How Marketing Mix Modeling (MMM) helped me learn Linear Regression from first Principles. Some of you have appreciated my posts on Linear Regression and other statistics topics. Some of you also often ask me resources to learn Linear Regression. I always provide a list of books or articles that I have personally read. But when […]
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 and whistles (check the link under resources for a primer on MMM). If you must have noticed in Linear Regression, the confidence interval are always narrower than the prediction interval.
Why we report both confidence Interval and Prediction Interval in our MMM models Read More »
Selecting MMM models via AIC? Some key pointers 👇 The Akaike information criterion (AIC) is given by: AIC = 2k -2ln(L) where k is the number of parameters L is the likelihood The underlying principle behind usage of AIC is the ‘Information Theory’. Talking about information theory, we have been researching and implementing these concepts
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Calibration vs Validation A lot of people use calibration and Validation interchangeably. The two are not the same. ▪ Calibration In a regression setting, calibration of a model is about understanding the model fit. Goodness of fit measures like R squared values, P value, Standard Error, Cross validation and within sample MAPE/MAE/RMSE inform you how
Calibration vs Validation in MMM 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
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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. Multicollinearity is a information redundancy problem and Bayesian methodology can’t magically solve it. Rather the problem becomes worse in case of Bayesian MMM because your posterior distribution keeps getting wide
Bayesian MMM is not a silver bullet for MMM’s Multicollinearity issue Read More »
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 infer things about the population through the sample. For e.g. Population parameter is estimated through sample statistic. ▪ What is Bootstrapping? Bootstrapping simply put is a method of repeated sampling
How we build robust MMM models with help of Bootstrapping Read More »
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 »
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