Basic cost management concepts chapter 2mcgraw hillirwin

Basic Cost
Management Concepts
Chapter 2
McGraw-Hill/Irwin
Copyright © 2020 McGraw-Hill Education. All rights reserved.
No reproduction or distribution without prior written consent of
McGraw-Hill Education.
2-1
Chapter 2: Basic Cost Management Concepts
Learning Objective 2-1 – Explain what is meant by the word
cost.
2-2

Learning Objective 2-1. Explain what is meant by the word cost.
Process of Management
Decision
Making
Directing
Control
Planning
Strategy
Formulation
Managers need cost information to
perform each of these functions.
2-3
The process of management involves formulating strategy,
planning, control, decision making, and directing operational
activities. Management accounting information helps managers
perform each of these functions more effectively. (LO 2-1)
What Do We Mean By a Cost?
A cost
is the measure of
resources given
up to achieve a

particular purpose.
2-4
At the most basic level, a cost may be defined as the sacrifice
made, usually measured by the resources expended, or given up,
to achieve a particular purpose. The word cost can have
different meanings depending on the context in which it is used.
The important point is that different cost concepts and
classifications are used for different purposes. The purpose of
this chapter is to enable the users of this text to gain a firm
grasp of the cost terminology used in companies, and which will
be used throughout the text. (LO 2-1)
Learning Objective 2-2 – Distinguish among product costs,
period costs, and expenses.
2-5
Learning Objective 2-2. Distinguish among product costs,
period costs, and expenses.

Product Costs, Period Costs, and Expenses
Product costs are costs assigned to inventory, to goods that are
either purchased or manufactured for resale. Another term for
product cost is inventoriable cost.
Period costs are costs that are expensed during the time period
in which they are incurred.
Expenses are the consumption of assets for the purpose of
generating revenue.
2-6
One important way of classifying costs is by the timing of their
recognition as expenses for financial reporting.
An expense is defined as the cost incurred when a resource
(asset) is used up or sold for the purpose of generating revenue.
The resource used up can be:
cash, expended directly;
a promise to use up cash in the future, recognized as a liability
(accounts payable); or
the reduction in value of a recorded asset such as plant and
equipment (via depreciation) or inventory (via cost of goods
sold).
The term product cost and period cost are used to describe the
timing with which various expenses are recognized.
A product cost is a cost assigned to goods that were either
purchased or manufactured for resale. Another term for product
cost is inventoriable cost, since a product cost is stored as the
cost of inventory until the goods are sold. In the period of the

sale, the product costs are recognized as an expense called cost
of goods sold.
Period costs are all costs that are not product costs. They are
expensed in the period they are incurred. All research and
development, selling, and administrative costs are treated as
period costs. This is true in manufacturing, retail, and service
industry firms. (LO 2-2)
Learning Objective 2-3 – Describe the role of costs in published
financial statements.
2-7
2-7
Learning Objective 2-3. Describe the role of costs in published
financial statements.
Cost Classifications on Financial Statements – Income
Statement
Product Costs

Cost of goods sold
Period Costs
Operating expenses
2-8
Product costs appear on the income statement as cost of goods
sold in the period in which the products were sold. This is true
for merchandising and manufacturing companies.
Period costs appear on the income statement in the period in
which they were incurred. Selling and administrative expenses
are an example of period costs. (LO 2-3)
Cost Classifications on Financial Statements – Balance Sheet (1
of 4)
Merchandiser
Current Assets
Cash
Receivables
Prepaid Expenses
Merchandise Inventory

Manufacturer
Current Assets
Cash
Receivables
Prepaid Expenses
Inventories
Raw Materials
Work in Process
Finished Goods
2-9
Since retailers, wholesalers, and manufacturers sell
inventoriable products, their balance sheets are also affected by
product costs. Merchandisers, such as Wal-Mart, list the product
cost as merchandise inventories on the balance sheet in the
current assets section. (LO 2-3)
of 4)
Merchandiser
Current Assets
Cash
Receivables
Prepaid Expenses
Manufacturer
Current Assets
Cash

Receivables
Inventories
Raw Materials
Work in Process
Finished Goods
Those materials waiting to be processed.
2-10
Manufacturers have three types of inventory. Raw-material
inventory includes all materials before they are placed into
production. (LO 2-3)
of 4)
Merchandiser
Current Assets
Cash
Receivables
Prepaid Expenses
Manufacturer
Current Assets
Cash
Receivables
Prepaid Expenses

Inventories
Raw Materials
Work in Process
Finished Goods
Partially completed products – material to which some labor
and/or overhead has been added.
2-11
Work-in-process inventory refers to manufactured products that
are only partially completed at the date when the balance sheet
is prepared. (LO 2-3)
of 4)
Merchandiser
Current Assets
Cash
Receivables
Prepaid Expenses
Manufacturer
Current Assets
Cash
Receivables
Prepaid Expenses
Inventories
Raw Materials

Work in Process
Finished Goods
Completed products awaiting sale.
2-12
Finished-goods inventory refers to manufactured goods that are
complete and ready for sale. The values of the work-in-process
and finished-goods inventories are measured by their product
costs. (LO 2-3)
Learning Objective 2-4 – List and describe four types of
manufacturing processes.
2-13
2-13
Learning Objective 2-4. List and describe four types of
manufacturing processes.

Types of Production Processes
2-14
Although there are tens of thousands of manufacturing firms,
their basic production processes can be classified into four
standard types. The nature of the product and the manufacturing
process define the manufacturing costs incurred. The
management team is in a better position to manage these costs if
they understand the relationship of the production process and
the types of costs incurred. This exhibit defines and describes
the four standard categories of manufacturing processes. We
will study of the role of managerial accounting and cost
management in each of these manufacturing processes. (LO 2-4)
Learning Objective 2-5 – Give examples of three types of
manufacturing costs.
2-15
2-15

Learning Objective 2-5. Give examples of three types of
manufacturing costs.
Manufacturing Costs
The
Product
Direct
Labor
Manufacturing Overhead
Direct
Material
2-16
Managerial accountants classify costs by the functional area of
the organization to which costs relate. Some examples of
functional areas are manufacturing, marketing, administration,
and research and development. Manufacturing costs are further
classified into the following three categories: direct material,
direct labor, and manufacturing overhead. (LO 2-5)

Direct Material
Example:
Steel used to
manufacture
the automobile.
Cost of raw material that is used to
make, and can be conveniently
traced, to the finished product.
2-17
Material that is consumed in the manufacturing process, is
physically incorporated in the finished product, and can be
traced to products conveniently is called direct material.
Examples include the sheet metal in an automobile and a
microprocessor chip in a computer. (LO 2-5)
Cost of salaries, wages, and fringe
benefits for personnel who work
directly on manufactured products.
Direct Labor
Example:
Wages paid to an
automobile assembly
worker.
2-18

