Business Statistics
Course: BUSN 2429
Instructor: Bassem Hamid
“Introduction to Business Statistics”
(Chapter 1 & 7)
1
Business Statistics Map
Introduction +
Descriptive Statistics
Probability +
Probability Distributions
Inferential Statistics
1. Introduction to Business Statistics
2. Displaying Descriptive Statistics
3. Calculating Descriptive Statistics
4. Introduction to Probabilities
5. Discrete Probability Distribution
6. Continuous Probability Distribution
7. Sampling & Sampling Distribution
8. Confidence Intervals
9. Hypothesis Test-One Sample Test
10. Hypothesis Test-Two Samples Test
14. Correlation and Single Regression Model
2
Do You Know The Meaning of The
Following Terms;
Problem, Statistics, Data, Sample,
Population, Information?
These Terms are the Elements of the
Research Project Diagram!
Elements of
The
Research
Diagram
Do You Know How Information
(Knowledge) is Created?
The Information is Created Through
Conducting a Research Project!
The Output
of The
Research
Diagram
Outlines
This chapter covers the following points:
• #1 Business Statistics
• #2 Research Project Diagram/Steps
• #3 Why Sample?
• #4 Sampling Methods
• #5 Data Classifications
• #6 Data Collection Methods
• #7 Ethics and Statistics
4
Objectives
After completing this chapter, you will be able to:
• #1 Define statistics terms; business statistics, data, variables,
population and sample
• #2 Distinguish between descriptive and inferential statistics
• #3 Understand how statistics is used in the business world
• #4 Classify data by the level of measurement
• #5 Understand the difference between data collection methods and
sampling methods
• #6 Understand the ethical implications of misusing statistics
5
#1 Business Statistics
• #1.1 Statistics Definition
• #1.2 Statistic Terms
• #1.3 Statistics Branches
• #1.4 Statistics Applications
6
#1.1 Statistics Definition
• Statistics is the mathematical science that deals with collection, analysis and
presentation of data
oStatistics includes converting data into meaningful information through
statistical tools and techniques
oInformation is used to make business decision by the managers
Data
Collect Data
(Raw Materials)
Statistical Tools
Descriptive
Inferential
Information
Convert
(Workstation)
Present Information
(Processed Data)
Process Concept: Input, Workstation & Output
7
#1.2 Statistics Terms
• Population: consists of all possible subjects of interest
• Sample: is group of subjects selected from population
• Variables: Characteristic that can assume different values (e.g., Student grade
or X)
• Data: are the values assigned to the Measurement or Observation (Values that
the variables can assume. e.g., Student grade, X= 90)
Population
(Data)
Work Station
Information
(Statistics Tools)
(Make Decision)
Sample
(Data)
8
Example: Population vs. Sample
The warehouse has just received 1,000 pcs of cellphone cover.
Can we measure the width of all cellphones to ensure they are confirming to specifications?
Population
The variable: The width of the cell phone cover
The data: W = 40 MM
Test Center
It is not FEASIBLE
1000 Pcs
Sample
It is FEASIBLE
Test Center
50 Pcs
9
#1.3 Statistics Branches
Descriptive Statistics & Inferential Statistics
10
Descriptive Statistics
• Descriptive Statistics includes
• Collecting, summarizing, and displaying data using graphs, charts & tables
(Sample or Population)
The average
spending in
the summer
activities is
$1200
Average Spending ($)
Sample
Randomly selected (30 Students)
Bar Chart
Summer Activities
$1200
Summer
11
Descriptive Statistics
• Descriptive Statistics includes
• Collecting, summarizing, and displaying data using graphs, charts & tables
(Sample or Population)
The average
spending in
the summer
activities is
$1200
Average Spending ($)
Sample
Randomly selected (30 Students)
Bar Chart
Summer Activities $
$1200
Gender Breakdown
30%
70%
Male
Female
Summer
12
Descriptive Statistics
• Descriptive Statistics includes
• Collecting, summarizing, and displaying data using graphs, charts & tables
(Sample or Population)
The average
spending in the
summer activities
is $1250
Average Spending ($)
Population
My Stat class (300 Students)
Bar Chart
Summer Activities $
$1250
Gender Breakdown
30%
70%
Male
Female
Summer
13
Inferential Statistics
• Inferential Statistics includes
• Making claims or conclusions about the population based on a sample and
probability theories
Population
My Stat class (300 Students)
What is average
spending in the
summer activities?
