Business Analytics
Data Analysis and Decision
Making (7e)
S. Christian Albright
Wayne L. Winston
Chapter 1 Introduction to Data Analysis and Decision Making
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed with a certain product or service or
otherwise on a password-protected website or school-approved learning management system for classroom use.
1-1 Introduction
(slide 1 of 3)

Living in the age of technology has implications for
everyone entering the business world.
 Technology
makes it possible to collect huge amounts of
data.
 Technology has given more people the power and
responsibility to analyze data and make decisions.

A large amount of data already exists and will only
increase in the future.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Introduction
(slide 2 of 3)

One of the hottest topics in today’s business world is
business analytics, also called data analytics.
 These
terms encompass all of the types of analysis
discussed in this book.
 Business analytics typically implies the analysis of very
large data sets. (For this reason, the term big data has
also become popular.)
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Introduction
(slide 3 of 3)


By using quantitative methods to uncover the
information in these data sets and then acting on
this information—again guided by quantitative
analysis—companies are able to gain a competitive
advantage.
The goal of this book is to teach you how to use a
variety of quantitative methods to analyze data
and make decisions in a very hands-on way.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-2a The Methods
(slide 1 of 3)

This book combines topics from two separate fields:
statistics and management science.
 Statistics
is the study of data analysis.
 Management science is the study of model building,
optimization, and decision making.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
The Methods
(slide 2 of 3)

Three important themes run through this book:
 Data
analysis—includes data description, data
inference, and the search for relationships in data.
 Decision making—includes optimization techniques for
problems with no uncertainty, decision analysis for
problems with uncertainty, and structured sensitivity
analysis.
 Dealing with uncertainty—includes measuring
uncertainty and modeling uncertainty explicitly.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
The Methods
(slide 3 of 3)

The figure below shows where these themes and
subthemes are discussed in the book.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-2b The Software
(slide 1 of 6)

The software included in new copies of this book,
together with Microsoft Excel®, provides a powerful
combination that can be used to analyze a wide
variety of business problems.
 Excel—the
most heavily used spreadsheet package on
the market. In this edition, we use Excel 2016.
 The
file excel_tutorial.xlsm explains some of the
“intermediate” features of Excel—features that we expect
you to be able to use.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
The Software
(slide 2 of 6)

Features of Excel® 2016
 Analysis
ToolPak – has tools for data analysis, including
correlation, regression, and inference.
 Solver Add-in—uses powerful algorithms to perform
spreadsheet optimization.
 SolverTable Add-in—shows how the optimal solution
changes when certain inputs change.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
The Software
(slide 3 of 6)

Palisade DecisionTools Suite
 @RISK—can
run multiple replications of a spreadsheet
simulation, perform a sensitivity analysis, and generate
random numbers from a variety of probability
distributions.
 RISKOptimizer
combines optimization with simulation.
 BigPicture—a
smart drawing add-in used to represent
the elements and relationships in a model so that you
can better visualize the problem.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
The Software
(slide 4 of 6)

Palisade DecisionTools Suite
 StatTools—generates
statistical output quickly in an
easily interpretable form.
 PrecisionTree—used to analyze decisions with
uncertainty. The primary method for performing this
type of analysis is to draw a decision tree.
PrecisionTree does this in a very clever and intuitive
way.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
The Software
(slide 5 of 6)

Other programs in DecisionTools® Suite
 NeuralTools—mimics
the working of the human brain to
find “neural networks” that quantify complex nonlinear
relationships.
 TopRank—a “what-if” add-in used for sensitivity
analysis to see which inputs have the largest effect on a
given output.
 Evolver—although we will not use it in this book, Evolver
provides an alternative to Excel’s built-in Solver add-in
for optimization.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
The Software
(slide 6 of 6)

DADM_Tools Add-In
 Palisade
software may not be available in your place
of employment.
 Albright developed an add-in called DADM_Tools that
implements decision trees and simulation, as well as
forecasting and several basic data analysis tools.
 This add-in is freely available from the author’s website
and is free.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-3 Introduction to Spreadsheet Modeling


