Data correlation analysis is an essential tool in data analysis, used to measure the relationship between two or more numerical variables. This process helps identify hidden patterns in the data and supports decision-making based on statistical evidence.

Notes
Requires at least two numerical columns in the dataset to perform the analysis.
Correlations do not imply causation – even if two variables are correlated, it does not mean that one causes the other.
Data must be numerical and homogeneous – if a column contains mixed values (numbers and text), it will be excluded from the analysis.
Results may be influenced by missing data or outliers, which could distort the calculated correlation.

The EasySheet Pro feature provides an automated correlation analysis between the numerical columns of an Excel dataset, generating a detailed report that includes:

Correlation matrix Across all numerical variables.
Identification of significant correlations
Distinguishing between weak, moderate, and strong correlations.

Cross-correlation analysis To examine the highest correlation value between variables while considering different time lags.

This feature is useful for

Analyzing the influence between variables
Determining which variables have a strong or weak relationship with each other.

Identifying possible cause-effect relationships
Exploring correlations between temporal data or performance indicators.

Optimizing predictive models
Selecting the most relevant variables for statistical or machine learning models.