1. Chapter 1 The Where, Why, and How of Data Collection
Business Statistics:
A Decision-Making Approach
7th Edition
Chapter 1 The Where, Why, and How of Data Collection

2. What is Statistics?
Statistics is the development and application of methods to collect, analyze and interpret data. 
Statistics is a discipline which is concerned with:
designing experiments and other data collection
summarizing information to aid understanding
drawing conclusions from data
estimating the present or predicting the future.

3. Populations and Samples
A Population is the set of all items or individuals of interest
Examples: All likely voters in the next election
All parts produced today
All sales receipts for November
A Sample is a subset of the population
Examples: 1000 voters selected at random for interview
A few parts selected for destructive testing
Every 100th receipt selected for audit

4. Population vs. Sample Population Sample a b c d b c ef gh i jk l m n
o p q rs t u v w
x y z
b c
g i n
o r u y

5. Why Sample? Less time consuming than a census
Less costly to administer than a census
It is possible to obtain statistical results of a sufficiently high precision based on samples.

6. Nonstatistical Sampling
Sampling Techniques
Sampling Techniques
Nonstatistical Sampling
Statistical Sampling
(Simple)
Random
Convenience
Systematic
Judgment
Cluster
Stratified
Not interested in……

7. (Probability Sampling)
Statistical Sampling
Items of the sample are chosen based on known or calculable probabilities
Statistical Sampling
(Probability Sampling)
(Simple) Random
Stratified
Systematic
Cluster
Video Clip

8. Random Sampling
Every possible sample of a given size has an equal chance of being selected
Selection may be with replacement or without replacement
The sample can be obtained using a table of random numbers or computer random number generator

9. Stratified Random Sampling
Watch Video Clip: Samples and Surveys (#14) 
More often used than “Systematic” and “Cluster”
Stratified random sampling could be used to divide the employees into groups with similar characteristics that might affect preferences like marital status or age and then simple random samples can be taken from each group.

10. Systematic Random Sampling
Decide on sample size: n (sample)
Divide frame of N (population) individuals into groups of k individuals: k=N/n
Randomly select one individual from the 1st group
Select every kth individual thereafter
N = 64
n = 8
k = 8
First Group

11. Cluster Sampling
Divide population into several “clusters,” each representative of the population
Select a simple random sample of clusters
All items in the selected clusters can be used, or items can be chosen from a cluster using another probability sampling technique
Population divided into 16 clusters.
Randomly selected clusters for sample

12. Two Basic Divisions of Statistics
Descriptive statistics
Descriptive statistics are numbers that are used to summarize and describe data. 
Examples:
Average salary of various occupations
Median house price in Bakersfield, CA
Descriptive statistics do not infer the properties of the population from which the sample was drawn --- do not involve generalization

13. Descriptive Statistics
Collect data
e.g., Survey, Observation,
Experiments
Present data
e.g., Charts and graphs
Characterize data
e.g., Sample mean =

14. Inferential Statistics
You have been hired by the National Election Commission to examine how the American people feel about the fairness of the voting procedures in the U.S. Who will you ask?
Ask every single American
Ask randomly selected a small group (sample) of Americans and then draw inferences about the entire country from their responses.

15. Inferential Statistics
are used to draw inferences about a population from a sample (two main methods: estimation and hypothesis testing).
Sample selection is “critical” matter….
Not from a particular state
Not from a particular party

16. Tools for Collecting Data
Data Collection Methods
Domino Pizza example from What is Statistics? (#1)
Also Watch Video Clip: Samples and Surveys (#14) 
Experiments
Written questionnaires
Telephone surveys
Direct observation and personal interview

17. Survey Design Steps Define the issue Define the population of interest
What are the purpose and objectives of the survey?
How will the survey be administered?
(e.g. phone, , face to face)
Define the population of interest
Develop survey questions
Make questions clear and unambiguous
Use universally-accepted definitions
Limit the number of questions

18. Survey Design Steps Pre-test the survey
(continued)
Pre-test the survey
Pilot test with a small group of participants
Assess clarity and length
Determine the sample size and sampling method
Select sample and administer the survey

19. Types of Questions Closed-end Questions
Select from a short list of defined choices
Example: Major: __business __liberal arts
__science __other
Open-end Questions
Respondents are free to respond with any value, words, or statement
Example: What did you like best about this course?
Demographic Questions
Questions about the respondents’ personal characteristics
Example: Gender: __Female __ Male

20. Observations and Interviews
Data collected is observed and recorded based on what takes place
Very subjective
Example: Observe reactions of customers to a new store layout
Interviews
Can be structured – fixed set of questions
Can use a variety of questions
Requires more time from the researcher

21. Data Collection Pitfalls
Interview bias
Non response
Selection bias
Observer bias
Measurement error
Internal/External validity
The objective is to collect accurate and reliable data!

22. Data (variable) Types Data Qualitative (Categorical) Quantitative
(Numerical)
Discrete
Continuous
Examples:
Marital Status
Political Party
Eye Color
(Defined categories)
Examples:
Weight
Voltage
(Measured characteristics)
Examples:
Number of Children
Defects per hour
(Counted items)

23. Qualitative vs. Quantitative Variables (Data)
Qualitative variables (data) take on values that are names or labels.
Quantitative variables are numerical. They represent a measurable quantity.
Quantitative variables can be further classified as discrete or continuous: If a variable can take on any value between its minimum value and its maximum value, it is called a continuous variable; otherwise, it is called a discrete variable.
Video clip (easy and simple): Introduction to Variables

24. Data Measurement Levels (Please see the Video Clip: Scales of Measurement)
Highest Level
Complete Analysis
Measurements
Ratio/Interval Data
Rankings
Ordered Categories
Higher Level
Mid-level Analysis
Ordinal Data
Categorical Codes ID Numbers Category Names
Lowest Level
Basic Analysis
Nominal Data

25. Data Types Time Series Data Cross Section Data
Ordered data values observed over time
Cross Section Data
Data values observed at a fixed point in time

26. Data Types Sales (in $1000’s) 2003 2004 2005 2006 Atlanta 435 460 475
490
Boston
320
345
375
395
Cleveland
405
390
410
Denver
260
270
285
280
Time Series Data
Cross Section Data