2.13.1.16 qtest

Menu Information

Statistics:Descriptive Statistics:Dixon's Q Test

Brief Information

Perform Dixon's Q-test to identify outliers

Additional Information

Minimum Origin Version Required: 9.0 SR0

Menu accessible from 9.1 SR0

Command Line Usage

1. qtest

2. qtest ix:=col(2)[1:7] alpha:=0.01

3. qtest ix:=col(1)[3:10] alpha:=0.05 box:=1

X-Function Execution Options

Please refer to the page for additional option switches when accessing the x-function from script

Variables

Display
Name 		Variable
Name 		I/O
and
Type 		Default
Value 		Description
Input ix

Input

vector

<active>
 Must be 3 to 10 data from one column.
Significance Level alpha

Input

double

0.05

Option list:

  • 0.1
  • 0.05
  • 0.01
Data Point with Largest Q ox

Output

double

<unassigned>
 The value of the suspected point
Data Index with Largest Q index

Output

int

<unassigned>
 Row index of suspected point
Largest Q Value qstat

Output

double

<unassigned>
 The calculated Q value from suspected point
Critical Q Value critical

Output

double

<unassigned>
 The critical Q value at the specified significance level
Test Significance sig

Output

int

<unassigned>
 sig=1 means there is an outlier, sig=0 means there is no outlier
Conclusion conclusion

Output

string

<unassigned>
 A statement of conclusion indicating the statistical result
Outlier Plot box

Input

int

0
 Specify whether to generate an outlier plot. box=1 means to generate, and box=0 means not to generate.
Qtest Plot Data rd

Output

ReportData

[<input>]<new>
 The worksheet range to put the plot data for outlier plot, if generating an outlier plot is selected.
Qtest Report rt

Output

ReportTree

[<input>]<new>
 The worksheet range to put the report table.

Description

Used to test outlier for a dataset with 3 to 10 data points, at significance level 0.01, 0.05 or 0.1.

Algorithm

For a series of results from repeated measurements:

  1. Rank n results in ascending order and give x_{1} to x_{n} value.
  2. Calculate the test statistic Q, following:
    Q=\frac{ x_{2}-x_{1} }{x_{n}-x_{1} } (lowest observation)
    or
    Q=\frac{ x_{n}-x_{n-1} }{x_{n}-x_{1} } (highest observation)
  3. Compare the calculated Q value with Qcritical

References

Stephen L R. Ellison, Vicki J. Barwick and Trevor J Duguid. Farrant. 2009. Practical Statistics for the Analytical Scientist. The Royal Society of Chemistry, Cambridge, UK.

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grubbs

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