Predictive Analytics and Inferential Statistics Analysis 2022

Running head: PREDICTIVE STATISTICS 1
Predictive Analytics and Inferential Statistics
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PREDICTIVE STATISTICS 2
Introduction
At present, data refers to excessively fast and big. Conventional database techniques,
therefore, cannot process such data efficiently. Thus techniques to manage, distribute, store,
capture, and analyze diverse data is known as big data. The key features of big data involve
volatility, validity, variety, veracity, velocity, and volume.
Big data process refers to the process of organizing, gathering, and analyzing huge sets of
data known as the big data to discover useful information and patterns, and Big Data is important
since it can assist the company in comprehending the information contained in data. Big data
analytics is important in identifying the data that is most crucial to the business as well as future
decisions of the business (Chiang, 2012). Analytics who work with big data normally want the
information or knowledge coming from data analysis. Big data analytics is divided into four
categories: These are Descriptive, Diagnostic, Predictive, and Prescriptive (Chiang, 2012).
The Prescriptive Analytics
Predictive analytics make use variables that can be analyzed and measured to predict the
behavior of machinery, individuals, or any other entities. For instance, an insurance company is
capable of taking into consideration the safety variables such as driving record, type of vehicle,
location, gender, and age, when issuing and pricing auto insurance policies. Many variables are
incorporated into a predictive model to assess future probabilities with a certain level of

PREDICTIVE STATISTICS 3
reliability. Predictive analytics relies on methodologies, algorithms, such as decision trees, time
series analysis, and regression models.
Prescriptive analytics shows the actions that ought to be undertaken. Predictive
analytics refers to the analysis of situations or scenarios of what is likely to happen (Chiang,
2012). It is also the use of algorithms, data, and machine learning methods to identify the
chances of results of future based on past data (Chiang, 2012). It uses known results to create a
model that can be utilized to forecast the values of new or data. It involves modeling results in
predictions that denotes the probability values for new or different data. The predictive analytics
is used to predict the behavior, drive decision making, improve performance, and predict trends.
Prediction in health care
YiChuanWang concluded that big data come as a result of controlling big volume of data.
Data from different sources are structured and arranged in the first layer and are obtained and
converted by use of transformation engines and kept. The data is mapped in the analytics and
processed. Cases from a different area under different parameters are gathered, which are then
analyzed (Chiang, 2012). The analysis is done according to a particular criteria map decreased
algorithms by use of Apache Hadoop.
Today, Higher Education is operating in a competitive and complex environment, and
thus, has to face its problems. Different stakeholders in higher education have created a way