A common theme in this book is spreadsheet
modeling, where the essential elements of a
business problem are entered and related in an
Excel spreadsheet for further analysis.
The goal of this section is to get you “up to speed”
in using Excel effectively for the rest of the book.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-3a Basic Spreadsheet Modeling:
Concepts And Best Practices (slide 1 of 2)

Most spreadsheet models involve inputs, decision
variables, and outputs.
 The
inputs have given fixed values, at least for the
purposes of the model.
 The decision variables are those a decision maker
controls.
 The outputs are the ultimate values of interest; they are
determined by the inputs and the decision variables.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-3a Basic Spreadsheet Modeling:
Concepts And Best Practices (slide 2 of 2)


Spreadsheet modeling is the process of entering the
inputs and decision variables into a spreadsheet
and then relating them appropriately, by means of
formulas, to obtain the outputs.
After this you may
 Perform
a sensitivity analysis,
 Maximize or minimize a particular output,
 Create charts to show how certain parameters are
related.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
MAKING DECISIONS AND LOOKING UP VALUES
17
MAKING DECISIONS
The Comparison Operators:
• = (means equal to)
• > (means greater than)
• < (means less than)
• >= (means greater than or equal to)
• <= (means less than or equal to)
• <> (means not equal to)
*When Excel evaluates an expression with one of these operators, it generates the
logical value TRUE or FALSE
MAKING DECISIONS AND LOOKING UP VALUES
MAKING DECISIONS
18
The Comparison Operators:
2 important points:
1. When using comparison operators, be careful when choosing the one that exactly
reflects what you want.
For example (if the content of A1 is 60), =A1>60 is different from =A1>=60.
=A1>60 will give FALSE, =A1>=60 will give TRUE.
2.
The result of the operation with a comparison operator is always TRUE or FALSE
– a logical value.
MAKING DECISIONS AND LOOKING UP VALUES
19
MAKING DECISIONS
The IF function:
The structure of the IF function is:
=IF(logical_test, value_if_true, value_if_false)
• The logical_test can be any value or expression that can be evaluated to TRUE or
FALSE (for example, A23>90)
• Value_if_true and value_if_false can be simple or complex expressions (including
other functions).
MAKING DECISIONS AND LOOKING UP VALUES
20
MAKING DECISIONS
The nested IF function:
The value_if_true and value_if_false can be additional IF functions to create more
sophisticated decision structures :
Example:
Suppose the interest rate on a loan will be;
6% if the loan amount is less than $1m
5% for loans amounts between $1 and $2m
4% for loans above $2m
We can use this formula:
=IF(A1<1000000, 0.06,IF(A1<2000000,0.05, 0.04)
MAKING DECISIONS AND LOOKING UP VALUES
21
MAKING DECISIONS
The IFERROR function:
This function returns a value you specify if a formula evaluates to an error, otherwise it
returns the result of the formula.
The syntax is: IFERROR(value, value_if_error)
Example:
Cell F57 =IFERROR(A1/B1, “Error in calculation”)
Cell A1 = 5
If B1=0, cell F57 will read “Error in calculation”
If B1 is not zero, cell F57 will display the correct value.
MAKING DECISIONS AND LOOKING UP VALUES
MAKING DECISIONS
22
The AND and OR functions:
1.
The AND function has the structure AND(logical1, logical2,…) where the
arguments are conditions.
For example, =AND(A1>0, B1>0, C1>0) will return TRUE only if A1, B1, and C1
are all greater than 0.
2.
The OR function has the structure OR(logical1, logical2,…) where the arguments
are conditions.
For example, =OR(A1>0, B1>0, C1>0) will return TRUE if at least one of A1, B1,
and C1 is greater than 0.
MAKING DECISIONS AND LOOKING UP VALUES
23
MAKING DECISIONS
The MAX and MIN functions:
• These functions return the largest and smallest value in a list of arguments.
• MAX(number1, number2,…)
• MIN(number1, number2,…)
*The arguments can be numbers, references, and ranges.
Example 1.1: Ordering NCAA T-Shirts
(slide 1 of 4)
Objective: To build a spreadsheet model in a series
of stages, with all stages being correct but each
stage being more readable and flexible than the
previous stages.
 Solution: The formulas allow for the order quantity to
be less than, equal to, or greater than demand. If
demand is greater than the order quantity, Randy will
sell all the T-shirts ordered for $18 each. If demand
is less than the order quantity, Randy will sell as many
T-shirts as are demanded at the $18 price and all
leftovers at the $6 price.

© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.1: Ordering NCAA T-Shirts
(slide 1 of 4)
1.
2.
3.
If demand is greater than the order quantity, Randy
will sell all the T-shirts ordered for $18 each.
If demand is less than the order quantity, Randy will
sell as many T-shirts as are demanded at the $18
price and
All leftovers will be sold at $6 price.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.1: Ordering NCAA T-Shirts
(slide 2 of 4)




IF
Excel’s IF function has the syntax
=IF(condition, result_if_True,result_if_False).
The condition is any expression that is either true or
false.
The two expressions result_if_True and result_if_False
can be any expressions you would enter in a cell:
numbers, text, or other Excel functions (including other
IF functions).
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.1: Ordering NCAA T-Shirts
(slide 3 of 4)

Spreadsheet Layout and Documentation
 think
carefully about your spreadsheet layout and then
document your work carefully.
 For layout, consider whether certain data are best
oriented in rows or columns, whether your work is better
placed in a single sheet or in multiple sheets, and so on.
 For documentation, use descriptive labels and headings,
color coding, cell comments, and text boxes to make
your spreadsheets more readable.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.1: Ordering NCAA T-Shirts
(slide 4 of 4)
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-3b Cost Projections


In the following example, a company wants to
project its costs of producing products, given that
material and labor costs are likely to increase
through time.
We build a simple model and then use Excel’s
charting capabilities to obtain a graphical image of
projected costs.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.2: Projecting the Costs of Bookshelves
at Woodworks (slide 1 of 3)
Objective: To learn good spreadsheet practices, to
create copyable formulas with the careful use of
relative and absolute addresses, and to create line
charts from multiple series of data.
 Solution: The completed spreadsheet model
appears in Figure 1.7, on the following slide, and in
the file Bookshelf Costs Finished.xlsx.

© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.2: Projecting the Costs of Bookshelves
at Woodworks (slide 2 of 3)
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.2: Projecting the Costs of Bookshelves
at Woodworks (slide 3 of 3)

Steps:
Inputs
 Design output table
 Projected unit costs of wood
 Projected unit labor costs
 Projected bookshelf costs
 Chart

© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-3c Breakeven Analysis



Many business problems require you to find the
appropriate level of some activity.
This might be the level that maximizes profit (or
minimizes cost), or it might be the level that allows a
company to break even—no profit, no loss.
The following example illustrates a typical
breakeven analysis.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.3: Breakeven Analysis at Quality
Sweaters (slide 1 of 10)


Objective: To learn how to work with range names,
to learn how to answer what-if questions with oneway data tables, to introduce Excel’s Goal Seek
tool, and to learn how to document and audit Excel
models with cell comments and Excel’s formula
auditing tools.
Solution: The completed spreadsheet model
appears in Figure 1.9 on the following slide. (See
the file Breakeven Analysis Finished.xlsx.)
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.3: Breakeven Analysis at Quality
Sweaters (slide 2 of 10)
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.3: Breakeven Analysis at Quality
Sweaters (slide 5 of 10)

Inserting Cell Comments
 Inserting comments in cells is a great way to
document non-obvious aspects of your spreadsheet
models.
 To enter a comment in a cell, right-click the cell,
select the Insert Comment item, and type your
comment.
 This creates a little red mark in the cell, indicating a
comment, and you can see the comment by moving
the cursor over the cell.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.3: Breakeven Analysis at Quality
Sweaters (slide 7 of 10)

Forming a One-Way Data Table
Data tables are also called what-if tables.
 They let you see what happens to selected outputs as
selected inputs change.

© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
WHAT-IF QUESTIONS
38
Data Tables
- The Data Table tool is used to create dynamic tables to show how some of the
outputs of the model would look given a series of different values for one or more
inputs to the model.
- It is important to note that the input and output cells that a data table uses must be
on the same worksheet that you create the data table.
- Two types of data tables:
1. One-input table allows you to show the values of several output variables for
a range of one input variable.
2. A two-input data table allows you to show the values of one output variable
for ranges of two input variables.
WHAT-IF QUESTIONS
39
Creating a one-input Data Table
Example:
We want to create the after-tax income of a business that buys and sells just one
product.
It sold 100 units of the product at $12 each and its only cost was that of buying the
product at $10 each.
The business income tax rate is 40% fixed.
Solution:
• In this model, there are three independent variables; i) number of units sold, ii) sales
price, and iii) purchase price.
• There are five dependent variables; i) revenue, ii) cost of goods sold, iii) before-tax
income, iv) income tax, and v) after-tax income.
40
• We want to calculate the revenue and after-tax income of the business for the range
of number of units sold in cells D6:D11.
• We can do it one by one manually but Data Table can do it faster
• We want the revenues to be in cells E6:E11. In E5, enter ‘=B6’ to tell the Data Table
that the values of the dependent variables, which should enter below E5 will come from
cell B6.
• Similarly, the after-tax incomes will go to cells F6:F11. In F5 enter ‘=B13’.
• Select D5:F11 and select Data  Data tools  What-if Analysis  Data table.
• Since the values of the independent variables are in a column D6:D11 (not in a row),
click the box next to column input cell and click B3 (because this is where the values of
the independent variable have to be substituted).
• Click OK and Excel will fill up the table with values for revenue and after-tax income.
Example 1.3: Breakeven Analysis at Quality
Sweaters (slide 8 of 10)

The Power of Data Tables
 Unfortunately,
many Excel users are unaware of data
tables, which are among the most powerful and useful
tools Excel has to offer.
 After you have developed a model that relates inputs
to outputs, you can then build data tables in a matter
of seconds to see how the outputs vary as key inputs
vary over some range.
 Data tables are perfect for answering a large number
of what-if questions quickly and easily.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.3: Breakeven Analysis at Quality
Sweaters (slide 9 of 10)

Using Goal Seek
 The purpose of the Goal Seek tool is to solve one
equation in one unknown.
 It is used here to find the response rate that makes
profit equal to 0.
 Specifically, Goal Seek allows you to vary a single
input cell to force a single output cell to a selected
value.
 To use it, select Goal Seek from the What-If Analysis
dropdown list on the Data ribbon and fill in the
resulting dialog box.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.3: Breakeven Analysis at Quality
Sweaters (slide 10 of 10)

Using the Formula Auditing Tool
 You
can use a handy Excel tool to see how all the
parts in this example are related.
 This is the Formula Auditing tool, which is
available on the Formulas ribbon.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-3d Ordering with Quality Discounts and
Demand Uncertainty


In the following example, we again attempt to find
the appropriate level of some activity: how much of
a product to order when customer demand for the
product is uncertain.
Two important features of this example are the
presence of quantity discounts and the explicit use
of probabilities to model uncertain demand.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.4: Ordering With Quantity Discount
at Sam’s Bookstore (slide 1 of 6)


Objective: To learn how to build in complex logic
with IF formulas, to get help about Excel functions,
to learn how to use lookup functions, to see how
two-way data tables provide answers to more
extensive what-if questions, and to learn about
Excel’s SUMPRODUCT function.
Solution: The profit model appears in Figure 1.17,
on the following slide. (See the file Quantity
Discounts Finished.xlsx.).
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.4: Ordering With Quantity Discount
at Sam’s Bookstore (slide 2 of 6)
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.4: Ordering With Quantity Discount
at Sam’s Bookstore (slide 5 of 6)

Two-Way Data Table
A two-way data table allows you to see how a single
output cell varies as you vary two input cells.
 Unlike a one-way data table, only a single output cell can
be used.
 To create this type of table, enter a reference to the
output cell in the top-left corner of the table, enter
possible values of the two inputs below and to the right of
this corner cell, and select the entire table.
 Then select Data Table from the What-If Analysis
dropdown on the Data ribbon, and enter references to
the cells where the original two input variables live.

© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
WHAT-IF QUESTIONS
48
Creating a two-input Data Table
• A two-input data table displays the values of one dependent variable of a model
to ranges of values for two independent (input) variables.
• Let’s say we want to see how the after-tax income will vary with the number of
units sold and sales price/unit.
49
• Enter the desired values for the number of units sold in D16:D21 and the desired
values for sales price/unit in E15:I15, making sure to leave the corner cell, D15,
empty.
• In D15, enter =B13 to indicate where the values for the dependent variables should be
picked up.
• Now select D15:I21 and open the Data Table dialog box as before.
• In the box labeled Row input cell, enter B5.
• In the box labeled Column input cell, enter B3.
• Click OK
Example 1.4: Ordering With Quantity Discount
at Sam’s Bookstore (slide 6 of 6)

SUMPRODUCT
The SUMPRODUCT function takes two range arguments,
which must be exactly the same size and shape, and it
sums the products of the corresponding values in these
two ranges.
 This is an extremely useful function, especially when the
ranges involved are large.

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Example 1.4: Ordering With Quantity Discount
at Sam’s Bookstore (slide 3 of 6)

VLOOKUP

To use this function, first create a vertical lookup table,
with values to use for comparison listed in the left column
of the table and corresponding output values in as many
columns to the right as you like.
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with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.4: Ordering With Quantity Discount
at Sam’s Bookstore (slide 4 of 6)

VLOOKUP

The VLOOKUP function takes three or four arguments:
(1) the value you want to compare to the values in the left
column of the table;
 (2) the lookup table range;
 (3) the index of the column you want the returned value to
come from, where the index of the left column is 1, the index of
the next column is 2, and so on; and optionally
 (4) TRUE (for an approximate match, the default) or FALSE (for
an exact match). If you omit the last argument, the values in the
left column of the table must be entered in ascending order.