Sample
Randomly selected (30 Students)
The average
spending in
the summer
activities is
$1200
probability
theories
Conclusion
Based on sample mean and
probability theory, the spending
mean for the population is
between $1100 & $1300
14
Your Turn #1
Identify each of the following as either descriptive or inferential statistics
• Julie, who cuts and style hair in her salon, had 23 customers last weeks
• A recent poll showed that 75% of American had a favorable opinion of the
president of the united States
• The average exam score for my statistics exam was an 88
• Predicting election results by asking voters their intentions
15
Identify each of the following as either descriptive or inferential statistics
• Julie, who cuts and style hair in her salon, had 23 customers last weeks.
Descriptive Statistics
• A recent poll showed that 75% of American had a favorable opinion of the
president of the united States. Inferential Statistics (It is not feasible to ask
every American in the country about his opinion. Sample and probability are
used)
• The average exam score for my statistics exam was an 88. Descriptive
Statistics (Describe of sample data)
• Predicting election results by asking voters their intentions. Inferential
Statistics (Sample and probability are used)
16
#1.4 Statistics Application
• Marketing
oConduct a market research to identify the characteristics of target market
oThe income of a certain group is between $4000-$4800
• Operations
oDevelop a multi regression model to estimate the productivity based on its factors
oProductivity = Y= a.X1 (wage) + b.X2 (Experience) +c.X3(Training)
• Finance and Economics
oConstruct a scatter plot to show the relationship between construction materials and
house price
o There is a correlation between the above variables. The higher the materials, the
higher the house price.
17
Applying Concept
“Attendance and Grades”
• A study conducted on a sample of 500 students out of 10,000 students at
Manatee community College revealed that
oStudents who attended class 90% to 100 % of the time usually received an A.
oStudents who attended class 80% to less than 90 % of the time usually
received a B or C in the Class.
oStudents who attended class less than 80 % of the time usually received a D
or an F or eventually withdrew from the class. Please answer the following
questions.
18
• 1. What are the variables under study?
• 2. What are the data in the study?
• 3. Are descriptive, inferential or both statistics are used?
• 4. What are the population under study?
• 5. Was the sample collected? If so, from where?
• 6. From the information given, comment on the relationship between the
variables
19
• 1. What are the variables under study?
o The variables are grades and attendance
• 2. What are the data in the study?
o The data consists of specifics grades (A, B, C, D & F) and attendance numbers (80%)
• 3. Are descriptive, inferential or both statistics are used?
o These are descriptive statistics
• 4. What are the population under study?
o The population under study is the students at Manatee Community College
• 5. Was the sample collected? If so, from where?
o The data was collected from the sample of 500 students
• 6. From the information given, comment on the relationship between the
variables
o Based on the data, it appears that in general, the better your attendance, the higher
your grade
20
#2 Research Project Diagram and Steps
#2
#3
#4
Population
(Data)
Workstation
(Information)
Sample
(Data)
Define Population/Sample
“Sampling Methods”
-Random
-Systematic
-Stratified
-Cluster
Define Data
“Data Collection Methods”
“Data Types/levels”
-Direct Observation
-Focus Group
-Experiment
-Survey
-Interview
-Qualitative
-Quantitative
-Nominal Level
-Ordinal Level
-Interval Level
-Ratio Level
Select Statistical Tools
-Descriptive Statistics
-Inferential Statistics
#5
Solve Business Problem
Define Business Problem
#1
Research Project Steps
“Create Knowledge/Make a Decision/Solve a Problem”
The researcher should
1. Define the problem
2. Define the population/sample (Sampling Methods)
3. Define the nature of data (Data Collection Methods & Data Level/Types)
4. Use statistics tools to process the data and create Information
(Descriptive/Inferential Tools)
5. Create Knowledge/Make a Decision/Solve a Problem
Research Project Steps
“Create Knowledge/Make a Decision/Solve a Problem”
The researcher should
1. Define the problem
-State Clearly the Business problem
-The productivity (Output/Input = Unit produced/# of hours = 8 Units/8 hours = 1Unit per
hour) in XYZ factory is low. Why?
Does maturity matter?
is there a relationship between the employees’ age and their productivity?
