Background
The main objective of this blog is to briefly describe statistical analytical methods including some basic terminology in data analysis process. In the data analysis process, most of the analysts are not very clear where and which situations are required to apply statistical techniques. I believed my blogs would help you to understand which circumstances and where you can apply statistical analytical techniques in your organization data analysis process to identify the risks and mitigate business problem through data analysis process.
Overview of Statistical Analysis
Statistical analysis is a method of analysing and interpreting data to make meaningful conclusions. It is used in a wide range of fields, including science, business, and social sciences. To noted that advanced level of statistical analysis is a complex process and may require knowledge and basic understanding of some statistical terminology to properly understand the analytical results.
In general, statistical analysis involves four main steps:
1. Data Collection: The first step in statistical analysis is collecting the data. This can be done through surveys, experiments, and organization’s day to day transactional data. The data can be in the form of numbers, words, or images.
2. Data Cleaning: Next process needs to be cleaned data to remove any errors or inconsistencies. This includes checking for missing data, outliers, and other errors that could affect the results.
3. Data Analysis: After the data has been cleaned, statistical methods are used to analyze it. This includes descriptive, diagnostic, exploratory, predictive and prescriptive analysis depends on business problems such as summary analysis (mean, median, mode, variance, standard deviation, minimum, maximum etc.), inferential statistics such as hypothesis testing and regression analysis, correlation analysis, forecasting, segmentation, clustering and predictive model.
4. Interpretation: Finally, the results of the statistical analysis are interpreted to draw meaningful conclusions. This can involve drawing inferences about a population based on a sample or identifying relationships between variables underlined datasets.
However, there are many tools and resources available to help data analysts and business managers with non-statistical backgrounds people to understanding statistical analysis methods and its implications. These resources include online tutorials, textbooks, and some blogs like this blog.
Where Statistical Analysis sits on Data Analysis Process Flow?
Usually, statistical analysis fits all of four data analysis process follow. Below data analysis process flow explains what kind of statistical analysis best fits each of them.
Data Analysis Process Flow
The data analysis process will go through depends on the business problem we are trying to solve. Most business problems fall into the following five data analysis categories:
What happened? --> Descriptive Analysis
Most of the time, the first question the business leaders/stakeholders want to find an answer to before diving into any other exploration.
We can use descriptive analytics to understand what has happened in the past few months/years by running different hypothetical analysis.
Why did it happen? --> Diagnostic Analysis
When we know what happened through descriptive analysis, the next logical step could be to know why it happened. The diagnostic analysis process comes in handy and combining it with the descriptive analysis process would help the business take actionable decisions to mitigate business problem.
What relationship exists in my data? --> Exploratory Data Analysis/EDA
Exploratory analysis process is about analyzing the raw data to know what to learn and understand from it. It involves the use of different data visualizations techniques, so it helps to understand:
· the distribution of the variables in your data by examining their shape, whether they are right, left-skewed, or normally distributed, etc.
· detect eventual outliers that might exist in the data set and the relationship between all the data types.
· if there is a notion of temporality in our data set.
What Will happen? --> Predictive Analysis
In predictive analytics is all about trying to predict future trends based on diagnostic and exploratory analysis.
An efficient understanding of those trends and relationships in the data can guide the tasks that need to be performed, whether it is clustering, classification, regression analysis, etc. Once the data analyst has an idea of the business problem and scenario, then applying appropriate method to predict future trends and hidden patterns of underlined datasets. Now a days, advanced data science methods such as Machine Learning, Artificial Intelligence, Deep Learning methods apply in predict analysis process.
How will it happen? --> Prescriptive Analysis
Statistical analysts use prescriptive analytics to make recommendations for the future, which will allow the business to take the appropriate actions for a better return on investment in the short, medium, and long term while adapting their data collection strategy and ultimately realigning their performance indicator. Prescriptive analysis can answer such a business question like What happened? How, and Why it happened?
Thanks for reading. Will post statistical analysis application example in my next blog post. Please subscribe my blog for getting more exciting tips in data analysis and statistical analysis techniques.
Happy learning folks!
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