Difference between revisions of "Manuals/calci/INTERCEPT"
Jump to navigation
Jump to search
Line 10: | Line 10: | ||
*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, | + | *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. | ||
Line 16: | Line 16: | ||
*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. | + | *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 | + | *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 23:56, 25 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.