2. Define the population/sample (Sampling Methods)
3. Define the nature of data (Data Collection Methods & Data Level/Types)
4. Use statistics tools to process the data and create information (Descriptive/Inferential
Tools)
5. Create Knowledge/Make a Decision/Solve a Problem
Research Project Steps
“Create Knowledge/Make a Decision/Solve a Problem”
The researcher should
1. Define the problem
2. Define the population/sample (Sampling Methods)
-Sampling Methods: Random, Systematic, Stratified, Cluster
-Randomly select 60 employees (sample) from 1000 employees (population) in XYZ
factory
3. Define the nature of data (Data Collection Methods & Data Level/Types)
4. Use statistics tools to process the data and create information (Descriptive/Inferential
Tools)
5. Create Knowledge/Make a Decision/Solve a Problem
Research Project Steps
“Create Knowledge/Make a Decision/Solve a Problem”
The researcher should
1. Define the problem
2. Define the population/sample (Sampling Methods)
3. Define the nature of data (Data Collection Methods & Data Level/Types)
-Direct Observation, Focus Group, Experiment, Survey & Interview
-Qualitative, Quantitative, Nominal Level, Ordinal Level, Interval Level & Ratio Level
-Use survey. The data are Quantitative (Independent Variable: Employee age & Dependent
Variable: Employee Productivity)
4. Use statistics tools to process the data and create information (Descriptive/Inferential
Tools)
5. Create Knowledge/Make a Decision/Solve a Problem
Research Project Steps
“Create Knowledge/Make a Decision/Solve a Problem”
The researcher should
1. Define the problem
2. Define the population/sample (Sampling Methods)
3. Define the nature of data (Data Collection Methods & Data Level/Types)
4. Use statistics tools to process the data and create information
(Descriptive/Inferential Tools)
-Descriptive Tools & Inferential Tools
- Scatter Chart
5. Create Knowledge/Make a Decision/Solve a Problem
Research Project Steps
“Create Knowledge/Make a Decision/Solve a Problem”
Productivity
Dependent Variable
The researcher should
1. Define the problem
2. Define the population/sample (Sampling Methods)
3. Define the nature of data (Data Collection Methods & Data Level/Types)
4. Use statistics tools to process the data and create knowledge (Descriptive/Inferential
Tools)
5. Create Knowledge/Make a Decision/Solve a Problem
-There is a positive correlation between employee age and
productivity.
-Hire old age employees
Age
Independent Variable
Research Project Diagram and Steps
#2
#3
#4
Population
(Data)
Workstation
(Information)
Sample
(Data)
Define Population/Sample
Productivity is
“Sampling Methods”
decreasing ???
Why?
-Random
Is there any
relationship
between
employee’s age &
productivity???
(Randomly select 60 employees)
-Systematic
-Stratified
-Cluster
Define Data
“Data Collection Methods”
“Data Types/levels”
-Direct Observation
-Focus Group
-Experiment
-Survey
-Interview
-Qualitative
-Quantitative
-Nominal Level
-Ordinal Level
-Interval Level
-Ratio Level
#5
Solve Business Problem
Define Business Problem
#1
Select Statistical Tools
-Descriptive Statistics Hire old-age
(Scatterplot)
employees
-Inferential Statistics
#3 Why Sample?
• Population: consists of all possible subjects of interest
• Sample: is group of subjects selected from population
• Why Sample?
o Examining the entire population would be expensive and time consuming
o Can’t examine everything if the test is destructive
• Sample has three principle
o Examine part of the whole, Randomize and Sample size
• If a sample is selected properly and the analysis performed correctly, sample
information can be used to make an accurate assessment of the entire
population
29
#4 Sampling Methods
• Sampling From Population
oNonprobability Sampling
▪ Convenience
oProbability Sampling
▪ Simple Random
▪ Systematic
▪ Stratified
▪ Cluster
▪ Resampling
30
Research Project Diagram and Steps
#2
#3
#4
Population
(Data)
Workstation
(Information)
Sample
(Data)
Define Population/Sample
“Sampling Methods”
-Random
-Systematic
-Stratified
-Cluster
Define Data
“Data Collection Methods”
“Data Types/levels”
-Direct Observation
-Focus Group
-Experiment
-Survey
-Interview
-Qualitative
-Quantitative
-Nominal Level
-Ordinal Level
-Interval Level
-Ratio Level
Select Statistical Tools
-Descriptive Statistics
-Inferential Statistics
#5
Solve Business Problem
Define Business Problem
#1
Sampling Methods
Sampling from a Population
Probability Sampling
Simple
Random
Nonprobability Sampling
Convenience
Systematic
Stratified
Cluster
Resampling
32
Sampling Methods
Probability Sampling
Simple
Random
A probability sample is a sample in which each
member of the population has a known, nonzero,
chance of being selected for the sample
Systematic
Stratified
Cluster
Sample
Resampling
Population
33
Simple Random Sampling
Probability Sampling
Simple
