General Business 704
Data Analysis for
Managers
Introduction
The Course, Data, and Excel
Instructor/Class Info
Kholid eFendi
Ins.happy@yahoo.com
Ins.happy@hotmail.com
Class:
http://ins2happy.awardspace.com
Course Objectives






Master key terminology for data analysis
Understand key statistical concepts
Master the selection of appropriate
statistical tools
Master the use of Excel for the
calculation of statistics
Master the ability to interpret and explain
the meaning of statistical findings
Have fun
Class Motto
We will learn no statistic
before its time
Our focus is on making sense
of managerial situations and questions
through the use of statistics
Course Topics

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



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Data
Excel
Presenting & Describing Data
Estimation: Confidence Intervals
Hypothesis Testing
Simple Regression & Correlation
Multiple Regression
Bonus Session: Time Series,
Forecasting, & Smoothing
Readings & Class Time

Reading Assignments
Ch. 4 & 5 review on your own
 Ch. 10 covered in OIM 768: Quality Mgmt
Ch. 1, 1S, 2, 3, 6, 7, 8, & 9 overview
 Ch. 11 & 12 covered in depth
 Ch. 13 for reference purposes


Class Time
Lecture & Discussion
 Demonstrations
 Application Practice

Assignments

Homework


Individual & Team Cases


Demonstrate ability to do requested tasks
Demonstrate ability to: identify & apply
appropriate tools; & interpret/explain findings
Team Project

Demonstrate ability to: develop good
questions; identify appropriate data; identify &
apply appropriate tools; & interpret/explain
findings
Exams

Exam #1
Wednesday 9/23
 Covers material through Chapter 9


Final Exam
Wednesday 10/21
 Covers all course material

Grading
Activity
Homework #1
Homework #2
Homework #3
Individual Case #1
Individual Case #2
Team Case
Team Project
Exam #1
Final Exam
TOTAL POINTS
Points
50
50
50
100
100
100
250
100
200
1000
Objectives:
Data & Excel

Define statistics

Distinguish descriptive & inferential
statistics

Summarize the sources of data

Describe the types of data & scales

Explain the types of samples

Describe survey process & errors

Discuss the use of Excel
Some Important Issues

Collecting data


e.g., Survey
Presenting data


Data
Analysis
Why?
e.g., Charts &
tables
© 1984-1994 T/Maker Co.
DecisionMaking
Characterizing
data

e.g., Average
© 1984-1994
T/Maker Co.
Application Areas
Accounting


Auditing
Costing
Management


Finance


Financial trends
Forecasting
Describe employees
Quality improvement
Marketing


Consumer preferences
Marketing mix effects
Statistical Methods
Statistical
Methods
Descriptive
Statistics
Inferential
Statistics
Descriptive Statistics

Involves
Collecting data
 Presenting data
 Characterizing
data


50
$
25
0
Q1
Purpose

Describe data
Q2
Q3
Q4
_
X = 30.5 S2 = 113
Inferential Statistics

Involves
Estimation
 Hypothesis
testing


Purpose

Make decisions
about population
characteristics
Population?
Some Key Terms

Population (universe)


Sample


• S in Sample
& Statistic
Portion of population
Parameter


All items of interest
• P in Population
& Parameter
Summary measure about population
Statistic

Summary measure about sample
Why Collect Data?

Obtain input to a research study

Measure performance

Assist in formulating decision
alternatives

Satisfy curiosity

Knowledge for the sake of knowledge
Data Types
Data
Numerical
Categorical
(Quantitative)
(Qualitative)
Discrete
Continuous
Data Type Examples

Numerical

Discrete
 To
how many magazines do you subscribe
currently? ___ (Number)

Continuous
 How

tall are you? ___ (Inches)
Categorical
 Do
you own savings bonds? __ Yes __ No
How Are Data
Measured?