The cost of salaries, wages, and fringe benefits for personnel
who work directly on the manufactured products is classified as
direct-labor cost. (LO 2-5)
Manufacturing Overhead (1 of 3)
All other manufacturing costs
Materials used to support the production process. Examples:
lubricants and cleaning supplies used in an automobile assembly
plant.
Indirect
Labor
Indirect
Material
Other
Costs
2-19
All other costs of manufacturing are classified as manufacturing
overhead, which includes three types of costs: indirect material,
indirect labor, and other manufacturing costs. The cost of
materials that are required for the production process but do not
become an integral part of the finished product are classified as

indirect material costs. Materials that do become an integral
part of the finished product but are insignificant in cost are also
often classified as indirect material. (LO 2-5)
Cost of personnel who do not work directly on the product.
Examples: maintenance workers, janitors, and security guards.
Indirect
Labor
Indirect
Material
Other
Costs
2-20
The costs of personnel who do not work directly on the product,
but whose services are necessary for the manufacturing process,
are classified as indirect labor. (LO 2-5)
Examples: depreciation on plant and equipment, property taxes,
insurance, utilities, overtime premium, and unavoidable idle
time.
Indirect
Labor

Indirect
Material
Other
Costs
2-21
All other manufacturing costs that are neither material nor labor
costs are classified as manufacturing overhead. (LO 2-5)
Classifications of Costs in
Manufacturing Companies
Manufacturing costs are often combined as follows:
Prime
Cost
Conversion
Cost
Direct
Material
Direct
Labor
Manufacturing

Overhead
2-22
Direct material and direct labor are often referred to as prime
costs. Direct labor and overhead are often called conversion
costs, since they are the costs of “converting” raw material into
finished products. (LO 2-5)
Manufacturing Cost Flows
2-23
As direct material is consumed in production, its cost is added
to work-in-process inventory. Similarly, the costs of direct
labor and manufacturing overhead are accumulated in work in
process.
When products are finished, their costs are transferred from
work-in-process inventory to finished-goods inventory. The
total cost of direct material, direct labor, and manufacturing
overhead transferred from work-in-process inventory to
finished-goods inventory is called the cost of goods
manufactured.

The costs then are stored in finished goods until the time period
when the products are sold. At that time, the product costs are
transferred from finished goods to cost of goods sold, which is
an expense of the period when the sale is made. (LO 2-5)
Learning Objective 2-6 – Prepare a schedule of cost of goods
manufactured, a schedule of cost of goods sold, and an income
statement for a manufacturer.
2-24
Learning Objective 2-6. Prepare a schedule of cost goods
manufactured, a schedule of cost of goods sold, and an income
statement for a manufacturer.
Schedule of Cost of Goods Manufactured (1 of 4)
2-25

Manufacturers generally prepare a schedule of cost of goods
manufactured and a schedule of cost of goods sold to summarize
the flow of manufacturing costs during an accounting period.
Let’s take a look at the schedule of cost of goods manufactured
for Comet Computer Corporation. (LO 2-6)
2-26
Raw materials used in production is calculated by adding raw
materials purchases for the period to the beginning raw
materials inventory. This is the value of the raw materials that
were available for use during the period. The raw materials at
the end of the period is subtracted from raw materials available
to arrive at the raw materials used in production during the
period. (LO 2-6)

Include all direct labor costs incurred during the current period.
2-27
All direct labor costs incurred during the period are added to
raw materials used. (LO 2-6)
2-28
Costs incurred during the period that fall into the category of
manufacturing overhead are totaled and then added to raw
materials used and direct labor to arrive at total manufacturing
costs. (LO 2-6)
Schedule of Cost of Goods Manufactured - WIP

Beginning work-in-process inventory is carried over from the
prior period.
Ending work-in-process inventory contains the cost of
unfinished goods, and is reported in the current assets section of
the balance sheet.
2-29
The value of the work-in-process inventory at the beginning of
the period is added to the total manufacturing costs.
The value of the ending work-in-process inventory is subtracted
from the subtotal to arrive at cost of goods manufactured for the
period. (LO 2-6)
Income Statement for a Manufacturer
2-30

Now let’s look at the income statement for Comet. Remember,
cost of goods sold is the expense measured by the cost of the
finished goods sold during a period of time. (LO 2-6)
2-31
Schedule of Cost of Goods Sold
The cost of goods sold amount can be obtained from the cost of
goods sold schedule. This schedule starts with the finished
goods inventory value at the beginning of the period. Cost of
goods manufactured from the cost of goods manufactured
schedule is added to arrive at cost of goods available for sale.
The value of the finished goods inventory at the end of the
period is deducted to determine the cost of goods sold amount
for the period. (LO 2-6)
Learning Objective 2-7 – Understand the importance of
identifying an organization's cost drivers.
2-32

2-32
Learning Objective 2-7. Understand the importance of
identifying an organization’s cost drivers.
Cost Drivers
2-33
Activities that cause costs to be incurred are called COST
DRIVERS:
The phrase different costs for different purposes is often us ed to
convey the notion that different characteristics of costs can be
important to understand in a variety of managerial situations.
One of the most important cost classifications involves the way
a cost changes in relation to changes in the activity of the
organization. Activity refers to a measure of the organization’s
output of products or services. The activities that cause costs to
be incurred are also called cost drivers. (LO 2-7)

Learning Objective 2-8 – Describe the behavior of variable and
fixed costs, in total and on a per-unit basis.
2-34
2-34
Learning Objective 2-8. Describe the behavior of variable and
fixed costs, in total and on a per-unit basis.
Cost Classifications
Cost behavior means how a cost will react to changes in the
level of business activity.
Total variable costs change when activity changes.
Total fixed costs remain unchanged when activity changes.
2-35
After identifying costs and cost drivers, management should
examine the relationship of various costs to the activities

performed. Such a relationship is referred to as cost behavior.
Two types of cost behavior are variable costs and fixed costs. A
variable cost changes in total in direct proportion to a change in
the level of activity (or cost driver). A fixed cost remains
unchanged in total even if the level of activity (or cost driver)
varies. (LO 2-8)
Total Variable Cost Example
Your total monthly bill is based on how many movies you
watch.
Prime (for-fee) Movies Watched
Total Amazon Prime Bill
2-36
For example, assume you are a new subscriber to Amazon Prime
video. If you don’t watch any of the (for-fee) movies, your
charges will be zero. (LO 2-8)
Variable Cost Per Unit Example
The cost per movie watched is constant. For example, @
$4.00 per movie.
Movies Watched

Per Movie Charge
2-37
The cost per movie is constant, regardless of the number of
movies watched. In our example, the cost is $4.00 per movie no
matter if you watch one movie, four movies, or ten movies. (LO
2-8)
Total Fixed Cost Example
Your monthly subscription cost does not change when you
watch movies on streaming services such as Netflix that charge
a flat monthly fee.
Number of Netflix Movies Watched
Monthly Netflix Charge
2-38
A fixed cost remains unchanged in total as the level of activity

