Hey! So, I’ve been working on something similar, and yeah, z-scores are def the way to go for normalizing data to a 3 sigma model. Basically, you calculate the z-score for each data point using (x - mean) / std_dev. This scales your data so that 99.7% of it falls within 3 sigma.
One thing to watch out for is outliers—if you have extreme values, they can mess with your mean and std_dev. Maybe try trimming or winsorizing your data first? Also, check out Python’s `scipy.stats.zscore` or R’s `scale()` function if you’re coding. Super handy tools!
One thing to watch out for is outliers—if you have extreme values, they can mess with your mean and std_dev. Maybe try trimming or winsorizing your data first? Also, check out Python’s `scipy.stats.zscore` or R’s `scale()` function if you’re coding. Super handy tools!