MINISTRY OF EDUCATION AND TRAINING
SOCIALIST REPUBLIC OF VIETNAM
UNIVERSITY OF ECONOMICS HCMC
Independence – Liberty – Happines
PTCT.QT.xx.03
BACHELOR PROGRAM
(Higher education program)
MAJOR:
MINOR:
SYLLABUS
1. Course name:
Applied Statistics for Business and Economics
2. Course code:
3. Teaching Department:
Statistics - Data Analysis Department
4. Level of competency: apply for students at the first academic year
5. Credits: 3
6. Time allocation:
+ Theories: 30
+ Group works, practice, discussion: 15.
+ Self-study: 90
7. Prequisite courses:
none
8. Course description:
Applied Statistics for Economics and Business course provides in a systematic way
statistical methods including gathering data on economic & business phenomena and
processing collected data into useful information which can be used as facts for
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making decisions for management of economy and society. These methods include:
descriptive statistics, statistical inference, index- number, and forecasting.
This course is also to introduce students to popular and easy-to-use packages as a tool
to perform statistical techniques to save time and effort, to increase the level of
accuracy of the results, and to help students familiar with output interpretation.
9. Course Learning Outcomes – CLOs:
After completing the course, students will have better statistical thinking and
quantitative analysis capability. In particular, students will be able to:
9.1 Knowledge:
-
CLO1.1: Present basic concepts in statistics.
CLO1.2: Understand and give interpretation as well as examples for the
applications of statistics in the fields of economics and business, especially in
the fields studied.
- CLO1.3: Know primary and secondary data sources.
- CLO1.4: Distinguish descriptive statistics and statistical inference.
- CLO1.5: Understand the concept of probability and its application in statistical
inference.
9.2 Skills:
-
CLO2.1: Know how to get secondary data.
CLO2.2: Know how to get primary data.
CLO2.3: Summarize data in the form of graphs or numerical measures.
CLO2.4: Implement statistical inference including statistical estimate and
hypothesis testing.
CLO2.5: Perform time series analysis and basic prediction.
CLO2.6: Convey the significance of the statistical results for economic and
business decision-making.
9.3 Autonomy and Responsibility:
-
CLO3.1: Work in groups to complete group tasks.
CLO3.2: Proactively work to complete assignments on time.
CLO3.3: Actively listen to lecturers’ feedback to implement adjustment.
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Course learning outcomes matrix:
Program Learning Outcomes (PLOs)
Course Learning Outcomes
(CLOs)
PLO1.1
CLO1.1 Present basic concepts in
statistics.
P
CLO1.2 Understand and give
interpretation as well as examples
for the applications of statistics in
the fields of economics and
business, especially in the fields
studied.
S
PLO1.2
PLO1.3
PLO1.4
PLO1.5
PLO2.1
PLO2.2
P
P
P
P
P
H
P
P
S
CLO1.3 Know primary and
secondary data sources.
P
CLO1.4 Distinguish descriptive
statistics and statistical inference.
P
CLO1.5 Understand the concept
of probability and its application
in statistical inference.
P
H
H
CLO2.1 Know how to get
secondary data.
P
CLO2.2 Know how to get
primary data.
P
3
PLO2.3
PLO2.4
PLO2.5
PLO3.1
PLO3.2
PLO3.3
PLO3.4
CLO2.3 Summarize data in the
form of graphs or numerical
measures.
P
P
H
H
P
CLO2.4 Implement statistical
inference including statistical
estimate and hypothesis testing.
P
H
P
P
P
CLO2.5 Perform time series
analysis and basic prediction.
P
P
H
P
CLO2.6 Convey the significance
of the statistical results for
economic and business decisionmaking.
CLO3.1: Work in groups to
complete group tasks.
P
P
P
P
H
P
H
H
P
P
H
P
H
P
H
H
H
P
H
H
H
CLO3.2: Proactively work to
complete assignments on time.
CLO3.3: Actively listen to
lecturers’ feedback to implement
adjustment.
P
Note:
P: Partial supported
S: Supported
H: Highly supported
4
P
H
H
10. Learning materials:
10.1 Text books:
[1] Anderson, David R., Sweeney, Dennis J., Williams, Thomas A., Statistics for
Business and Economics, 11th ed., South-Western CENGAGE LEARNING, 2011.
Anderson, David R., Sweeney, Dennis J., Williams, Thomas A., Thống Kê trong Kinh
Tế Và Kinh Doanh, UEH Publishing, 2020.
[2] Slide in English and Vietnamese.
10.2 References:
[1] Hoàng Trọng, Chu Nguyễn Mộng Ngọc, Phân tích dữ liệu nghiên cứu với SPSS,
Hồng Đức Publishing, 2008
10.3 Others:
Practice data set with text book 1 provided in the course on UEH LMS
Results of the Vietnam Census 2019 [https://www.gso.gov.vn/du-lieu-va-so-lieuthong-ke/2019/12/ket-qua-tong-dieu-tra-dan-so-va-nha-o-thoi-diem-0-gio-ngay-01thang-4-nam-2019/]
Results of the survey on Vietnam's living standards in 2018
[https://www.gso.gov.vn/du-lieu-va-so-lieu-thong-ke/2020/05/ket-qua-khao-sat-mucsong-dan-cu-viet-nam-nam-2018/]
Explanation of terms, content and method of calculating some price index
[https://www.gso.gov.vn/du-lieu-dac-ta/2019/03/giai-thich-thuat-ngu-noi-dung-vaphuong-phap-tinh-mot-so-chi-tieu-thong-ke-gia/]
5
11. Course teaching plan:
Session
(hour no.)
