Introduction to the Course
• Teaching staff: Assoc.Prof. Tran Thi Bich
Statistics Faculty
Email: bichtt@neu.edu.vn
• Lectures: as indicated
• Tutorials: 30 minutes - 1 hour, from lecture 2 to 7
• Materials:
- Gerald Keller (2011).Statistics for Management
and Economics (9th Edition).
- SPSS Statistics: Guide to Data Analysis
STATISTICS IN BUSINESS &
ECONOMICS
Advanced Educational Program
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Goals
Topics will be covered
 Develop an understanding of a variety of
quantitative and statistical techniques that can be
applied to a wide range of business situations.
• Data Presentation: Tables and Charts
• Data Presentation: Central Tendency and
Dispersion
• Probability and Random variables
• Sampling distribution and Estimation
• Hypothesis Testing
• Regression Analysis
 Use statistical software applications to perform
essential and practical data analysis for business
organizations.
 Demonstrate the ability to prepare and analyze a
variety of data sets for specific purposes.
 Determine which statistical techniques are best
suited to which organizational problems.
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How to achieve 10% of participation?
Assessment
• Contribution in class and tutorials.
• Before a new lecture, you need to work on
the topic’s main points that are assigned.
• You may also ask question.
• Compliance with the class rules:
• Participation: 10%
• Mid-term exams: 40%
– Two midterm exams, each accounts for 20%
• Final exams: 50%
• Further details (including date of exams and
deadline of assignments) given during the
course.
– Come prepared – read the readings assigned.
– Turn off cell phones during classes.
– Use laptop only when being indicated
– Do not engage in individual discussions
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Mid-term exams: 40%
• One group mid-term exam:
How to do the group mid-term exam
• You select an article which is suited to the topic.
• Your work includes two parts:
o Part 1: summarise the article
20%
Topic for group mid-term exams:
- Descriptive statistics
- Probability
- Sampling distribution and Estimation
- Hypothesis testing
• Individual in-class mid-term exam:
- What is the issue of interest?
- Why do you care about the technique as the organisation
manager?
- You need to find two additional sources and cite those
references.
- I am interested in your opinion which you need a few
different viewpoints to develop.
20%
o Part 2: Data analysis to a specific organisational
problem.
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Timetable for group mid-term exam
Hints for success
• You are assigned the topic on Monday.
• Attend lectures and tutorials, supplement given
materials with your own comments and notes.
• Work carefully on the tutorials – doing them is how
you will understand and learn
• Time spent trying questions is well spent
• Constantly REFER to notes
• Use resources provided
• You need to send your work to the class by the
following Thursday.
• Other students have 3-3.5 days to read your work and
prepare for their discussion.
• Each group has maximum 10’ for the presentation,
which is followed by 10’ individual discussions.
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Outline
Lecture 1
• Introduction to statistics
• Basic concepts: variables and data
• Getting acquainted with SPSS
Introduction to Statistics and
SPSS
Reading materials:
Chap 1, 2 (Keller)
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What is statistics?
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What is statistics?
• This is not a matter of ordering soup! Statistics
involves matters of life and death…
– If the probability of getting accident of an airplane is
1/10,000, what is your chance of survival when you are
on board?
– How do you know?
• To accomplish the above feat, Statisticians rely on
three related disciplines:
– Data analysis
– Probability
– Statistical inference
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The question is …
So…
• How to get useful information from data to make a
correct decision? It’s not easy! 
• Statistics is all about collecting, organising and
interpreting data
• Statistics is a way to get information from data and
make decisions under uncertainty
• Statistical analysis of data uses statistical
modelling and probability: our main focus is on
data and techniques for analysing data
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Why is statistics important?
Why is statistics important?
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Financial management (capital budgeting)
Marketing management (pricing)
Marketing research (consumer behaviour)
Operations management (inventory)
Accounting (forecasting sales)
Human resources management (performance
appraisal)
• Information systems
• Economics (summarising, predicting)
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Types of statistics
Questions for Data Analysis
• Descriptive statistics:
• In the descriptive statistics:
 Collecting, organising, summarising, and presenting data
 E.g: graphical techniques;
numerical techniques
 What is the general pattern of the studied variable
 Applied for population or sample
• In the exploratory statistics:
• Exploratory statistics:
 What is the relationship amongst variables?
