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Introduction to Statistical Data Analysis: To your knowledge, statistics is a branch of mathematics that...

Introduction to Statistical Data Analysis:

To your knowledge, statistics is a branch of mathematics that is used to analyze the data that can then be described, summarized, and compared to. And thus statistical data analysis is known to be a process where numerous statistical functions are performed that involves the collection of data. With that, this data is also interpreted and validated in sequence. For the analysis of statistical data, a variety of statistical tools like SAS, SPSS, STATA or Tableau for Visualization is used.

What is the use of statistical data analysis?

Statistical data analysis is widely used in situations like

  • Gathering research interpretations and statistical modeling.
  • With that, statistical data analysis is also used in designing surveys and studies.
  • Furthermore, statistical data analysis is also used in business intelligence organizations that have to deal with large volumes of data on a regular basis.

Main goal of statistical data analysis:

The main goal of statistical data analysis is to identify different trends. To explain this heading, let us take a look at the following example.

This method can be easily applied in a retailing business that can help uncover patterns in unstructured and semi-structured consumer data. This data can then be used for making a variety of powerful decisions. And thus, this can enhance customer experience and increase the number of sales which in return is prolific for the business.

Applications of statistical data analysis:

Statistical data analysis has a variety of applications used in the following fields.

  • Statistical analysis of market research.
  • Business intelligence (BI).
  • Machine learning.
  • Data analytics in big data.
  • Deep learning.
  • Financial and economic analysis.

And many more.

Key types of statistical data analysis:

Following are the key types of statistical data analysis.

Descriptive statistical analysis:

With the help of descriptive statistical analysis, we get a clear description of the data we have. Meaning, descriptive statistical analysis gives out the summary of data in such a way that is more meaningful and leads to effective interpretation of the data. However, we cannot get to any conclusion using descriptive analysis but yes we do get a clear picture of what is in the data. In short, we get to know the quantitative description of our data.

Inferential statistical analysis:

Now comes the second type which is the inferential statistical analysis. As we have discussed above the descriptive statistics helps to summarize the characteristic of a data set but not gives us any conclusion. And thus inferential statistics is the one that helps us to make predictions and then come to a conclusion based on the given data.

Let us explain in detail how it works.

As soon as you collect data from a sample, the inferential statistics analysis can then be used to understand the larger population from the origin of the taken sample. We can use inferential statistics to make estimates about population. And it can be used to draw conclusions about populations.

Mechanistic analysis:

Mechanistic analysis has been found out to be the least common type among all. Yet it is considered the most worthy in the process of big data analytics and biological science. Mechanistic analysis is deployed for understanding how things happen overall rather than understanding how specific things will take place anteriorly.

It uses a very simple approach where you get a clear understanding of individual changes in variables that result in changes in other variables correspondingly. Whereas, at the same time it excludes external influences and predicts that the entire system is influenced by its own internal element’s interaction.

Predictive analysis:

Predictive analysis is such a type of statistical data analysis that is used to make a prediction of different future events. You can also find out what is likely to take place next. This is all done based on the current and past facts and figures. It works in a way that it used statistical techniques and machine learning algorithms for describing the possibility of future outcomes and behaviors.

Predictive analysis

Prescriptive analysis:

What prescriptive analysis does is examine the data with the aim to find out what should be done. This type of statistical data analysis is used widely for identifying the best possible action for any situation. What makes this type unique from others is it provides actual answer instead of driving exclusions. Prescriptive analysis focus on discovering the optimal suggestion for decision making. The different techniques which are used under prescriptive analysis are simulation, graph, analysis, complex event processing, machine learning, and more.

Casual analysis:

Casual analysis is another type of statistical data analysis which is the field of experimental design and statistics used to establish cause and effect. There are four elements involved here which are correlation, information-theoretical mechanism, a sequence in time, and lastly eliminating the possibility of common and alternative causes.

Exploratory Data Analysis (EDA):

The exploratory data analysis is known to be a critical process of performing initial investigations on data. The purpose here is to discover patterns and spot anomalies if any. With that, it is also used for testing hypothesis and keeps a check on the assumptions with the help of summary statistics and graphical representations.

This is a type of method that is fully focused on analyzing different patterns in the data and to recognize the potential relationships. Similarly, EDA can be approached for finding out unknown associations within the data and inspecting missing data from collected data.

Conclusion:

The above key types of statistical data analysis are different from one another and serve different purpose. So all you have to do is read the details of the key types of statistical data analysis and then understand how each of them is used to analyze data.

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