This document defines key concepts in econometrics and linear regression. It begins by defining econometrics as the application of economic theory, mathematical models, and statistical models to analyze economic data and empirically test economic theories. It then defines related concepts like economic theory, mathematical models, statistical models, and economic models. The document also distinguishes between econometrics and statistics, and economic models versus econometric models. Finally, it discusses the purpose of econometrics and types of statistics before providing an overview of simple linear regression.

1. ECONOMETRICS
LINEAR REGRESSION
BY
BUSARI, Rasheed Ajibola

2. WHAT IS ECONOMETRICS
Econometrics can be defined as application of ECONOMIC THEORY, MATHEMATICAL MODEL AND STATISTICAL
MODEL to the analysis of economic data with a purpose of giving EMPIRICAL CONTENT to the economic theories
and verifying them or refuting them.
WHAT IS ECONOMIC THEORY
Economic theories try to explain economic phenomena, to interpret why and how the economy behaves and what is
the best to solution - how to influence or to solve the economic phenomena. They are comprehensive system of
assumptions, hypotheses, definitions and instructions what should be done in a certain economic situation.
WHAT IS MATHEMATICAL MODEL
A mathematical model is a description of a system using mathematical concepts and language.
WHAT IS STATISTICAL MODEL
A statistical model is usually specified as a mathematical relationship between one or more random variables and
other non-random variables. As such, a statistical model is "a formal representation of a theory

3. WHAT IS AN ECONOMIC MODEL
An economic model is a simplified description of reality, designed to yield hypotheses about economic behavior that
can be tested. An important feature of an economic model is that it is necessarily subjective in design because there
are no objective measures of economic outcomes.
WHAT IS A MODEL
A model is a simplified representation of a real-world process. Some school of thought believes that a simple model
should represent a complex theory while some believe that a complex model is the best to reflect the complex nature
of the theory. However, simplicity of a model leads to two criticism: OVERSIMPLIFICATION ANS UNREALISTIC
ASSUMPTION.
DIFFERENCE BETWEEN ECONOMETRICS AND STATISTICS
Econometrics is often “theory driven” while statistics tends to be “data driven”

4. DIFFERENCE BETWEEN ECONOMIC MODEL AND ECONOMETRIC MODEL
 An economic model is a set of assumptions that describes the behaviour of an economy, or more generally, a
phenomenon while an econometric model consists of a set of equations describing the behaviour.
 An economic model is deterministic in nature while econometric model is realistic in nature.
 C=a + bY….. consumption function (an economic model)
 C= a+bY + u (an econometric model)
 An econometric model consist of the following
 A set of behavioural equations derived from the economic model
 A statement of whether there are errors of observation in the observed variables
 A specification of the probability distribution of the disturbances and error of measurement

5. PURPOSE OF ECONOMETRICS
The aims of econometrics are
 Formulation of hypothesis (economic models, economic theory)
 Estimation and testing of these models with observed data
 Use of these models for prediction and policy purpose (forecasting)
TYPES OF STATISTICS
 Descriptive statistics: summarizes and describe a body of date. Eg chart graph, measure of central tendency
(mean, median, mode), etc
 Inferential statistics: the process of reaching generalization about the whole (population) by examining a portion
(sample). While bringing out a sample from population, we must have error term or disturbances error.

7. SIMPLE LINEAR REGRESSION
Regression analysis is concerned with describing and evaluating the causal relationship between a given variable
(often called the explained or dependent variable) and one or more other variables (often called explanatory or
independent variables).
Thus, a linear regression is the evaluation of the causal relationship between a dependent variable and independent
variable.
 𝑌 = α + β𝑋 + μ
 Where Y is the explained or dependent or regressand variable
 X is the explanatory or independent or regressor variable
 μ is the error term
 α β are coefficients (intercepts and slope)
The sources of error term could be the following;
• Unpredictable element of randomness in human behaviour
• Effect of a large number of variables that have been omitted
• Measurement of error in y.

15. THANK YOU FOR
YOUR TIME