1. PowerPoint Presentation Package to Accompany:
A Course in Business Statistics
(3rd Edition)
by Shannon/Groebner/Fry/Smith

2. The Where, Why, and How of Data Collection
Chapter 1
The Where, Why, and How of Data Collection

3. Chapter 1 - Chapter Outcomes
After studying the material in this chapter, you should:
• Know the key data collection methods.
• Know the difference between a population and a sample.
• Understand how to categorize data by type and level of measurement.

4. Chapter 1 - Chapter Outcomes (continued)
After studying the material in this chapter, you should:
• Understand the similarities and differences between different sampling methods.
• Know how to set up a computer file for data storage.

5. Business Statistics
Business statistics offers students the necessary tools for effectively converting sets of data into usable information.

6. Business Statistics
Business statistics consists of a set of tools and techniques that are used to convert data into meaningful information for a business environment.

7. Descriptive Statistics
Descriptive Statistics consists of the tools and techniques designed to describe data, such as charts, graphs, and numerical measures.

8. Descriptive Statistics - Examples of Descriptive Methods -
• Histograms
• Bar charts
• Average or Arithmetic Mean

9. Descriptive Statistics (Figure 1-1)

10. Descriptive Statistics (Figure 1-2: Histogram)

11. Descriptive Statistics (Figure 1-3: Bar Chart)
PATIENTS BY GENDER

12. Descriptive Statistics
AVERAGE
The sum of all the values divided by the number of values. In equation form:
where:
N = number of data values
xi = ith data value

13. Inferential Statistics
Inferential Statistics consists of techniques that allow a decision-maker to reach a conclusion about characteristics of a larger data set based upon a subset of those data

14. Two Basic Categories of Statistical Inference Tools
• Estimation
• Hypothesis Testing

15. Data Types • Primary Data • Secondary Data
Those that are collected by you or another
person with whom you are closely associated.
• Secondary Data
Those that are collected and compiled by an outside source or by someone in your organization who may later provide access to the data to other users.

16. Tools for Collecting Data
• Experiments
• Telephone Surveys
• Mail Questionnaires
• Direct Observation and Personal Interview

17. An experiment is any process that generates data as its outcome.
Experiments
An experiment is any process that generates data as its outcome.

18. Major Steps for a Telephone Survey
• Define the Issue
• Define the Population of Interest
• Develop Survey Questions
• Pre-test the Survey
• Determine the Sample Size and Sampling Method
• Select Sample and Make Calls

19. Written Surveys
Open ended questions are questions that allow respondents the freedom to respond with any value, words, or statements of their own choosing.

20. Written Surveys
Closed-ended questions are questions that require the respondent to select from a short list of defined choices.

21. Written Surveys
Demographic questions are questions relating to the respondents’ own characteristics, backgrounds, and attributes.

22. Written Survey Steps • Define the Issue
• Define the Population of Interest
• Design the Survey Instrument
• Pre-test the Survey
• Determine Sample Size and Sampling Method
• Select Sample and Send Surveys

23. Populations and Samples
A population is a set of specific data values on all objects or individuals.

24. Populations and Samples
A sample is a subset of the population.

25. Parameters and Statistics
Descriptive numerical measures calculated from the entire population are called parameters.
Corresponding measures for a sample are called statistics.

26. Sampling Techniques
Non-statistical sampling techniques refer to those methods of sampling using influence, judgment, or other non-chance processes.

27. Sampling Techniques
Convenience sampling is a sampling technique that selects items from the population based upon accessibility and ease of selection.

28. Sampling Techniques
Statistical sampling techniques refer to those methods of sampling that use selection techniques based upon chance selection.

29. Statistical Sampling • Simple Random Sampling
Types of statistical sampling include:
• Simple Random Sampling
• Stratified Random Sampling
• Systematic Sampling
• Cluster Sampling

30. Statistical Sampling
Simple random sampling refers to a method of selecting items from a population such that every possible sample of a specified size has an equal chance of being selected.

31. Statistical Sampling
Stratified random sampling refers to a sampling method in which the population is divided into subgroups called strata so that each population item belongs to only one strata. The objective is to form strata such that the population values of interest in each strata are as much alike as possible.

32. Stratified Sampling Example (Figure 1-13)
Population
Cash holdings of All Financial Institutions in the United States
Financial Institutions
Stratified Population
Large Institutions
Medium Size Institutions
Small Institutions
Stratum 1
Select n1
Stratum 2
Select n2
Stratum 3
Select n3

33. Statistical Sampling
Systemic random sampling refers to a sampling technique that involves selecting the kth item in the population after randomly selecting a starting point between 1 and the kth values. The value of k is determined as the ratio of the population size over the desired sample size.

34. Statistical Sampling
Cluster sampling refers to a method by which the population is divided into groups, or clusters, that are each intended to be mini-populations. A random sample of m clusters is selected. Individual items are then selected randomly from each of the m clusters.

35. Cluster Sampling Example (Figure 1-14)
Mid-Level Managers by Location for Morrison-Knudsen Construction Company
Algeria
Illinois
Scotland
California
Alaska
New York
Florida
Idaho
Mexico
Australia 25 42 22
105 20 36 52
152 76 37

36. Quantitative and Qualitative Data
Data that are numeric and that define value or quantity are quantitative data.
Data whose measurement scale is inherently categorical are qualitative data.

37. Time Series Data and Cross-Sectional Data
Time series data consist of a set of ordered data values observed at successive points in time.
Cross-sectional data are a set of data values observed at a fixed point in time.

38. Data Measurement Levels
• Nominal Data
• Ordinal (Rank) Data
• Interval Data
• Ratio Data

39. Data Level Hierarchy (Figure 1-15)
Highest Level
Complete Analysis
Ratio/Interval Data
Measurements
Higher Level
Mid-level Analysis
Rankings
Ordered Categories
Ordinal Data
Categorical Codes ID Numbers Category Names
Lowest Level
Basic Analysis
Nominal Data

40. Key Terms • Average • Business Statistics • Census
• Closed-end questions
• Cluster sample
• Convenience sample
• Cross-sectional data
• Data check sheets
• Demographic questions
• Experiment
• Experimental design
• Interval data
• Nominal data
• Nonstatistical sampling
• Open-end questions
• Ordinal data

41. Key Terms (continued) • Population • Primary data • Qualitative data
• Quantitative data
• Ratio data
• Sample
• Secondary data
• Simple random sampling
• Statistical inference tools
• Statistical sampling
• Stratified random sampling
• Structured review
• Systematic random sampling
• Time series data
• Unstructured review