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. 
Modern statistical methods involve the design and analysis of experiments and surveys, the quantification of biological, social and scientific phenomenon and the application of statistical principles to understand more about the world around us. 
Statistics is a discipline which is concerned with:
designing experiments and other data collection,
summarizing information to aid understanding,
drawing conclusions from data, and
estimating the present or predicting the future.

3. 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

4. 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

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
Please read the book

8. Example of Random Sampling
Suggesting how the statistical sampling techniques can be used to gather data on employees' preferences for scheduling vacation times.
Simple random sampling could be used by assigning each employee a number and then using a random number generator to select employees.
Table and Excel Toolpak

9. Simple 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

10. Type of Statistics Descriptive statistics Inferential statistics
Mathematical methods (such as mean, median, standard deviation) that summarize and interpret some of the properties of a set of data (sample) but do not infer the properties of the population from which the sample was drawn.
Inferential statistics
Mathematical methods (such as hypothesis development) that employ probability theory for deducing (inferring) the properties of a population from the analysis of the properties of a set of data (sample) drawn from it. It is concerned also with the precision and reliability of the inferences it helps draw.

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

12. Inferential Statistics
Making statements about a population by examining sample results
Sample statistics Population parameters
(known) Inference (unknown, but can
be estimated from
sample evidence)

13. Inferential Statistics
Drawing conclusions and/or making decisions concerning a population based on sample results.
Estimation
e.g., Estimate the population mean weight using the sample mean weight
Hypothesis Testing
e.g., Use sample evidence to test the claim that the population mean weight is 120 pounds

14. Tools for Collecting Data
Data Collection Methods
Experiments
Written questionnaires
Telephone surveys
Direct observation and personal interview

15. Survey Design Steps Define the issue Define the population of interest
what are the purpose and objectives of the survey?
Define the population of interest
Develop survey questions
make questions clear and unambiguous
use universally-accepted definitions
limit the number of questions

16. 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

17. 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

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

19. Qualitative vs. Quantitative Variables (Data)
Qualitative variables (data) take on values that are names or labels.
Example: the color of a ball (e.g., red, green, blue) or the breed of a dog (e.g., collie, shepherd, terrier)  
Quantitative variables are numerical. They represent a measurable quantity.
Example: # of students in CSUB or # of people in Bakersfield

20. Discrete vs. Continuous Variables (Data)
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.
Example: The fire department mandates that all fire fighters must weigh between 150 and 250 pounds. The weight of a fire fighter would be an example of a continuous variable; since a fire fighter's weight could take on any value between 150 and 250 pounds. 

21. Discrete vs. Continuous Variables (Data)
Example: If we flip a coin and count the number of heads. The number of heads could be any integer value between 0 and plus infinity. However, it could not be any number between 0 and plus infinity. That is, we could not, for example, get 2.3 heads. Therefore, the number of heads must be a discrete variable.

22. Data Measurement Levels
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

23. Nominal
Nominal basically refers to categorically discrete data such as name of your school, type of car you drive or name of a book. This one is easy to remember because nominal sounds like name (they have the same Latin root). 

24. Ordinal
Ordinal refers to quantities that have a natural ordering. The ranking of favorite sports, the order of people's place in a line, the order of runners finishing a race or more often the choice on a rating scale from 1 to 5. With ordinal data you cannot state with certainty whether the intervals between each value are equal. For example, we often using rating scales (Likert-Scale questions). On a 10 point scale, the difference between a 9 and a 10 is not necessarily the same difference as the difference between a 6 and a 7. This is also an easy one to remember, ordinal sounds like order.

25. Interval
Interval data is like ordinal except we can say the intervals between each value are equally split. The most common example is temperature in degrees Fahrenheit. The difference between 29 and 30 degrees is the same magnitude as the difference between 78 and 79 (although I know I prefer the latter). With attitudinal scales and the Likert questions you usually see on a survey, these are rarely interval, although many points on the scale likely are of equal intervals.

26. Ratio
Ratio data is interval data with a natural zero point. For example, time is ratio since 0 time is meaningful. Degrees Kelvin has a 0 point (absolute 0) and the steps in both these scales have the same degree of magnitude.

27. 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

28. 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