Category: Data Science

Abhijit Telang introduces the concept of conjoint analysis and shows how you can implement this in R:

We will need to typically transform the problem of utility modeling from its intangible, abstract form to something that is measurable. That is, we wish to assign a numeric value to the perceived utility by the consumer, and we want to measure that accurately and precisely (as much as possible).

This is where survey design comes in, where, as a market researcher, we must design inputs (in the form of questionnaires) to have respondents do the hard work of transforming their qualitative, habitual, perceptual opinions into  simplified, summarized aggregate values which are expressed either as a numeric value or on a rank scale.

I tend to shy away from this kind of analysis because it runs a huge risk of trying to turn ordinal utility rankings into cardinal functions.

Miroslav Rajter builds a Monty Hall problem simulator using R:

The original and most simple scenario of the Monty Hall problem is this: You are in a prize contest and in front of you there are three doors (A, B and C). Behind one of the doors is a prize (Car), while behind others is a loss (Goat). You first choose a door (let’s say door A). The contest host then opens another door behind which is a goat (let’s say door B), and then he ask you will you stay behind your original choice or will you switch the door. The question behind this is what is the better strategy?

This is something that puzzled me for a very long time. This is fundamentally a Bayesian problem built around processing new information, and once I understood that, the answer was a lot clearer. H/T R-Bloggers.