 Applied for population or sample
 Identifying relationships/associations
 E.g: factor analysis
• In the inferential statistics:
• Inferential statistics:
 What can we infer for the population based on sample
results
 Applied for population
 Estimating, predicting, and making decisions about
population based on sample data
 E.g: estimation;
hypothesis testing
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Basic concepts: variables and data
Types of data
• A variable is some characteristics of population or
sample items
Data
 Eg:
• Height of students
• Occupation of students upon graduation
Qualitative
• Data are the observed values of a variable
 Eg:
• Height of 10 students: 1.6, 1.7, 1.55, 1.59, 1.5, 1.58,
1.64, 1.67, 1.58, 1.55
• Occupation of 5 students: teller, accountant, IT,
marketing manager, teacher
Nominal
Discrete
Continuous
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Ordinal
Quantitative
(also called
Interval)
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Quantitative data
Qualitative data (Categorical data)
• Quantitative data are real number (can be measured)
• Qualitative is the kind of data that cannot be measured
(quantified)
 Eg:
• Marital status: single, married, divorced, and widowed
• Study performance of students: poor, fair, good, very good,
excellent
• Mid-term test marks of 10 students: 7, 8, 10, 5, 5, 6, 8, 9, 9, 7
• Weights of postal packages
• Monthly salary
• More classification: qualitative data can be classified as
Nominal and Ordinal data
 Nominal data: cannot be quantified with any meaningful
unit
• More classification: quantitative data can be classified as
interval and ratio data
– Interval data: it includes all characteristics of ordinal data but the
interval b/w values is meaningful and has no meaningful
interpretation of zero on the scale
 Eg:
- Marital status: single, married, divorced, and widowed
 Ordinal data: a sort of nominal data but their values are
in order
• Temperature: 10oC, 20oC, 40oC
– Ratio data: data is based on a scale with a known unit of
measurement and a meaningful interpretation of zero on the scale
 Eg:
- Study performance of students: poor, fair, good, very good,
excellent
- Opinions of consumers: strongly disagree, somewhat disagree,
neither disagree nor agree, agree, strongly agree
• Weights of postal packages
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Activity 1
More on quantitative data
• Quantitative data can be divided into two types: discrete or
continuous
• For each of the following examples of data, determine
the type:
– Discrete data: take only integer value
 Eg:
i. The number of miles joggers run per week
• Number of children in family
ii.The starting salaries of graduates of advanced program
– Continuous data: can take any value
 Eg:
iii.The months in which a firm’s employees choose to take
their vacations
• Monthly salary
iv.The occupation of graduates of Advanced Program
v. Teachers’ ranking
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Sources of secondary data
Data sources
• Internet research
• Based on the method of collecting information,
data can be classified as Primary and Secondary
• Search through Vietcombank website (www.vietcombank.com.vn) to
know the exchange rate
• Search through Google to gather information about the performance
of private firms in Vietnam since ‘Doi moi’
– Primary data: collected by the organisation itself for
the particular purpose
• Government data and official publications
• The General Statistics Office (GSO) of Vietnam provides all socioeconomic information (www.gso.gov.vn)
• Data from surveys carried out by GSO and other organisations
• The Ministry of Finance (www.mof.gov.vn) provides information on
budget statement
 Eg: you need to know the consumer’s behaviour of
Techcombank, then collect this information yourself
– Secondary data: collected by other organisations for
other purposes
• Internal and by-product data: data collected from different departments
in an organisation and used all together
 Eg: the Vietnam household living standards survey in
2010
• Data from Sale Department, Human resource Department => decision
making
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Population
• Population is a set of all items or people that share
some common characteristics
 A census is obtained by collecting information
about every member of a population
Basic concepts: population and sample
- Collect the height of Vietnamese citizens
- Verify the quality of all products that are produced by
factory X
• Parameter: a descriptive measure of a population
( ,  2 )
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Sample
Reasons to take sample (1)
• A sample is a smaller group of the population.
A sample survey is obtained by collecting
information of some members of the population
- Collect the height of 1,000 Vietnamese citizens
• A census can give accurate data but population is so
large
- Verify the quality of a proportion of products that are
produced by factory X
• Statistics: a descriptive measure of a sample
(x, s2 )
• Sampling: taking a sample from the population
Collecting information from the entire population is
time-consuming,
expensive,
and
sometimes
impossible.
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Reasons to take sample (2)
Important requirements
• If we are willing to accept less than 100% accuracy,
we collect information from a sample, calculate its
statistics and use statistical inference to infer values
of parameters of the population
A sample must be representative for the
population. That means the profile of the
sample is the same as that of the population
=> apply sampling techniques
• We can do this because: a certain sample size
ensures that results from the sample are as accurate
as those of the population
The sample size is large enough
• And benefits of sample: sample allows to investigate
more detailed information
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Moving from population to sample
You need a sampling frame
Population
Sampling frame
(a list of all items of
the population)
Sample
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The second requirements
Getting acquainted with SPSS
The sample size is large enough
• Import the file ‘assignment 1 data set.xls’ into
SPSS and get familiar with SPSS.
This question will be answered in lecture 7
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