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Context 04050100.xhp par_id7951902 help.text

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Context	English	Chinese (Simplified)
04050100.xhp par_id180820161612524298 help.text	<emph>Polynomial</emph> trend line: regression through equation <item type="literal">y=Σ</item><sub><item type="literal">i</item></sub><item type="literal">(a</item><sub><item type="literal">i</item></sub><item type="literal">∙x</item><sup><item type="literal">i</item></sup><item type="literal">)</item>. Intercept <item type="literal">a</item><sub><item type="literal">0</item></sub> can be forced. Degree of polynomial must be given (at least 2).	「<emph>多项式</emph>」趋势线: 通过方程「<item type="literal">y=Σ</item><sub><item type="literal">i</item></sub><item type="literal">(a</item><sub><item type="literal">i</item></sub><item type="literal">∙x</item><sup><item type="literal">i</item></sup><item type="literal">)</item>」进行回归计算。截断「<item type="literal">a</item>」<sub><item type="literal">0</item></sub> 可以强制。必须给出多项式的度数 (至少为 2)。
04050100.xhp par_id180820161612525364 help.text	<emph>Logarithmic</emph> trend line: regression through equation <item type="literal">y=a∙ln(x)+b</item>.	「<emph>对数</emph>」趋势线: 通过方程「<item type="literal">y=a∙ln(x)+b</item>」回归。
04050100.xhp par_id180820161612526680 help.text	<emph>Exponential</emph> trend line: regression through equation <item type="literal">y=b∙exp(a∙x)</item>.This equation is equivalent to <item type="literal">y=b∙m</item><sup><item type="literal">x</item></sup> with <item type="literal">m=exp(a)</item>. Intercept <item type="literal">b</item> can be forced.	「<emph>指数</emph>」趋势线: 通过方程「<item type="literal">y=b∙exp(a∙x)</item>」回归。该方程等效于「<item type="literal">y=b∙m</item><sup><item type="literal">x</item></sup>」其中「<item type="literal">m=exp(a)</item>」。截断「<item type="literal">b</item>」可以强制。
04050100.xhp par_id180820161612527230 help.text	<emph>Power</emph> trend line: regression through equation <item type="literal">y=b∙x</item><sup><item type="literal">a</item></sup>.	「<emph>幂</emph>」趋势线: 通过方程「<item type="literal">y=b∙x</item><sup><item type="literal">a</item></sup>」回归。
04050100.xhp par_id180820161617342768 help.text	<emph>Moving average</emph> trend line: simple moving average is calculated with the <emph>n</emph> previous y-values, <emph>n</emph> being the period. No equation is available for this trend line.	「<emph>移动平均</emph>趋势线: 使用「<emph>n</emph>」个之前的 y 数值计算简单的移动平均， <emph>n</emph>」为期数。该类趋势线无公式可用。
04050100.xhp hd_id9204077 help.text	Constraints	约束
04050100.xhp par_id7393719 help.text	The calculation of the trend line considers only data pairs with the following values:	趋势线的计算只考虑带有下列值的数据对:
04050100.xhp par_id7212744 help.text	Logarithmic trend line: only positive x-values are considered.	对数趋势线: 只考虑正的 x 值。
04050100.xhp par_id1664479 help.text	Exponential trend line: only positive y-values are considered, except if all y-values are negative: regression will then follow equation <item type="literal">y=-b∙exp(a∙x)</item>.	指数趋势线: 只考虑正的 y 值，除非所有 y 值均为负: 那样的话回归将采用方程「<item type="literal">y=-b∙exp(a∙x)</item>」。
04050100.xhp par_id8734702 help.text	Power trend line: only positive x-values are considered; only positive y-values are considered, except if all y-values are negative: regression will then follow equation<item type="literal"> y=-b∙x</item><sup><item type="literal">a</item></sup>.	指数趋势线: 只考虑正的 x 值；只考虑正的 y 值，除非所有 y 值均为负: 那样的话回归将采用方程「<item type="literal"> y=-b∙x</item><sup><item type="literal">a</item></sup>」。