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with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
MAKING DECISIONS AND LOOKING UP VALUES
53
LOOKUP (Vector form):
Looks in a one-row or one-column range for a value and returns a value from the same position
in another one-row or one-column range.
LOOKUP(lookup_value, lookup_vector, result_vector)
Lookup_value – the value that LOOKUP searches for in the first vector. It can be a
number, text, a logical value, or a name or reference that refers to
a value.
Lookup_vector – a range that contains only one row or one column. It can be text,
numbers, or logical values.
- The values must be in ascending order: -2,-1, 0, 1, A-Z, FALSE,TRUE
- Uppercase and lowercase are considered equivalent
Result_vector – a range that contains only one row or column. It must be the same
size as the look_up vector.
MAKING DECISIONS AND LOOKING UP VALUES
54
LOOKING UP VALUES
• If LOOKUP cannot find the lookup_value, it matches the largest value in the
look_upvector that is less than the lookup_value.
• This makes it possible to look up values where the lookup_value falls in a range
instead of matching a specific value.
• If the lookup_value is smaller than the smallest value in the lookup_vector,
LOOKUP gives the #N/A! error value.
=LOOKUP(29000,D9:D14,H9:H14) will return 15%
=LOOKUP(55000,D9:D14,H9:H14) will return 25%
=LOOKUP(390000,D9:D14,H9:H14) will return 35%
55
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with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
MAKING DECISIONS AND LOOKING UP VALUES
56
HLOOKUP:
- Searches for a value in the top row of a table or an array (range) of values and
then returns the value from a specified row in the same column of the table or array
- Use HLOOKUP when your comparison values are located in a row across the top of
a table of data, and you want to look down a specified number of rows.
Syntax:
HLOOKUP(lookup_value, table_array, row_index_num, range_lookup)
• Lookup_value is the value to be found in the first row of the table. It can be a
value, a reference, or a text string
• Table_array is a table of information in which a data is looked up.
• Row_index_num is the row number in table_array from which the matching value
will be returned. A row_index_num of 1 returns the first row value in table_array,
row_index_num of 2 returns the second row value in table_array and so on.
• Range_lookup is a logical value that specifies whether you want HLOOKUP to find
an exact match or an approximate match. If TRUE or omitted, an approximate
match is returned.
MAKING DECISIONS AND LOOKING UP VALUES
57
VLOOKUP:
- Searches for a value in the leftmost column of a table or an array (range) of values
and then returns the value from a specified column in the same row of the table or
array
- Use VLOOKUP when your comparison values are located in a column to the left of
a table of data.
Syntax:
VLOOKUP(lookup_value, table_array, column_index_num, range_lookup)
• Lookup_value is the value to be found in the first column of the table. It can be a
value, a reference, or a text string
• Table_array is a table of information in which a data is looked up.
• Row_index_num is the row number in table_array from which the matching value
will be returned. A row_index_num of 1 returns the first row value in table_array,
row_index_num of 2 returns the second row value in table_array and so on.
• Range_lookup is a logical value that specifies whether you want VLOOKUP to find
an exact match or an approximate match. If TRUE or omitted, an approximate
match is returned.
=VLOOKUP(140000, D9:I14, 3) will return $16,056.25
= VLOOKUP(63000, D9:I14, 3) will return $4,481.25
= VLOOKUP(140000, D9:I14, 3, FALSE) will return #N/A! (no exact match)
= VLOOKUP(140000, D9:I14, 3, TRUE) will return $16,056.25
58
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with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
B5 = VLOOKUP(B3, D10:I15,3) + VLOOKUP(B3, D10:I15, 5)*(B3-VLOOKUP(B3, D10:I15, 1)
59
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with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
MAKING DECISIONS AND LOOKING UP VALUES
60
MATCH:
- Returns the relative position of an item in a range of contiguous cells in a row or a column
that matches a specified value.
- Even though it is called MATCH, it does not need to find an exact match (unless you
specifically require it).
- Use MATCH, instead of VLOOKUP, when you need the position of an item in a range instead
of the item itself.
Syntax:
MATCH(lookup_value, lookup_array, match_type)
•
•
•
Lookup_value is the value you use to find the value you want to find in a table.
Lookup_array is a range of contiguous cells containing possible lookup values. It can be an
array or an array reference.
Match_type is the number -1, 0, or 1. It defines how Excel matches lookup_value with values
in lookup_array.
- Match_type = 1 ; MATCH finds the largest value that is less than or equal to
lookup_value. Lookup_array can be in any order.
- Match_type = 0 ; MATCH finds the first value that is exactly equal to
lookup_value.
Lookup_array must be in ascending order.
- Match_type = -1 ; MATCH finds the smallest value that is greater than or equal to
lookup_value. Lookup_array must be in descending order.
- If match_type is omitted, it is assumed to be 1.
=MATCH(64000, D9:D14, 1) will return 3
=MATCH(32550, D9:D14, 0) will return 3
=MATCH(64000, D9:D14, 0) will return N/A! (no exact match)
=MATCH(64000, D9:D14, -1) will return N/A! (values not in descending order)
61
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with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
MAKING DECISIONS AND LOOKING UP VALUES
62
CHOOSE:
- Based on a specified index number, this function chooses a value from the list of its
arguments.
Example:
=CHOOSE(1, “Mon”, “Tue”, “Wed”, “Thu”, “Fri”, “Sat”, “Sun”) will return Mon
=CHOOSE(4, “Mon”, “Tue”, “Wed”, “Thu”, “Fri”, “Sat”, “Sun”) will return Thu
MAKING DECISIONS AND LOOKING UP VALUES
63
INDEX:
- Returns the value of an element in a table or an array, selected by the row and
column number indexes
Syntax:
INDEX(array, row_num, col_num)
• Array is a range of cells.
• Row_num selects the row in array from which to return a value. If row_num is
omitted, col_num is required.
• Col_num selects the column in array from which to return a value. If col_num is
omitted, row_num is required.
=INDEX(D9:I14, 2, 3) will return $802.50
64
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with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
1-3e Estimating the Relationship Between
Price and Demand


The following example illustrates a very important
modeling concept: estimating relationships between
variables by curve fitting.
The ideas can be illustrated at a relatively low level
by taking advantage of some useful Excel tools.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.5: Estimating Sensitivity of Demand to
Price at Links Company (slide 1 of 5)


