ef\u4ee5\u662f\u4efb\u610f\u5185\u5bb9\uff0c\u591a\u4e2a\u5143\u7d20\u4e4b\u95f4\u4ee5<code>,<\/code>\u9694\u5f00\u3002<\/li>\n<li>\u5173\u4e8e\u5217\u8868\uff0c\u8be6\u60c5\u53c2\u8003\uff1a<a href=\"https:\/\/www.runoob.com\/python\/python-lists.html\">\u83dc\u9e1f\u6559\u7a0b\uff1aPython \u5217\u8868(List)<\/a><\/li>\n<li><code>dfs[&#039;NP_Loan&#039;] = stat_y<\/code>\uff1a\u8868\u793a\u5c06\u53d8\u91cf<code>stat_y<\/code>\u8d4b\u503c\u7ed9\u53d8\u91cf<code>dfs<\/code>\u4e2d\u540d\u79f0\u4e3a<code>&#039;NP_Loan&#039;<\/code>\u7684\u5217\uff1b\u5176\u4ed6\u53d8\u91cf\u8d4b\u503c\uff0c\u4f9d\u6b21\u7c7b\u63a8\u3002<\/li>\n<\/ul>\n<p><strong>\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\uff1a\u5c55\u793a\u7edf\u8ba1\u6307\u6807<\/strong><\/p>\n<pre><code class=\"language-python\">print(&quot;\u6570\u636e\u7684\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\uff1a&quot;)\nprint(dfs, &quot;\\n&quot;)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_08.png?w=750&#038;ssl=1\" alt=\"\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>print(&quot;\u6570\u636e\u7684\u8be6\u7ec6\u7edf\u8ba1\u6307\u6807\uff1a&quot;)<\/code>\u8868\u793a\u5c06<code>&quot;&quot;<\/code>\u4e2d\u7684\u6587\u5b57\uff08<a href=\"https:\/\/www.runoob.com\/python\/python-strings.html\">\u5b57\u7b26\u4e32<\/a>\uff09\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>print(dfs, &quot;\\n&quot;)<\/code>\uff1a\u663e\u793a\u540d\u79f0\u4e3a<code>dfs<\/code>\u53d8\u91cf\u7684\u7b80\u8981\u7edf\u8ba1\u7ed3\u679c\uff0c\u7136\u540e\u56de\u8f66\uff0c\u6362\u884c\u3002<\/li>\n<li><code>print()<\/code>\u662fpython\u663e\u793a\u8f93\u51fa\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06\u53d8\u91cf\uff0c\u6570\u636e\uff0c\u8fd0\u884c\u7ed3\u679c\u7b49\u4ee5\u6587\u672c\u7684\u65b9\u5f0f\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>\u201d\\n&quot;<\/code>\uff1a\u8868\u793a\u56de\u8f66\u3001\u6362\u884c\uff0c\u5176\u529f\u80fd\u662f\uff1a\u5c06\u524d\u540e\u4e24\u6b21\u663e\u793a\u7684\u7ed3\u679c\u4ee5\u7a7a\u884c\u9694\u5f00\u3002<\/li>\n<\/ul>\n<p><strong>\u5c55\u793a\u6570\u636e\u7684\u534f\u65b9\u5dee\u77e9\u9635<\/strong><\/p>\n<pre><code class=\"language-python\">print(&quot;\u6570\u636e\u7684\u534f\u65b9\u5dee\u77e9\u9635\uff1a&quot;)\nprint(df.cov(), &quot;\\n&quot;)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_09.png?w=750&#038;ssl=1\" alt=\"\u534f\u65b9\u5dee\u77e9\u9635\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>print(&quot;\u6570\u636e\u7684\u534f\u65b9\u5dee\u77e9\u9635\uff1a&quot;)<\/code>\u8868\u793a\u5c06<code>&quot;&quot;<\/code>\u4e2d\u7684\u6587\u5b57\uff08<a