Parameter: a numerical measurement describing some characteristic of a POPULATION
Statistic: a numerical measurement describing some characteristic of a SAMPLE
Quantitative (or numerical) data consists of numbers representing counts or measurements
Categorical (or qualitative / attribute) data consists of names or labels
It is important to include appropriate units of measurements (such as $, ft, m).
Discrete data result when the data values are quantitative and the number of values is finite, or countable (for example, number of tosses of a coin before getting tails).
Continuous (numerical) data result from infinitely many possible quantitative values where the collection of values is not countable (for example, the lengths of distances from 0 to 12 cm).
Levels of measurement are important because they tell us which computations and statistical methods are appropriate for that type of data.
Big data: data sets so large and so complex that their analysis is beyond the capabilities of traditional software tools.
Data science: an area of study that involves applications of statistics, computer science, software engineering, and some other relevant fields.
A data value is missing completely at random if the likelihood of its being missing is independent of its value or any of the other values in the data set.
A data value is missing not at random if the missing value is related to the reason that it is missing.
To correct for missing data:
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