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PowerPoint PresentationPowerPoint PresentationPackage to Accompany:Package to Accompany:
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
©
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