Data analysis

Our team leverages extensive theoretical and practical expertise in statistical, mathematical and machine learning models, employing suitable computational techniques to produce robust results.

Our team has vast experience in statistical and mathematical analyses of datasets based on their solid theoretical and practical backgrounds. We use appropriate computational techniques for a given problem and available data to produce robust results. Our approach includes

• Designing a Statistical Analysis Plans (SAP) e.g., sample size calculation, statistical methodology etc.

• Descriptive statistics e.g., Chi-square tests, Pearson correlation tests

• Statistical linear and nonlinear models which make assumptions of how data is distributed e.g., Classic regression models, Time series analysis, Distributed lag linear and non-linear models, Spatial analysis, Non-linear growth/decay models, Markov models, Cohort models etc.

• Bayesian analysis using different techniques and Markov Chain Monte Carlo (MCMC) methods

• Mathematical models that emulate biological or other processes that generate observed outcomes in order to infer parameters that we incorrectly or do not observed in real life.

• Using tools for programming, data analysis and visualization include Stan, R, R Shinny, R Markdown, Stata, SPSS