href=\"https:\/\/www.runoob.com\/python\/python-strings.html\">\u5b57\u7b26\u4e32<\/a>\uff09\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>print(df.cov(), &quot;\\n&quot;)<\/code>\uff1a\u663e\u793a\u540d\u79f0\u4e3a<code>df<\/code>\u53d8\u91cf\u7684\u534f\u65b9\u5dee\u77e9\u9635\uff0c\u7136\u540e\u56de\u8f66\uff0c\u6362\u884c\u3002<\/li>\n<li><code>.cov()<\/code>\uff1a\u8ba1\u7b97\u6570\u636e\u5e8f\u5217\u534f\u65b9\u5dee\u7684\u51fd\u6570\u3002<\/li>\n<li><code>print()<\/code>\u662fpython\u663e\u793a\u8f93\u51fa\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06\u53d8\u91cf\uff0c\u6570\u636e\uff0c\u8fd0\u884c\u7ed3\u679c\u7b49\u4ee5\u6587\u672c\u7684\u65b9\u5f0f\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>\u201d\\n&quot;<\/code>\uff1a\u8868\u793a\u56de\u8f66\u3001\u6362\u884c\uff0c\u5176\u529f\u80fd\u662f\uff1a\u5c06\u524d\u540e\u4e24\u6b21\u663e\u793a\u7684\u7ed3\u679c\u4ee5\u7a7a\u884c\u9694\u5f00\u3002<\/li>\n<\/ul>\n<p><strong>\u5c55\u793a\u6570\u636e\u7684\u76f8\u5173\u7cfb\u6570\u77e9\u9635<\/strong><\/p>\n<pre><code class=\"language-python\">print(&quot;\u6570\u636e\u7684\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff1a&quot;)\nprint(df.corr(), &quot;\\n&quot;)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_10.png?w=750&#038;ssl=1\" alt=\"\u76f8\u5173\u7cfb\u6570\u77e9\u9635\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>print(&quot;\u6570\u636e\u7684\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff1a&quot;)<\/code>\u8868\u793a\u5c06<code>&quot;&quot;<\/code>\u4e2d\u7684\u6587\u5b57\uff08<a href=\"https:\/\/www.runoob.com\/python\/python-strings.html\">\u5b57\u7b26\u4e32<\/a>\uff09\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>print(df.corr(), &quot;\\n&quot;)<\/code>\uff1a\u663e\u793a\u540d\u79f0\u4e3a<code>df<\/code>\u53d8\u91cf\u7684\u76f8\u5173\u7cfb\u6570\u77e9\u9635\uff0c\u7136\u540e\u56de\u8f66\uff0c\u6362\u884c\u3002<\/li>\n<li><code>.corr()<\/code>\uff1a\u8ba1\u7b97\u6570\u636e\u5e8f\u5217\u76f8\u5173\u7cfb\u6570\u7684\u51fd\u6570\u3002<\/li>\n<li><code>print()<\/code>\u662fpython\u663e\u793a\u8f93\u51fa\u51fd\u6570\uff0c\u53ef\u4ee5\u5c06\u53d8\u91cf\uff0c\u6570\u636e\uff0c\u8fd0\u884c\u7ed3\u679c\u7b49\u4ee5\u6587\u672c\u7684\u65b9\u5f0f\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>\u201d\\n&quot;<\/code>\uff1a\u8868\u793a\u56de\u8f66\u3001\u6362\u884c\uff0c\u5176\u529f\u80fd\u662f\uff1a\u5c06\u524d\u540e\u4e24\u6b21\u663e\u793a\u7684\u7ed3\u679c\u4ee5\u7a7a\u884c\u9694\u5f00\u3002<\/li>\n<\/ul>\n<p><strong>\u4e00\u5143\u7ebf\u6027\u56de\u5f52\uff1a\u5b9a\u4e49\u53d8\u91cf<\/strong><\/p>\n<pre><code class=\"language-python\">X = sm.add_constant(x1)      # \u6dfb\u52a0\u5e38\u6570\u9879<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u4e3a\u53d8\u91cf<code>x1<\/code>\u6dfb\u52a0\u4e00\u5217\u53d6\u503c\u4e3a1\u7684\u5217\uff0c\u5e76\u5c06\u7ed3\u679c\u8d4b\u503c\u7ed9\u53d8\u91cf<code>X<\/code>\u3002\u76f8\u5f53\u4e8e\u5728\u56de\u5f52\u7684\u89e3\u91ca\u53d8\u91cf\u4e2d\u6dfb\u52a0\u4e00\u4e2a\u5e38\u6570\u5217\uff0c\u4f5c\u4e3a\u56de\u5f52\u7684\u5e38\u6570\u9879\u3002<\/li>\n<\/ul>\n<p><strong>\u4e00\u5143\u7ebf\u6027\u56de\u5f52\uff1aOLS\u56de\u5f52<\/strong><\/p>\n<pre><code class=\"language-python\">OLS_model = sm.OLS(y, X).fit()<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u8c03\u7528<code>Statsmodels<\/code>\u7a0b\u5e8f\u5305\u4e2d\u7684<code>.OLS()<\/code>\u51fd\u6570\uff0c\u62df\u5408<code>y<\/code>\u548c<code>X<\/code>\uff0c\u5e76\u5c06\u62df\u5408\u7ed3\u679c\u8d4b\u503c\u7ed9\u53d8\u91cf<code>OLS_model<\/code>\u3002<\/li>\n<li><code>sm<\/code>\uff1a\u7a0b\u5e8f\u5305<code>Statsmodels<\/code>\u7684\u7b80\u5199\uff0c\u4ee3\u7801\u5f00\u5934\u5bfc\u5165\u7a0b\u5e8f\u5305\u65f6\u8bbe\u7f6e\u3002<\/li>\n<li><code>.fit()<\/code>\uff1a\u62df\u5408\u51fd\u6570\uff0c\u7528\u4e8e\u83b7\u53d6\u56de\u5f52\u7ed3\u679c\u3002<\/li>\n<\/ul>\n<p><strong>\u4e00\u5143\u7ebf\u6027\u56de\u5f52\uff1a\u5c55\u793a\u56de\u5f52\u7ed3\u679c<\/strong><\/p>\n<pre><code class=\"language-python\">print(&quot;OLS\u56de\u5f52\u7ed3\u679c\uff1a&quot;)\nprint(OLS_model.summary(), &quot;\\n&quot;)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_11.png?w=750&#038;ssl=1\" alt=\"OLS\u56de\u5f52\u7ed3\u679c\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>print(&quot;OLS\u56de\u5f52\u7ed3\u679c\uff1a&quot;)<\/code>\u8868\u793a\u5c06<code>&quot;&quot;<\/code>\u4e2d\u7684\u6587\u5b57\uff08<a href=\"https:\/\/www.runoob.com\/python\/python-strings.html\">\u5b57\u7b26\u4e32<\/a>\uff09\u663e\u793a\u5728\u7a0b\u5e8f\u8fd0\u884c\u7a97\u53e3\u3002<\/li>\n<li><code>print(OLS_model.summary(), &quot;\\n&quot;)<\/code>\uff1a\u5c55\u793aOLS\u56de\u5f52\u7ed3\u679c\u7684\u4e3b\u8981\u5185\u5bb9\uff0c\u7136\u540e\u56de\u8f66\uff0c\u6362\u884c\u3002<\/li>\n<li><code>.summary()<\/code>\uff1a\u83b7\u53d6\u53d8\u91cf<code>OLS_model<\/code>\u4e2dOLS\u56de\u5f52\u7684\u4e3b\u8981\u5185\u5bb9\u3002<\/li>\n<\/ul>\n<p><strong>\u4e00\u5143\u7ebf\u6027\u56de\u5f52\uff1a\u5c55\u793a\u56de\u5f52\u65b9\u7a0b<\/strong><\/p>\n<pre><code class=\"language-python\">dfc = pd.DataFrame(OLS_model.params).round(4)       # \u83b7\u53d6\u56de\u5f52\u53c2\u6570,\u4fdd\u7559\u5c0f\u6570\u70b9\u540e4\u4f4d\uff0c\u8d4b\u503c\u7ed9dfc\ndfc = dfc.astype(&quot;string&quot;)                          # \u8f6c\u6362dfc\u7684\u683c\u5f0f\u4e3a\u5b57\u7b26\u4e32\uff0c\u91cd\u65b0\u8d4b\u503c\u7ed9dfc\nbeta0 = dfc.iloc[0, 0]                              # \u83b7\u53d6dfc\u7684\u7b2c1\u4e2a\u503c\uff0c\u8d4b\u503c\u7ed9beta0\nbeta1 = dfc.iloc[1, 0]                              # \u83b7\u53d6dfc\u7684\u7b2c2\u4e2a\u503c\uff0c\u8d4b\u503c\u7ed9\u5b9a\u4e49beta1\nOLS_equation = &quot;Y=&quot; + beta0 + &quot;+&quot; + beta1 + &quot;*X&quot;    # \u5b9a\u4e49\u56de\u5f52\u65b9\u7a0b\uff0c\u65b9\u5f0f\uff1a\u5b57\u7b26\u4e32\u62fc\u63a5\nprint(&quot;\u4e00\u5143\u7ebf\u6027\u56de\u5f52\u65b9\u7a0b\u4e3a\uff1a&quot;+OLS_equation, &quot;\\n&quot;)       # \u663e\u793a\u56de\u5f52\u65b9\u7a0b<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_12.png?w=750&#038;ssl=1\" alt=\"\u56de\u5f52\u65b9\u7a0b\" style=\"zoom: 80%;\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li><code>OLS_model.params<\/code>\uff1a\u8868\u793a\u83b7\u53d6\u53d8\u91cf<code>OLS_model<\/code>\u4e2dOLS\u56de\u5f52\u7684\u53c2\u6570<\/li>\n<li><code>.round(4)<\/code>\uff1a\u8868\u793a\u5bf9\u53d8\u91cf\u53d6\u503c\u4fdd\u7559\u5c0f\u6570\u70b9\u540e4\u4f4d<\/li>\n<li><code>dfc.astype(&quot;string&quot;)<\/code>\uff1a\u8868\u793a\u5c06\u53d8\u91cf<code>dfc<\/code>\u7684\u683c\u5f0f\u8f6c\u6362\u4e3a\u5b57\u7b26\uff08string\uff09\u4e32\u683c\u5f0f\u3002<\/li>\n<li><code>.loc[]<\/code>\uff1a\u5b9a\u4f4d\u51fd\u6570\uff0c\u4f9d\u636e\u884c\u7d22\u5f15\u548c\u5217\u7d22\u5f15\u5b9a\u4f4dDataFrame\u6570\u636e\u4e2d\u7684\u5143\u7d20\u3002\u4f8b\u5982\uff1a<code>dfc.iloc[1, 0]<\/code>\u8868\u793adfc\u53d8\u91cf\u4e2d\u884c\u7d22\u5f15\u4e3a1\uff0c\u5217\u7d22\u5f15\u4e3a0\u7684\u5143\u7d20\u3002\uff08\u6ce8\u610f\uff1apython\u4e2d\uff0c\u884c\u7d22\u5f15\u548c\u5217\u7d22\u5f15\u9ed8\u8ba4\u4ece0\u5f00\u59cb\u3002\uff09<\/li>\n<\/ul>\n<p><strong>\u7ed8\u5236\u8d8b\u52bf\u7ebf\u4e0e\u6563\u70b9\u56fe\uff1a\u5b9a\u4e49\u51fd\u6570<\/strong><\/p>\n<pre><code class=\"language-python\">def fitted_scatter(x_var, y_var, y_fit_var, x_label_str, y_label_str, x_unit_str, y_unit_str, scatter_c, plot_c, equation_str): # \u51fd\u6570\u540d\u79f0\uff08\u53c2\u6570\uff09 \n    plt.rcParams[&#039;font.sans-serif&#039;] = [&#039;SimHei&#039;]                                  # \u5b9a\u4e49\u5b57\u4f53\uff0c\u663e\u793a\u4e2d\u6587\n    plt.rcParams[&#039;axes.unicode_minus&#039;] = False                                    # \u5b9a\u4e49\u5b57\u4f53\uff0c\u663e\u793a\u4e2d\u6587\n    title_str = y_label_str + &quot;\u4e0e&quot; + x_label_str + &quot;\u6563\u70b9\u56fe&quot;                        # \u5b9a\u4e49\u6807\u9898\u5b57\u7b26\u4e32\n    y_label_str = y_label_str + y_unit_str                                        # y\u8f74\u6807\u7b7e+\u5355\u4f4d\n    x_label_str = x_label_str + x_unit_str                                        # y\u8f74\u6807\u7b7e+\u5355\u4f4d\n    