Why we report both confidence Interval and Prediction Interval in our MMM models
Why we report both confidence Interval and Prediction Interval in our MMM models. MMM is a type of linear regression but with lot more bells
Marketing Mix Modeling
Selecting MMM models via AIC? Some key pointers
Selecting MMM models via AIC? Some key pointers 👇 The Akaike information criterion (AIC) is given by: AIC = 2k -2ln(L) where k is the
Calibration vs Validation in MMM
Calibration vs Validation A lot of people use calibration and Validation interchangeably. The two are not the same. ▪ Calibration In a regression setting, calibration
Bayesian MMM is not a silver bullet for MMM’s Multicollinearity issue
I came across a post few days back which stated that Bayesian Methodologies are better at handling Multicollinearity in MMM. This is simply not true.
How we build robust MMM models with help of Bootstrapping
In statistics, especially inferential statistics, the corner stone paradigm is that of sample-population. We most often don’t have the population details. We hence try to
Bayesian MMM and The Richard McElreath’s Quartet
A week ago, I talked about epistemic uncertainty in Bayesian framework as a result of uninformative priors. That post drew expected reactions and many of
The Problem of priors in Bayesian MMM
In any data science project, the biggest hurdle is translating the business problem into a statistics/ML problem. Lot of things gets lost in this translation
Why Rows > Columns in MMM
If your job involves dealing with tabular data, then it is always prudent to ask for more data (rows) rather than ask for more features
Why Heteroscedasticity matters in Marketing Mix Modeling (MMM)
MMM is something that always leads you to reminisce about the learning one had or hadn’t during their statistics course. 15 years ago, when I was
A high t statistic does not indicate strong relationship between dependent variable and Independent variable
Gentle Reminder : A high t statistic does not indicate strong relationship of IVs with the DV. Just the other day, I saw a post
