Statistical analysis refers to tools and techniques that give meaning to data, revealing insights, solutions, and evidence. Generally, this includes studying, clearing and transforming data to produce useful and relevant results backed by scientific and mathematical rigorousness. The scope of analysis ranges from simple summaries and exploratory analyzes to complex modeling and hypothesis testing. Our analysis service provides the information you need to make smart decisions by letting your data speak. We use the most advanced statistical methods to ensure that we provide the most accurate and representative use of data, thus enabling interpretation in the most efficient and useful way.    

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We can list some of the analysis methods we use as follows; 

• Group means comparison (T-Test, Anova, Wilcoxon, Mann Whitney U, Kruskal-Wallis, Chi-Square, A / B Testing)

• Correlation analysis (Pearson, Spearman, Kendall)

• Power and sample size estimation

• Longitudinal (Panel) data set analysis (GLMs, GLMM, GEE)

• Confirmatory and exploratory factor analysis (CFA, EFA)

• Reliability and internal consistency (ICC, Cohen's Kappa, Cronbach's Alpha)

• Regression (Linear and Polynomial Models, SVR, Naive Bayes, Random Forest)

• Classification (Logistic regression, Roc-Curve Analysis, SVM, Decision Trees)

• Clustering (K-Means, Hierarchical, Latent Class, Mean Shift)

• Time series models, Survival models