plt.scatter(x_var, y_var, color=scatter_c, label=&quot;\u5b9e\u9645\u503c&quot;)                     # \u6563\u70b9\u56fe\n    plt.plot(x_var, y_fit_var, color=plot_c, label=&quot;\u9884\u6d4b\u503c&quot;)                       # \u8d8b\u52bf\u7ebf\n    plt.title(title_str, fontsize=&quot;xx-large&quot;, fontweight=&quot;bold&quot;)                  # \u6807\u9898\n    plt.xlabel(x_label_str)                                                       # \u663e\u793ax\u8f74\u6807\u7b7e\n    plt.ylabel(y_label_str)                                                       # \u663e\u793ay\u8f74\u6807\u7b7e\n    plt.text(240, 7, s=equation_str, fontsize=&quot;x-large&quot;)                          # \u663e\u793a\u56de\u5f52\u65b9\u7a0b\uff0c\u4f4d\u7f6e\u81ea\u5b9a\u4e49\n    plt.legend()                                                                  # \u663e\u793a\u56fe\u4f8b\n    plt.show()                                                                    # \u663e\u793a\u6574\u4e2a\u56fe\u5f62\n    return<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u5b9a\u4e49\u4e00\u4e2a\u7ed8\u5236\u8d8b\u52bf\u7ebf\u548c\u6563\u70b9\u56fe\u7684\u51fd\u6570<code>fitted_scatter()<\/code>\u3002<\/li>\n<li>\u51fd\u6570\u5b9a\u4e49\uff1a\u51fd\u6570\u662f\u6307\u4e00\u6bb5\u529f\u80fd\u5b8c\u6574\uff0c\u53ef\u4ee5\u91cd\u590d\u4f7f\u7528\u7684\u4ee3\u7801\u7247\u6bb5\u3002<\/li>\n<li>\u51fd\u6570\u8bf4\u660e\uff1apython\u4e2d\uff0c\u51fd\u6570\u5b9a\u4e49\u4ee5<code>def<\/code>\u5f00\u59cb\uff0c<code>return<\/code>\u7ed3\u675f\u3002<\/li>\n<li>\u51fd\u6570\u4f7f\u7528\u573a\u666f\uff1a\u5f53\u67d0\u4e00\u7279\u5b9a\u529f\u80fd\u9700\u8981\u591a\u6b21\u5b9e\u73b0\uff0c\u5efa\u8bae\u5b9a\u4e49\u4e3a\u51fd\u6570\uff0c\u91cd\u590d\u8c03\u7528\u3002<\/li>\n<li>\u5173\u4e8e\u51fd\u6570\uff0c\u8be6\u60c5\u53c2\u8003\uff1a<a href=\"https:\/\/www.runoob.com\/python\/python-functions.html\">Python \u51fd\u6570<\/a><\/li>\n<\/ul>\n<p><strong>\u7ed8\u5236\u8d8b\u52bf\u7ebf\u4e0e\u6563\u70b9\u56fe\uff1a\u5b9a\u4e49\u53d8\u91cf<\/strong><\/p>\n<pre><code class=\"language-python\">y_fitted = OLS_model.predict()<\/code><\/pre>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u83b7\u53d6\u53d8\u91cf<code>OLS_model<\/code>\u4e2dOLS\u56de\u5f52\u7684\u62df\u5408\u503c\uff0c\u8d4b\u503c\u7ed9\u53d8\u91cf<code>y_fitted<\/code>\u3002<\/li>\n<li><code>OLS_model.predict()<\/code>\uff1a\u8868\u793a\u83b7\u53d6\u53d8\u91cf<code>OLS_model<\/code>\u4e2dOLS\u56de\u5f52\u7684\u62df\u5408\u503c\u3002<\/li>\n<\/ul>\n<p><strong>\u7ed8\u5236\u8d8b\u52bf\u7ebf\u4e0e\u6563\u70b9\u56fe\uff1a\u5b9a\u4e49\u6807\u7b7e\uff08\u7565\uff09<\/strong><\/p>\n<p>\u8bf4\u660e\uff1a\u4e0a\u8ff0\u4ee3\u7801\u5df2\u7ecf\u5b9a\u4e49\uff0c\u8fd9\u91cc\u76f4\u63a5\u5f15\u7528\u3002<\/p>\n<p><strong>\u7ed8\u5236\u8d8b\u52bf\u7ebf\u4e0e\u6563\u70b9\u56fe\uff1a\u7ed8\u56fe<\/strong><\/p>\n<pre><code