Session 1
(5)
Content
(chapter, section)
Chapter 1: Data and Statistics
1. Applications in business and
economics
2. Data
3. Data sources
4. Descriptive statistics
5. Statistical inference
6. Computers and statistical analysis
7. Ethical guidelines for statistical
practice
Chapter 2: Descriptive Statistics:
Tabular and Graphical
Presentations
Teaching method
Learning materials
(chapter, section)
Student works in detail
Corresponding
CLO
General method: lecture
[1] chapter 1, 2
combined with specific
explanations and
questions to help students
identify the main points of
the content through
situations.
Read materials in advance
CLO1.1
Form groups of 3-5 students each
CLO1.2
Website reference:
CLO1.3
http://www.gso.gov.vn
CLO1.4
Provide case study: we
need statistical data to
make a decision; why we
need descriptive statistics;
why we need statistical
inference; using
incomplete statistical
information, distorting the
facts.
https://unstats.un.org/home/
CLO2.1
http://data.un.org/
CLO2.2
https://www.fitchsolutions.com/
CLO2.3
http://www.sbv.gov.vn
https://www.euromonitor.com/
https://www.nielsen.com/vn/vi/
https://www.ibm.com/analytics/s
pss-statistics-software
Keyword for search: infographic.
1. Summarizing qualitative data
2. Summarizing quantitative data
3. Exploratory data analysis: the stemand-leaf display
4. Crosstabulations and scatter diagrams
5. Introduction of statistical packages
Group work: discuss to identify a
problem that requires statistical
study and identify the data to be
collected. Lecturer comments.
Homework: do the exercises in
chapters 1 and 2.
6
CLO3.1
Session 2
(5)
Chapter 3: Descriptive Statistics:
Numerical Measures
1. Measures of location
2. Measures of variability
3. Measures of distribution shape,
relative location, and detecting outliers
4. Exploratory data analysis
5. Measures of association between two
variables
6. The weighted mean and working with
grouped data
7. Manual computer program
Give situations so that
students understand the
meaning of measuring the
location and variation for
decision making.
[1] chapter 3
Practice by doing a group
work being a project to
collect primary data and
present the results of
descriptive statistics.
Read materials in advance
CLO2.1
Group work: identify topic,
define the research problem,
state the research objective, and
the variables needed for data
collection.
CLO2.2
Homework
The groups present their
research plans; the
lecturers make comments
and exchange opinions
CLO2.3
CLO2.6
CLO3.1
CLO3.2
Cengage's Mindtap exercises
chapters 1,2,3 (optional)
CLO3.3
Read materials in advance
CLO1.5
Troubleshooting questions about
exercises
Session 3
(5)
Chapter 4: Introduction to
Probability
1. Experiments, counting rules, and
assigning probabilities
2. Events and their probabilities
3. Some basic relationships of
probability
4. Conditional probability
5. Bayes’ theorem
Chapter 5: Discrete Probability
Distributions
1. Random variables
2. Discrete probability distributions
Give situations when we
make a decision and the
results are uncertain, so
the decision-maker needs
to know the likelihood
that it will happen to have
the proper response.
Give situation to explain
why it is necessary to
know the probability
distribution of the random
variable.
The groups present
questionnaires, lecturer
gives comments and
exchanges ideas.
7
[1] chapter 4, 5
Homework
Group work: questionnaire and
data collection plan.
CLO3.1
CLO3.2
CLO3.3
3.
4.
5.
6.
Session 4
(5)
Expected value and variance
Binomial probability distribution
Poisson probability distribution
Hypergeometric probability
distribution (self study)
Chapter 6: Continuous Probability
Distributions
1. Uniform probability distribution
2. Normal probability distribution
3. Normal approximation of binomial
probabilities
4. Exponential probability distribution
(self study)
Chapter 7: Sampling and Sampling
Distributions
1.
2.
3.
4.
Give situation to explain
why it is necessary to
know the probability
distribution of the
continuous random
variable.
[1] chapter 6, 7
Read materials in advance
CLO1.5
Homework
Group work: revise the
questionnaire and start to collect
data.
Exercises on Mindtap Cengage
chapters 4,5,6,7 (optional)
Give an example of data
standardization to get the
standardized normal
probability distribution,
which makes it possible to
calculate probabilities
quickly by lookup tables
and statistical functions.
CLO3.1
CLO3.2
CLO3.3
Sampling
Point estimation
Give the situation why it
Introduction to sampling distributions is necessary to know the
Sampling distribution of sample mean sample probability
distribution and the
x
application of the sample
5. Sampling distribution of sample
distribution in interval
proportion p
estimation and hypothesis
6. Properties of point estimators
testing.
