ট্রেন্ড লাইন

Trend lines can be added to all 2D chart types except for Pie and Stock charts.

এই নির্দেশাবলীতে সন্নিবেশ করতে...

Choose Insert - Trend Line (Charts)


নোট আইকন

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.


  1. To insert a trend line for a data series, first double-click the chart to enter edit mode and select the data series in the chart to which a trend line is to be created.

  2. Choose Insert - Trend Line, or right-click the data series to open the context menu, and choose Insert Trend Line.

  3. 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.

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

note

The menu item Insert - Trend Line is only available when the chart is in edit mode. It will appear grayed out if the chart is in edit mode but no data series is selected.


সংশ্লিষ্ট ডাটা ক্রমের জন্য ট্রেন্ড লাইনের একই রং রয়েছে। সারির বৈশিষ্ট্যাবলী পরিবর্তন করার জন্য, ট্রেন্ড লাইন নির্বাচন করুন এবং বিন্যাস - বিন্যাস নির্বাচন - সারি নিরবাচন করুন।

note

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


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.

নোট আইকন

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.


Trend Lines Curve Types

The following regression types are available:

সীমাবদ্ধতা

ট্রেন্ড লাইনের গণনা নিম্নোক্ত মানসহ কেবলমাত্র ডাটা জোড়া বিবেচনা করে:

তদনুসারে আপনার ডাটা রূপান্তর করা উচিত; প্রকৃত ডাটার অনুলিপিতে কাজ করা এবং অনুলিপিকৃত ডাটা রূপান্তর করা সর্বোৎকৃষ্ট।

Calculate Parameters in Calc

আপনি ক্যাল্‍ক ফাংশন ব্যবহার করে প্যারামিটারও নিম্নরূপে গণনা করতে পারেন।

সরল নির্ভরণ সমীকরণ

সরল নির্ভরণ y=m*x+bসমীকরণটি অনুসরণ করে।

m = SLOPE(Data_Y;Data_X)

b = INTERCEPT(Data_Y ;Data_X)

এর দ্বারা নির্ধারণকরণের সহগ গণনা করুন

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))

সূচকীয় নির্ভরণ সমীকরণ

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.

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)

দ্বিতীয় ধরনের ফাংশনের জন্য চলকসমূহ নিম্নোক্তভাবে গণনা করা হয়:

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

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

এর দ্বারা নির্ধারণকরণের সহগ গণনা করুন

r2 = RSQ(LN(Data_Y);Data_X)

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

ঘাত নির্ভরণ সমীকরণ

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))

বহুপদী নির্ভরণ সমীকরণ

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.

X/Y Error Bars

LINEST function

LOGEST function

SLOPE function

INTERCEPT function

RSQ function

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