I’ve been diving deep into some regression analysis lately, and I’m running into a bit of a conundrum with identifying outliers, especially when I’m applying logarithmic scaling to my dataset. You know how sometimes you can have those weird data points that just don’t seem to fit in? Like, they’re way off in left field, and it can really skew your results if you don’t catch them.

So here’s the situation: I’ve got this dataset that I think is pretty clean, but after applying logarithmic scaling to the predictors, I started noticing some points that look suspicious. I know that logarithmic transformation can help with normalizing data, especially when there’s a lot of skewness involved, but what happens to those outliers? Do they still stand out in the same way, or does the transformation change their positioning?

I’m really curious about the best methods to effectively identify these anomalies post-transformation. Like, should I be looking at scatter plots or using statistical tests to see how these points compare to the others? I’ve heard of things like Z-scores and the IQR method, but do these still hold up after I transform my data? And what about other techniques? I’ve come across some clustering methods and density-based approaches, which seem interesting but a bit complicated.

Have you experienced something similar? What strategies have you found useful for spotting these outliers after applying a log transformation? I’m eager to hear your thoughts or any resources you might recommend. I want to make sure I’m not missing any critical insights that could skew my analysis. Let’s brainstorm some ideas together!