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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 Bayesian loyalists provided only hand wavy refutations. ICYMI the link to post is in comments. Anyhow, I stumbled upon an interesting tweet from Richard McElreath. I have named it ‘Richard […]
Bayesian MMM and The Richard McElreath’s Quartet 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 »
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 (columns). There is no point in having more columns which are shallow (i.e. missing data). We at Aryma Labs deal with tabular data a lot, especially in the form Marketing
Why Rows > Columns in MMM 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 is
Why Heteroscedasticity matters in Marketing Mix Modeling (MMM) Read More »
Interesting Fact: Imagine you sculpted your distribution out of wood and tried to balance it on your finger. The balance point would be the mean regardless of the shape of the distribution !! Now why is that? This is where the concept of Moments come into play. Many of you might be wondering, how did
Moments – Statistics’ physics connection Read More »
Seeing Statistical Tests through the lens of Signal vs Noise. Most statistical tests are designed to discern signal from the noise. Let me take a classic example – ANOVA The F test for one way ANOVA is given as follows: F= Variance between treatments / Variance within treatments. One could look at the numerator as
Seeing Statistical Tests through the lens of Signal vs Noise Read More »
Gentle Reminder : A high t statistic does not indicate strong relationship of IVs with the DV. Just the other day, I saw a post (again) stating that a t statistic indicates strength of relationship of IVs to DVs. So here is a gentle reminder (sharing it again). The high t statistic here does not
When we talk about application of statistics during world war 2, somehow the image of the airplane with red dots (survivorship bias) comes to mind. Don’t worry I am not going delve on that again 😅 There are however other lesser known statistical applications during world war 2 which had a huge impact in the
Are you confused about the meaning of ‘statistic’ in statistics? You’re not alone. Many blogs and posts on the internet use the term loosely. In statistics, a statistic is defined at a sample level, whereas a parameter is defined at a population level. To estimate the parameter, we use a statistic. One of the mental
What does ‘statistic’ in statistics mean? Read More »
A lot of people blame Confidence Intervals and its ‘unintuitive’ nature for switching to Bayesian side of things. But if you are in Marketing Mix modeling domain, frequentist concept of confidence interval makes more sense. Before I elaborate, let me provide a quick recap of what exactly is Confidence Interval. 📌 What is confidence Interval?
Confidence intervals in a way are barometer of your Marketing Mix Models Read More »
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