Random
*
A simple random sample is a sample in which
every member of the population has an equal
chance of being chosen
Systematic
Stratified
Cluster
Sample
Resampling
Population
34
Systematic Sampling
In systematic sampling, every kth member of the
population is chosen for the sample. The value of
k is determined by dividing the size of the
population (N ) by the size of the sample (n)
Probability Sampling
Simple
Random
Systematic
Stratified
*
1 2 3 4 5 6 7 8 9 10
1
2
3 1
2 3
1
2
3
Cluster
Resampling
K=3
Population, N
Sample, n
35
Systematic Sampling
• Formula for the Systematic Sampling Constant, k:
N
k=
n
N = Size of the population
n = Size of the sample
• Example:
oSelect a systematic sample of size n = 30 from a population of N = 270
N 270
k= =
=9
n
30
oFrom a list of all population values, choose every 9th value for the sample
o9, 18, 27, 36 etc..270
36
Systematic Sampling
• Advantages of systematic sampling:
o Easy to do manually
o Can avoid bias by not allowing judgment or convenience to affect the sample
• Disadvantages:
o One concern about systematic sampling is periodicity, which is a pattern in the
population that is consistent with the value of k
o Example: Sampling every 8 hours might obtain values only from the beginning or end
of a shift, which might not be representative of all values during the day
37
Stratified Sampling
Probability Sampling
Stratified sampling divides the population into
mutually exclusive groups, or strata. Each one is
considered as part of the population
Simple
Random
A random sample from each strata is selected
Systematic
Stratified
Cluster
*
Strata are based on important variables that can
have an impact on the data collected and the
results that are achieved
Resampling
38
Population
Production Shifts
Strata
Select a random
sample from each
group
First Shift
(Part of the Population)
Sample
Second Shift
(Part of the Population)
Sample
Third Shift
(Part of the Population)
Gather All
Selected
Samples to
form the
final sample
Sample
Sample
This type includes dividing the population into groups (Strata) according to some characteristics that
are important to the study and then select a random sample from each group
Using stratified sampling helps ensure that all shifts are represented in the sample
Stratified Sampling
• Examples of Stratified Sample:
oFor factory production shift, strata could be 1st shift, 2nd shift, and 3rd shift
oFor an undergraduate population, strata could be class standing for Freshman,
Sophomore, Junior, and Senior
oFor a population of workers, strata might be different age categories of
workers, young, old, etc.
oFor a population of firms, strata might be large, mid and small
• Using stratified sampling helps ensure that all classes, shifts, or ages are
represented in the sample
40
Cluster Sampling
Probability Sampling
Cluster sampling involves dividing the population
into mutually exclusive groups, or clusters, that
are each representative of the population (Mini
Population)
Simple
Random
Systematic
Then randomly select clusters to form the final
sample
Stratified
Cluster
*
These clusters are often selected based on
geography to help simplify the sampling process
Resampling
41
Population
Clusters
Mini Population 1
(Hospital1:
Surrey)
Patients in
Hospitals in
Greater
Vancouver
Use all members of
the randomly
selected clusters to
form the final
sample
Sample
Mini Population 2
(Hospital2:
Burnaby)
Mini Population n
(Hospital n: Other
cities)
This type includes dividing the population into groups (Clusters) by some means (Geographic Area)
and then the researcher randomly select some of the cluster and use all members of the selected as
samples. It would be very costly and time consuming to obtain random sample of patients since they
are spread over a large area
Cluster Sampling
• Examples of cluster sample:
o Individual cities where a new product is introduced
o Customer account balances arranged in clusters by first letter of last name
o Patients in greater Vancouver
o Students in higher education institutions (Universities and colleges)
43
Cluster vs. Stratified
(Economical Sampling Process)
(Accurate Sampling Process)
Susan would like to conduct a survey of homeowners in the Meadowbrook neighborhood
to get their opinions on proposed road modifications in the area.
What are the sampling method options for Susan?
o Susan selects the first 20 homes that she passes as she walks into the entrance of the
neighborhood. (Convenience sample)
o Susan selects every third house on each street in the neighborhood. (Systematic
sample)
o Susan randomly chooses two streets in the neighborhood and selects every home on
these streets. (Cluster sample)
o Susan ensures that her sample contains a number of two-story, split-level, and ranch
homes in her sample that corresponds to the number of homes in the neighborhood.