7. Other sampling methods (self study)
Session 5
(5)
Chapter 8: Interval Estimation
1. Population mean
2. Determining the sample size
Give decision-making
situations that require
information from interval
estimation and hypothesis
8
[1] chapter 8, 9
Read materials in advance
CLO1.5
Homework
CLO2.4
Group work: continue to collect
CLO2.6
3. Population proportion
testing
data.
Cengage's Mindtap exercises
chapters 8,9,10 (optional)
Chapter 9: Hypothesis Tests
1. Developing null and alternative
hypotheses
2. Type I and type II errors
3. Population mean
4. Population proportion
5. Hypothesis testing and decision
making
6. Calculating the probability of type II
errors (self study)
7. Determining the sample size for a
hypothesis test about a population
mean (self study)
Session 6
(5)
Chapter 10: Statistical Inference
About Means and Proportions with
Two Populations
1. Inferences about the difference
between two population mean
2. Inferences about the difference
between two population means:
matched samples
CLO3.1
CLO3.2
Give decision-making
situations that require
information from
hypothesis testing with
two populations
[1] chapter 10
Read materials in advance
CLO1.5
Homework
CLO2.4
Group work: summarize and
present data, perform statistical
inference
CLO2.6
CLO3.1
CLO3.2
CLO3.3
3. Inferences about the difference
between two population proportions
Troubleshooting questions about
exercises
Session 7
(5)
Mid-term exam: chapters 4,5,6,7 (20%)
CLO3.1
Group project submission (20%)
CLO3.2
CLO3.3
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Session 8
(5)
Chapter 11: Index Numbers
1.
2.
3.
4.
5.
6.
7.
Give decision-making
situations that require
Price relatives
information from price or
Aggregate price indexes
quantity changes over
Computing an aggregate price index time.
from price relatives
Some important price indexes
Deflating a series by price indexes
Price indexes: other considerations
Quantity indexes
[1] chapter 17, 18
Read materials in advance
CLO2.1
Homework
CLO2.5
Cengage Mindtap Exercises
(optional)
CLO2.6
Chapter 12: Forecasting
1. Components of a time series
2. Smoothing methods
3. Trend projection
4. Trend and seasonal components
5. Regression analysis
Give situations which
need to forecast in
economics and business
6. Qualitative approaches (self study)
Session 9
(5)
Review, questions and answers
Homework
Announce process score
Total:
45
10
12. Student workload:
Students must perform the following tasks:
-
Read the material in advance for the coresponding session and do all the
activities detailed in Section 11;
Students must actively give their opinions in situations given in lectures;
Actively participate and contribute to group work.
13. Student assessment system:
-
Quiz:
Mid-term exam:
Final exam (project):
30%
20%
50%
Scoring guide/Rubric
Rubric 1. Group work assessment
Weight
(%)
Excellent
(100%)
Good
(75%)
20
Proactively and
actively discuss
with the lecturer
and make fine
adjustments
Participate in
discussion with
the lecturer and
make required
adjustments
10
Very detailed
and clear data
collection plan
10
Collect data to
meet the
requirements of
sample size and
survey object
Data summary and
presentation
10
Summarizing
and presenting
data is very easy
to understand,
very easy to
visualize
Statistical Inference
10
Good statistical
inference
Component
Identify research
problems, research
objectives, research
content
Data collection plan
Data collection
11
Moderately
detailed and
clear data
collection plan
Data collection
is moderately
right on sample
size and
audience
requirements
Summarizing
and presenting
data is
moderately
easy to
understand,
moderately
easy to
visualize
Moderately
good statistical
Average
(50%)
Poor
(0%)
Less
participation in
discussion and
with the
lecturer and
less adjustment
Less detailed
and less clear
data collection
plan
Data collection
is less
approriate with
sample and
audience size
requirements
No
participation in
discussion and
with the
lecturer and no
adjustment
Summarizing
and presenting
data is able to
understand,
able to
visualize
Summarizing
and presenting
data is difficult
to understand,
difficult to
visualize
Acceptable
statistical
Poor statistical
inference
Sketchy data
collection plan
Actively
collect data
that is not on
demand on
sample size
and object
Significance and
application of the
results of statistical
analysis
20
Good
interpretation of
the significance
of statistical
results to
decision making
Presentation quality
20
Creatively,
appropriate
inference
Moderately
good
interpretation
of the
significance of
statistical
results to
decision
making
Appropriate
inference
Acceptable
interpretation
of the
significance of
statistical
results to
decision
making
Poor
interpretation
of the
significance of
statistical
results to
decision
making
Somewhat
appropriate
Inappropriate
14. Student support:
Lecturers will answer questions from students on the online forum, and schedule at
least one session each week to be present in the school office to meet directly with
students to solve complicated problems directly. Student reception schedule and online
faculty forum must notify students from the first session and announce on the course
on the UEH LMS.
HCMC, February 26, 2021
SCHOOL HEAD’S APPROVAL
COMPOSER
(signature and full name)
(signature and full name)
Hà Văn Sơn
Hoàng Trọng – Trần Hà Quyên
12