Statistical thinking and analysis
The reference page every formula on this site links to. Percentages, averages, spread, probability, tests, regression, elasticity, compounding and lifetime value, each from scratch with one shop's numbers worked through.
How to size a test from the lift you need, read a confidence interval instead of a green tick, and run a full factorial that reveals interactions.
Sample sizes, spend per platform, time at a small daily budget, kill rules for losing ads, how many tests fit your traffic, which channel fits your stage and what a forecast needs, with every formula and table.
How structural equation modelling shows which guest experiences actually drive loyalty: a hotel path model, total effects, fit in plain words and an investment decision.
How media mix modelling uses adstock and saturation curves to estimate the marginal ROI of every channel and where the next pound should go, worked through on an invented hamper shop with the formulas, the numbers and the checks.
Fifteen concrete jobs that data engineers, analysts and data scientists do for a SaaS business and an online shop, in the order they should be done, each rated for importance and worked through with invented numbers you can check.
How to put an honest number on one more email, webinar invitation or retargeting ad: response curves, marginal value, the full cost of a contact and holdout groups, worked through for an invented Manchester SaaS.
k means, latent class analysis and RFM compared on an invented hamper shop: how to choose the number of segments, profile and name them, check that they are stable and put them to work.
A/B tests, A/B/n tests, bandits, multivariate tests, holdouts, geographic tests, audience splits and time based tests: what each one answers, what it needs and where it breaks.
How PCA and factor analysis turn 24 survey questions or 18 behavioural variables into a few dimensions you can name, score and act on, with a hotel survey and a hamper shop worked through.
When the ad budget is set from sales, sales and spend cause each other. Why one regression then overstates the ad effect, and how two stage least squares corrects it.
How to forecast weekly demand with seasonal dummies, handle autocorrelation, judge models on a holdout and plan stock and staff against an 80% error band.
Hick, Fitts, Miller, the peak end rule, the serial position effect, cognitive load, friction and framing: what each piece of research actually found, what it does not prove, and how to use it to review a page, with worked examples.
Ten pieces of maths that turn growth data into decisions: lifetime value against acquisition cost, payback, cohorts, survival curves, Bayesian tests, bandits, attribution and mix models, power laws, price elasticity and retention, each with a worked example.
How panel regression separates a good shop from a good quarter, why pooled data lies about marketing, and how fixed effects give you an honest like for like sales figure.
How survival analysis tells you when customers leave, not just whether, and how to turn Kaplan Meier curves and a Cox model into a lifetime value you can defend.
Seven in 10 carts are abandoned and a sixth of US retail sales come back. Two predictive systems, an abandonment risk scorer and a return risk predictor, built in Python and wired into Solidus, with the business case worked through properly.
How logistic regression ranks customers, where the next brochure stops paying for itself, and how the same model turns baskets into cross sell recommendations.
Orders, tickets, visits: when the outcome is a count, ordinary regression misbehaves. Poisson regression, rate ratios, exposure and overdispersion, worked through on a Leeds hamper shop.
Why a newsletter keeps producing orders for weeks, how the Almon polynomial lag model measures that curve, and how an invented Leeds hamper shop could use it to pick a three weekly send cadence.
A year of hamper sales, one log log regression and an elasticity of minus 1.6: how to model demand with OLS, read the output and find the price that pays.
A finding says what happened. An insight names the decision it changes, prices it and states the risk. How to tell them apart, score them and write one.
Mean, median, standard deviation, confidence intervals, p values and correlation, worked through on an invented hamper shop's orders so you can read marketing numbers honestly.
What a p value really tells you, how wide the margin of error on a conversion rate is, and why a significant lift can still lose you money, worked through for an invented Leeds candle shop.
Why the average order value hides the shape of your data, and how the median, spread, quartiles, honest outlier handling and a look at segments give you numbers you can actually decide on.
