📄 多元非线性回归分析 概述.htm
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<p class=MsoNormal align=center style='text-align:center'><b><span
style='font-size:18.0pt;mso-bidi-font-size:12.0pt;font-family:宋体;mso-ascii-font-family:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>多元非线性回归分析</span></b><b><span
style='font-size:18.0pt;mso-bidi-font-size:12.0pt'> </span></b><b><span
style='font-size:18.0pt;mso-bidi-font-size:12.0pt;font-family:宋体;mso-ascii-font-family:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>概述</span></b><b><span
lang=EN-US style='font-size:18.0pt;mso-bidi-font-size:12.0pt'><o:p></o:p></span></b></p>
<p class=MsoNormal><b><span lang=EN-US style='font-size:22.0pt;mso-bidi-font-size:
12.0pt;font-family:宋体'><span style="mso-spacerun: yes"> </span></span></b><span
style='font-size:14.0pt;mso-bidi-font-size:12.0pt;font-family:宋体'>在当前各种科学实验中，都会产生大量的实验测试数据；这些数据在一定概率统计误差（随机干扰）内，在数学上都可以以某种形式的确定性函数来描述其变化规律。<span
lang=EN-US><o:p></o:p></span></span></p>
<p class=MsoNormal style='text-indent:28.0pt;mso-char-indent-count:2.0;
mso-char-indent-size:14.0pt;mso-char-indent-size:14pt'><span style='font-size:
14.0pt;mso-bidi-font-size:12.0pt;font-family:宋体'>回归分析的任务就是对这些受随机干扰下的测试数据“回归”其原有的变化规律。<span
lang=EN-US><o:p></o:p></span></span></p>
<p class=MsoNormal style='text-indent:28.0pt;mso-char-indent-count:2.0;
mso-char-indent-size:14.0pt;mso-char-indent-size:14pt'><span style='font-size:
14.0pt;mso-bidi-font-size:12.0pt;font-family:宋体'>在实际的实验测试数据中，数据符合的数学函数很多时候并不是线性的（或者不能简单的转化为线性关系）；这时候，就需要使用<span
style='color:blue'>非线性的回归分析</span>方法来处理。<span lang=EN-US><o:p></o:p></span></span></p>
<p class=MsoNormal style='text-indent:28.0pt;mso-char-indent-count:2.0;
mso-char-indent-size:14.0pt;mso-char-indent-size:14pt'><span style='font-size:
14.0pt;mso-bidi-font-size:12.0pt;font-family:宋体'>“多元非线性回归分析”<b><span
style='color:blue'>程序设计的目标</span></b>就是要处理具有<span style='color:blue'>多变元</span>（二维数据、三维数据<span
lang=EN-US>,甚至N维数据场）、<span style='color:blue'>多待定系数</span>、<span
style='color:blue'>非线性</span>的回归模型。<o:p></o:p></span></span></p>
<p class=MsoNormal style='text-indent:28.0pt;mso-char-indent-count:2.0;
mso-char-indent-size:14.0pt'><span lang=EN-US style='font-size:14.0pt;
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12.0pt'><![if !supportEmptyParas]> <![endif]><o:p></o:p></span></p>
<p class=MsoNormal align=center style='text-align:center'><span lang=EN-US
style='font-size:9.0pt;mso-bidi-font-size:12.0pt'><a href="多元非线性回归分析%20帮助主题.htm"><span
style='font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-hansi-font-family:
"Times New Roman"'>帮助主题</span></a><o:p></o:p></span></p>
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