Objective: To illustrate Excel’s Trendline tool, and to
illustrate conditional formatting.
Solution: This example is divided into two parts:
estimating the relationship between price and
demand, and creating the profit model. (Both can
be found in the file Golf Club Demand
Finished.xlsx.)
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.5: Estimating Sensitivity of Demand to
Price at Links Company (slide 2 of 5)

Estimating the Relationship Between Price and
Demand
Here is a scatterplot of demand versus price.
 We consider 3 possibilities: linear, power, and
exponential curves.

© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.5: Estimating Sensitivity of Demand to
Price at Links Company (slide 3 of 5)

Estimating the Relationship Between Price and
Demand

The best fitting curve is the power model.
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.5: Estimating Sensitivity of Demand to
Price at Links Company (slide 4 of 5)

Maximizing Profit
© 2020 Cengage. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Example 1.5: Estimating Sensitivity of Demand to
Price at Links Company (slide 5 of 5)

Sensitivity to Variable Cost
 How
does the best price change as the unit variable
cost changes?
 Create a chart of maximum profit (or best price) versus
unit cost.
 The maximum profit decreases at a decreasing rate as
the unit cost increases.
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with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
WHAT-IF QUESTIONS
71
Scenario Manager
- In a table created with Scenario Manager, you can show the values of any number
of dependent variables for several sets of values (each called a scenario) of a
group of independent variables.
Example:
Using the same example, let’s define a pessimistic scenario in which the company will
sell only 50 units at a price of $11 each and an optimistic scenario in which it can sell
125 units at $13 each. In both scenarios, the other independent variables will remain
at the same levels as shown in the model.
The output variables that we are interested in are revenue and after-tax income.
Name: B3 Sales, B5 SPrice, B6 Revenue, B13 ATIncome
WHAT-IF QUESTIONS
• To create a scenario table, you have to first create your scenarios.
72
• Data  Data tools  What-if Analysis  Scenario Manager
• Click the Add button. In the scenario name box, type “Pessimistic”. In the Changing cells
box, enter the address of the cells for the independent variables which values you want
to change separated by commas; in this case they are B3and B5. Click OK.
• In the Scenario Values box that comes up, enter ’50’ in the box next to Sales and ’11’ in
the box next to SPrice to define the scenario. Click OK.
• To create the optimistic scenario, click the Add button and repeat the previous steps.
However, in this case, you do not need to reenter the cell addresses in the Changing
cells box; you just need to enter the values for Sales and SPrice in the Scenario Values
dialog box.
• To create the scenario table, in the Scenario Manager dialog box, click the Summary
button. In the Scenario Summary dialog box in the Results cell box, enter the addresses
for the dependent variables that you want to show in your table, separated by commas;
in this case they are B6 and B13. In Report type, click Summary. Click OK.
WHAT-IF QUESTIONS
73
• To create a scenario table, you have to first create your scenarios.
• Data  Data tools  What-if Analysis  Scenario Manager
WHAT-IF QUESTIONS
74
• Click the Add button. In the scenario name box, type “Pessimistic”. In the Changing cells
box, enter the address of the cells for the independent variables which values you want
to change separated by commas; in this case they are B3and B5. Click OK.
WHAT-IF QUESTIONS
75
• In the Scenario Values box that comes up, enter ’50’ in the box next to Sales and ’11’ in
the box next to SPrice to define the scenario. Click OK.
WHAT-IF QUESTIONS
• To create the optimistic scenario, click the Add button and repeat the previous steps.
However, in this case, you do not need to reenter the cell addresses in the Changing
76
cells box; you just need to enter the values for Sales and SPrice in the Scenario Values
dialog box.
• To create the scenario table, in the Scenario Manager dialog box, click the Summary
button. In the Scenario Summary dialog box in the Results cell box, enter the addresses
for the dependent variables that you want to show in your table, separated by commas;
in this case they are B6 and B13. In Report type, click Summary. Click OK.
WHAT-IF QUESTIONS
77