(or cost driver) changes. For example, if you are a Netflix
subscriber your monthly subscription is the same every month,
regardless of the number of movies or shows that you watch.
This is classified as a fixed cost. It does not change when
activity changes. (LO 2-8)
Graph of Unit Fixed Cost
The average cost per movie decreases as more Netflix movies
are watched.
Number of Movies Watched
Monthly Subscription per Movie Watched
2-39
The cost of your monthly subscription can be divided by the
number of movies watched to arrive at the average cost per
movie. The more movies that you watch, the average cost per
movie decreases. (LO 2-8)
Cost Classifications - Summary
2-40

In summary, the total variable cost changes as activity changes.
But the cost per unit of variable costs stays the same, regardless
of the level of activity. On the other hand, total fixed costs stay
the same, regardless of the level of activity. However, the fixed
cost per unit changes (in the opposite direction) as the level of
activity changes. (LO 2-8)
Cost Management Systems
Objectives
Measure the cost of resources consumed.
Identify and eliminate non–value-added costs.
Determine efficiency and effectiveness of major activities.
Identify and evaluate new activities that can improve
performance.
2-41
A cost management system is a planning and control system
with four objectives:
Measure the cost of the resources consumed in performing the
organization’s significant activities and measure the unused
capacity of those resources.
Identify and eliminate non–value-added costs. These are the
costs of activities that can be eliminated with no deterioration
of product quality, performance, or perceived value.
Determine the efficiency and effectiveness of all major
activities performed in the enterprise.
Identify and evaluate new activities that can improve the future

performance of the organization.
Notice that the emphasis in a cost management accounting
system is on the activities. Using an activity-based costing
system to improve the operations of an organization is called
activity-based management or ABM. We will learn a lot more
about these concepts. (LO 2-8)
Learning Objective 2-9 – Distinguish among direct, indirect,
controllable, and uncontrollable costs.
2-42
2-42
Learning Objective 2-9. Distinguish among direct, indirect,
controllable, and uncontrollable costs.
Direct and Indirect Costs
Direct costs
Costs that can be easily and conveniently traced to a product or
department.
Example: cost of paint in the paint department of an automobile
assembly plant.
Indirect costs
Costs that must be allocated in order to be assigned to a product
or department.

Example: cost of national advertising for an airline is indirect to
a particular flight.
2-43
An entity, such as a particular product, service, or department,
to which a cost is assigned is called a cost object. A cost that
can be traced to a particular cost object is called a direct cost of
that cost object. A cost that is not directly traceable to a
particular cost object is called an indirect cost of that cost
object.
Whether a cost is a direct cost or an indirect cost of a
department often depends on which department is under
consideration. A cost can be a direct cost of one department or
subunit in the organization but an indirect cost of other
departments. While the salary of a General Electric Company
plant manager is an indirect cost of the plant’s departments, the
manager’s salary is a direct cost of the plant. An important
objective of a cost management system is to trace as many costs
as possible directly to the activities that cause them to be
incurred. Sometimes called activity accounting, this process is
vital to management’s objective of eliminating non–value-added
costs. These are costs of activities that can be eliminated
without deterioration of product quality, performance, or
perceived value. (LO 2-9)
Controllable and Uncontrollable Costs
A cost that can be significantly influenced by a manager is a
controllable cost.

2-44
Another cost classification that can be helpful in cost control
indicates the controllability of a cost item by a particular
manager. If a manager can control or heavily influence the level
of a cost, then that cost is classified as a controllable cost of
that manager. Costs that a manager cannot influence
significantly are classified as uncontrollable costs of that
manager. Many costs are not completely under the control of
any individual. In classifying costs as controllable or
uncontrollable, managerial accountants generally focus on a
manager’s ability to influence costs. The question is not, Who
controls the cost? but, Who is in the best position to influence
the level of a cost item? This exhibit lists several cost items,
along with their typical classification as controllable or
uncontrollable. (LO 2-9)
Learning Objective 2-10 – Define and give examples of an
opportunity cost, an out-of-pocket cost, a sunk cost, a
differential cost, a marginal cost, and an average cost.
2-45
2-45

Learning Objective 2-10. Define and give examples of an
opportunity cost, an out-of-pocket cost, a sunk cost, a
differential cost, a marginal cost, and an average cost.
Opportunity Costs
Example: If you were not attending college, you could be
earning $30,000 per year. Your opportunity cost of attending
college for one year is $30,000.
2-46
An opportunity cost is defined as the benefit that is sacrificed
when the choice of one action precludes taking an alternative
course of action. Opportunity costs arise in many business
decisions. (LO 2-10)
Out-of-Pocket Costs
Those costs that require the payment of cash or other assets as a
result of its incurrence.
These costs should be considered when making decisions.
No reproduction or distribution without prior written …

Data Visualization with R
Rob Kabacoff
2018-09-03
2
Contents
Welcome 7
Preface 9
How to use this book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 9
Prequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 10
Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 10
1 Data Preparation 11
1.1 Importing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 11
1.2 Cleaning data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 12
2 Introduction to ggplot2 19

2.1 A worked example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 19
2.2 Placing the data and mapping options . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 30
2.3 Graphs as objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 32
3 Univariate Graphs 35
3.1 Categorical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 35
3.2 Quantitative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 51
4 Bivariate Graphs 63
4.1 Categorical vs. Categorical . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 63
4.2 Quantitative vs. Quantitative . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 71
4.3 Categorical vs. Quantitative . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 79
5 Multivariate Graphs 103
5.1 Grouping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 103
6 Maps 115

6.1 Dot density maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 115
6.2 Choropleth maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 119
3
4 CONTENTS
7 Time-dependent graphs 127
7.1 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 127
7.2 Dummbbell charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 130
7.3 Slope graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 133
7.4 Area Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 135
8 Statistical Models 139
8.1 Correlation plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 139
8.2 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 141
8.3 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 145

8.4 Survival plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 147
8.5 Mosaic plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 150
9 Other Graphs 153
9.1 3-D Scatterplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 153
9.2 Biplots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 159
9.3 Bubble charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 161
9.4 Flow diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 163
9.5 Heatmaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 168
9.6 Radar charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 174
9.7 Scatterplot matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 176
9.8 Waterfall charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 178
9.9 Word clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 180

10 Customizing Graphs 183
10.1 Axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 183
10.2 Colors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 187
10.3 Points & Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 193
10.4 Legends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 195
10.5 Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 197
10.6 Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 199
10.7 Themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 206
11 Saving Graphs 219
11.1 Via menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 219
11.2 Via code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 219
11.3 File formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 219
11.4 External editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 221

CONTENTS 5
12 Interactive Graphs 223
12.1 leaflet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 223
12.2 plotly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 223
12.3 rbokeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 226
12.4 rCharts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 226
12.5 highcharter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 226
13 Advice / Best Practices 231
13.1 Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 231
13.2 Signal to noise ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 232
13.3 Color choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 234
13.4 y-Axis scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 234

