The relationship between Curse of Dimensionality and Degrees of Freedom
Statisticians/Data scientists often remark “Don’t add more variables, you won’t have enough degrees of freedom” What does that mean ? Lets take an example of
statistics
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.
Why was ANOVA invented ?
I have interviewed many statisticians over the years. One misconception many still have is that, “If we have more than 2 groups, then we can’t
Explaining the ‘Hourglass’ shape of Confidence Interval
Couple of weeks back I wrote a post on “Why we report both confidence Interval and Prediction Interval in our MMM models.” If one were
What the word ‘confidence’ in Confidence Interval Signifies
A lot of people switch to Bayesian methods not because it is better than Frequentist ones, but mainly because they find it hard to wrap
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
