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A Course in Business A Course in Business StatisticsStatistics(3rd Edition)

by Shannon/Groebner/Fry/Smithby Shannon/Groebner/Fry/Smith

Chapter 1Chapter 1

The Where, Why, and The Where, Why, and How of Data CollectionHow of Data Collection

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Chapter 1 - Chapter 1 - Chapter Chapter OutcomesOutcomes

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.

Chapter 1 - Chapter 1 - Chapter Chapter OutcomesOutcomes

(continued)(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.

Business StatisticsBusiness Statistics

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

Business StatisticsBusiness Statistics

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

Descriptive StatisticsDescriptive Statistics

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

Descriptive StatisticsDescriptive Statistics- Examples of Descriptive - Examples of Descriptive

Methods -Methods -

• HistogramsHistograms

• Bar chartsBar charts

• Average or Arithmetic MeanAverage or Arithmetic Mean

Descriptive StatisticsDescriptive Statistics((Figure 1-1Figure 1-1))

Length of Stay Age (Years) Sex (M/F/U) Total Charges3 78 F 5.4193 74 F 4.57511 89 M 12.0313 81 M 3.6189 87 F 12.8073 65 M 5.2963 90 M 3.4533 61 M 1.7603 90 F 3.2905 78 M 6.2543 78 F 3.8962 71 M 1.7953 76 M 9.2653 76 F 3.283

Descriptive StatisticsDescriptive Statistics(Figure 1-2: Histogram)(Figure 1-2: Histogram)

BAKER CITY HOSPITAL - LENGTH OF STAY DISTRIBUTION

0

10

20

30

40

50

60

70

0<2 2<4 4<6 6<8 8<10 10<12 12<14 14<16 16<18

PATIENTS BY GENDER

44.20%

55.80%

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00%

Females

Males

PATIENTS BY GENDER

Descriptive StatisticsDescriptive Statistics(Figure 1-3: Bar Chart)(Figure 1-3: Bar Chart)

Descriptive StatisticsDescriptive Statistics

AVERAGEAVERAGEThe sum of all the values divided by the number of values. In equation form:

where:

N = number of data values

xi = ith data value

valuesdataofnumbertotal

valuesxofsum

N

xN

ii

1Average

Inferential StatisticsInferential Statistics

Inferential StatisticsInferential 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

Two Basic Categories of Two Basic Categories of Statistical Inference ToolsStatistical Inference Tools

• EstimationEstimation• Hypothesis TestingHypothesis Testing

Data TypesData Types

• Primary DataPrimary Data Those that are collected by you or anotherperson with whom you are closely

associated.

• Secondary DataSecondary 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.

Tools for Collecting DataTools for Collecting Data

• ExperimentsExperiments• Telephone SurveysTelephone Surveys• Mail QuestionnairesMail Questionnaires• Direct Observation Direct Observation

and Personal and Personal InterviewInterview

ExperimentsExperiments

An experimentexperiment is any process that generates data as its outcome.

Major Steps for a Major Steps for a Telephone SurveyTelephone Survey

• Define the IssueDefine the Issue• Define the Population of InterestDefine the Population of Interest• Develop Survey QuestionsDevelop Survey Questions• Pre-test the SurveyPre-test the Survey• Determine the Sample Size and Sampling Determine the Sample Size and Sampling

MethodMethod• Select Sample and Make CallsSelect Sample and Make Calls

Written SurveysWritten Surveys

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

Written SurveysWritten Surveys

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

Written SurveysWritten Surveys

Demographic questionsDemographic questions are questions relating to the respondents’ own characteristics, backgrounds, and attributes.

Written Survey StepsWritten Survey Steps

• Define the IssueDefine the Issue• Define the Population of InterestDefine the Population of Interest• Design the Survey InstrumentDesign the Survey Instrument• Pre-test the SurveyPre-test the Survey• Determine Sample Size and Sampling Determine Sample Size and Sampling

MethodMethod• Select Sample and Send SurveysSelect Sample and Send Surveys

Populations and SamplesPopulations and Samples

A populationpopulation is a set of specific data values on all objects or individuals.

Populations and SamplesPopulations and Samples

A samplesample is a subset of the population.

Parameters and StatisticsParameters and Statistics

• Descriptive numerical measures calculated from the entire population are called parametersparameters.

• Corresponding measures for a sample are called statisticsstatistics.

Sampling TechniquesSampling Techniques

Non-statistical sampling Non-statistical sampling techniquestechniques refer to those methods of sampling using influence, judgment, or other non-chance processes.

Sampling TechniquesSampling Techniques

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

Sampling TechniquesSampling Techniques

Statistical sampling Statistical sampling techniquestechniques refer to those methods of sampling that use selection techniques based upon chance selection.

Statistical SamplingStatistical Sampling

Types of statistical sampling include:

• Simple Random SamplingSimple Random Sampling

• Stratified Random SamplingStratified Random Sampling

• Systematic SamplingSystematic Sampling

• Cluster SamplingCluster Sampling

Statistical SamplingStatistical Sampling

Simple random samplingSimple 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.

Statistical SamplingStatistical Sampling

Stratified random samplingStratified 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.

Stratified Sampling ExampleStratified Sampling Example(Figure 1-13)(Figure 1-13)

PopulationPopulation

Cash holdings of All Financial

Institutions in the United States

Large Institutions

Medium Size Institutions

Small Institutions

Stratified PopulationStratified Population

Stratum 1

Stratum 2

Stratum 3

Select n1

Select n2

Select n3

Financial Institutions

Statistical SamplingStatistical Sampling

Systemic random samplingSystemic 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.

Statistical SamplingStatistical Sampling

Cluster samplingCluster 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.

Cluster Sampling ExampleCluster Sampling Example(Figure 1-14)(Figure 1-14)

25 42 22 105 20 36 52 152 76 37

Algeria Illinois Scotland California Alaska New York Florida Idaho Mexico Australia

Mid-Level Managers by Location for Morrison-Knudsen Construction Company

Quantitative and Quantitative and Qualitative DataQualitative Data

•Data that are numeric and that define value or quantity are quantitative quantitative datadata.•Data whose measurement scale is inherently categorical are qualitative qualitative datadata.

Time Series Data and Time Series Data and Cross-Sectional DataCross-Sectional Data

•Time series dataTime series data consist of a set of ordered data values observed at successive points in time.•Cross-sectional dataCross-sectional data are a set of data values observed at a fixed point in time.

Data Measurement LevelsData Measurement Levels

• Nominal Nominal DataData

• Ordinal Ordinal (Rank) Data(Rank) Data

• Interval Interval Data Data

• Ratio DataRatio Data

Data Level HierarchyData Level Hierarchy(Figure 1-15)(Figure 1-15)

Ratio/Interval Data

Ordinal Data

Nominal Data

Highest Level

Complete Analysis

Higher Level

Mid-level Analysis

Lowest Level

Basic Analysis

Categorical Codes ID Numbers Category Names

Rankings

Ordered Categories

Measurements

Key TermsKey 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

Key TermsKey Terms(continued)(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