13.5 Attribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 238
13.6 Going further . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 238
13.7 Final Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 239
A Datasets 241
A.1 Academic salaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 241
A.2 Starwars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 241
A.3 Mammal sleep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 241
A.4 Marriage records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 242
A.5 Fuel economy data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 242
A.6 Gapminder data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 242
A.7 Current Population Survey (1985) . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 242
A.8 Houston crime data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 242
A.9 US economic timeseries . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . 243
A.10 Saratoga housing data . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 243
A.11 US population by age and year . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 243
A.12 NCCTG lung cancer data . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 243
A.13 Titanic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 243
A.14 JFK Cuban Missle speech . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 244
A.15 UK Energy forecast data . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 244
A.16 US Mexican American Population . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 244
B About the Author 245
C About the QAC 247
6 CONTENTS
Welcome
R is an amazing platform for data analysis, capable of creating

almost any type of graph. This book helps
you create the most popular visualizations - from quick and
dirty plots to publication-ready graphs. The
text relies heavily on the ggplot2 package for graphics, but
other approaches are covered as well.
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 Interna-
tional License.
My goal is make this book as helpful and user-friendly as
possible. Any feedback is both welcome and
appreciated.
7
8 CONTENTS
Preface
How to use this book
You don’t need to read this book from start to finish in order to
start building effective graphs. Feel free to
jump to the section that you need and then explore others that
you find interesting.
Graphs are organized by
• the number of variables to be plotted
• the type of variables to be plotted

• the purpose of the visualization
Chapter Description
Ch 1 provides a quick overview of how to get your data into R
and how to prepare it
for analysis.
Ch 2 provides an overview of the ggplot2 package.
Ch 3 describes graphs for visualizing the distribution of a single
categorical (e.g. race)
or quantitative (e.g. income) variable.
Ch 4 describes graphs that display the relationship between two
variables.
Ch 5 describes graphs that display the relationships among 3 or
more variables. It is
helpful to read chapters 3 and 4 before this chapter.
Ch 6 provides a brief introduction to displaying data
geographically.
Ch 7 describes graphs that display change over time.
Ch 8 describes graphs that can help you interpret the results of
statistical models.
Ch 9 covers graphs that do not fit neatly elsewhere (every book
needs a miscellaneous
chapter).
Ch 10 describes how to customize the look and feel of your
graphs. If you are going to
share your graphs with others, be sure to skim this chapter.
Ch 11 covers how to save your graphs. Different formats are
optimized for different
purposes.
Ch 12 provides an introduction to interactive graphics.

Ch 13 gives advice on creating effective graphs and where to go
to learn more. It’s
worth a look.
The Appendices describe each of the datasets used in this book,
and provides a short blurb about
the author and the Wesleyan Quantitative Analysis Center.
There is no one right graph for displaying data. Check out the
examples, and see which type best fits
your needs.
9
10 CONTENTS
Prequisites
It’s assumed that you have some experience with the R language
and that you have already installed R and
RStudio. If not, here are some resources for getting started:
• A (very) short introduction to R
• DataCamp - Introduction to R with Jonathon Cornelissen
• Quick-R
• Getting up to speed with R
Setup
In order to create the graphs in this guide, you’ll need to install
some optional R packages. To install all of
the necessary packages, run the following code in the RStudio
console window.

pkgs <- c("ggplot2", "dplyr", "tidyr",
"mosaicData", "carData",
"VIM", "scales", "treemapify",
"gapminder", "ggmap", "choroplethr",
"choroplethrMaps", "CGPfunctions",
"ggcorrplot", "visreg",
"gcookbook", "forcats",
"survival", "survminer",
"ggalluvial", "ggridges",
"GGally", "superheat",
"waterfalls", "factoextra",
"networkD3", "ggthemes",
"hrbrthemes", "ggpol",
"ggbeeswarm")
install.packages(pkgs)
Alternatively, you can install a given package the first time it is
needed.
For example, if you execute
library(gapminder)
and get the message
Error in library(gapminder) : there is no package called
‘gapminder’
you know that the package has never been installed. Simply
execute
install.packages("gapminder")
once and

library(gapminder)
will work from that point on.
https://cran.r-project.org/
https://www.rstudio.com/products/RStudio/#Desktop
https://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-
Intro.pdf
https://www.datacamp.com/courses/free-introduction-to-r
http://www.statmethods.net
Chapter 1
Data Preparation
Before you can visualize your data, you have to get it into R.
This involves importing the data from an
external source and massaging it into a useful format.
1.1 Importing data
R can import data from almost any source, including text files,
excel spreadsheets, statistical packages, and
database management systems. We’ll illustrate these techniques
using the Salaries dataset, containing the 9
month academic salaries of college professors at a single
institution in 2008-2009.
1.1.1 Text files
The readr package provides functions for importing delimited
text files into R data frames.
library(readr)

# import data from a comma delimited file
Salaries <- read_csv("salaries.csv")
# import data from a tab delimited file
Salaries <- read_tsv("salaries.txt")
These function assume that the first line of data contains the
variable names, values are separated by commas
or tabs respectively, and that missing data are represented by
blanks. For example, the first few lines of the
comma delimited file looks like this.
"rank","discipline","yrs.since.phd","yrs.service","sex","salary"
"Prof","B",19,18,"Male",139750
"Prof","B",20,16,"Male",173200
"AsstProf","B",4,3,"Male",79750
"Prof","B",45,39,"Male",115000
"Prof","B",40,41,"Male",141500
"AssocProf","B",6,6,"Male",97000
Options allow you to alter these assumptions. See the
documentation for more details.
11
https://www.rdocumentation.org/packages/readr/versions/0.1.1/t
opics/read_delim
12 CHAPTER 1. DATA PREPARATION
1.1.2 Excel spreadsheets
The readxl package can import data from Excel workbooks.
Both xls and xlsx formats are supported.

library(readxl)
# import data from an Excel workbook
Salaries <- read_excel("salaries.xlsx", sheet=1)
Since workbooks can have more than one worksheet, you can
specify the one you want with the sheet option.
The default is sheet=1.
1.1.3 Statistical packages
The haven package provides functions for importing data from a
variety of statistical packages.
library(haven)
# import data from Stata
Salaries <- read_dta("salaries.dta")
# import data from SPSS
Salaries <- read_sav("salaries.sav")
# import data from SAS
Salaries <- read_sas("salaries.sas7bdat")
1.1.4 Databases
Importing data from a database requires additional steps and is
beyond the scope of this book. Depending on
the database containing the data, the following packages can
help: RODBC, RMySQL, ROracle, RPostgreSQL,
RSQLite, and RMongo. In the newest versions of RStudio, you
can use the Connections pane to quickly access
the data stored in database management systems.

