Missing Values or Data Points in SimplexNumerica

This video tutorial focuses on the crucial task of managing missing data within the SimplexNumerica software environment. Dealing with missing values is a fundamental step in data analysis, and SimplexNumerica provides several powerful and flexible methods to address this common issue, ensuring the integrity and accuracy of your final results.

What You Will Learn:
Identification: Quickly spot and identify cells containing missing data points within your datasets.

Default Behavior: Understand how SimplexNumerica automatically handles missing values during various operations and calculations.

Techniques for Handling Missing Data: We demonstrate and explain the most effective methods to deal with gaps in your data, including:

Imputation Methods: Using statistical techniques (like mean, median, or mode) to fill in the missing data points.

Exclusion: Properly removing rows or columns with missing values where appropriate.

Specific Functions: Utilizing SimplexNumerica's built-in functions designed for robust missing value management.

Whether you are a student, researcher, or professional, mastering missing value treatment in SimplexNumerica will significantly improve the quality of your statistical modeling and data visualizations.