The Wildman of Analytics

We talk analytics and tell it as it is.

Bayesian Reasoning: Gender Inference from a Specimen Measurement
Imagine that we have a population of something composed of two subset populations that, while distinct from each other, share a common characteristic that can be measured along some kind of scale. Furthermore, let’s assume that each subset population expresses this characteristic with a frequency distribution unique to each. In other words, along the scale of measurement for the characteristic, each subset displays varying levels of the characteristic among its members. Now, we choose a specimen from the larger population in an unbiased manner and measure this characteristic for this specific individual. Are we justified in inferring the subset membership of the specimen based on this measurement alone? Baye’s rule (or theorem), something you may have heard about in this age of exploding data analytics, tells us that we can be so justified as long as we assign a probability (or degree of belief) to our inference. The following discussion provides an interesting way of understanding the process for doing this. More importantly, I present how Baye’s theorem helps us overcome a common thinking failure associated with making inferences from an incomplete treatment of all the information we should use. I’ll use a bit of a fanciful example to convey this understanding along with showing the associated calculations in the R programming language.

Are you asking the right questions?
Whether you are a businessman or a practicing professionals such as an attorney, a doctor or a consultant, the ability to ask the right questions is imperative along with the ability to capture the information that is important when an answer is provided. Sometimes knowing where to start is the toughest aspect of solving a problem. Usually a sound approach is breaking out complex problems into smaller more manageable components; as the old adage goes “Do you know how to eat an elephant? One bite at a time!” Check out how to break-down tough problems by following the simple 5W question framework.

Perceptions and popularity of analytics technologies over time
While doing some market analysis, we decided to take a look at how search terms were being used in Google around the world as they related to advanced analytics technologies. We picked five terms to compare over five years: risk analysis, big data, machine learning, Monte Carlo method, and forecasting. We then proceeded to download the data and apply some quick and dirty forecasting to see what would happen to the popularity of the search terms overtime.