The empirical study and quantitative recognition of the relationships that describe the behavior of economic phenomena are necessary and essential for the integration and documentation of economic theory. Econometrics serves as the "bridge" between economic theory and practice, which, due to the uniqueness of economic variables, has acquired a distinct entity and position in the field of sciences. Econometrics has not only contributed to the establishment and evolution of economic science but has also helped in its development at a theoretical level, as several economic theories have emerged from the empirical study of economic phenomena. Additionally, econometrics contributes to the process of making managerial and economic decisions, as its empirical results can be used not only to recognize the behavior of the phenomenon but also to create predictions regarding its future developments.

The purpose of this textbook is twofold. On the one hand, it aims to provide the reader with the necessary statistical tools required in every econometric analysis, and on the other hand, it aims to familiarize the reader with the concept, purpose, and functioning of econometrics, thus introducing them to the methodology of econometric analysis. For this reason, the presentation of the chapters has been made in a simple manner, so that their content is easily understandable, and the theoretical presentation of econometrics is accompanied by empirical applications.

In Chapter 1, the methodology of econometrics is analyzed, according to which the quantitative relationship between the various variables involved in the interpretation of economic phenomena is determined. Chapter 2 develops the concept of a random variable as determined by its values and probability function. Chapter 3 describes certain theoretical distributions of random variables, which are necessary for econometric analysis. Chapter 4 refers to the concept and methodology of statistical inference, according to which the value and range of values for each parameter are determined based on the algebraic form of its estimator and the observations of the sample. Chapter 5 analyzes all forms of statistical tests that can appear and be used in econometrics, while Chapter 6 introduces the concept of regression analysis and its differences from correlation analysis.

Subsequently, Chapter 7 describes the estimation method of the simplest form of linear model, namely the simple linear model, in which only two variables are involved. Chapter 8 presents the statistical inference of the parameters of the simple linear model, as well as the method of creating predictions from this model. Chapter 9 develops the estimation method of a multiple linear model, in which the behavior of the dependent variable is interpreted by more than one independent variable, and Chapter 10 analyzes all the methods of statistical inference of the parameters of a multiple linear model, as well as the method of creating predictions.

Finally, Chapters 11 to 14 address the problems that may arise in the analysis of regression when some of the assumptions of the model are violated.

Specifically, in chapter 11, the problem of multicollinearity is analyzed, which refers to the existence of linear correlation between independent variables. In chapter 12, the problem of heteroscedasticity is discussed, which refers to the violation of the assumption of constant and equal variance of the random error term. Chapter 13 addresses the issue of autocorrelation, which refers to the violation of the assumption of independence of the random error term. Finally, chapter 14 presents the problem of bias in the estimators of the model coefficients, as well as the problem of normality, which arises in regression analysis when the assumption of normality of the random error term is violated.

We would like to thank Professor Paul Newbold of the University of Nottingham for his constructive comments on certain points of the book, as well as Professor Anil Bera of the University of Illinois at Urbana-Champaign.