Books

• 
  Fouskakis, D. (2021). Data Analysis using R. (862 pages), 2nd Edition. Tsotras, Athens, Greece. (in Greek).

  This book, besides constituting a detailed guide to the use of the statistical programming language R, also employs the particular language in order to implement the statistical methods developed herein, covering - among others - descriptive statistics, construction of graphs, simulation methods, hypothesis testing, regression analysis and analysis of variance. The methods included are illustrated in a concise and simple way, avoiding elaborate mathematical foundation, and emphasis is given on the circumstances under which they can be applied, on the corresponding assumptions and how to check that these are valid and finally on the interpretation of the results that these methods produce. As a next step, the way to perform these methods in R is demonstrated. Meanwhile, the book provides the reader with a vast range of examples pertaining to several research fields, such as Medicine, Social Sciences, Business Administration, Engineering, Economic Sciences etc. Thus, the reader is guided in a well-structured and comprehensive way to assimilate the theory and practices applicable to basic data analysis problems.
  
  Additions to the 2nd Edition:

• The apply() Functions in R.
• Greek Characters in R.
• RStudio and R Markdown.
• Centering, Standardization and Normalization of the Values of the Quantitative Variables in a Linear Model.
• Wrong use of the Coefficient of Determination in the Linear Models.
• Multiplicative Models.
• Methods of Comparing Linear Models.
• ggplot2, data.table, shiny.
• Binary Logistic Regression.
• Binary Classification.
• Cross Validation.



• 
  Kokolakis, G. and Fouskakis, D. (2009). Statistical Theory & Applications. (370 pages). Symeon. Athens, Greece. (in Greek).

  The present book delves into the progressive and systematic development of the mathematical foundations that lay beneath a mathematically sound methodology of statistical inference. Meanwhile, emphasis is given on the assumptions that ensure the validity of a statistical technique. By this way, the reader becomes capable of understanding the necessary conditions under which an applied statistical method is appropriate, thus rendering the ensuing conclusions reliable – or not. A variety of illustrations and applications not only throw light on the theory but also provide the reader with a broad range of experiences related to the statistical analysis of data. The book covers topics such as: Sampling Theory, Descriptive Statistics, Estimation Theory, Hypothesis Testing, Regression Analysis, Analysis of Variance (ANOVA), Analysis of Categorical Data and Non-parametric Statistical Methods, while some elements of Probability theory are included in the appendix.