In most real-life studies, auxiliary variables are available and are employed to explain and understand missing data patterns and to evaluate and control causal relations with variables of interest.

Fuzzy statistics and random variables represent a progressive fusion of traditional probability theory with the principles of fuzzy logic, enabling the treatment of imprecision and vagueness ...

Non-discretionary or environmental variables are regarded as important in the evaluation of efficiency in Data Envelopment Analysis (DEA), but there is no consensus on the correct treatment of these ...

For big data arriving in streams online updating is an important statistical method that breaks the storage barrier and the computational barrier under certain circumstances. In the regression context ...

By using the right data type for a particular variable and understanding how to convert data types, you can write efficient code and reduce errors in your program.

The Statistics and Data Science curriculum links faculty and students from across the college interested in learning things from data. One important goal is to prepare students for careers in data ...

suppresses the reporting of univariate statistics computes additional percentiles for two variables creates an output data set with descriptive statistics and percentiles prints the output data set.