Regression Analysis Form

Overview

Statistical analysis demands arise almost in any case where a given dataset is investigated, in scientific research as well as industrial projects. A lot of procedures, techniques, mathematical models and computer codes have been proposed, however, they can be grouped in four simple steps: data preprocessing, variables distributions, pairwise correlations, and multivariate modeling.

Topics

Descriptive statistics: min, max, median, percentiles, variance. Distributions, fitting and outliers. Correlation coefficients, covariance, chi2 test. Analysis of variance & Effect size. Time-series, smoothing, moving statistics, predictions. Regression Analysis: Data preparation, Normalization, Outliers. Coefficients, p-value, residuals, heteroscedasticity, bias. Importance vs Significance. Test sets, Ensemble models & Stepwise regression. Logistic & Nonlinear Regression. Conceptual Interpretatio.

Course Instructors: Dr. Nikolaos Bakas, Prof. Spyros Makridakis

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