
Explore statistical concepts explained and applied in R, and learn to interpret results for scientific papers. Master analyses, including regression, ANOVA, logistic and time analysis, with reproducible results.
Install R and RStudio on Windows, compare the classic R console with the desktop RStudio IDE, and explore creating variables, loading packages, plotting, and basic data analyses.
Import and prepare the dataset in R by reading the file, attaching variables, and creating a data frame named D for 32 observations of six variables for a multiple linear regression.
Explore logistic regression for binary outcomes using the logit link, modeling probability with age-based investments, fitting in R, visualizing results, and comparing with linear regression.
Compare odds, odds ratios, and probability in logistic regression using log and exponential transformations of coefficients. Apply generalized linear models to discrete count data and interpret model scales.
Explore fitting logistic and related binomial models in R, comparing odds and risk ratios with confidence intervals, examining convergence, and using display, plotting, and Wald tests for model robustness.
This course takes you from basic statistics and linear regression into more advanced concepts, such as multivariate regression, anovas, logistic and time analyses. It offers extensive examples of application in R and complete guidance of statistical validity, as required for in academic papers or while working as a statistician.
Statistical models need to fulfill many requirements and need to pass several tests, and these make up an important part of the lectures.
This course shows you how to understand, interpret, perform and validate most common regressions, from theory and concept to finished (gradable) paper/report by guiding you through all mandatory steps and associated tests.
Taught by a university lecturer in Econometrics and Math, with several international statistical journal publications and a Ph.D. in Economics, you are offered the best route to success, either in academia or in the business world.
The course contents focus on theory, data and analysis, while triangulating important theorems and tests of validity into ensuring robust results and reproducible analyses. Start learning today for a brighter future!