Слайд 2Module Aims:
To foster in students confidence to cope with the processing and
analyzing of quantitative information.
To provide an appreciation of numerical and statistical concepts relevant to the business environment.
Слайд 3Learning outcomes:
apply numerical skills to business and/or engineering problems
present statistical data in
a variety of formats, including electronic means
apply basic rules of algebra and calculus
using spreadsheets summarize numerical data into averages and deviations and apply them to a variety of business problems.
Слайд 4In brief, you will learn how ...:
To appreciate benefit of numerical data
for businesses
To make decisions based on the numerical data
To interpret and represent numerical data in a most appropriate way depending on your aims
To solve statistics and calculus problems using various quantitative methods
Note: You can find out more about module content in module syllabus and 12-week teaching schedule.
Слайд 5Teaching methods:
1-hour online lecture each week (online)
2-hour tutorial each week (offline)
1-hour workshop
each week (offline)
You will learn the theory and its application
Слайд 6Assessment methods:
Two assessments (or components):
In-class test (30%+10%).
30% goes to an in-class test
in Teaching Week 6
10% goes to weekly online mini-quizzes
Final exam (60%) in Final exam week
True/false
Theory description
Problem solving
Open ended questions
Multiple choice
Слайд 7
by appointment
Room IB 205
EXT: 546
Слайд 8Lecture outline
DATA
the meaning and types of data
sources of data
the scales
of measurements for data
DATA REPRESENTATION TECHNIQUES AND TOOLS
analyze the quantitative and qualitative data;
display data in the form of table;
display data in the form of graph.
Слайд 9What is data? (1)
Data –
the facts and figures that are collected, analyzed
and summarized.
Examples: data about people, countries, employees
nature, universities, number of products sold, costs, prices,
movies, cars, hospitals, registration numbers, tax codes etc
Слайд 10What is data? (2)
Data may be obtained through already existing-sources or through
statistical studies.
1. already existing-source:
Salaries, sales, advertising costs, inventory levels can be disclosed from a company,
2. from a statistical study:
an experiment, a questionnaire, a survey, etc
Слайд 11Primary and Secondary data
Primary data – the data that are obtained as
a result of conducting a questionnaire, a survey, an interview, an observation, etc.
Examples:__________________________________________
Secondary data – the data that come from existing sources. Government institutions, healthcare facilities, Internet and others can provide a great deal of information in a ready-to-estimate format.
Examples:__________________________________________
Слайд 12Questions:
What data is more costly (expensive):
primary or secondary?
What data is
more reliable (trustworthy):
primary or secondary?
Слайд 13Statistical data
Q: What are the components of the statistical table?
Слайд 14Components of the tabular data
Element – the entity or item on which
data are collected.
Examples: Westminster College, Yale Univ., etc
Variable – a characteristic of interest for an element.
Examples: Enrollment, type, etc
Observation – a set of measurements collected for a particular element.
Examples: 953, coed, public, $6,140, etc
Слайд 15Main types of data
Qualitative data provide labels or names for variables. They
can be nonnumeric descriptions or numeric codes.
Examples: Coed, Public, etc
Quantitative data show an amount of variables. They indicate either “how much” or “how many” of something.
Examples: 953 students, $6,140 for Room & Boarding, etc
Слайд 16Question:
Consider this room as an element.
Are its variables such as,
Names
of students quantitative or qualitative?
Mode of students quantitative or qualitative?
Number of students quantitative or qualitative?
Слайд 17Quantitative Data
Discrete data – the data obtained as a result of counting.
Examples:
Number of enrolled students: 500, 1000, 2458, etc.
Continuous data – the data that can take any value within a continuum, limited only by the precision of the measurement instrument.
Examples: Length or height of some object: 5 cm, 5.35 cm,
Слайд 19SM for Qualitative Data (1)
Nominal scale – a scale of measurement that
uses name or label to define a characteristic of an element.
Слайд 21SM for Qualitative Data (2)
Ordinal scale – a scale of measurement that
is nominal and allows ranking or ordering the data according to some criteria.
Слайд 23SM for Quantitative Data (1)
Interval scale – a scale of measurement that
is ordinal and intervals between data can be used to compare variable observations.
Слайд 25SM for Quantitative Data (2)
Ratio scale – a scale of measurement that
is interval and allows considering the ratio of two data values.
Слайд 29Raw data
Raw data – the data that has not been processed (analyzed,
categorized, put in a table) yet.
Example:
Number of students (total 100), who attended 12 lectures: 100, 98, 85, 76, 64, 55, 76, 87, 96, 98, 99 & 100
Слайд 30Aggregate data
Aggregate data – the data that has already been processed to
serve one’s goal.
Example:
On four lectures, the attendance of students was lower than 80 and on other eight lectures it was greater or equal to 80.
(the raw data above have been analyzed).
Слайд 31
Cross-section data – data collected at the same point in time or
based on the same period of time.
Example:
Numbers of different models of automobiles produced by GM Uzbekistan in 2020.
Time series data – data that consist of observations collected at regular intervals over time.
Example:
Number of automobiles produced by GM Uzbekistan during the period from 2010 to 2020.
Слайд 32Population and Sample
Population – a collection of all elements of interest in
a particular study.
Sample – a subset of the population
Example:
All University students vs CIFS students
CIFS students vs 3CIFS1 group
Note: Data about a large group of elements are difficult
to collect due to various restrictions,
therefore only a small part of the group is considered.
Слайд 33Part 2. Data representation
PART II. Data representation tools and techniques
Слайд 34Section I Qualitative data:
Case 1. Research conducted on 50 individuals’ choice on
GM Uzbekistan automobiles.
Слайд 35Tabular Methods:
Frequency and Relative frequency tables
Слайд 38Quantitative data: Discrete
Case 2. The store sold the following numbers of refrigerators
on 30 different days. Analyze and present the data in tabular and graphical forms.
Слайд 39Tabular Methods:
Frequency, relative and cumulative frequency table
Range = 23 – 0
= 23; Group width = 23:5 = 4.6 ≈ 5;
Thus, make the group width = 5 for convenience.
Слайд 40Tabular Method:
Stem-and-Leaf diagram
Слайд 41Graphical Method: Histogram
Histogram
Слайд 42Graphical Method
Cumulative frequency
Слайд 43Quantitative data: Time series
Case 3. the following table shows the profit made
by three cotton companies over four years. Display this data graphically
Слайд 44Quantitative data: Time series
Times series graph (line graph)
Слайд 45Quantitative data: Time series
Case 4:
The company XYZ produces three types of
products (A, B, and C). The total sales of the Product A in 1999, 2000 and 2001 were £40,000, £45,000 and £50,000, of the Product B were £30,000, £40,000 and £50,000 and of the Product C were £50,000, £55,000 and £60,000 respectively. Construct a table for this data and illustrate it with a help of bar chart.
Слайд 47Graphical form
Component bar graph
Слайд 48Graphical form
Multiple bar graph
Слайд 50Concluding remarks:
Today, you learnt:
The components of statistical table;
The main types of data;
The
scales of measurement of the data
analyze statistical data;
use tabular methods to display data
use graphical methods to display data
Слайд 51Essential readings (Part 1)
Jon Curwin…, “Quantitative Methods…”, Chapters 1-2
Glyn Burton…, “Quantitative Methods…”,
Chapter 1
Richard Thomas, “Quantitative Methods…”, Chapter 1.1
Mik Wisniewski…, “Foundation Quantitative…”, Chapter 3
Clare Morris, “Quantitative Approaches…”, Chapter 3