Guided model selection

Describe your research design

Answer a few questions about your goal and data. The recommendation will include a rationale and compatible alternatives—not just a model name.

Click to auto-fill the form with inputs that would recommend different models
Write your research question in simple terms, as if you're explaining it to a friend.
More examples: • Is there a relationship between study time and test scores? • Do different teaching methods affect student performance? • How does age influence blood pressure?
Think about what you want to achieve with your analysis.
This is what you're trying to understand or predict in your study.
For clustering, this field is optional since clustering algorithms don't always need a dependent variable.
Select all types of data you're using to explain or predict your outcome.
Select all types of data you want to use for clustering.
Correlated variables move together (e.g., as height increases, weight tends to increase too).
This is the total number of data points or participants in your study.
Missing data are blank or empty values in your dataset.
Don't worry if you're not sure - we'll help you choose an appropriate model.
If you're not sure, that's okay! We'll consider all possibilities.
Sign in to add an optional AI review. The rules-based recommendation remains available without an account.

Statistical assistant