1.2 Cleaning data
The processes of cleaning your data can be the most time-
consuming part of any data analysis. The most
important steps are considered below. While there are many
approaches, those using the dplyr and tidyr
packages are some of the quickest and easiest to learn.
Package Function Use
dplyr select select variables/columns
dplyr filter select observations/rows
dplyr mutate transform or recode variables
dplyr summarize summarize data
dplyr group_by identify subgroups for further processing
tidyr gather convert wide format dataset to long format
tidyr spread convert long format dataset to wide format
https://db.rstudio.com/rstudio/connections/
1.2. CLEANING DATA 13
Examples in this section will use the starwars dataset from the
dplyr package. The dataset provides
descriptions of 87 characters from the Starwars universe on 13
variables. (I actually prefer StarTrek, but we
work with what we have.)
1.2.1 Selecting variables
The select function allows you to limit your dataset to specified
variables (columns).
library(dplyr)
# keep the variables name, height, and gender

newdata <- select(starwars, name, height, gender)
# keep the variables name and all variables
# between mass and species inclusive
newdata <- select(starwars, name, mass:species)
# keep all variables except birth_year and gender
newdata <- select(starwars, -birth_year, -gender)
1.2.2 Selecting observations
The filter function allows you to limit your dataset to
observations (rows) meeting a specific criteria.
Multiple criteria can be combined with the & (AND) and | (OR)
symbols.
library(dplyr)
# select females
newdata <- filter(starwars,
gender == "female")
# select females that are from Alderaan
newdata <- select(starwars,
gender == "female" &
homeworld == "Alderaan")
# select individuals that are from
# Alderaan, Coruscant, or Endor
homeworld == "Alderaan" |
homeworld == "Coruscant" |
homeworld == "Endor")

# this can be written more succinctly as
homeworld %in% c("Alderaan", "Coruscant", "Endor"))
1.2.3 Creating/Recoding variables
The mutate function allows you to create new variables or
transform existing ones.
library(dplyr)
# convert height in centimeters to inches,
# and mass in kilograms to pounds
newdata <- mutate(starwars,
height = height * 0.394,
mass = mass * 2.205)
The ifelse function (part of base R) can be used for recoding
data. The format is ifelse(test, return
if TRUE, return if FALSE).
library(dplyr)
# if height is greater than 180
# then heightcat = "tall",
# otherwise heightcat = "short"
heightcat = ifelse(height > 180,

"tall",
"short")
# convert any eye color that is not
# black, blue or brown, to other
eye_color = ifelse(eye_color %in% c("black", "blue", "brown"),
eye_color,
"other")
# set heights greater than 200 or
# less than 75 to missing
height = ifelse(height < 75 | height > 200,
NA,
height)
1.2.4 Summarizing data
The summarize function can be used to reduce multiple values
down to a single value (such as a mean). It
is often used in conjunction with the by_group function, to
calculate statistics by group. In the code below,
the na.rm=TRUE option is used to drop missing values before
calculating the means.
library(dplyr)
# calculate mean height and mass
newdata <- summarize(starwars,
mean_ht = mean(height, na.rm=TRUE),
mean_mass = mean(mass, na.rm=TRUE))

newdata
## # A tibble: 1 x 2
## mean_ht mean_mass
## <dbl> <dbl>
## 1 174. 97.3
# calculate mean height and weight by gender
newdata <- group_by(starwars, gender)
newdata <- summarize(newdata,
mean_ht = mean(height, na.rm=TRUE),
mean_wt = mean(mass, na.rm=TRUE))
newdata
## # A tibble: 5 x 3
## gender mean_ht mean_wt
## <chr> <dbl> <dbl>
## 1 female 165. 54.0
## 2 hermaphrodite 175. 1358.
## 3 male 179. 81.0
## 4 none 200. 140.
## 5 <NA> 120. 46.3
1.2.5 Using pipes
Packages like dplyr and tidyr allow you to write your code in a
compact format using the pipe %>% operator.
Here is an example.

library(dplyr)
# calculate the mean height for women by species
newdata <- filter(starwars,
gender == "female")
newdata <- group_by(species)
newdata <- summarize(newdata,
mean_ht = mean(height, na.rm = TRUE))
# this can be written as
newdata <- starwars %>%
filter(gender == "female") %>%
group_by(species) %>%
summarize(mean_ht = mean(height, na.rm = TRUE))
The %>% operator passes the result on the left to the first
parameter of the function on the right.
1.2.6 Reshaping data
Some graphs require the data to be in wide format, while some
graphs require the data to be in long format.
You can convert a wide dataset to a long dataset using
library(tidyr)
long_data <- gather(wide_data,
key="variable",
value="value",
sex:income)

Table 1.2: Wide data
id name sex age income
01 Bill Male 22 55000
02 Bob Male 25 75000
03 Mary Female 18 90000
Table 1.3: Long data
id name variable value
01 Bill sex Male
02 Bob sex Male
03 Mary sex Female
01 Bill age 22
02 Bob age 25
03 Mary age 18
01 Bill income 55000
02 Bob income 75000
03 Mary income 90000
Conversely, you can convert a long dataset to a wide dataset
using
library(tidyr)
wide_data <- spread(long_data, variable, value)
1.2.7 Missing data
Real data are likely to contain missing values. There are three
basic approaches to dealing with missing
data: feature selection, listwise deletion, and imputation. Let’s
see how each applies to the msleep dataset
from the ggplot2 package. The msleep dataset describes the

sleep habits of mammals and contains missing
values on several variables.
1.2.7.1 Feature selection
In feature selection, you delete variables (columns) that contain
too many missing values.
data(msleep, package="ggplot2")
# what is the proportion of missing data for each variable?
pctmiss <- colSums(is.na(msleep))/nrow(msleep)
round(pctmiss, 2)
## name genus vore order conservation
## 0.00 0.00 0.08 0.00 0.35
## sleep_total sleep_rem sleep_cycle awake brainwt
## 0.00 0.27 0.61 0.00 0.33
## bodywt
## 0.00
Sixty-one percent of the sleep_cycle values are missing. You
may decide to drop it.
1.2.7.2 Listwise deletion
Listwise deletion involves deleting observations (rows) that
contain missing values on any of the variables of
interest.
# Create a dataset containing genus, vore, and conservation.
# Delete any rows containing missing data.

newdata <- select(msleep, genus, vore, conservation)
newdata <- na.omit(newdata)
1.2.7.3 Imputation
Imputation involves replacing missing values with “reasonable”
guesses about what the values would have
been if they had not been missing. There are several
approaches, as detailed in such packages as VIM, mice,
Amelia and missForest. Here we will use the kNN function from
the VIM package to replace missing values
with imputed values.
# Impute missing values using the 5 nearest neighbors
library(VIM)
newdata <- kNN(msleep, k=5)
Basically, for each case with a missing value, the k most similar
cases not having a missing value are selected.
If the missing value is numeric, the mean of those k cases is
used as the imputed value. If the missing value
is categorical, the most frequent value from the k cases is used.
The process iterates over cases and variables
until the results converge (become stable). This is a bit of an
oversimplification - see Imputation with R
Package VIM for the actual details.
Important caveate: Missing values can bias the results of studies
(sometimes severely). If you
have a significant amount of missing data, it is probably a good
idea to consult a statistician or
data scientist before deleting cases or imputing missing values.
https://www.jstatsoft.org/article/view/v074i07/v74i07.pdf
https://www.jstatsoft.org/article/view/v074i07/v74i07.pdf

