Robust statistics – MAD method
February 2, 2015
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Tag Cloud
- ANOVA
- Design of Experiments
- Linear regression
- Decision Rule
- Significance testing
- Probability
- Randomization
- hypothesis testing
- outliers
- Sampling statistics
- Normal distribution
- Type I and II errors
- Sampling
- Sampling uncertainty
- Student's t-distribution
- Median
- Conformity testing
- Probability distribution
- Confidence interval
- Microbiology
- GUM
- uncertainty
- Decision rules
- profiency testing
- F-test
- Degrees of freedom
- Measurement error
- Control chart
- Risk
- Variance
- p-Value
- Precision
- Measurement bias
- ISO 17025
- propagation of uncertainty
- Monte Carlo
- Anderson-Darling
- repeatability
- interlab comparison
- standard uncertainty
- Bias
- measurement uncertainty
- t-test
- Method validation
- Confidence limits
- Risk assessment
- Excel spreadsheet
- Risk analysis
- Detection limit
- Accuracy
- Coverage factor
- Law of Averages
- IQR
- How to
- ISO FDIS 17025
- Central limit theorem
- Robust statistics
- standard additions method
- Internal quality control
- systematic error
- standard deviation
- Mean value
- ISO/IEC 17025
- analytical uncertainty
- Trueness
- Aerobic plate count
- Divisor
- Compliance
- Chi-square
- ISO 17025:2017
- Recovery
- Shewhart
- She
- Cross-checks
- Factorial design
- quartile
Comments on: "Robust statistics – MAD method" (1)
[…] may wish to read our previous article https://consultglp.com/2015/02/02/robust-statistics-mad-method/ which demonstrates how the constant, 1.4826 is derived […]