Types of Statistical Analysis
Types of Statistical Analysis
The objective of statistical analysis is to collect data and further analyse the collected data. These data are large information, which needs computation for gaining relevant conclusions. The target of statistical analysis is to deduce information from a bulk of data and express them through graphs, calculations, charts, and tables.
Descriptive Statistical Analysis
The descriptive type of statistical analysis offers descriptions of the data. It summarises all the information about the collected data so that comprehensive meaning can be attained from the interpretation. Based on the usage of descriptive statistical analysis, the research attains the necessary conclusion, along with elaborate quantitative descriptions about the attained data.
For example:
To find out the performance of a student in a year, the calculation of average marks attained throughout the year should be assessed. The average calculation is the summation of the marks that the student acquired in every subject in a year divided by the total count of the subjects. The attained average is the single number that describes the entire performance scale of the student.
Inferential Statistical Analysis
The scope of inferential statistical analysis is to offer generalisation about the information of huge data set through the mode of sampling. In this analytical venture, the researcher considers a sample to represent bulk data. The approach follows the proceedings of either-
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Estimating Parameters, or
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Testing Hypothesis
For example:
Considering unbiased 100 to 200 people to represent the population of a particular location is called sampling. Inferential statistical analysis critically evaluates the information collected from the sample to derive a relevant conclusion.
Predictive Statistical Analysis
The objective of predictive statistical analysis is to make predictions based on regular events from the past. It analyses a series of events so that necessary possibilities can be enlisted about ‘what will happen in future. This analytical approach is used in every domain of life. Based on complex event processing, graph analysis, simulation, algorithms, business rules, and machine learning, it justifies the process of making decisions. These decisions show effectiveness on the subject to offer a solution to a future issue. This analytical approach is usually used to make predictions related to uncertainties and risks. The business domains of financial service, marketing, and online services imply predictive analysis to attain competitive advantages.
For example:
With the advent of social media platforms, users get recommendations for different sources of entertainment and shopping. These recommendations are based on the data collected from the online streaming platforms attained through the analysis of data used by the user. The history, shopping sites, search keywords, are the means to predict the right recommendations to the user to spend money for entertainment and shopping.
Prescriptive Statistical Analysis
The prescriptive statistical analysis aims to derive ‘what will happen, along with the precision on ‘when it will happen. Based on the statistical evaluations led by modelling, data mining, and artificial intelligence, the prescriptive statistical analysis combines the collected information from the internal sources, with data attained from third-party sources. From there, this analytical approach gains insight into the means of developing better possibilities for the respective operation or event. This analysis is capable of predicting what might happen in future and what should be done to gain the best from the consequences. It is the provision to gain insight into the selection of different choices for different actions based on former recommendations.
For example:
Self-driving cars like Waymo is the result of prescriptive statistical analysis. This car undergoes innumerable calculations to accomplish a trip. Based on prescribed situations and sensor-based information, this car makes decisions to drive by itself.
Causal Statistical Analysis
The relevance of causal statistical analysis is based on its capability to offer reasons to know ‘why’ certain even is happening that way. It is a statistical analysis that seeks to find the causes that lead to certain events of success or failure. This analytical approach is effective in resisting or preventing disasters at large.
For example:
In the case of managing the COVID-19 pandemic, the researchers analysed series of former epidemics through machine learning and a robust statistical algorithm. The objective is to find the causes of its spread and the ways to prevent it. Based on the cause-specific derivations, the researcher tried to incur the impacts of COVID-19 in future. Even the cases and developments of COVID-19 in 2020 are analysed to build better shields for restricting its spread in 2021.
Exploratory Data Analysis
Exploratory data analysis (EDA) is focused on evaluating different sets of data and thereby summarising the core relevant concerns to the research question. It is identified as an exponential entity to inferential statistics, which is expressed through various visualisation methods. The data scientists use this statistical analysis to identify patterns and from there gain insight into the domain of unknown knowledge hidden within. These derivations are expressed through either graphical or non-graphical representations, following the process of -
Find unknown relationships à Check hypotheses à Make assumptions
For example:
In a process of predicting the trend of giving tip in a dining party to the waiter, the variables considered are the amount of the tip, total bill amount, gender or the payer, section of smoking or the non-smoking area, day, time and party size. An EDA speculation derives that hypothetically tipping can depend on the count of people available at the dining party. As number of people attending the party increases, the bill amount increases and hence tipping decreases.
Mechanistic Analysis
Mechanistic Statistical Analysis is applied in terms of big industries. The core approach of this statistical analysis aims to understand the exact kind of changes in the considered variables, which are subject to lead to other forms of variables. The entire process depends on assumption mechanised through a given systematic approach. It is liable to be affected by the interaction among the internal components. There is no room for influence from external components.
For example:
The incidences of car crashes can be analysed through mechanical statistical analysis. In doing so innumerable information about the ways passengers and drivers react to the crashes can be marked under variables. All these variables can be used for the development of a determined mode of the mechanistic model giving details about the impacts and reactions on crashes. These data can be further developed for generating safety features in the cars.
Eventually, it can be stated that the application of different types of statistical analysis can be implemented to varied kinds of information. For all these types of statistical analyses, the core objective is to derive results so that a better situation can be generated in future.