Chapter 2
Introduction to ggplot2
This section provides an brief overview of how the ggplot2
package works. If you are simply seeking code to
make a specific type of graph, feel free to skip this section.
However, the material can help you understand
how the pieces fit together.
2.1 A worked example
The functions in the ggplot2 package build up a graph in layers.
We’ll build a a complex graph by starting
with a simple graph and adding additional elements, one at a
time.
The example uses data from the 1985 Current Population Survey
to explore the relationship between wages
(wage) and experience (expr).
# load data
data(CPS85 , package = "mosaicData")
In building a ggplot2 graph, only the first two functions
described below are required. The other functions
are optional and can appear in any order.
2.1.1 ggplot
The first function in building a graph is the ggplot function. It

specifies the
• data frame containing the data to be plotted
• the mapping of the variables to visual properties of the graph.
The mappings are placed within the
aes function (where aes stands for aesthetics).
# specify dataset and mapping
library(ggplot2)
ggplot(data = CPS85,
mapping = aes(x = exper, y = wage))
Why is the graph empty? We specified that the exper variable
should be mapped to the x-axis and that the
wage should be mapped to the y-axis, but we haven’t yet
specified what we wanted placed on the graph.
19
https://ggplot2.tidyverse.org/
20 CHAPTER 2. INTRODUCTION TO GGPLOT2
0
10
20
30
40

0 20 40
exper
w
a
g
e
Figure 2.1: Map variables
2.1. A WORKED EXAMPLE 21
2.1.2 geoms
Geoms are the geometric objects (points, lines, bars, etc.) that
can be placed on a graph. They are added
using functions that start with geom_. In this example, we’ll
add points using the geom_point function,
creating a scatterplot.
In ggplot2 graphs, functions are chained together using the +
sign to build a final plot.
# add points
ggplot(data = CPS85,
mapping = aes(x = exper, y = wage)) +
geom_point()
0
10

20
30
40
0 20 40
exper
w
a
g
e
The graph indicates that there is an outlier. One individual has a
wage much higher than the rest. We’ll
delete this case before continuing.
# delete outlier
library(dplyr)
plotdata <- filter(CPS85, wage < 40)
# redraw scatterplot
ggplot(data = plotdata,
geom_point()
A number of parameters (options) can be specified in a geom_
function. Options for the geom_point function
include color, size, and alpha. These control the point color,
size, and transparency, respectively. Trans-

0
10
20
0 20 40
exper
w
a
g
e
Figure 2.2: Remove outlier
0
10
20
0 20 40
exper

w
a
g
e
Figure 2.3: Modify point color, transparency, and size
parency ranges from 0 (completely transparent) to 1 (completely
opaque). Adding a degree of transparency
can help visualize overlapping points.
# make points blue, larger, and semi-transparent
geom_point(color = "cornflowerblue",
alpha = .7,
size = 3)
Next, let’s add a line of best fit. We can do this with the
geom_smooth function. Options control the type of
line (linear, quadratic, nonparametric), the thickness of the line,
the line’s color, and the presence or absence
of a confidence interval. Here we request a linear regression
(method = lm) line (where lm stands for linear
model).
# add a line of best fit.
geom_point(color = "cornflowerblue",
alpha = .7,

size = 3) +
geom_smooth(method = "lm")
Wages appears to increase with experience.
0
10
20
0 20 40
exper
w
a
g
e
Figure 2.4: Add line of best fit
2.1.3 grouping
In addition to mapping variables to the x and y axes, variables
can be mapped to the color, shape, size,

transparency, and other visual characteristics of geometric
objects. This allows groups of observations to be
superimposed in a single graph.
Let’s add sex to the plot and represent it by color.
# indicate sex using color
mapping = aes(x = exper,
y = wage,
color = sex)) +
geom_point(alpha = .7,
size = 3) +
geom_smooth(method = "lm",
se = FALSE,
size = 1.5)
0
10
20
0 20 40
exper
w
a
g
e

sex
F
M
The color = sex option is placed in the aes function, because we
are mapping a variable to an aesthetic.
The geom_smooth option (se = FALSE) was added to suppresses
the confidence intervals.
It appears that men tend to make more money than women.
Additionally, there may be a stronger relation-
ship between experience and wages for men than than for
women.
$0
$5
$10
$15
$20
$25
0 10 20 30 40 50
exper

w
a
g
e
sex
F
M
Figure 2.5: Change colors and axis labels
2.1.4 scales
Scales control how variables are mapped to the visual
characteristics of the plot. Scale functions (which start
with scale_) allow you to modify this mapping. In the next pl ot,
we’ll change the x and y axis scaling, and
the colors employed.
# modify the x and y axes and specify the colors to be used
mapping = aes(x = exper,
y = wage,
color = sex)) +
geom_point(alpha = .7,
size = 3) +
geom_smooth(method = "lm",
se = FALSE,
size = …

The Crucial Role of Managerial Accounting in a Dynamic
Business Environment
Chapter 1
McGraw-Hill/Irwin
1-1
Chapter 1: The Crucial Role of Managerial Accounting in a
Dynamic Business Environment
Learning Objective 1-1 – Define managerial accounting and
describe its role in the management process.
1-2
1-2

Learning Objective 1-1. Define managerial accounting and
describe its role in the management process.
Managerial accounting is the process of:
Identifying
Measuring
Analyzing
Interpreting
Communicating information
Managerial Accounting: A Business Partnership with
Management
1-3
Managerial accounting is an integral part of the management
process. It is the process of identifying, measuring, analyzing,
interpreting, and communicating information in pursuit of an
organization’s goals. (LO 1-1)
Learning Objective 1-2 – Explain four fundamental management
processes that help organizations attain their goals.
1-4

Learning Objective 1-2. Explain four fundamental management
processes that help organizations attain their goals.
Managing Resources, Activities, and People
An organization . . .
Organized set
of activities
Planning
Directing
Decision
Making
Controlling
1-5
Acquires Resources: (i.e. funding, patents, and buildings)
Hires People

The owners, directors, or trustees of an organization set its
goals, generally with the help of management. In pursuing its
goals, an organization acquires resources, hires people, and then
engages in an organized set of activities. It is up to the
management team to make the best use of the organization’s
resources, activities, and people in achieving the organization’s
goals. The day-to-day work of the management team comprises
four activities:
Decision making
Planning
Directing operational activities
Controlling
(LO 1-2)
Learning Objective 1-3 – List and describe five objectives of
managerial accounting activity.
1-6

