Introduction to Statistics. Introduction to the Statistical Package R. Descriptive Statistics: Quantitative variables, categorical variables, graphs in the statistical package R. Simulation: Distributions in the statistical package R, goodness-of-fit tests for distributions, weak law of large numbers, central limit theorem. Statistical Inference: Maximum likelihood estimators, confidence intervals, hypothesis testing. Regression Analysis: Simple linear model, correlation coefficient, general linear model. Analysis of Variance: One-way analysis of variance, two-way analysis of variance.
- Teacher: Δημήτριος Φουσκάκης
ECTS : 5
Language : el
Learning Outcomes : Upon successful completion of the course, the student will be able to:
• Determine, depending on the research question posed, what type of statistical analysis should be applied, which assumptions need to be checked, and how to interpret the results obtained.
• Fully understand the significance of the data analysis methods taught and when they are applied.
• Explain the results obtained in simple terms.
• Implement the methods taught with the help of the R programming language.
• Acquire a fairly high level of proficiency in using R for data processing and presentation, as well as creating their own functions in it.
• Be guided in a structured and comprehensible way to internalize the theory and practices applied to basic data analysis problems, with the goal of informed decision-making.