(Stratified sample)
Cluster vs. Stratified
Population
Clusters
Population
Starta
45
Cluster vs. Stratified
Susan would like to conduct a survey of homeowners in the Meadowbrook neighborhood
to get their opinions on proposed road modifications in the area.
What are the sampling method options for Susan?
o Susan selects the first 20 homes that she passes as she walks into the entrance of the
neighborhood. (Convenience sample)
o Susan selects every third house on each street in the neighborhood. (Systematic
sample)
o Susan randomly chooses two streets in the neighborhood and selects every home on
these streets. (Cluster sample)
o Susan ensures that her sample contains a number of two-story, split-level, and ranch
homes in her sample that corresponds to the number of homes in the neighborhood.
(Stratified sample)
Street n
Street 3
Street 2
Street 1
Cluster vs. Stratified
Susan would like to conduct a survey of homeowners in the Meadowbrook neighborhood
to get their opinions on proposed road modifications in the area.
What are the sampling method options for Susan?
o Susan selects the first 20 homes that she passes as she walks into the entrance of the
neighborhood. (Convenience sample)Main Street
o Susan selects every third house on each street in the neighborhood. (Systematic
sample)
o Susan randomly chooses two streets in the neighborhood and selects every home on
these streets. (Cluster sample)
o Susan ensures that her sample contains a number of two-story, split-level, and ranch
homes in her sample that corresponds to the number of homes in the neighborhood.
(Stratified sample)
Cluster vs. Stratified
Cluster 1
Street n
Street 3
Street 2
Street 1
Cluster 2
Susan would like to conduct a survey of homeowners in the Meadowbrook neighborhood
to get their opinions on proposed road modifications in the area.
What are the sampling method options for Susan?
o Susan selects the first 20 homes that she passes as she walks into the entrance of the
neighborhood. (Convenience sample)
o Susan selects every third house on each street in the neighborhood. (Systematic
sample)
o Susan randomly chooses two streets in the neighborhood and selects every home on
these streets. (Cluster sample)
o Susan ensures that her sample contains a number of two-story, split-level, and ranch
homes in her sample that corresponds to the number of homes in the neighborhood.
(Stratified sample)
Strata 1
Cluster vs. Stratified
Strata 2
Strata 3
Susan would like to conduct a survey of homeowners in the Meadowbrook neighborhood
to get their opinions on proposed road modifications in the area.
What are the sampling method options for Susan?
o Susan selects the first 20 homes that she passes as she walks into the entrance of the
neighborhood. (Convenience sample)
o Susan selects every third house on each street in the neighborhood. (Systematic
sample)
o Susan randomly chooses two streets in the neighborhood and selects every home on
these streets. (Cluster sample)
o Susan ensures that her sample contains a number of two-story, split-level, and ranch
homes in her sample that corresponds to the number of homes in the neighborhood.
(Stratified sample)
Resampling
Resampling is a statistical technique
where many samples are repeatedly drawn
from a population
Probability Sampling
Simple
Random
Systematic
One type of resampling methods is the
bootstrap method
Stratified
Cluster
Resampling
*
Involves using computer software to
extract many samples with replacement in
order to estimate a parameter of the
population, such as a mean or proportion
50
Nonprobability Sampling
Nonprobability Sampling
Convenience
A nonprobability sample is a sample in
which the probability of a population
member being selected for the sample is
not known
51
Nonprobability Sampling
Nonprobability Sampling
Convenience
*
A convenience sample is used when
sample values are selected simply because
they are easily accessible
• Advantages:
o Quick and easy to get sample data
o Provides general information about the population
• Disadvantages:
o May not be representative of the population
52
Your Turn #2
Identify the type of sampling technique for each of the following:
• The first Monday of each month, I ask my customers who come to my store to fill
out a satisfaction survey
• I randomly select four stores in a mall and ask each customer in those stores about
his or her opinion of the latest health care legislation
• I position myself on a busy intersection of a city street and ask people what their
opinions are of a local sports team
• Susan would like to conduct a survey of homeowners in the Meadowbrook
neighborhood to get their opinions on proposed road modifications in the area.
Susan ensures that her sample contains a number of two-story, split-level, and
ranch homes in her sample that corresponds to the number of homes in the
neighborhood.
• Using computer software, I randomly select 20 employees to participate in a job
satisfaction survey.