Learning Objective 1-3. List and describe five objectives of
managerial accounting activity.
Objectives of Managerial Accounting Activity
Providing information for decision making and planning.
Assisting managers in directing and controlling activities.
Motivating managers and other employees toward the
organization’s goals.
Measuring performance of subunits, activities, managers, and
other employees within the organization.
Assessing the organization’s competitive position and working
with other managers to ensure the organization’s long-run
competitiveness in its industry.
1-7
Managerial accounting adds value to an organization by
providing information, assisting in directing and controlling
activities, motivating managers and employees towards the
organization’s goals, measuring performance of subunits,
activities, managers, and other employees within the
organization. Managerial accounting also helps in assessing the
organization’s competitive position and working with other
managers to ensure the organization’s long-run competiveness

in its industry. (LO 1-3)
How Managerial Accounting Adds Value to the Organization
Shortly after its IPO, Facebook shares were trading 50% below
their initial share price. Management accountants analyzed the
costs and benefits of different courses of action to help improve
the company’s monetization. By mid-2018, the stock was
trading at five to six times the IPO price.
Though slow to embrace online sales because of their
investment in brick-and-mortar stores, Walmart sees an
opportunity in providing what Amazon cannot with their
physical networks. Management accounting helps identify cost
efficiencies from using Walmart’s massive existing supply
chain to innovatively deliver products.
REI see an opportunity to monetize the Internet in conjunction
with their stores as well. They offer events and skills clinics to
draw consumers in. When services are provided free of charge,
it must be demonstrated that this will lead to increased sales
and net profit. Managerial accounting tools help provide that
insight.
News organizations that were built on print ad revenues are
trying to monetize their online presences in a world where
online content is generally free. Managerial accounting
techniques help to divide costs and allocate revenue among
different channels to better understand profitability.
1-8
Many companies have started to use managerial accounting to
monetize the internet. Monetizing means finding a way to

generate revenues from users in order to make a profit after the
costs of providing the internet service or content. This slide
shows real-world companies using managerial accounting to
increase profits. (LO 1-3)
The Balanced Scorecard
Financial Perspective
Goals Measures
Customer Perspective
Goals Measures
Operations Perspective
Goals Measures
Innovation Perspective
Goals Measures
How do we look to owners?
How do customers see us?
How can we continue to improve and create value?
In which activities must we excel?
1-9
The balanced scorecard is one example of a managerial
accounting tool that is used to assess competitive position and
ensure long-run competiveness. It is a model of business
performance evaluation that balances measures of financial
performance, internal operations, innovation and learning, and
customer satisfaction. If an organization is to remain viable in a
changing and ever more competitive business environment, its
managers need to continually ask the questions emphasized in
the balanced scorecard. (LO 1-3)

Learning Objective 1-4 – Explain the major differences between
managerial and financial accounting.
1-10
Learning Objective 1-4. Explain the major differences between
managerial and financial accounting.
Managerial versus Financial Accounting
Accounting System
(accumulates financial and
managerial accounting data in the cost accounting system)
Managerial Accounting
Information for decision
making, planning, and controlling an organization’s
operations.
Financial Accounting
Published financial
statements and other
financial reports.
Internal
Users
External

Users
1-11
Both managerial and financial accounting information draw
upon data from an organization’s basic accounting system. One
part of that system, the cost accounting system, accumulates
cost data for both managerial and financial accounting.
Managerial accounting information is used for decision making,
planning, directing and controlling an organization's operations,
and assessing its competitive position. It is intended for
managers of all levels within the organization. Financial
accounting information is used to prepare the published
financial statements and other financial reports. It is intended
for external users, such as stockholders, financial analysts,
lenders, unions, consumer groups, and governmental agencies.
(LO 1-4)
Users of Information
1-12

This exhibit summarizes the differences between managerial
and financial accounting. Managerial accounting reports are
intended for managers within the organization and are not
subject to any regulation. The data are drawn from the basic
accounting system as well as other sources. The reports often
focus on departments or subunits and are based on historical
data, estimates, and future projections. Financial accounting
reports are intended for external users and are subject to
regulation. The data are drawn almost completely from the basic
accounting system and are almost exclusively historical. (LO 1-
4)
Learning Objective 1-5 – Describe the accounting and finance
structure in an organization.
1-13
Learning Objective 1-5. Describe the accounting and finance
structure in an organization.
Line and Staff Positions
A line position is directly involved in the provision of goods or
services.

Example: A production supervisor in a manufacturing plant
A staff position supports and assists line positions.
Example: A cost accountant in the manufacturing plant
CEO
1-14
Every manager must have an understanding of basic managerial
accounting concepts and tools. Line positions are directly
involved in providing goods and services while staff positions
support the line positions. Management accountants are staff
positions that need to work closely with line management. (LO
1-5)
Learning Objective 1-6 – Describe the roles of an organization’s
chief financial officer (CFO) or controller, treasurer, and
internal auditor.
1-15

Learning Objective 1-6. Describe the roles of an organization’s
chief financial officer (CFO) or controller, treasurer, and
internal auditor.
CFO or Controller
The chief managerial and financial accountant is responsible
for:
Supervising accounting personnel
Preparation of information and managerial and financial reports
Analysis of accounting information
Planning and decision making
1-16
The chief financial officer, also known as the controller, is the
title given to the top managerial and financial accountant. This
person is responsible for accounting personnel and pr eparing
the information and reports used in both managerial and
financial accounting. The CFO is an integral member of the
management team, often involved in planning and decision
making at all levels and across all functional areas of the
enterprise. (LO 1-6)
Treasurer
Responsible for raising capital and safeguarding the
organization’s assets.
Supervises relationships with financial institutions
Works with investors and potential investors
Manages investments
Establishes credit policies
Manages insurance coverage

1-17
The treasurer typically is responsible for raising capital and
safeguarding the organization’s assets. In addition, the treasurer
manages the organization’s investments, its credit policy, and
its insurance coverage. (LO 1-6)
Internal Auditor
Responsible for reviewing accounting procedures, records, and
reports in both the controller’s and the treasurer’s areas of
responsibility
Expresses an opinion to top management regarding the
effectiveness of the organization’s accounting system and its
system of internal controls
1-18
The internal auditor is responsible for reviewing the accounting
procedures, records, and reports in both the controller’s and the
treasurer’s areas. The internal auditor expresses an opinion to
top management about the effectiveness of the accounting
system. (LO 1-6)
Learning Objective 1-7 – Understand and explain the value

chain concept.
1-19
Learning Objective 1-7. Understand and explain the value chain
concept.
Product
Design
Research
and
Development
Strategic Cost Management and the Value Chain
Securing raw
materials and
other resources
Production
Marketing
Distribution
Customer
Service
Start

1-20
Usually many activities are involved in securing basic raw
materials and turning them into valuable products or services.
The set of linked, value-creating activities, ranging from
securing basic raw materials and energy to the ultimate delivery
of products and services, is called the value chain. Although
there may be only one organization involved in a particular
value chain, usually there are many. This exhibit shows a
typical list of activities that most businesses need to coordinate
in the value chain. (LO 1-7)
Learning Objective 1-8 – Explain how investments in capacity
affect managerial decision making.
1-21
Learning Objective 1-8. Explain how investments in capacity
affect managerial decision making.

