Articles by Michael Grogan

Bayesian Statistics: Analysis of Health Data

March 10, 2019 | Michael Grogan

CategoriesRegression Models Tags Bayesian Analysis Linear Regression R Programming t-test The premise of Bayesian statistics is that distributions are based on a 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 ...
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Robust Regressions: Dealing with Outliers in R

February 26, 2019 | Michael Grogan

Robust Regressions in R CategoriesRegression Models Tags Machine Learning Outlier R Programming Video Tutorials It is often the case that a dataset contains significant outliers – or observations that are significantly out of range from the majority of other observations in our dataset. Let us see how we can use robust ...
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Multilevel Modelling in R: Analysing Vendor Data

February 10, 2019 | Michael Grogan

CategoriesRegression Models Tags Linear Mixed Model Linear Regression R Programming 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 ...
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Visualizing New York City WiFi Access with K-Means Clustering

February 5, 2019 | Michael Grogan

CategoriesAdvanced Modeling Tags K Means R Programming Unsupervised Learning 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 can ...
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Kalman Filter: Modelling Time Series Shocks with KFAS in R

February 1, 2019 | Michael Grogan

CategoriesAdvanced Modeling Tags R Programming Time Series When it comes to time series forecasts, conventional models such as ARIMA are often a popular option. While these models can prove to have high degrees of accuracy, they have one major shortcoming – they do not typically account for “shocks”, or sudden changes ...
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Analysing UK Traffic Trends with PCA

October 31, 2018 | Michael Grogan

CategoriesVisualizing Data Tags Data Visualisation Principal Component Analysis R Programming Tips & Tricks The PCA (also known as Principal Component Analysis) is quite a handy tool for solving unsupervised learning problems. In other words, PCA can allow us to group unsupervised data into meaningful clusters, and visualize this in a way ...
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neuralnet: Train and Test Neural Networks Using R

October 9, 2018 | Michael Grogan

CategoriesAdvanced Modeling Tags Data Visualisation Neural Networks Prediction R Programming A neural network is a computational system that creates predictions based on existing data. Let us train and test a neural network using the neuralnet library in R. How To Construct A Neural Network? A neural network consists of: Input ...
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Working with panel data in R: Fixed vs. Random Effects (plm)

October 6, 2018 | Michael Grogan

Working with panel data in R: Fixed vs. Random Effects CategoriesAdvanced Modeling Tags Linear Regression Logistic Regression R Programming Video Tutorials Panel data, along with cross-sectional and time series data, are the main data types that we encounter when working with regression analysis. Types of data Cross-Sectional: Data collected at ...
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