Statistics for business decision making and analysis robert stine and foster chapter 01

Chapter 1

Introduction

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1.1 What is Statistics?
Some Basic Ideas


Statistics as a discipline



Questions we can answer with statistics



Variation, patterns and models

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1.1 What is Statistics?
Statistics as a Discipline


Science and art of extracting answers from data



A statistic is a property of data



Statistics can be numbers (like an average) or
graphs that display information (like a timeplot)

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1.1 What is Statistics?
Typical Questions Statistics Help to Answer


Which customers are interested in an MP3
player?



How much are customers willing to pay for an
MP3 player?



Why does a shopper choose a particular box of
cereal?

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1.1 What is Statistics?
Variation, Patterns and Models


Variation refers to differences among items /
individuals or fluctuations over time



Patterns are systematic or predictable features in
data



Models break down variation into a pattern and
unexplained variation
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1.1 What is Statistics?
Statistical Models


Key to extracting answers from data



Good models simplify reality



Finding real patterns helps us to understand data,
plan for the future, and make better decisions

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1.2 Previews
Predicting Employment

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1.2 Previews
Predicting Employment


Back in September 2005, how could you forecast
employment in October?



Use monthly employment data for U.S. beginning

January 2003.



Prepare a timeplot (a chart of values ordered in
time) to display monthly employment data
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1.2 Previews
Timeplot of Employment Data

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1.2 Previews
Linear Pattern and Region of Uncertainty

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1.2 Previews
Conclusions


Extrapolating the pattern (line) into the future
provides a forecast for October



Actual employment for October is less than
expected and below the anticipated range



This surprising result breaks from the pattern and
deserves attention (the Katrina effect?)
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1.2 Previews
Pricing a Car

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1.2 Previews
Pricing a Car


Is $22,000 too much to spend on a 2002 BMW
325i with 53,000 miles?



Get prices for a sample of certified used BMW’s
from the Philadelphia area in November 2005



Prepare a scatterplot (a graph showing pairs of
values) of price versus mileage for this sample
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1.2 Previews
Scatterplot of Price Versus Mileage

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1.2 Previews
Line and Region of Uncertainty

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1.2 Previews
Conclusions


The range of uncertainty is so wide because

factors other than mileage affect price



This model uses all of the data to answer the
question about one used car (borrowing strength)



$22,000 isn’t too much to pay (it is less than
predicted by our line and within the range)
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1.3 How to Use This Book
The Four M’s





Motivation: reason for answering a question or
solving a problem
Method: plan for addressing the problem
Mechanics: performing the calculations,
generating graphs and displays
Message: explanation of conclusions

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