Moderation Analysis

Moderation Analysis — find out WHEN an effect is strong and when it fades. Does the effect of X on Y depend on a third variable W? This app fits the moderated regression, tests the X·W interaction, and draws the simple-slopes fan that makes "it depends" obvious. Fully offline. Real relationships are rarely one-size-fits-all. Stress (X) lowers wellbeing (Y) — but maybe far less for people with strong social support (W). Practice improves performance — but more so for those with natural talent. That "the effect changes with W" is MODERATION, and it shows up as an interaction term in the regression. This app estimates it, tests it, and turns it into a picture. No sign-up. No internet needed. No data collected. Just clear, instant statistics. WHAT YOU CAN DO • Data — type your own predictor (X), moderator (W) and outcome (Y) into a clean row editor and name each variable. Everything recomputes as you type. • Slopes — see the simple-slopes plot: the X→Y line drawn at LOW, MEAN and HIGH levels of W. Lines that fan out mean W moderates the effect; parallel lines mean it does not. Each simple slope gets its own value and significance test. • Concept — learn what moderation means in everyday language, why we add the product X·W, and see the offline, beautifully typeset formulas with no overlap. • Simulate — set the main effects of X and W and the interaction strength with sliders that have + / - steppers, pick a sample size, and watch the slopes fan out as the interaction grows. • Examples — load ready-made studies (stress & support, practice & talent, price & income and more) with one tap. WHAT YOU GET • The moderated regression: b₀, b₁ (X), b₂ (W) and b₃ (the X·W interaction). • A significance test for b₃ — is the moderation real? • Simple slopes of X at low, mean and high W, each with its own test. • A simple-slopes fan plot that turns the interaction into a picture. • Mean-centred predictors, so b₁ reads as X's effect at the average W. WHY YOU'LL LIKE IT • Fully offline — every regression, slope and formula works with no connection. • See the moderation — the fan plot makes "the effect depends on W" obvious. • Honest statistics — the interaction term is tested, not assumed. • Hands-on — type exact data, or build a sample with sliders and + / - steppers. • Clean "Crimson Charcoal" design, easy on the eyes. • Privacy-friendly — your data stays on your device. Whether you are a student learning interactions for a methods class, a researcher probing a moderator, or anyone curious about when an effect is strong and when it fades, Moderation Analysis gives you the model, the test and the picture in seconds. Download it, enter your X, W and Y, and see whether the effect really depends.