Introduction to Statistical Analysis Types
Statistical Analysis is the science of collecting, exploring, organizing, exploring patterns and trends using one of its types i.e. Descriptive Type (for describing the data), Inferential Type(to generalize the population), Prescriptive, Predictive, Exploratory and Mechanistic Analysis to answer the questions such as, “What might happen?”, “What should be done?”, and “Why”, etc. Due to this most of the business relies on these statistical analysis results to reduce the risk and forecast trends to stay in the competition.
Different Types of Statistical Analysis
Given below are the types of statistical analysis:
- Descriptive Type of Statistical Analysis
- Inferential Type of Statistical Analysis
- Prescriptive Analysis
- Predictive Analysis
- Causal Analysis
- Exploratory Data Analysis
- Mechanistic Analysis
1. Descriptive Type of Statistical Analysis
Descriptive statistical analysis as the name suggests helps in describing the data. It gets the summary of data in a way that meaningful information can be interpreted from it. Using descriptive analysis, we do not get to a conclusion however we get to know what in the data is i.e. we get to know the quantitative description of the data.
For instance, consider a simple example in which you must determine how well the student performed throughout the semester by calculating the average. This average is nothing but the sum of the score in all the subjects in the semester by the total number of subjects. This single number is describing the general performance of the student across a potentially wide range of subject experiences.
Whenever we try to describe a large set of observations with a single value, we run into the risk of either distorting the original data or losing any important information. The student average won’t determine the strong subject of the student. It won’t tell you the specialty of the student or you won’t come to know which subject was easy or strong. In spite of these limitations, Descriptive statistics can provide a powerful summary which may be helpful in comparisons across the various unit.
There are two types of statistics that are used to describe data:
- Measures of central tendency: In this, a single value attempts to describe the data by using its central position with the given set. They are also classified as a summary set. In order to get the central value, they use averaging(mean), median or mode.
- The measure of spread: In this, the data is summarized by describing how well the data is spread out. For example, if the mean score of 100 students is 55 then there will be students whose score will be less than 55 or more than 55. Which means their score will be spread out in a way that their mean is 55. To describe the spread, we can use either of the statistical technique i.e. range, quartiles, variation, standard deviation, and absolute deviation.
2. Inferial Statistics
The group of data that contains the information we are interested in is known as population. Inferential Statistics is used to make a generalization of the population using the samples. Where the sample is drawn from the population itself. It is necessary that the samples properly demonstrate the population and should not be biased. The process of achieving these kinds of samples is termed as sampling. Inferential Statistics comes from the fact that the sampling naturally incurs sampling errors and is thus not expected to perfectly represent the population.
There are two types of Inferential Statistics method used for generalizing the data:
- Estimating Parameters
- Testing of Statistical Hypothesis
The above two are the main types of statistical analysis.
3. Prescriptive Analysis
“What should be done?” Prescriptive Analysis work on the data by asking this question. It is the common area of business analysis to identify the best possible action for a situation. Its whole idea is to provide advice that aims to find the optimal recommendation for a decision-making process. It is related to descriptive and predictive analysis. The descriptive analysis describes the data i.e. what has happened, and predictive analytics predicts what might happen prescriptive analysis find the best option among the available choice.
Techniques used in the prescriptive analysis are simulation, graph analysis, business rules, algorithms, complex event processing, and machine learning.
4. Predictive Analysis
“What might happen?” Predictive analysis is used to make a prediction of future events. It is based upon the current and historical facts. It uses statistical algorithm and machine learning techniques to determine the likelihood of future results, trends based upon historical and new data and behavior. Business is implementing predictive analytics to increase the competitive advantage and reduce the risk related to an unpredictable future. The main users of predictive analysis are marketing, financial service, online service providers and insurance companies. Techniques used in Predictive analysis are data mining, modeling, A.I., etc.
5. Causal Analysis
“Why?” CausalAnalysis helps in determining why things are the way they are. Since the current business world is full of events that might lead to failure, Causal Analysis seeks to identify the reason for it. It tries to get the root cause, i.e. the basic reason why something can happen. This is a common technique used in the IT industry for the quality assurance of the software. And industries that address major disasters.
6. Exploratory Data Analysis
It is an Exponential to the inferential statistics and is mostly used by the data scientists. It is an analytical approach that focuses on identifying patterns in the data and figure out the unknown relationships. The purpose of Exploratory Data Analysis is to get check the missing data, find unknown relationships and check hypotheses and assumptions. It shouldn’t be used alone as it only provides a birds-eye view of the data and gets some insight into it. It is the first step in data analysis that should be performed before the other formal statistical techniques.
7. Mechanistic Analysis
Mechanistic Analysis plays an important role in big industries. Though it is not among the common type of statistical analysis methods still it’s worth discussing. It is used for understanding the exact changes in the given variable that leads to the other variables. It works on the assumption that the given system gets affected by the interaction of its internal component. It does not consider external influence. It is useful in a system containing clear definitions like biological science.
Conclusion
In this article, we understood the different types of statistical analysis methods. There is a vast career in this field. Businesses from hotels, clothing designs, music stores, vendors, marketing and even politics rely heavily on the data to stay ahead. Other fields include Medical, Psychologist, etc. Since data on its own can be helpful Statistical Analysis helps in gaining the insight.
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