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The FINV(p, df1, df2) function is the inverse function for FDIST(x, df1, df2). For any particular x, FDIST(x, df1, df2) returns the probability that an F-distributed random variable with df1 and df2 degrees of freedom is greater than or equal to x.

The FINV(p, df1, df2) function returns the value of x where FDIST(x, df1, df2) returns p. Therefore, FINV is evaluated by a search process that returns the appropriate value of x by evaluating FDIST for various candidate values of x until it finds a value of x where FDIST(x, df1, df2) is "acceptably close" to p.

`FINV(p, df1, df2)`

y | x | forecast | error |

10 | 1 | =FORECAST(B2, $A$2:$A$7, $B$2:$B$7) | =C2-A2 |

16 | 2 | =FORECAST(B3, $A$2:$A$7, $B$2:$B$7) | =C3-A3 |

33 | 3 | =FORECAST(B4, $A$2:$A$7, $B$2:$B$7) | =C4-A4 |

36 | 4 | =FORECAST(B5, $A$2:$A$7, $B$2:$B$7) | =C5-A5 |

47 | 5 | =FORECAST(B6, $A$2:$A$7, $B$2:$B$7) | =C6-A6 |

51 | 6 | =FORECAST(B7, $A$2:$A$7, $B$2:$B$7) | =C7-A7 |

LINEST output | |||

=LINEST(A2:A7,B2:B7,TRUE,TRUE) | =LINEST(A2:A7,B2:B7,TRUE,TRUE) | ||

=LINEST(A2:A7,B2:B7,TRUE,TRUE) | =LINEST(A2:A7,B2:B7,TRUE,TRUE) | ||

=LINEST(A2:A7,B2:B7,TRUE,TRUE) | =LINEST(A2:A7,B2:B7,TRUE,TRUE) | ||

=LINEST(A2:A7,B2:B7,TRUE,TRUE) | =LINEST(A2:A7,B2:B7,TRUE,TRUE) | ||

=LINEST(A2:A7,B2:B7,TRUE,TRUE) | =LINEST(A2:A7,B2:B7,TRUE,TRUE) | ||

=DEVSQ(A2:A7) | |||

=SUMSQ(D2:D7) | |||

=((A16-A17)/1)/(A17/4) | |||

=FDIST(A18,1,4) | |||

=FINV(A19,1,4) | |||

=FINV(0.05,1,4) |

- In Microsoft Office Excel 2007, click the
**Home**tab, click**Format**in the**Cells**group, and then click**AutoFit Column Width**. - In Excel 2003, point to
**Column**on the**Format**menu, and then click**AutoFit Selection**.

The F-statistic is calculated by computing two different sums of squares. First, compute the total sum of squares, the sum of squared deviations of the y-values about their mean. This result is in cell A16. This value provides a measure of the goodness of fit of the model with a slope that is forced to 0. The second sum of squares is found by first computing the forecast y-values for specific x-values (in cells C2:C7), finding the differences between forecast and actual y-values (cells D2:D7), and then summing the squares of these differences. The result is in cell A17. The value in cell A17 is smaller than the value in cell A16 because there is a better fit with the best value of the slope than with a slope that is forced to 0. The F-statistic for the test is computed in cell A18 and its value agrees with that shown in the LINEST output in cell A13.

The FDIST value in cell A19 shows the probability of an F-statistic value that is greater than the value that is obtained in cell A18 under the null hypothesis that there is no relationship between the x-values and the y-values. The value of FINV in cell A20 illustrates the inverse relationship between FINV and FDIST. The value of FINV in cell A21 provides the cutoff that is used for a significance test with a probability 0.05 of rejecting the null hypothesis when it is true. In this example, the observed F-statistic (106.67) is much higher than this cutoff (7.71), so the null hypothesis of no relationship between the x-values and the y-values is rejected.

- The accuracy of FDIST
- The design of the search process and the definition of "acceptably close"

- BETAINV
- CHIINV
- FINV
- GAMMAINV
- TINV

- BETADIST
- CHIDIST
- FDIST
- GAMMADIST
- TDIST

Properties

Article ID: 828332 - Last Review: 01/18/2007 00:42:18 - Revision: 2.2

- Microsoft Office Excel 2007
- Microsoft Office Excel 2003

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