Difference between revisions of "Manuals/calci/INTERCEPT"

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*Regression methods nearly to the simple ordinary least squares also exist.  
 
*Regression methods nearly to the simple ordinary least squares also exist.  
 
*i.e.,The Least Squares method relies on taking partial derivatives with respect to the slope and intercept which provides a solvable pair of equations called normal equations.
 
*i.e.,The Least Squares method relies on taking partial derivatives with respect to the slope and intercept which provides a solvable pair of equations called normal equations.
*Suppose there are <math> n </math> data points  <math> {y_{i}, x_{i}}</math>, where i = 1, 2, …, n.
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*Suppose there are <math> n </math> data points  <math> {y_{i}, x_{i}}</math>, <math>where i = 1, 2,...n</math>
 
*To find the equation of the regression line:<math> a=\bar{y}-b.\bar{x}</math>.
 
*To find the equation of the regression line:<math> a=\bar{y}-b.\bar{x}</math>.
 
*This equation will give a "best" fit for the data points.  
 
*This equation will give a "best" fit for the data points.  
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*The slope is calculated by:<math> b=\frac{\sum_{i=1}^{n} {(x_{i}-\bar{x})(y_{i}-\bar{y})}} {\sum_{i=1}^{n}{(x_{i}-\bar{x})}^2}</math>.  
 
*The slope is calculated by:<math> b=\frac{\sum_{i=1}^{n} {(x_{i}-\bar{x})(y_{i}-\bar{y})}} {\sum_{i=1}^{n}{(x_{i}-\bar{x})}^2}</math>.  
 
*In this formula<math> \bar{x}</math> and<math> \bar{y}</math> are the sample means  AVERAGE of <math> x</math>  and <math> y </math>.  
 
*In this formula<math> \bar{x}</math> and<math> \bar{y}</math> are the sample means  AVERAGE of <math> x</math>  and <math> y </math>.  
*In <math>INTERCEPT(y,x)</math> , the arguments can be numbers, names, arrays, or references that contain numbers.
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*In <math>INTERCEPT(y,x)</math>, the arguments can be numbers, names, arrays, or references that contain numbers.
 
* The arrays  values are  disregarded when it is contains text, logical values or empty cells.  
 
* The arrays  values are  disregarded when it is contains text, logical values or empty cells.  
*This function will return the result as error when any one of the argument is nonnueric or x and y is having different number of data points and there is no data.
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*This function will return the result as error when any one of the argument is non-numeric or <math>x</math> and <math>y</math> is having different number of data points and there is no data.
  
 
==Examples==
 
==Examples==

Revision as of 00:56, 26 December 2013

INTERCEPT(y,x)


  • is the set of dependent data
  • is the set of independent data.

Description

  • This function is calculating the point where the line is intesecting y-axis using dependent and independent variables.
  • Using this function we can find the value of when is zero.
  • The intercept point is finding using simple linear regression.
  • It is fits a straight line through the set of points in such a way that makes vertical distances between the points of the data set and the fitted line as small as possible.
  • Regression methods nearly to the simple ordinary least squares also exist.
  • i.e.,The Least Squares method relies on taking partial derivatives with respect to the slope and intercept which provides a solvable pair of equations called normal equations.
  • Suppose there are data points ,
  • To find the equation of the regression line:.
  • This equation will give a "best" fit for the data points.
  • The "best" means least-squares method. Here b is the slope.
  • The slope is calculated by:.
  • In this formula and are the sample means AVERAGE of and .
  • In , the arguments can be numbers, names, arrays, or references that contain numbers.
  • The arrays values are disregarded when it is contains text, logical values or empty cells.
  • This function will return the result as error when any one of the argument is non-numeric or and is having different number of data points and there is no data.

Examples