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# 趋势线

趋势线可以添加于除饼形图和股价图之外的所有类型的2D图表上。

要访问此命令...

Choose Insert - Trend Line (Charts)

If you insert a trend line to a chart type that uses categories, like Line or Column, then the numbers 1, 2, 3, … are used as x-values to calculate the trend line. For such charts the XY chart type might be more suitable.

- To insert a trend line for a data series, select the data series in the chart. Choose Insert - Trend Line, or right-click to open the context menu, and choose Insert Trend Line.

- Mean Value Lines are special trend lines that show the mean value. Use Insert - Mean Value Lines to insert mean value lines for data series.

  如果已选定某个数据序列的元素，则此命令仅适用于该数据序列。如果未选定任何元素，则此命令适用于所有数据序列。

- To delete a trend line or mean value line, click the line, then press the Del key.

A trend line is shown in the legend automatically. Its name can be defined in options of the trend line.

趋势线与对应的数据序列具有相同的颜色。要更改线条属性，请选择趋势线并选择格式 - 格式选择 - 线条。

## Trend Line Equation and Coefficient of Determination

When the chart is in edit mode, LibreOffice gives you the equation of the trend line and the coefficient of determination R2, even if they are not shown: click on the trend line to see the information in the status bar.

To show the trend line equation, select the trend line in the chart, right-click to open the context menu, and choose Insert Trend Line Equation.

To change format of values (use less significant digits or scientific notation), select the equation in the chart, right-click to open the context menu, and choose Format Trend Line Equation - Numbers.

Default equation uses x for abscissa variable, and f(x) for ordinate variable. To change these names, select the trend line, choose Format - Format Selection – Type and enter names in X Variable Name and Y Variable Name edit boxes.

To show the coefficient of determination R2, select the equation in the chart, right-click to open the context menu, and choose Insert R2.

If intercept is forced, coefficient of determination R2 is not calculated in the same way as with free intercept. R2 values can not be compared with forced or free intercept.

## 趋势线曲线类型

The following regression types are available:

- 线性回归线：通过等式y=a∙x+b进行回归计算。.可以强制设置截距b.

- Polynomial trend line: regression through equation y=Σi(ai∙xi). Intercept a0 can be forced. Degree of polynomial must be given (at least 2).

- Logarithmic trend line: regression through equation y=a∙ln(x)+b.

- Exponential trend line: regression through equation y=b∙exp(a∙x).This equation is equivalent to y=b∙mx with m=exp(a). Intercept b can be forced.

- Power trend line: regression through equation y=b∙xa.

- Moving average trend line: simple moving average is calculated with the n previous y-values, n being the period. No equation is available for this trend line.

## 约束

趋势线的计算只考虑带有下列值的数据对：

- Logarithmic trend line: only positive x-values are considered.

- Exponential trend line: only positive y-values are considered, except if all y-values are negative: regression will then follow equation y=-b∙exp(a∙x).

- 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 y=-b∙xa.

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。

## Calculate Parameters in Calc

您也可以使用以下的 Calc 函数计算参数。

### 线性回归方程式

线性回归遵循如下方程式 y=m*x+b。

m = SLOPE(Data_Y;Data_X)

b = INTERCEPT(Data_Y ;Data_X)

计算决定系数通过

r2 = RSQ(Data_Y;Data_X)

Besides m, b and r2 the array function LINEST provides additional statistics for a regression analysis.

### The logarithmic regression equation

The logarithmic regression follows the equation y=a*ln(x)+b.

a = SLOPE(Data_Y;LN(Data_X))

b = INTERCEPT(Data_Y ;LN(Data_X))

r2 = RSQ(Data_Y;LN(Data_X))

### 指数回归方程式

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。

The exponential regression follows the equation y=b*exp(a*x) or y=b*mx, which is transformed to ln(y)=ln(b)+a*x or ln(y)=ln(b)+ln(m)*x respectively.

a = SLOPE(LN(Data_Y);Data_X)

第二变分的变量计算如下：

m = EXP(SLOPE(LN(Data_Y);Data_X))

b = EXP(INTERCEPT(LN(Data_Y);Data_X))

计算决定系数通过

r2 = RSQ(LN(Data_Y);Data_X)

Besides m, b and r2 the array function LOGEST provides additional statistics for a regression analysis.

### 幂回归方程式

For power regression curves a transformation to a linear model takes place. The power regression follows the equation y=b*xa, which is transformed to ln(y)=ln(b)+a*ln(x).

a = SLOPE(LN(Data_Y);LN(Data_X))

b = EXP(INTERCEPT(LN(Data_Y);LN(Data_X))

r2 = RSQ(LN(Data_Y);LN(Data_X))

### 多项式回归方程式

For polynomial regression curves a transformation to a linear model takes place.

Create a table with the columns x, x2, x3, … , xn, y up to the desired degree n.

Use the formula =LINEST(Data_Y,Data_X) with the complete range x to xn (without headings) as Data_X.

The first row of the LINEST output contains the coefficients of the regression polynomial, with the coefficient of xn at the leftmost position.

The first element of the third row of the LINEST output is the value of r2. See the LINEST function for details on proper use and an explanation of the other output parameters.

相关主题

X/Y Error Bars

LINEST function

LOGEST function

SLOPE function

INTERCEPT function

RSQ function