Copyright © 2010 Pearson Education, Inc.  Publishing as Prentice Hall

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Chapter 1: Data and Business Decisions

Statistics, Data Analysis, and Decision Modeling, Fourth Edition

James R. Evans

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“In God We Trust; All Others Use Data” Modern organizations manage by fact for

performance evaluation, improvement, and decision making

Some organizations ignore data: They may not fully understand what to measure

or how to measure. They may be reluctant to spend the required

time and effort. They may feel they can make decisions by

instinct and do not need data. They may fear discovering problems or poor

performance that data may uncover.

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Data, Information, and Analysis Information derives from the analysis of

data Analysis refers to extracting larger

meaning from data to support evaluation and decision making.

Data are also used as key inputs to decision models – logical or mathematical representations of problems or business situations.

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Statistics Statistics – the science of collecting,

organizing, analyzing, interpreting, and presenting data for the purpose of gaining insight and making better decisions.

Applications abound in all business disciplines, manufacturing and quality control, health care, sports, and daily life.

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Statistical Thinking A philosophy of learning and action for

improvement based on three principles: All work occurs in a system of

interconnected processes Variation exists in all processes –

systematic ways of doing things that achieve desired results

Variation must be understood and reduced

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Variation Common causes of variation – complex

interactions of variation in materials, tools, machines, operators, and the environment Individual sources are not easily understood

and cannot be controlled Special causes of variation – variation arising

from external sources not inherent in a process Can be identified and controlled or explained

Many managers do not properly distinguish between these two causes, confuse them, and as a result, often make poor decisions

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Six Sigma and Statistical Thinking Six Sigma - a business process

improvement approach that seeks to find and eliminate causes of defects and errors, reduce cycle times and cost of operations, improve productivity, better meet customer expectations, and achieve higher asset utilization and returns on investment in manufacturing and service processes.

The term “six sigma” is a measure signifying at most 3.4 errors or defects per million opportunities

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Six Sigma Problem Solving DMAIC (Define, Measure, Analyze,

Improve, and Control) Uses a wide variety of statistical and

process improvement tools. Many companies report positive

financial results from Six Sigma initiatives

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Metrics and Measurement Metric - a unit of measurement that

provides a way to objectively quantify performance. Examples: profit, ROI, market share,

customer satisfaction, defects, order accuracy

Measurement – the act of obtaining data.

Measure – numerical information that results from measurement

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Discrete and Continuous Metrics

Discrete Metrics – derived from counts E.g., number of defects per unit of

production, percentage of on-time flight arrivals, number of complaints per customer, percentage of “top box” responses in a satisfaction survey

Continuous Metrics –based on a continuous scale of measurement

E.g., delivery time, number of ounces in a bottle of beer, monthly revenues, diameter of a drilled hole, balance in your checking account, time spent on homework

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Six Sigma Metrics Defects per unit Errors per opportunity Defects per million opportunities (dpmo)

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Types of Business Data – “Balanced Scorecard”

Financial Perspective – profitability, revenue growth, ROI, EPS,…

Internal Perspective – quality levels, productivity, process yields, cycle time, cost,…

Customer Perspective – service levels, satisfaction ratings, repeat business, complaints,…

Innovation and Learning Perspective – intellectual assets, employee satisfaction, market innovation, training effectiveness, supplier performance,…

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Baldrige National Quality Award Results Categories Product and service outcomes Customer-focused outcomes Financial and market outcomes Workforce-focused outcomes Process effectiveness outcomes Leadership outcomes

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Using a Balanced Scorecard

Lagging measures (outcomes) Leading measures (performance drivers) Statistical relationships Examples

IBM Rochester: causal relationships between people skills, quality, customer satisfaction, and financial/market share performance

Sears: employee attitudes predict behavior, which predicts customer retention, which predicts financial performance

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Sources of Data Internal – obtained from company

records, databases, etc. External – obtained from published

sources, external databases, the internet

Generated – obtained from surveys, focus groups, etc.

