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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
Selecting MMM models via AIC? Some key pointers Read More »
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
Why Linear Regression is not all about predictions Read More »
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 »
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 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 »
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