Analysis of numeric variables

exploratory statistics

slug: recipe-exploratory-statistics-analysis-of-numeric-variables

Recipe: Analysis of numeric variables

category: exploratory statistics

Problem

You need to understand the distribution and characteristics of numeric variables:

• detect outliers or extreme values
• summarize key statistics (mean, median, standard deviation)
• identify patterns for further analysis or transformation

Solution

Follow these steps to perform univariate analysis:

• load the dataset
• apply univariate analysis to the target numeric fields
• review summary statistics and visualizations
• optionally filter or flag outliers for cleaning

Step Sequence

load step -> univariate step -> filter step

Input Datasets

• transactions_clean — cleaned transactional data
• Notes: focus on numeric fields like amount, quantity, discount

Output Dataset

• numeric_summary — table summarizing each numeric variable
• Notes: includes count, mean, median, min, max, standard deviation, and optionally flagged outliers

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load the dataset	Supports local file, database, or API sources
univariate step	Compute summary statistics for numeric variables	Example: calculate mean, median, SD for amount
filter step	Optionally flag or remove outliers	Example: filter transactions with amounts > 3 SDs from mean

Variations & Extensions

• Apply chart step to visualize distributions (histogram, boxplot)
• Combine with calculate step to create normalized or transformed fields
• Use compare step to compare numeric distributions across different datasets or periods

Concepts Demonstrated

• Univariate statistical analysis
• Outlier detection
• Data summarization for numeric fields
• Sequencing statistical steps

Related Recipes

• Frequency analysis of categorical data
• Correlation analysis between numeric variables

Notes & Best Practices

• Always inspect extreme values before filtering or transforming
• Document transformations applied for reproducibility
• Consider visualizing distributions for better insight