Decoding the Regression Matrix: P-Values, Standard Errors, and Other Mystical Creatures Explained

So, you’ve run a regression. Your screen is full of numbers, symbols, and maybe even a Greek letter or two (hello, β). You’re feeling like a data wizard, but… what do all these numbers actually mean? Don’t worry — interpreting regression outputs can be like unlocking a treasure chest, but without the risk of getting a splinter. Let’s break it down, step by step, and have a laugh while we’re at it!

Step 1: The Coefficient (β) — The King of the Regression Kingdom

Ah, the coefficient, or as we’ll call it, “The Beta Boss”. This number tells you how much a change in one variable (let’s say temperature) affects your outcome (like ice cream sales). If β is positive, an increase in temperature means more ice cream sold. If it’s negative, well, maybe it’s more soup sales instead.

Anecdote: “The first time I saw a negative β, I thought my model was broken. Turns out, not everyone buys sunglasses when it rains. Who knew?”

Step 2: Standard Error (SE) — Your Model’s Nervous Friend

The standard error is the jittery sidekick of your coefficient. Think of it as your data’s way of saying, “How confident am I in this β value?” If the standard error is small, it’s like your data is confidently standing tall. If it’s large, it’s waving its arms around, saying, “I’m not so sure about…