53
Identify the type of sampling technique for each of the following:
• The first Monday of each month, I ask my customers who come to my store to fill out a
satisfaction survey. Systematic sampling
• I randomly select four stores in a mall and ask each customer in those stores about his or her
opinion of the latest health care legislation. Cluster sampling
• I position myself on a busy intersection of a city street and ask people what their opinions are
of a local sports team. Convenience sampling
• Susan would like to conduct a survey of homeowners in the Meadowbrook neighborhood to
get their opinions on proposed road modifications in the area. Susan ensures that her sample
contains a number of two-story, split-level, and ranch homes in her sample that corresponds to
the number of homes in the neighborhood. Stratified sampling
• Using computer software, I randomly select 20 employees to participate in a job satisfaction
survey. Simple random sampling
54
#5 Data Classification
• #5.2.0 Data Definition
• #5.2.1 Data can be divided into the following groups
• #5.2.1.1 Qualitative Data
• #5.2.1.2 Quantitative Data
• #5.2.2 Data can be divided into the following groups
• #5.2.2.1 The Nominal Level of Measurement (Qualitative)
• #5.2.2.2 The Ordinal Level of Measurement (Qualitative)
• #5.2.2.3 The Interval Level of Measurement (Quantitative)
• #5.2.2.4 The Ratio Level of Measurement (Quantitative)
55
Research Project Diagram and Steps
#2
#3
#4
Population
(Data)
Workstation
(Information)
Sample
(Data)
Define Population/Sample
“Sampling Methods”
-Random
-Systematic
-Stratified
-Cluster
Define Data
“Data Collection Methods”
“Data Types/levels”
-Direct Observation
-Focus Group
-Experiment
-Survey
-Interview
-Qualitative
-Quantitative
-Nominal Level
-Ordinal Level
-Interval Level
-Ratio Level
Select Statistical Tools
-Descriptive Statistics
-Inferential Statistics
#5
Solve Business Problem
Define Business Problem
#1
#5.2.0 Data Definition
• Variables: Characteristic that can assume different values (Student grade/X)
• Data: are the values assigned to the Measurement or Observation (Values that
the variables can assume (Student grade, X = 90)
57
#5.2.1.1 Qualitative Data
• Qualitative Data: are non numerical variables that can not be ranked or
ordered and can be placed into distinct categories according to some
characteristics or attributes
oGeographic locations (Burnaby, Richmond)
oGender preference (Female or Male)
58
#5.2.1.2 Quantitative Data
• Quantitative data: are numerical variables that can be ordered or ranked.
oPeople can be ranked according to the value of their experience
▪ 10-15 years work experience-10 persons/Class A
▪ 16-20 years work experience-15 person/Class B
oDiscrete: assume only certain value. It is obtained by counting and do not
include fractions and decimals.
▪ The # of students in a classroom (0,1,2,3)
oContinuous: assume infinite number of values between any two specific
values (Range). They are obtained by measuring and often include fractions
and decimals
▪ The value of the temperature is (15.768 C or -8.5 C - 40.563 C)
59
#5.2.2.1 The Nominal Level of Measurement (Qualitative)
• The Nominal Level of Measurements: Classifies data into categories in
which no order or ranking can be imposed on the data. It strictly deals
with qualitative data.
oClassifies university instructors according to the subject taught (Physics,
Math)
oClassifies people according to their gender (Male, Female)
60
#5.2.2.2 The Ordinary Level of Measurement (Qualitative)
• The Ordinal level of Measurements: Classifies data into categories that can
be ranked; however, precise difference between the ranks does not exist (It can
not be measured). It strictly deals with qualitative data.
oClassifies people according to their build (small, medium & large)
oClassifies people according to their education level (PhD, Master & Bachelor)
No Exact Limit Between Bodies
61
#5.2.2.3 The Interval Level of Measurement (Quantiative)
• The Interval Level of Measurements: Classifies data into categories;
however, ranks and precise difference between units of measure does exist (It
can be measured but there is no meaningful zero). It strictly deals with
quantitative data.
oTemperature. There is a difference between 24 degree and 25 degree, but 0
degree does not mean that there is no heat at all.
oStudents' marks. There is a differences between 59 and 60.
