Question 1

How do I calculate linear regression with the stats package in Go?

Accepted Answer

Use the LinearRegression function with a Series of Coordinate structs. First, convert your data points to Coordinates, then call the function to get the regression series. The README provides example usage and godoc has detailed documentation.

Question 2

Stats or gonum for statistics in Go?

Accepted Answer

Stats is lightweight and dependency-free, ideal for basic to intermediate statistical needs like descriptive stats and regression. Gonum is more comprehensive with numerical computing features but has external dependencies. Choose Stats for simplicity and Gonum for advanced mathematics.

Question 3

Is the stats package production-ready?

Accepted Answer

Yes, with 100% test coverage, thorough error handling, and no dependencies, it's reliable for production. However, verify it covers all statistical functions required for your specific use case, as it may lack some advanced methods.

Question 4

How to handle errors from stats functions?

Accepted Answer

Most functions return an error as the second value—check for errors like ErrEmptyInput or ErrNaN and handle them in your code. The README lists common error types for guidance.

Question 5

Can I use stats with different data types like integers?

Accepted Answer

Yes, use the LoadRawData function to convert various types (int, interface{}, etc.) to Float64Data. This provides flexibility, but note that all computations internally use float64.

Question 6

How does stats compare to Python's statistics libraries?

Accepted Answer

Stats is a Go-native library with no dependencies, offering similar core functions but without Python's extensive ecosystem. It's best for Go applications where performance and minimal dependencies are key, unlike Python's broader data science tooling.

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