Error Analysis Assignment Help & Homework Answers


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What is Errors Analysis ?

Error analysis in statistics involves the investigation of errors and how they occur. in statistics  the goal of error analysis is to determine whether the data has been affected by a systematic bias that may have occurred at any point during data collection or analysis.

Systematic bias can be caused by human factors such as measurement inaccuracies, instrument faults, and observer biases; variability in natural phenomena; or environmental conditions of the experiment. While error analysis methods vary between disciplines, many applied research statistics textbooks will provide an outline for how to investigate potential sources of systematic errors affecting a dataset. Also usually an overall conclusion on the quality of the data is given.

The potential sources of systematic bias that can affect a sample of data may be:

  1. Human factors (errors and/or deliberate measurement errors)
  2. Instrumental errors (such as instrumentation malfunctions, inaccurate or inconsistent measurements)
  3. Physically induced errors (for example, temperature variations that cause calibration problems)
  4. Sample design errors (such as undersampling)
  5. Random error
  6. Statistical errors

Types Of Errors

Random errors– random errors are errors that occur due to chance; they cannot be predicted or controlled in any way. These errors may be caused by human factors such as mistakes in data collection and analysis. Instrumental errors as well as physical variables can be random and cause random errors. Sometimes the results obtained from experimental data may not be completely representative of the true estimates of a parameter. More often though, random errors will bias the true estimates of the parameter (e.g., the mean value of a sample can slightly differ from that of an ‘ideal’ sample). Random error is also known as “random potency error” or “random error”.

  • Statistical errors -are individuals who perform an error analysis on a dataset. Statistical errors include sampling error, measurement error and measurement bias.
  • Sampling error: The term ‘sampling error’ is used for both the overall quantity of data collected and for the average value of that data. This is because it refers to the variability in a sample of data, which depends both on the sampling method (such as whether you take a sample from your entire population or just from one group) and on the objective (such as whether you wish to obtain an estimate or measure the true value).
  • Sampling error can be reduced in a number of ways: these include using larger samples, increasing the number of replicates (in case of a time series), using surrogate variables (such as an average instead of an individual value), taking repeated measurements, and carrying out blind variations.
  • Measurement Error– is the difference between an individual measurement and its real value. In a historical database where individuals are measured over time, these errors can be measured by studying the records (observations) in the database. They involve raw data for which there is no processing applied in order to remove any processing errors, such as transcription mistakes. The most common type of measurement error is “misspeaking”, where a person says something when they mean something else (for example, “I arrived at that party” instead of “I got there”).
  • Systematic error -occurs in measurements taken at a single instant of time. It is caused where measurement errors are constant over time (they are not random). Systematic error can only be removed by applying a mathematical correction.
  • Instrumentation error and environmental error. Instrumentation errors include incorrect or inaccurate methods of measurement, electrical interference in the equipment, and recording problems due to poor storage conditions or poor maintenance. While, Environmental error occurs when objects do not behave in a way that has been predicted by theory and also where they react differently to different temperatures or light intensities.

Importance Of  Errors Analysis

  1. Errors analysis help in identifying root causes
  2. Main objectives of errors analysis is to reduce correct and eliminate errors
  3. It will help in refining the system
  4. Errors analysis is one of the activities that would be carried out after a project has been completed or an experiment has been completed
  5. Forecast cost savings from eliminating error, preventing future similar errors from occurring
  6. Support decision making by verifying assumptions and maximizing benefits of opportunities with data available via statistical analyses
  7. Error analysis is a powerful tool to improve the overall effectiveness of any project or the process
  8. Reducing defects, errors and rework immediately improves profitability by increasing output while reducing costs
  9. In recent times, it has been used extensively in all spheres such as in business and non-profit organizations
  10. Errors analysis is one of the important tools of quality management system and it is a vital part of continual improvement
  11. t will help in identifying causes that lead to a defect so that actions can be taken to eliminate these defects from happening again

Topics Studied Under Error Analysis

  1. Variance analysis
  2. Root cause analysis
  3. Value analysis
  4. Hazard analysis
  5. Risk analysis
  6. Statistical process control (SPC)
  7. Study of variation (SOV)

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