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dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language. [1]
Note that winsorizing is not equivalent to simply excluding data, which is a simpler procedure, called trimming or truncation, but is a method of censoring data.. In a trimmed estimator, the extreme values are discarded; in a winsorized estimator, the extreme values are instead replaced by certain percentiles (the trimmed minimum and maximum).
If the regression errors are independent, but have distinct variances , then = (, …,) which can be estimated with ^ = ^. This provides White's (1980) estimator, often referred to as HCE (heteroskedasticity-consistent estimator):
R is a programming language for statistical computing and data visualization.It has been adopted in the fields of data mining, bioinformatics and data analysis. [9]The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data.
RStudio IDE (or RStudio) is an integrated development environment for R, a programming language for statistical computing and graphics. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser .
names or codes allocated using a regime involving multiple (concurrent) issuers of unique identifiers that are each assigned mutually exclusive partitions of a global address space such that the unique identifiers assigned by each issuer in each exclusive address space partition are guaranteed to be globally unique.
No two distinct rows or data records in a database table can have the same data value (or combination of data values) in those candidate key columns since NULL values are not used. Depending on its design, a database table may have many candidate keys but at most one candidate key may be distinguished as the primary key.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.