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

2. 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.

3. Objective in Business Statistics
Describe
Descriptive Statistics
Compare
Relate
Inferential Statistics

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

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

6. 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

7. 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

8. Two Basic Categories of Statistical Inference Tools
Estimation
Hypothesis Testing

9. 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.

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

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

12. Major Steps for a Telephone or Written 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 Administer

13. Surveys Demographic questions Closed-ended questions
Open ended questions

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

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

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

17. Sampling Techniques
Non-statistical sampling techniques refer to those methods of sampling using influence, judgement, or other non-chance processes.
Example: Convenience sampling -- sample from the population based upon accessibility and ease of selection.

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

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

20. 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.

21. 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 are as much alike as possible.

22. Stratified Sampling Example (Figure 1-13)
Population
Financial Institutions
Stratified Population

23. Stratified Sampling Example (Figure 1-13)
Population
Cash holdings of All Financial Institutions in the United States
Financial Institutions
Stratified Population

24. Stratified Sampling Example (Figure 1-13)
Population
Cash holdings of All Financial Institutions in the United States
Financial Institutions
Stratified Population

25. Stratified Sampling Example (Figure 1-13)
Population
Cash holdings of All Financial Institutions in the United States
Financial Institutions
Stratified Population
Large Institutions
Stratum 1
Select n1

26. 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
Stratum 1
Select n1
Stratum 2
Select n2

27. 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

28. 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 k. The value of k is determined as the ratio of the population size over the desired sample size.

29. 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.

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

31. Cluster Sampling Example (Figure 1-14)
Mid-Level Managers by Location for Morrison-Knudsen Construction Company
Illinois
Scotland
Florida 42 22 52
All members selected from these clusters

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

33. 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.

34. Data Measurement Levels
Nominal Data
Ordinal (Rank) Data
Interval Data
Ratio Data

35. 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