If you’re exploring the world of data science, you’ve probably heard about ANOVA (Analysis of Variance). It’s a basic statistical method used to test if the means (average values) of three or more groups are different from each other.
There are two commonly used types of ANOVA: One-Way ANOVA and Two-Way ANOVA. They sound similar but solve different problems.
One-Way ANOVA
This is used when your data has one independent variable (factor).
Example: Checking if students from three different colleges score differently in mathematics.
Here, the factor is only “college”.
Two-Way ANOVA
This is used when your data has two independent variables (factors).
Example: Checking if student scores vary based on both “teaching method” and “gender”.
It also tells you if these two factors interact and together affect the result.
Key difference:
One-Way ANOVA → One factor
Two-Way ANOVA → Two factors + their interaction
ANOVA is used in fields like marketing, healthcare, and education. If you’re aiming for a career in data science, mastering topics like ANOVA is a strong starting point.
Platforms like Zenoffi E-Learning Labb offer Advanced and P.G. Diploma programs in Data Science & GEN AI — among the best data science courses in India to learn these skills with hands-on practice.
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