Manuals/calci/INTERCEPT

INTERCEPT(y,x)

• $y$ is the set of dependent data
• $x$ 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 $y$ when $x$ is zero.
• The intercept point is finding using simple linear regression.
• It is fits a straight line through the set of $n$ 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 $n$ data points ${y_{i},x_{i}}$, where i = 1, 2, …, n.
• To find the equation of the regression line:$a=bar(y)-b.bar(x)$.
• 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:$b={\frac {\sum _{i=1}^{n}{(x_{i}-{\bar {(}}x))(y_{i}-{\bar {(}}y))}}{\sum _{i=1}^{n}{(x_{i}-bar(x))}^{2}}}$.
• In this formula$bar(x)$ and$bar(y)$ are the sample means AVERAGE of $x$ and $y$.
• In $INTERCEPT(y,x)$ , 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 nonnueric or x and y is having different number of data points and there is no data.

INTERCEPT(Y,X)

Where Y is the dependent set of observations or data, and

Y is the independent set of observations or data.

---

This function calculates  the point at which a line will intersect the y-axis using the  available x-values and y-values.

---

·         An array contains text, logical values, or empty cells that are ignored; but, the cells with the value zero are included.

·          INTERCEPT shows the error value, when Y and X have a dissimilar number of data points.

Formulas:-

·          The equation to calculate the intercept of the regression line, a, is:

where b is the slope, and is calculated as:

and where x and y are the sample means AVERAGE(Y) and AVERAGE(X).

---

INTERCEPT

---

Lets see an example,

INTERCEPT(Y, X)

B                        C

10                     13

8                        11

15                      18

6                        12

12                      10

=INTERCEPT(B2:B6,C2:C6) is 1.2268

---

Syntax

---

Remarks

---

Examples

---

Description

---

	Column1	Column2	Column3	Column4
Row1	10	13	1.226804
Row2	8	11
Row3	15	18
Row4	6	12
Row5	12	10
Row6

File:Calci1.gif

---