The premise of Bayesian statistics is that distributions are based on personal belief about the shape of such a distribution, rather than the classical assumption which does not take such subjectivity into account. In this regard, Bayesian statistics defines distributions […]

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# Multilevel Modelling in R: Analysing Vendor Data

One of the main limitations of regression analysis is when one needs to examine changes in data across several categories. This problem can be resolved by using a multilevel model, i.e. one that varies at more than one level and […]

Continue reading »# Visualizing New York City WiFi Access with K-Means Clustering

Visualization has become a key application of data science in the telecommunications industry. Specifically, telecommunication analysis is highly dependent on the use of geospatial data. This is because telecommunication networks in themselves are geographically dispersed, and analysis of such dispersions […]

Continue reading »# Cumulative Binomial Probability with R and Shiny

In conducting probability analysis, the two variables that take account of the chance of an event happening are N (number of observations) and λ (lambda – our hit rate/chance of occurrence in a single interval). When we talk about a […]

Continue reading »# plyr and dplyr: Data Manipulation in R

The purpose of the plyr and dplyr libraries in R is to manipulate data with ease. As we’ve seen in a previous post, there are various methods of wrangling and summarising data in R. However, wouldn’t it be great if […]

Continue reading »# Decision Trees and Random Forests in R

Decision trees are a highly useful visual aid in analysing a series of predicted outcomes for a particular model. As such, it is often used as a supplement (or even alternative to) regression analysis in determining how a series of […]

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