Intials and surname : C Mthombeni
Student no : 62426613
Module code : ECS3706
Exam date : 28 October 2022
QUESTION A1 (20 marks)
(a)
Explain the following terms
i. Estimated regression equation
usually population size may be too large or unknown therefore
inference is made on the sample drawn from the population using
the estimated regression equation which is the statistical
technique that attempts to explain movements in one dependent
variable given independent variables
ii. Expected value
Expected value is the anticipated long-run average value of X if
this die is rolled over and over and over and also The expected
value is an average of the possible outcomes weighted by their
respective probabilities
iii. Independent variable
Independent variable is the variable that is changed or controlled in
sceientific experiment to test the effects on the dependent variavle
or variable that are manipulated or are changed by researchers and
whose effects are measured and compared
iv. Estimator
An estimator - A sample statistic that will be used to estimate the
value of the population parameter.
v. Ordinary Least Squares
is a statistical estimator which is usually applied to sample data
where the sample is a
subset of the population. These sample estimates are not
necessarily accurate. There is the
risk that the sample estimates may be in error
B.
i. Why is the left-hand variable in the equation and not ?
There are two logical options when you vary t: either the value of
the left-hand side changes, or it doesn’t. If it changes, then the right
side must change as well, since they are equal. But the right-hand
side can’t change when you vary t, since it is not a function of t!
Therefore, since varying t produces no change in the left-hand-side,
then the left-hand side must be constant. And since it is equal to the
right-hand side, then they are both (the same) constant
ii. The researcher did not include the stochastic error term in
the estimated equation. Was this a mistake? Why or why not?
An error term is a residual variable produced by a statistical or
mathematical model, which is created when the model does not
, fully represent the actual
This study source was downloaded by 100000847097152 from CourseHero.com on 03-12-2023 14:50:21 GMT -05:00
, relationship between the independent variables and the dependent
variables. As a result of this incomplete relationship, the error term
is the amount at which the equation may differ during empirical
analysis.
The error term Is also known as the residual, disturbance, or
remainder term, and is variously represented in models by the
letters e, ε, or u.
iii. If the researcher changes units of INC from “Rand” to
“thousands of rand.” Will the estimated coefficients change? Be
specific.
Yes , everything will change since variables changed
iv. What additional variables would you add to this equation?
P values and coefficients in regression analysis work together to tell
you which relationships in your model are statistically significant
and the nature of those relationships. The linear regression
coefficients describe the mathematical relationship between each
independent variable and the dependent variable. The p values for
the coefficients indicate whether these relationships are
statistically significant
QUESTION A2 (20 marks)
(a) Using practical examples, discuss the six steps in applied
regression analysis
STEPS IN APPLIED REGRESSION ANALYSIS
Once a dependent variable is chosen, however, it’s logical to follow
this sequence:
1. Review the literature and develop the theoretical model.
The best data analysts don’t start with data, but with theory!
Econometric decisions, ranging from which variables to include to
which functional form to employ, are determined by the underlying
theoretical model.
It’s smart to review the scholarly literature, recent issues of the
Journal of Economic Literature or a business oriented
publication of abstracts on your topic. Pay attention to the
bibliographies. If a topic will be so new or so obscure there are
two possible strategies.
1. Try to transfer theory from a similar topic to yours.
2. If all else fails, pick up the telephone and call someone who
works in the field you’re investigating.
2. Specify the model: Select the independent variables and
the functional form.
The most important step is the specification of the theoretical
regression model. After selecting the dependent variable, the
specification of a model involves choosing the following components:
1. The independent variables and how they should be measured
2. The functional (mathematical) form of the variables, and
3. The properties of the stochastic error term.
The elements of specification are determined on the basis of
economic theory. A mistake in any of the three elements
Student no : 62426613
Module code : ECS3706
Exam date : 28 October 2022
QUESTION A1 (20 marks)
(a)
Explain the following terms
i. Estimated regression equation
usually population size may be too large or unknown therefore
inference is made on the sample drawn from the population using
the estimated regression equation which is the statistical
technique that attempts to explain movements in one dependent
variable given independent variables
ii. Expected value
Expected value is the anticipated long-run average value of X if
this die is rolled over and over and over and also The expected
value is an average of the possible outcomes weighted by their
respective probabilities
iii. Independent variable
Independent variable is the variable that is changed or controlled in
sceientific experiment to test the effects on the dependent variavle
or variable that are manipulated or are changed by researchers and
whose effects are measured and compared
iv. Estimator
An estimator - A sample statistic that will be used to estimate the
value of the population parameter.
v. Ordinary Least Squares
is a statistical estimator which is usually applied to sample data
where the sample is a
subset of the population. These sample estimates are not
necessarily accurate. There is the
risk that the sample estimates may be in error
B.
i. Why is the left-hand variable in the equation and not ?
There are two logical options when you vary t: either the value of
the left-hand side changes, or it doesn’t. If it changes, then the right
side must change as well, since they are equal. But the right-hand
side can’t change when you vary t, since it is not a function of t!
Therefore, since varying t produces no change in the left-hand-side,
then the left-hand side must be constant. And since it is equal to the
right-hand side, then they are both (the same) constant
ii. The researcher did not include the stochastic error term in
the estimated equation. Was this a mistake? Why or why not?
An error term is a residual variable produced by a statistical or
mathematical model, which is created when the model does not
, fully represent the actual
This study source was downloaded by 100000847097152 from CourseHero.com on 03-12-2023 14:50:21 GMT -05:00
, relationship between the independent variables and the dependent
variables. As a result of this incomplete relationship, the error term
is the amount at which the equation may differ during empirical
analysis.
The error term Is also known as the residual, disturbance, or
remainder term, and is variously represented in models by the
letters e, ε, or u.
iii. If the researcher changes units of INC from “Rand” to
“thousands of rand.” Will the estimated coefficients change? Be
specific.
Yes , everything will change since variables changed
iv. What additional variables would you add to this equation?
P values and coefficients in regression analysis work together to tell
you which relationships in your model are statistically significant
and the nature of those relationships. The linear regression
coefficients describe the mathematical relationship between each
independent variable and the dependent variable. The p values for
the coefficients indicate whether these relationships are
statistically significant
QUESTION A2 (20 marks)
(a) Using practical examples, discuss the six steps in applied
regression analysis
STEPS IN APPLIED REGRESSION ANALYSIS
Once a dependent variable is chosen, however, it’s logical to follow
this sequence:
1. Review the literature and develop the theoretical model.
The best data analysts don’t start with data, but with theory!
Econometric decisions, ranging from which variables to include to
which functional form to employ, are determined by the underlying
theoretical model.
It’s smart to review the scholarly literature, recent issues of the
Journal of Economic Literature or a business oriented
publication of abstracts on your topic. Pay attention to the
bibliographies. If a topic will be so new or so obscure there are
two possible strategies.
1. Try to transfer theory from a similar topic to yours.
2. If all else fails, pick up the telephone and call someone who
works in the field you’re investigating.
2. Specify the model: Select the independent variables and
the functional form.
The most important step is the specification of the theoretical
regression model. After selecting the dependent variable, the
specification of a model involves choosing the following components:
1. The independent variables and how they should be measured
2. The functional (mathematical) form of the variables, and
3. The properties of the stochastic error term.
The elements of specification are determined on the basis of
economic theory. A mistake in any of the three elements
