Difference between revisions of "Manuals/calci/FTEST"
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*The F-test is designed to test if two population variances are equal. | *The F-test is designed to test if two population variances are equal. | ||
*It does this by comparing the ratio of two variances. | *It does this by comparing the ratio of two variances. | ||
− | *So, if the variances are equal, the ratio of the variances will be 1.Let X1, ... | + | *So, if the variances are equal, the ratio of the variances will be 1. |
− | *It's sample means: X | + | *Let X1,...Xn and Y1...Ym be independent samples each have a Normal Distribution . |
− | *The sample variances : | + | *It's sample means: <math>\bar X=\frac{1}{n} \sum_(i=1)^n Xi</math> and <math>\bar Y =\frac {1}{m} \sum_{i=1}^m Yi</math> . |
− | *Then the | + | *The sample variances : |
− | *In FTEST(ar1,ar2) where ar1 is the data of first array,ar2 is the data of second array. | + | <math>SX^2=\frac{1}{n-1} \sum_{i=1}^n (Xi-\bar X))^2</math> |
− | *The array may be any numbers, names, or | + | and : |
− | * | + | <math>SY^2=\frac{1}{m-1} \sum_{i=1}^m (Yi-\bar Y)^2. |
− | When the | + | *Then the Test Statistic = <math>\frac {Sx^2}{Sy^2}<math> has an F-distribution with <math>n−1</math> and <math>m − 1</math> degrees of freedom. |
+ | *In FTEST(ar1,ar2) where <math>ar1</math> is the data of first array, <math>ar2</math> is the data of second array. | ||
+ | *The array may be any numbers, names, or references that contains numbers. | ||
+ | *values are not considered if the array contains any text, logical values or empty cells. | ||
+ | When the <math>ar1</math> or <math>ar2</math> is less than 2 or the variance of the array value is zero, then this function will return the result as error. | ||
+ | |||
==Examples== | ==Examples== | ||
Revision as of 03:23, 10 December 2013
FTEST(ar1,ar2)
- and are array of data.
Description
- This function gives the result of F-test.
- The F-test is designed to test if two population variances are equal.
- It does this by comparing the ratio of two variances.
- So, if the variances are equal, the ratio of the variances will be 1.
- Let X1,...Xn and Y1...Ym be independent samples each have a Normal Distribution .
- It's sample means: and .
- The sample variances :
and : Failed to parse (syntax error): {\displaystyle SY^2=\frac{1}{m-1} \sum_{i=1}^m (Yi-\bar Y)^2. *Then the Test Statistic = <math>\frac {Sx^2}{Sy^2}<math> has an F-distribution with <math>n−1} and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle m − 1} degrees of freedom.
- In FTEST(ar1,ar2) where is the data of first array, is the data of second array.
- The array may be any numbers, names, or references that contains numbers.
- values are not considered if the array contains any text, logical values or empty cells.
When the or is less than 2 or the variance of the array value is zero, then this function will return the result as error.
Examples
1.DATA1 DATA2
15 21 27 12 19 30 32 11
FTEST(B4:B8,C4:C8)=0.81524906747183 2.DATA 1={5,8,12,45,23}; DATA2={10,20,30,40,50}
FTEST(A1:A5,C1:C5)=0.9583035732212274
3. DATA1={14,26,37};DATA2={45,82,21,17} FTEST(B1:B3,C1:C4}=0.26412211240525474 4.DATA1={25},DATA2={45,65} FTEST(B1,C2:C3)=NAN
See Also