| Teaching
Philosophy |
'Whatever be the detail
with which you cram your students, the chance of them meeting
in after-life exactly that detail is almost infinitesimal; and
if they do meet it they will probably have forgotten what you
taught them about it. The really useful training yields a comprehension
of a few general principles with a thorough grounding in the
way they apply to a variety of concrete details. In subsequent
practice, the students will have forgotten your particular detail,
but they will remember by an unconscious common sense how to
apply principles to immediate circumstances." |
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Alfred
N. Whitehead: The Aims of Education & other Essays. |
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| Introduction |
This
course is intended to provide a foundation in those applied
statistical concepts, techniques and methods that students are
like to encounter within an economic and business environment.
The objective is to familiarize the student with the underlying
principles essential to the decision-makers understanding of
the reliability of such. |
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| Course
Content |
- Forecasting with
the Regression Model
- Simple Linear
Regression
- Multiple Linear
Regression
- Forecasting with
the Time Series Model
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| Course
Objectives |
On
completion of this course, the student should be able to do
the following:
- For estimators:
determine whether an estimator is biased or efficient
- Calculate the
least squares estimates of the parameters of the single
and multiple regression models and use knowledge of their
distribution for hypothesis testing and development of confidence
intervals.
- Test a given
linear regression model's fit to a given data set
- Assess the appropriateness
of the linear regression model for a given data set by checking
for such irregularities as heteroscedasticity, serial correlation,
and multicollinearity
- Develop deterministic
forecasts from time series data, using simple extrapolation
and moving average models, applying smoothing techniques
when appropriate.
- Use the concept
of the autocorrelation function of a stochastic process
to test the process for stationarity
- Test the hypothesis
that a given stochastic process is Random Walk.
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| Course
Material |
References
(Texts, Sites,…)
Econometric
Models & Econometric Forecasts
Robert S. Pindyck & Daniel L. Rubinfield
Introduction to Econometrics
Christopher Dougherty:
Business Statistics in Practice
Bowerman, OConnell & Hand
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| Course
Structure |
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| Lectures |
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Tuesdays |
10:00
- 11:00 & 12:00 - 1:00 |
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Thursdays |
12:00
- 2:00 |
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| Consultation
Hours: |
Tuesdays
& Wednesdays 10:00 - 12:00 |
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| Assignments: |
Assignments
are useful practice for exam preparation, and also may provide
evidence of ability for border-line exam candidates. Therefore,
it is in your best interest to complete and submit all assignments
on time. |
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| Assessment
Method |
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In-course
Test (20%): |
2
hour written paper
October 19th
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Final
Exam (80%): |
2
hour written paper. |
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| Handouts |
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| Lecture
Plan |
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| I |
FORECASTING
WITH REGRESSION MODELS (objectives 1-4) |
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[Reading
EM 3] |
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Simple Linear
Regression analysis- development of a
statistical model using single numerical independent variable
X to predict the numerical dependent variable Y.
Correlation analysis- measure of the strength of the
association between two numerical variables.
Goodness
of fit & Testing Hypotheses
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| NB: |
IN-COURSE
TEST covers Part I |
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[Reading
EM 4] |
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Multiple
Linear Regression analysis- development of a
statistical model using more than one numerical independent
variable X to predict the numerical dependent variable Y.
Goodness of fit & Testing Hypotheses
Departures
from regression assumptions: - Heteroscedasticity, Serial
correlation, Multicollinearity. |
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| II |
FORECASTING
WITH TIME SERIES MODELS (objectives 5-7) |
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[Handout] |
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Random
Walk Model
Moving Average Model
Autoregressive Model
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| NB: |
IN-COURSE
TESTS: October 3… Topic I; November 14… Topic
II |
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| Practice
& Review Sheets |
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| Problem
Papers |
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| Past
Exam Papers |
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- Final Exam December
2004 (doc,
pdf)
- Incourse Test
- October 2005 (doc, pdf)
- Incourse Test
Solutions - October 2005 (doc,
pdf)
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| Notices |
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