class=\"language-python\">fitted_scatter(x1, y, y_fitted, x1_label_str, y_label_str, &quot;\u4ebf\u5143&quot;, &quot;\u4ebf\u5143&quot;, &quot;blue&quot;, &quot;black&quot;, OLS_equation)<\/code><\/pre>\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn.seit2019.xyz\/pic\/python\/5.2_lec02_13.png?w=750&#038;ssl=1\" alt=\"\u8d8b\u52bf\u7ebf\u4e0e\u6563\u70b9\u56fe\" \/><\/p>\n<p>\u8bf4\u660e\uff1a<\/p>\n<ul>\n<li>\u529f\u80fd\uff1a\u8c03\u7528\u4e0a\u8ff0\u81ea\u5b9a\u4e49\u51fd\u6570<code>fitted_scatter()<\/code>\uff0c\u7ed8\u5236\u8d8b\u52bf\u7ebf\u548c\u6563\u70b9\u56fe\u3002<\/li>\n<li><code>()<\/code>\u5185\u4e3a\u51fd\u6570\u53c2\u6570\uff0c\u53c2\u6570\u6b21\u5e8f\u4e0e\u51fd\u6570\u5b9a\u4e49\u4fdd\u6301\u4e00\u81f4\u3002<\/li>\n<li>\u53c2\u6570\u542b\u4e49\uff08\u4f9d\u6b21\u4e3a\uff09\uff1ax\u53d8\u91cf\uff0cy\u53d8\u91cf\uff0cy\u53d8\u91cf\u7684\u62df\u5408\u503c\uff0cx\u53d8\u91cf\u6807\u7b7e\uff0cy\u53d8\u91cf\u6807\u7b7e\uff0cx\u53d8\u91cf\u5355\u4f4d\uff0cy\u53d8\u91cf\u5355\u4f4d\uff0c\u6563\u70b9\u7684\u989c\u8272\uff0c\u8d8b\u52bf\u7ebf\u7684\u989c\u8272\uff0c\u56de\u5f52\u65b9\u7a0b\u3002<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>5.2.0 \u8981\u70b9 Python\u57fa\u672c\u64cd\u4f5c \u81ea\u5b9a\u4e49\u7ed8\u56fe\u51fd\u6570\uff1aXY\u6563\u70b9\u56fe\uff0c\u8d8b\u52bf\u548c\u6563\u70b9\u56fe\u51fd\u6570 \u81ea\u5b9a\u4e49\u5e8f\u5217x\u7684 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29,30],"tags":[],"class_list":["post-4823","post","type-post","status-publish","format-standard","hentry","category-29","category-30"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/seit2019.xyz\/index.php?rest_route=\/wp\/v2\/posts\/4823","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/seit2019.xyz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/seit2019.xyz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/seit2019.xyz\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/seit2019.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4823"}],"version-history":[{"count":0,"href":"https:\/\/seit2019.xyz\/index.php?rest_route=\/wp\/v2\/posts\/4823\/revisions"}],"wp:attachment":[{"href":"https:\/\/seit2019.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4823"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/seit2019.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4823"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/seit2019.xyz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4823"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}