Data Splitting Techniques in Advanced Statistics

Running head: ADVANCED STATISTICS
Advanced Statistics
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1ADVANCED STATISTICS
Table of Contents
Introduction......................................................................................................................................2
Discussion........................................................................................................................................2
Conclusion.......................................................................................................................................3
References........................................................................................................................................4

2ADVANCED STATISTICS
Introduction
As discussed by Prajapati & Ghosh (2015), Data splitting is the act of portioning the
available data into two portions mainly required for the cross-validator purpose. One section of
the data needs to develop a predictive model and the other section needs to evaluate the
performance of the model. The assimilation of the data for the statistical analysis is seen to be
taken into consideration the various type the factors which may need to “filter out” cases or rows
from a dataset. This often requires dividing the dataset into separate pieces. The subset is known
as the selection of the cases extracted from the dataset to match specific criteria. This is often
known as filtering of a dataset in include some cases. The split action assists in partitioning of a
dataset as it separates the dataset into two or more new datasets as result. To combine multiple
streams of data, the researchers often apply append or merge technique. The append allows
adding additional rows to the attributable table. However, when the datasets are merged or
joined, the additional columns are added to it (Larkoski et al., 2017).
Discussion
The various issues of appropriate data splitting may be handled as a statistical sample
problem. The several types of the classical statistical sampling techniques are seen to consist of
the techniques which are conducive in splitting data. The basic elements for splitting data sets
can be segregated into six main categories as per their “principles, goals, algorithmic and
computational complexity”. These categories are seen with “simple random sampling, trial and
error methods, systematic sampling, convenience sampling, CADEX, DUPLEX and stratified
sampling”. The simple random technique is method of splitting the data into uniform
distribution. The “trial and error method” aims to overcome the high amount variances when
using SRS by repeating the samples several times and calculating the average. In this method the
data splitting aims at minimizing the statistical difference between the T and its subsets (Liu et
al., 2015).
The implementation of systematic sampling allows for distributing the datasets as per the output
variable. This is mostly ideal for splitting the datasets into multimedia data and gene sequence.
In the convenience sampling the dataset T is split according to the discrete blocks. For example,