There are three dimensions to Extreme Data - volume, velocity, and variety, - and management of these dimensions is critical when it comes to winning the battle for control of enterprise data amid the conflicting requirements of business intelligence and reporting vs. data mining and statistical analysis. Management of the data is often part of an Information Lifecycle Management strategy (ILM) or Nearline approach.
Extreme numbers in the data as compared the the rest of the data are called OUTLIERS.
Extreme data refers to data points that exist at the outer edges of a dataset, often representing rare or unusual events. This type of data can include outliers, anomalies, or extreme values that can significantly influence statistical analyses and modeling. Analyzing extreme data is crucial in fields like finance, insurance, and climate science, where understanding rare events can inform risk assessment and decision-making. Properly handling extreme data is essential to avoid misleading conclusions in research and applications.
when there are extreme values in the data
Such a data point is called an outlier.
They are a simple measure of the spread of the data, which is not affected by extreme values.
Extreme numbers in the data as compared the the rest of the data are called OUTLIERS.
Extreme test data is data that is on the boundary. eg if you were asked to enter an age between 1 - 100 extreme test data would be 0 and 101 It is on the boundary of normal test data (Normal test data is within the boundary) Hope i could help
Extreme data refers to data points that exist at the outer edges of a dataset, often representing rare or unusual events. This type of data can include outliers, anomalies, or extreme values that can significantly influence statistical analyses and modeling. Analyzing extreme data is crucial in fields like finance, insurance, and climate science, where understanding rare events can inform risk assessment and decision-making. Properly handling extreme data is essential to avoid misleading conclusions in research and applications.
To find the lower extreme, you need to identify the smallest value in a data set. To find the upper extreme, you need to identify the largest value in the data set. These values represent the lowest and highest points of the data distribution.
median
when there are extreme values in the data
They are called extreme values or outliers.
Outlier
Such a data point is called an outlier.
They are a simple measure of the spread of the data, which is not affected by extreme values.
No because an outlier is an extreme data point that can almost be ignored.
It is a value which appears not to fit in with the other data elements.