DIFFERENT TECHNIQUES OF STATISTICAL ANALYSIS

Statistical analysis refers to the science of collection, organization, and exploration of trends using certain types of methods in order to answer questions such as “what could happen?”, “Why did this happen” etc. Most of the businesses today rely upon statistical analysis to predict what kind of products and services will perform well while also identifying who could potentially be their competitors.

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 analysis refers to the method of statistical analysis in which data is described. The summary of the data is presented in a way that all the meaningful information is easily interpreted from it. Using descriptive analysis may not assure that a conclusion can be derived from the information but the quantitative description of the data can be inferred.

An example of this analysis: If a teacher needs to determine how well a student is performing throughout a semester she/he is likely to calculate an average. The average is going to be total score in all subjects in that semester and dividing it by total number of subjects that he or she has studied. This number which will be a result of this calculation will determine the performance of the student in a number of subjects.

When someone tries to describe an observation using a single value there is always a chance of the data getting distorted in some way or omission of certain important information. In this case, determining of a students average score will not be helpful in determining which subject has she or he excelled in. There are limitations of this method but the kind of summary it provides is helpful in drawing comparisons.

There are two types of statistics that are used to describe data:

  • Measures of central tendency: In this type of description a center point is chosen from the provided set of data, they are classified as a summary set. In order to determine the center point mean, median or mode is used.
  • Measuring spread: In this type of description the data is presented in a summary form by providing an explanation regarding how the data is spread out. For example: If a mean score out of 200 college students is 65 then the results of their exams will be laid out in a manner that 65 will be the mean. The spread can be described by using either of the techniques.

2. Inferial Statistics

The data which contains the information we need is referred to as “population”. Inferential Statistics is used to generalize the population by the use of samples. The sample is chosen from the population. Samples should depict the population & remain unbiased. The whole process of determining these samples is called sampling. Inferential Statistics is a result of the fact that whenever there is sampling there are bound to be certain errors and therefore they cannot depict the population perfectly.

There are two types of Inferential Statistics method:

  • Estimating Parameters
  • Testing of Statistical Hypothesis

3. Prescriptive Analysis

Prescriptive analysis focuses on the aspect of “what needs to be done”. This analysis is a part of the analysis which is done for businesses in order to identify what would be the best plan of action under those circumstances. The aim of the prescriptive analysis is to make recommendations when a decision is being made. This type of analysis is also co-related to descriptive as well as predictive analysis. Descriptive Analysis as the name suggests is used to describe the date and Predictive Analysis is used to make predictions regarding the best action to choose.

Some techniques which are used as a form of prescriptive analysis are:

  • Simulation
  • Graph Analysis
  • Business Rules
  • Algorithms
  • Complex Event Processing
  • Machine Learning

4. Predictive Analysis

Predictive analysis is used to predict the kind of events that may occur in the future. The prediction is made on the basis of current information available and the historical data. This method uses statistical algorithms along with techniques of machine learning to determine what sort of results can be expected in the future and trends that are likely to become popular based upon analysis of data. Businesses have been using predictive analysis to get an edge over their competitors and come up with risk management plans due to uncertainty. Predictive analysis is used by marketing experts, those involved in financial or online services and even insurance companies.

Techniques used in predictive analysis are:

  • Data Mining
  • Modeling
  • A.I , etc

5. Causal Analysis

Casual analysis helps in answering the question “why” when we analyze data. It determines the reason behind any occurrence which has been noticed. The business world is dynamic, which means that there are constantly changes happening which could probably also lead to failure. Casual analysis identifies the reason behind why the business might fail. It attempts to single out the sole problem due to which the business could suffer. This technique is fairly common in the IT sector and is used for quality assessment of software while addressing issues within the software.

6. Exploratory Data Analysis

Exploratory data analysis is an exponential related to inferential statistics and is most commonly seen being used by data scientists. It is the type of analysis which tries to decipher the pattern which is occurring in the data and determining if there is any kind of relationship between them. Missing data can also be found with the help of exploratory data analysis. However, it is not recommended that this be the only analysis technique used because it might only provide one sided insight on it. Exploratory data analysis is considered to be the first step of data analysis and it must be used prior to other techniques.

7. Mechanistic Analysis

Mechanistic analysis has played an integral role within large industries. This is not considered a commonly used statistical analysis method. It is used in order to understand what changes have occurred in any variables and how they lead to other variables. It functions on a presumption that the system is going to be impacted when components which are internal interact. However, it does not take into consideration that there is a possibility of influence. A system which is well equipped with crystal clear definitions can use this technique.

Conclusion

Many industries use data analysis techniques for various purposes. Data constitutes the basis of many actions, plans and strategies. It may be business, medical field, psychology, everyone is dependent on data and its analysis to find conclusions and conduct various kinds of studies. Different techniques may be used depending on the motive of the analysis and which technique would provide the best possible results. If you need help writing content for your economics assignments contact our subject experts at MyAssignmentBuddy today and get a special 20% discount.

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