ECS3706/ASSIGNMENT 1/2025
Assignment 01 Semester 01
Econometrics
ECS3706
Semester 1
Department of Economics
This tutorial letter contains Assignment 1 Questions.
BARCODE
, 1 Which of the following statements is/are correct?
a Simple linear regression explores the relationship between only two variables
b Simple linear regression explores the relationship between more than one
dependent and one independent variable
c Multiple linear regression explores the relationship between one dependent
variable and more than one independent variable.
[1] a
[2] a and b
[3] b and c
[4] a and c
[5] Not [1], [2], [3] or [4]
Linear regression is a mathematical method used for analysing how the variation in one variable
can explain the variation in another variable. For example, we can use linear regression analysis
to forecast inflation, given unemployment data.
In simple linear regression, we assume linear relationships exist between the dependent and
independent variables. The aim is to fit a line to the observations of (Xis) and (Yis) to minimize
the squared deviations from the line. To accomplish this, we use the least squares criterion.
The following is a simple linear regression equation:
Y = b0+b1X1+εi , i =1,2,…,n
Where:
Y = Dependent variable.
b0 = Intercept.
b1 = Slope coefficient.
X = Independent variable.
ε = Error term (Noise).
b0 and b1 are known as regression coefficients. The equation above implies that the dependent
is equivalent to the intercept (b0) plus the product of the slope coefficient (b1) and the
independent variable plus the error term.
The error term is equal to the difference between the observed value of YY and the one expected
from the underlying population relation between XX and Y
2
Assignment 01 Semester 01
Econometrics
ECS3706
Semester 1
Department of Economics
This tutorial letter contains Assignment 1 Questions.
BARCODE
, 1 Which of the following statements is/are correct?
a Simple linear regression explores the relationship between only two variables
b Simple linear regression explores the relationship between more than one
dependent and one independent variable
c Multiple linear regression explores the relationship between one dependent
variable and more than one independent variable.
[1] a
[2] a and b
[3] b and c
[4] a and c
[5] Not [1], [2], [3] or [4]
Linear regression is a mathematical method used for analysing how the variation in one variable
can explain the variation in another variable. For example, we can use linear regression analysis
to forecast inflation, given unemployment data.
In simple linear regression, we assume linear relationships exist between the dependent and
independent variables. The aim is to fit a line to the observations of (Xis) and (Yis) to minimize
the squared deviations from the line. To accomplish this, we use the least squares criterion.
The following is a simple linear regression equation:
Y = b0+b1X1+εi , i =1,2,…,n
Where:
Y = Dependent variable.
b0 = Intercept.
b1 = Slope coefficient.
X = Independent variable.
ε = Error term (Noise).
b0 and b1 are known as regression coefficients. The equation above implies that the dependent
is equivalent to the intercept (b0) plus the product of the slope coefficient (b1) and the
independent variable plus the error term.
The error term is equal to the difference between the observed value of YY and the one expected
from the underlying population relation between XX and Y
2
