Commonly used statistical analysis methods

Data analysts for different business problems can produce a variety of specific data models to analyze the problem, using a variety of analytical methods to explore the data, here are the three most commonly used analytical methods, I hope to have a certain amount of help for your work

The visual charts in this article are produced using DataFocus data analysis tools.

1. Correlation Analysis

Correlation analysis shows how a variable is related to another variable. For example, it shows whether piece-rate pay leads to higher productivity.

2. Regression Analysis

Regression analysis is a quantitative prediction of the difference between the value of one variable and the value of another. Regression models the relationship between dependent and explanatory variables, which are usually plotted on a scatter plot. You can also use regression lines to show whether these relationships are strong or weak.

Also note that outliers on a scatterplot are important. For example, outlying data points may represent inputs from a company's most critical suppliers or best-selling products. However, the nature of the regression line usually allows you to ignore these outliers.

3. Hypothesis Testing

Hypothesis testing is a method of statistical analysis in mathematical statistics that is based on certain assumptions and moves from sample to population. Mainly for the need to solve problems, some hypotheses about the overall study. Usually, two statistical data sets are compared, or a data set obtained through sampling is compared with a synthetic data set from an idealized model. Hypotheses of a statistical relationship between the two datasets are proposed and used as an alternative to the idealized null hypothesis. It is suggested that there is no relationship between the two datasets.

After mastering the basic graphical and analytical methods of data analysis, data analysts believe that there is one thing to keep in mind, "Don't start data analysis without identifying how you can articulate the problem you are trying to solve." In short, if you can't explain the business problem you're trying to solve with data analytics, then no data analytics can solve it.