statistical-analysis-basic

To calculate descriptive statistics, this Skill computes mean, median, standard deviation, and percentiles to summarize your dataset. It utilizes Python libraries to provide these fundamental calculations and help you understand data distributions without requiring deep statistical knowledge.

Identifying outliers in your data distribution is handled by calculating Z-scores or using the Interquartile Range (IQR). This approach quickly finds unusual data points within your dataset, making it useful for validating business metrics and understanding data spread.

Yes, you can perform basic hypothesis testing without deep statistical knowledge. The Skill executes simple tests like t-tests and proportion tests to validate hypotheses, enabling quick validation of business metrics and initial data exploration directly on your datasets.

To identify basic trends and seasonality in time-series data, the Skill performs trend analysis on your datasets. This allows you to quickly spot directional movements and seasonal patterns during initial data exploration without requiring complex manual calculations.

You need standard Python libraries to run basic stats and EDA, as the Skill utilizes them for calculations and visualizations. No specific dependencies are listed, but a Python environment is required to execute the descriptive statistics and hypothesis testing functions.