62
#5.2.2.4 The Ratio Level of Measurement (Quantitative)
• The Ratio level of Measurements: Possesses all the characteristics of interval
measurements and a true zero does exist. In addition, true ratios exist when the
same variable is measured on two different members of the population. It
strictly deals with quantitative data.
o“0” income means there is no income
oOne person can lift 200 pound while the other can lift 100 pound. The ratio is
2:1
63
Qualitative Data
Qualitative Data
Quantitative Data
Nominal
Ordinal
Classifies data into
categories in which
no order or ranking
Classifies data into
categories that can be
ranked
Classifies data into
categories that can be
ranked
Precise difference
between the ranks does
not exist
Precise difference between
units of measure does exist
Interval
A true zero does not exist
“0” degree does not mean
that there is no heat at all.
Male & Female
Big, Medium & Small
Quantitative Data
Ratio
Possesses all the
characteristics of
interval measurements
A true zero does exist
“0” income means
there is no income
A:90-100
B:80-89.99
C:70-79.99
64
Your Turn #3
Identify the type of data (Quantitative/Qualitative) and level of measurement
for each of the following data sources
• Your IQ scores
• The price for one gallon of gasoline
• The letter grade earned in your statistics course
• The number of boxes of Frosted Flakes on the shelf of a grocery store
• The types of cars driven by students in your class
65
Identify the type of data (Quantitative/Qualitative) and level of measurement for
each of the following data sources
• Your IQ scores. Quantitative/Interval. The difference between IQ scores are
meaningful, but there is no true zero point because an IQ of “0” does not indicate
the absence of intelligence
• The price for one gallon of gasoline. Quantitative/Ratio. The difference prices are
meaningful, and there is a true zero point because gasoline that is “0” per gallon is
free
• The letter grade earned in your statistics course. Qualitative/Ordinal. You can rank
letter grades, but the difference between the grades cannot be consistently
measured
• The number of boxes of Frosted Flakes on the shelf of a grocery store.
Quantitative/Ratio. The differences between inventory levels are meaningful, and
there is a true zero boxes on the shelf indicates an absence of the product.
• The types of cars driven by students in your class. Qualitative/Nominal. The types
of cars are merely labels with no ranking or meaningful difference.
66
#6 Data Collection Methods
• #6.1.1 Secondary Data
• #6.1.2 Primary Data
• #6.1.2.1 Direct Observation
• #6.1.2.2 Focus Group
• #6.1.2.3 Experiment
• #6.1.2.4 Survey
• #6.1.2.5 Interview
67
Research Project Diagram and Steps
#2
#3
#4
Population
(Data)
Workstation
(Information)
Sample
(Data)
Define Population/Sample
“Sampling Methods”
-Random
-Systematic
-Stratified
-Cluster
Define Data
“Data Collection Methods”
“Data Types/levels”
-Direct Observation
-Focus Group
-Experiment
-Survey
-Interview
-Qualitative
-Quantitative
-Nominal Level
-Ordinal Level
-Interval Level
-Ratio Level
Select Statistical Tools
-Descriptive Statistics
-Inferential Statistics
#5
Solve Business Problem
Define Business Problem
#1
#6.1.1 Secondary Data
• Secondary Data: are the data collected by someone else and made available
for others to use.
oU.S. Department of labor collect tons of data on topics such as consumer
prices, inflation and unemployment
oIndividuals or organization do not have source of control over the reliability of
the data
Secondary Data
Primary Data
Direct Observation
Focus Group
Data Sources
Statistic Tools
Information
Experiment
Survey
Interview
69
#6.1.2 Primary Data
• Primary Data: are data collected by the person or organization that
eventually use the data
oExpensive to acquire
oThe individuals or organization have source of control over the reliability of
the data
Secondary Data
Primary Data
Direct Observation
Focus Group
Data Sources
Statistic Tools
Information
Experiment
Survey
Interview
70
#6.1.2.1 Primary Data
• Direct Observation: is a method of gathering data while the subject of interest
are in their natural environment, often unaware they are being watched
oWatching how baby interacts with a toy
oThe subject will be unlikely influenced by the data collection process
Secondary Data
Primary Data
Direct Observation
Focus Group
Data Sources
Statistic Tools
Information
Experiment
Survey
Interview
71
#6.1.2.2 Primary Data
• Focus Group: is a direct observational technique whereby individuals are
often paid to discuss their attitudes towards products or services in a group
setting controlled by moderator
oStudents and instructors are used as focused group to obtain a new textbook
feedback
Secondary Data
Primary Data
Direct Observation
Focus Group
Data Sources
Statistic Tools
Information
Experiment
Survey
Interview
72
#6.1.2.3 Primary Data
• Experiment: The subjects are exposed to certain treatments and the data of
interest are recorded
• The Golden Brown color of the French fries has many influential factor that determine
their color (e.g. The time of the frying, temperature, thickness of the potato and type of
potato, etc.)
