The word uncertainty means different things in Bayesian MMM vs Frequentist MMM. From a Frequentist perspective, the uncertainty is due sampling variability. That is, there is a true fixed parameter of the population. But given the sample at hand, you may or may not capture this true parameter. In MMM parlance, one could think of […]
Bayesian uncertainty ≠ Frequentist uncertainty Read More »
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 Multi linear regression When building a multi linear regression model, adding more independent variables into the model reduces the degrees of freedom. This is also related to the “curse of
The relationship between Curse of Dimensionality and Degrees of Freedom Read More »
Often I come across posts and comments from people where they make claims like ‘Linear regression is all about predictions’. Well they are wrong but I don’t quite blame them. Thanks to the machine learning take over of statistical nomenclatures, any prediction task is now labelled as ‘Regression task’ !! This is of course two
Why Linear Regression is not all about predictions Read More »
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 perform t-test and that is why we perform ANOVA”. It is not that we can’t perform t-test with more than 2 groups. We certainly can. The real reason why ANOVA
Why was ANOVA invented ? Read More »
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 to notice the shape of the confidence Interval, one would notice that it is in the shape of ‘hourglass’ or ‘sand clock’. Now why is that? Well, the answer again
Explaining the ‘Hourglass’ shape of Confidence Interval Read More »
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 their heads around Frequentist concepts. One such concept is Confidence Interval. One of the common misconception people have wrt to CI is that “it is the range in which the
What the word ‘confidence’ in Confidence Interval Signifies Read More »
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 which eventually leads to inaccurate models and unhappy customers. In MMM, especially Bayesian MMM, this ‘lost in translation’ problem is more pronounced. The client is sold the magic that through
The Problem of priors in Bayesian MMM Read More »
MMM is for enterprises of all sizes. Of late I came across a lot of posts proclaiming that MMM is not ideal for SMBs. I want to dispel some myths on this. 📌Small Marketing Spends Small marketing spends is not a show stopper. Not statistically and not marketing wise either. There are various statistical techniques
You can do MMM with small marketing spends Read More »
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 student, I attended one statistics seminar. In it the professor told “Always be testing your assumptions”. That statement rings true even now, especially in MMM. In MMM, inference
Why Heteroscedasticity matters in Marketing Mix Modeling Read More »
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