Nominal scale




Categories
 e.g., Male-female
Count




Ordinal scale



Categories
Ordering implied
 e.g., High-low
Count
Interval scale

Equal intervals
No true 0
e.g., Degrees Celsius
Measurement
Ratio scale




Equal intervals
True 0
Meaningful ratios
e.g., Height in inches
Data Sources
Data
Sources
Secondary
Primary
Experiment
Survey
Observation
Published
(& On-Line)
Basic Survey Steps

Define purpose


Design
questionnaire
Collect data
(field work)

Prepare data


Select sample
design


Sample type
Sample size

Edit
Code

Analyze data

Interpret findings

Report results
Questionnaire Design

Question content

Mode of response

Question wording

Question
sequence

Layout

Pilot testing
© 1984-1994 T/Maker Co.
Why Sample?

Destruction of test
units
 Quality control

Accurate &
reliable results

Pragmatic reasons
 Time
 Cost
Types of Samples
Type of
Sample
Non
Probability
Probability
Simple
Random
Judgment
Quota
Chunk
Systematic
Stratified
Cluster
Types of Samples
Type of
Sample
Non
Probability
Probability
Simple
Random
Judgment
Quota
Chunk
Systematic
Stratified
Cluster
Simple
Random Sample

Each population element
has an equal chance of
being selected

Selecting 1 subject does
not affect selecting
others

May use random number
table, lottery, ‘fish bowl’
© 1984-1994
T/Maker Co.
Random Number
Table
Column
Row
00000
12345
00001
67890
11111
12345
11111
67890
01
49280
88924
35779
00283
02
61870
41657
07468
08612
03
43898
65923
25078
86129
OR: Use Excel to generate random numbers
Types of Samples
Type of
Sample
Non
Probability
Probability
Simple
Random
Judgment
Quota
Chunk
Systematic
Stratified
Cluster
Systematic Sample

Every kth element Is
selected after a
random start within
the first k elements

Skip interval, k, is
Population size
Sample size

Used in telephone
surveys
© 1984-1994 T/Maker Co.
Types of Samples
Type of
Sample
Non
Probability
Probability
Simple
Random
Judgment
Quota
Chunk
Systematic
Stratified
Cluster
Stratified Sample

Divide population into
subgroups




Mutually exclusive
Exhaustive
At least 1 common
characteristic of
interest
Select simple random
samples from
subgroups
All Students
Commuter
s
Residents
Sample
Types of Samples
Type of
Sample
Non
Probability
Probability
Simple
Random
Judgment
Quota
Chunk
Systematic
Stratified
Cluster
Cluster Sample

Divide population
into clusters

Companies (Clusters)
If managers
are elements then
companies are clusters

Select clusters
randomly

Survey all or a random
sample of elements in
cluster
Sample
Types of Samples
Type of
Sample
Non
Probability
Probability
Simple
Random
Judgment
Quota
Chunk
Systematic
Stratified
Cluster
Nonprobability
Samples

Judgment
Use experience to select sample
 Example: Test markets


Quota


Similar to stratified sampling
except no random sampling
Chunk (convenience)

Use elements most available
Errors Due to
Sampling
Coverage (Frame) Error
Sampling Error
Nonresponse &
Measurement Error
Total Population
(Students)
Sample Frame
(Students in
Phone Book)
Planned Sample
(Selected Students)
Actual
Sample
The Use of Excel

Microsoft Excel 97 Windows/ 98 Mac

Graduate Lab has Excel 97

Our analyses done with Excel 97/98

Data disk with book for practice

Study Chapter 1S

Review update to 1S for Excel 97
Objectives:
Data & Excel

Define statistics

Distinguish descriptive & inferential
statistics

Summarize the sources of data

Describe the types of data & scales

Explain the types of samples

Describe survey process & errors

Discuss the use of Excel
Course Objectives






Master key terminology for data analysis
Understand key statistical concepts
Master the selection of appropriate
statistical tools
Master the use of Excel for the
calculation of statistics
Master the ability to interpret and explain
the meaning of statistical findings
Have fun