Secondary Data
Primary Data
Direct Observation
Focus Group
Data Sources
Statistic Tools
Information
Experiment
Survey
Interview
73
#6.1.2.4 Primary Data
• Survey: includes directly asking people a series of questions and can be
administered by e-mail, via the Web, through the mail. Face to face or over the
telephone
oThe survey questions should be carefully designed to avoid bias
Secondary Data
Primary Data
Direct Observation
Focus Group
Data Sources
Statistic Tools
Information
Experiment
Survey
Interview
74
#6.1.2.5 Primary Data
• Interview: is used to gather data from people.
oStructured Interview: Interview in which questions are scripted.
oUnstructured Interview: Interview that begin with one or more broadly questions,
with further questions being based on the responses
Secondary Data
Primary Data
Direct Observation
Focus Group
Data Sources
Statistic Tools
Information
Experiment
Survey
Interview
75
Your Turn #4
Identify the data required for each example are primary or secondary. For primary
data, determine the best way in which the data should be collected. |In other words,
should the data be collected via observation, experiment or survey?
• Apple would like to measure the satisfaction levels of customers who purchased its
new iPad product.
• A manager of an electronics store would like to investigate the impact that price has
on the demand for laptop computers. Each week, the price of a Dell laptop is
adjusted and the demand for each week is recorded.
• Cleveland State University needs to determine the current inflation rate to
determine the annual salary increase for its staff for the upcoming year.
• McDonald’s would like to determine the average wait time for customers who use
its drive-through windows during the lunch hour.
76
Identify the data required for each example are primary or secondary. For primary
data, determine the best way in which the data should be collected. In other words,
should the data be collected via observation, experiment or survey?
• Apple would like to measure the satisfaction levels of customers who purchased its
new iPad product. Primary data through a survey
• A manager of an electronics store would like to investigate the impact that price
has on the demand for laptop computers. Each week, the price of a Dell laptop is
adjusted and the demand for each week is recorded. Primary data through an
experiment
• Cleveland State University needs to determine the current inflation rate to
determine the annual salary increase for its staff for the upcoming year. Secondary
data
• McDonald’s would like to determine the average wait time for customers who use
its drive-through windows during the lunch hour. Primary data through direct
observation
77
Applying Concept “Safe Travel”
• Read the following information about the transportation industry and
answer the following questions
• The chart shows the number of job-related injuries for each of the
transportation industries
• Industry
Number of Injuries
oRailroad
4520
oIntercity Bus
5100
oSubway
6850
oTrucking
7144
oAirline
9950
78
• 1. What are the variables under study?
• 2. Categorize each variables as quantitative or qualitative
• 3. Categorize each quantitative variable as discrete or continuous
• 4. Identify the level of measurement for each variable
• 5. What is the message of this graph?
79
• 1. What are the variables under study?
• The variables are industry and number of job-related injuries
• 2. Categorize each variables as quantitative or qualitative
• The type of industry is a qualitative variables, while the number of job-related injuries
is quantitative
• 3. Categorize each quantitative variable as discrete or continuous
• The number of job-related injuries is discrete
• 4. Identify the level of measurement for each variable
• The type of industry is nominal, and the number of job-related injuries is ratio
• 5. What is the message of this graph?
• The rail-road is the safest transportation industry
80
#7 Ethics and Statistics
• Biased sample – a sample that does not represent the intended population
o Can lead to distorted findings
o Can occur either intentionally and unintentionally
• Changing the graph scale
vs.
• Choosing a sample that is not representative of the population
81
Summary
•
•
•
•
Statistics
Data, Variables
Sample, Population
Statistics Branches;
o Descriptive Statistics
o Inferential Statistics
•
•
•
•
Sampling Methods
Data Classification
Data Collection Methods
Research Project Diagram
82
References
1. Bluman, A. G. (2009). Elementary statistics: A step by step approach (6th
ed). McGraw-Hill Higher Education.
2. Donnely, R. (2019). Business statistics (3rd ed). Pearson Education.
3. Groebner, D., Shannon, P., Fry, P., and Smith, K., (2018). Business
statistics: A decision making approach (10th ed). Pearson Education.
4. Sharpe, N., DeVeaux, R.,Velleman, P., and Wright, D. (2017). Business
Statistics. (3rd). Pearson Education.
83