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Data Classification Type of Data

Cross-Sectional – measurements taken at one time period

Time series – data collected over time Number of Variables

Univariate– data consisting of a single variable to measure some entity

Multivariate– data consisting of two or more variables to measure some entity

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Cross-Sectional, Univariate

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Cross-Sectional, Multivariate

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Time Series, Univariate

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Time Series, Multivariate

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Data Classification Categorical (nominal) – data sorted into

mutually exclusive (an observation cannot belong to more than one category) categories Geographical region, type of employee,

gender, state of birth, type of automobile owned

Properties No quantitative relationships among

categories Statistics such as averages are usually

meaningless

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Data Classification Ordinal data – data ordered or ranked according

to some relationship to one another Ranking of colas in taste tests, employee

performance appraisals, satisfaction survey scales

Properties Categories can be compared with one another Statistics usually meaningless because of no

fixed units of measurement; i.e., differences are meaningless

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Data Classification Interval data – data that are ordered and

characterized by a specified measure of distance between observations, but with no natural zero. Temperature scales, time, survey scales

that are assumed to be interval Properties

Ratios are meaningless (50 degrees is not twice as hot as 25 degrees)

Differences are meaningful, so statistics such as averages may be compared

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Data Classification Ratio data – data that have a natural zero

Sales dollars, length, weight, time from start of a process, most business and economic data

Properties Strongest form of measurement; both ratios

and differences are meaningful

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Populations and Samples Population – all items of interest for a particular

decision or investigation All married drivers in the U.S. over age 25 All individuals who do not own a cell phone

Sample – a subset of a population Nielsen samples of TV viewers Accounting department samples of invoices

for audits Samples are used

To reduce costs of data collection When a full census cannot be taken

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Definition of a Statistic A statistic is a summary measure of sample

data used to describe a characteristic of a population or to draw inferences about the population. 100 owners of a certain car reported 85

problems in the first 90 days of ownership. The statistic “85” describes the number of problems per 100 cars during the first 90 days of ownership, and suggests that the entire population of owners of these cars experience an average of 0.85 problems per car.

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Statistical Methodology

Descriptive statistics – collection, organization, and description of data

Statistical inference – drawing conclusions about unknown characteristics of a population based on samples

Predictive statistics – inferring future values based on historical data

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Basic Excel Skills Opening, saving, and printing files Navigation Selecting ranges Inserting/deleting rows and columns Entering and editing text, data, and formulas Formatting data (number, currency, decimal) Working with text strings Performing basic arithmetic calculations Formatting text Modifying the appearance of a spreadsheet

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Office 2007 Toolbar

Office Button

Tabs

Groups

Buttons and Menus

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Copying Formulas Select a cell. Choose Edit…Copy (or click Copy

icon or press Ctrl-C ). Click on cell to copy to. Choose Edit…Paste (or click on Paste icon or press Ctrl-V ).

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Cell References Relative addressing: B5, G13 Absolute addressing: $B$5, $G13, K$11 Change reference using F4 key

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Functions Range functions: MIN, MAX, SUM,

AVERAGE, AND(condition 1, condition 2,…) OR(condition 1, condition 2,…) IF(condition, value if true, value if false) VLOOKUP(value, table range, column

number)

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Insert FunctionEasiest way to locatea particular functionand identify the correct arguments

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Other Useful Excel Tips Split screen Paste special Column and row widths Displaying formulas Displaying grid lines and row/column

headers for printing Filling a range with a series of numbers

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Excel Add-Ins Analysis Toolpak – included with Excel Prentice-Hall PHStat2 Crystal Ball TreePlan Premium Solver for Education SimQuick

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PHStat Tool: Stack and Unstack Data PHStat menu > Data Preparation >

Stack Data (or Unstack Data)

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PivotTables Create custom summaries and charts

from data Need a database with headers. Select

any cell and choose PivotTable Report from Data menu. Follow the wizard steps.

Drag and drop data items into or out of any of the fields

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Example: Portion of Excel File Accounting Professionals

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Create PivotTable

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PivotTable Structure

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PivotTable Examples

To change statistics, change Value Field Settings

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Value Field Settings

Click on the Options tab under PivotTable Tools in the menubar. In the Active Field group, click on Field Settings to change type of summary

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Changing PivotTable Views

Uncheck the boxes in the PivotTable Field List or drag the variable names to different field areas.

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PHStat Tool: One- and Two-Way Tables and Charts

Choose type of data

• Raw Categorical Data – single column range

• Table of Frequencies – two-column categories and frequency counts

Choose type of chart

(Two-way tables similar)

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One-Way Table Example