04050100.xhp par_id181279 help.text	You should transform your data accordingly; it is best to work on a copy of the original data and transform the copied data.	您应该相应地转换数据；最好在原始数据副本上工作并且转换复制的数据。
04050100.xhp hd_id180820161528021520 help.text	Calculate Parameters in Calc	在 Calc 中计算参数
04050100.xhp par_id7735221 help.text	You can also calculate the parameters using Calc functions as follows.	您也可以使用以下的 Calc 函数计算参数。
04050100.xhp hd_id5744193 help.text	The linear regression equation	线性回归方程式
04050100.xhp par_id9251991 help.text	The <emph>linear regression</emph> follows the equation <item type="literal">y=m*x+b</item>.	「<emph>线性回归</emph>」遵循如下方程式「<item type="literal">y=m*x+b</item>」。
04050100.xhp par_id7951902 help.text	m = SLOPE(Data_Y;Data_X)	m = SLOPE(Data_Y;Data_X)
04050100.xhp par_id6637165 help.text	b = INTERCEPT(Data_Y ;Data_X)	b = INTERCEPT(Data_Y ;Data_X)
04050100.xhp par_id7879268 help.text	Calculate the coefficient of determination by	计算决定系数通过
04050100.xhp par_id9244361 help.text	r<sup>2</sup> = RSQ(Data_Y;Data_X)	r<sup>2</sup> = RSQ(Data_Y;Data_X)
04050100.xhp par_id2083498 help.text	Besides m, b and r<sup>2</sup> the array function <emph>LINEST</emph> provides additional statistics for a regression analysis.	除了 m, b 和 r<sup>2</sup> 之外，数组函数「<emph>LINEST</emph>」为回归分析提供其他统计。
04050100.xhp hd_id2538834 help.text	The logarithmic regression equation	对数回归方程式
04050100.xhp par_id394299 help.text	The <emph>logarithmic regression</emph> follows the equation <item type="literal">y=a*ln(x)+b</item>.	「<emph>对数回归</emph>」遵循如下方程式「<item type="literal">y=a*ln(x)+b</item>」。
04050100.xhp par_id2134159 help.text	a = SLOPE(Data_Y;LN(Data_X))	a = SLOPE(Data_Y;LN(Data_X))
04050100.xhp par_id5946531 help.text	b = INTERCEPT(Data_Y ;LN(Data_X))	b = INTERCEPT(Data_Y ;LN(Data_X))
04050100.xhp par_id5649281 help.text	r<sup>2</sup> = RSQ(Data_Y;LN(Data_X))	r<sup>2</sup> = RSQ(Data_Y;LN(Data_X))
04050100.xhp hd_id7874080 help.text	The exponential regression equation	指数回归方程式
04050100.xhp par_id4679097 help.text	For exponential trend lines a transformation to a linear model takes place. The optimal curve fitting is related to the linear model and the results are interpreted accordingly.	对于指数回归曲线，转换产生了线性模型。最佳拟合曲线与线性模型相关，且结果被相应解释。
04050100.xhp par_id9112216 help.text	The exponential regression follows the equation <item type="literal">y=bexp(ax)</item> or <item type="literal">y=bm</item><sup><item type="literal">x</item></sup>, which is transformed to <item type="literal">ln(y)=ln(b)+ax</item> or <item type="literal">ln(y)=ln(b)+ln(m)*x</item> respectively.	指数回归曲线遵循如下方程式「<item type="literal">y=bexp(ax)</item>」或「<item type="literal">y=bm</item>」<sup><item type="literal">x</item></sup>，这两个方程式分别被转换为「<item type="literal">ln(y)=ln(b)+ax</item>」或「<item type="literal">ln(y)=ln(b)+ln(m)*x</item>」。
04050100.xhp par_id4416638 help.text	a = SLOPE(LN(Data_Y);Data_X)	a = SLOPE(LN(Data_Y);Data_X)
04050100.xhp par_id1039155 help.text	The variables for the second variation are calculated as follows:	第二变分的变量计算如下:
04050100.xhp par_id7184057 help.text	m = EXP(SLOPE(LN(Data_Y);Data_X))	m = EXP(SLOPE(LN(Data_Y);Data_X))

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04050100.xhp
par_id7951902
help.text

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sJhZJ

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04050100.xhp

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4 years ago

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9 months ago

Source string age

4 years ago

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zh-CN/helpcontent2/source/text/schart/01.po, string 208