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Explain Chi-Squared test to detect outlier? - EngineersHub
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Question
Anvesh Kanchibhotla
2 years ago
1 Answer(s) posted Write an answer 1889
Answer posted by Ruchitha
2 years ago

Chi-squared test for outlier:
Description:

• Performs a chi squared test for detection of one outlier in a vector.

### Usage:

• chisq.out.test(x, variance=var(x), opposite = FALSE)

• ### x: a numeric vector for data values.

Variance: known variance of population. if not given, estimator from sample is taken, but there is not so much sense in such test (it is similar to z-scores)

Opposite: a logical indicating whether you want to check not the value with largest difference from the mean, but opposite (lowest, if most suspicious is highest etc.).

### Details:

This function performs a simple test for one outlier, based on chisquared distribution of squared differences between data and sample mean. It assumes known variance of population. It is rather not recommended today for routine use, because several more powerful tests are implemented.

### Examples

• set.seed(1234)

• x = rnorm(10)

• chisq.out.test(x)

• chisq.out.test(x,opposite=TRUE)

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Explain Chi-Squared test to detect outlier?
X
Explain Chi-Squared test to detect outlier?

Chi-squared test for outlier:
Description:

• Performs a chi squared test for detection of one outlier in a vector.

### Usage:

• chisq.out.test(x, variance=var(x), opposite = FALSE)

• ### x: a numeric vector for data values.

Variance: known variance of population. if not given, estimator from sample is taken, but there is not so much sense in such test (it is similar to z-scores)

Opposite: a logical indicating whether you want to check not the value with largest difference from the mean, but opposite (lowest, if most suspicious is highest etc.).

### Details:

This function performs a simple test for one outlier, based on chisquared distribution of squared differences between data and sample mean. It assumes known variance of population. It is rather not recommended today for routine use, because several more powerful tests are implemented.

### Examples

• set.seed(1234)

• x = rnorm(10)

• chisq.out.test(x)

• chisq.out.test(x,opposite=TRUE)

x