Theoretical capacity is the upper limit on the amount of goods
or services if everything works perfectly.
Practical capacity allows for normal occurrences such as cash
register downtime and cashier fatigue or illness.
1-22
Capacity
A key challenge in providing useful information is
understanding and correctly analyzing an organization’s
capacity and the cost of providing capacity. By capacity we
mean the upper limit on the amount of goods or services that an
organization can produce in a specified period of time.
Theoretical capacity is the upper limit on the amount of goods
or services if everything works perfectly.
Practical capacity allows for normal occurrences such as cash
register downtime and cashier fatigue or illness.
Most managers believe that practical capacity is a much more
useful measure of capacity. (LO 1-8)
Questions about Capacity
Important questions for the managerial accounting system to
address are:
What is an organization’s practical capacity?
What are the costs of the resources supplied to provide that

capacity?
How have those resources been used in creating value?
1-23
Important questions for the managerial accounting system to
address are (1) What is an organization’s practical capacity? (2)
What are the costs of the resources supplied to provide that
capacity? and (3) How have those resources been used in
creating value?
Unfortunately, it is not uncommon for managers to mistakenly
act like all the money spent on production should be divided up
over the products or services produced, without considering
whether some of that spending was for resources supplied but
unused. (LO 1-8)
Managing Capacity
Cost of resources supplied
Cost of resources used
Cost of resources unused (unused capacity)
1-24

It is very important to distinguish among the cost of resources
supplied, the cost of resources used, and the cost of resources
unused, which is the same as the cost of unused capacity. An
important task for any management team is to understand and
manage the cost of capacity. Providing them data about the
costs of unused capacity gives them an important tool. (LO 1-8)
Learning Objective 1-9 – Understand and explain big data and
data analytics and how they interact with managerial
accounting.
1-25
Learning Objective 1-9. Understand and explain big data and
data analytics and how they interact with managerial
accounting.
1-26
1-26
Big Data, Data Analytics, and Managerial Accounting

Big Data
Data Analytics
Data Governance
Data Science
How do we even begin to process the massive volume of both
structured and unstructured data available to management today
(Big Data)? Can we assure that such a large amount of data is
accurate, complete, and formatted in such a way that it can be
processed? What tools will help us to tease insights from it?
And how do we best connect those insights to our operations?
The process of making sense of big data and developing true
and helpful insights from it is called data analytics.
One of the primary challenges of big data relates to making sure
that so much data is valid and usable. This is called data
governance. The other key challenge of big data is to analyze
such a large volume of data and draw insights from it that are
useful for decision makers. The process of doing this is called
data science. (LO 1-9)
Learning Objective 1-10 – Discuss the professional
organizations and certifications in the field of managerial

accounting.
1-27
Learning Objective 1-10. Discuss the professional organizations
and certifications in the field of managerial accounting
Managerial Accounting as a Career
Professional Organizations
Institute of Management Accountants (IMA)
Publishes
Management
Accounting
and research
studies.
Administers
Certified
Management
Accountant
(CMA)
program
Develops
Standards of
Ethical
Conduct for
Management

Accountants
1-28
Managerial accountants are providers of information and are
often in touch with the heartbeat of the organization. They
frequently interact with sales personnel, finance specialists,
production people, and managers at all levels. To keep up with
new developments in their field, management accountants often
belong to one or more professional organizations. The largest is
the Institute of Management Accountants. The IMA publishes
journals, administers the Certified Management Accountant
program, and develops ethical standards for its members. A
recent study of managerial accountants practicing in the U.S.A.
showed professionals with a certification earning almost 50%
more in average total compensation than their noncertified
peers. (LO 1-10)
Learning Objective 1-11 – Describe the ethical responsibilities
and ethical standards that apply to managerial accounting.
1-29

Learning Objective 1-11. Describe the ethical responsibilities
and ethical standards that apply to managerial accounting.
Ethical Climate of Business
“Ethical behavior by managers in general, and accountants in
particular, is not a luxury or a discretionary ‘good thing to do.’
It is an absolute necessity for the smooth functioning of the
economy.”
1-30
The stream of corporate scandals has taught us that not only is
unethical behavior in business wrong in a moral sense, but it
also can be disastrous to the business and the economy. There
will most likely be reforms in corporate governance and
accounting. The Sarbanes-Oxley Act is one example. This act,
among other things, requires companies to establish, assess, and
regularly report on their internal controls over financial
reporting. This legislation also created the PCAOB or the Public
Company Accounting Oversight Board to establish auditing
standards and provide for an audit quality review process.

These scandals have served as a wake-up call to focus more on
ethical issues in practice and teaching. (LO 1-11)
Professional Ethics
Competence
Confidentiality
Integrity
Credibility
1-31
IMA Statement of Ethical Professional Practice
As professionals, managerial accountants have an obligation to
themselves, their colleagues, and their organizations to adhere
to high standards of ethical conduct. In recognition of this
obligation, the IMA has developed ethical standards for its
members, who are practitioners of managerial accounting and
financial management. In addition, it includes a section on
resolving ethical conflicts. (LO 1-11)
End of Chapter 1
1-32

Managerial AccountingFinancial Accounting
Users of
Information
Managers, within the organization.
Interested parties, outside the
organization.
RegulationNot required and unregulated, since it
is intended only for management.
Required and must conform to
generally accepted accounting
principles. Regulated by the Financial
Accounting Standards Board, and, to
a lesser degree, the Securities and
Exchange Commission.
Source of
Data
The organization's basic accounting
system, plus various other sources,
such as rates of effective products
manufactured, physical quantities of
material and labor used in production,
occupancy rates in hotels and
hospitals, and average take-off delays.
Almost exclusively drawn from the
organization's basic accounting
system, which accumulates financial
information.
Nature of
Reports and
Procedures

Reports often focus on subunits within
the organization, such as departments,
divisions, geographical regions, or
product lines. Based on a combination
of historical data, estimates, and
projections of future events.
Reports focus on the enterprise in its
entirety. Based almost exclusively on
historical transaction data.
Sheet1Managerial AccountingFinancial AccountingUsers of
InformationManagers, within the organization.Interested
parties, outside the organization.RegulationNot required and
unregulated, since it is intended only for management.Required
and must conform to generally accepted accounting principles.
Regulated by the Financial Accounting Standards Board, and, to
a lesser degree, the Securities and Exchange
Commission.Source of DataThe organization's basic accounting
system, plus various other sources, such as rates of effective
products manufactured, physical quantities of material and labor
used in production, occupancy rates in hotels and hospitals, and
average take-off delays.Almost exclusively drawn from the
organization's basic accounting system, which accumulates
financial information.Nature of Reports and ProceduresReports
often focus on subunits within the organization, such as
departments, divisions, geographical regions, or product lines.
Based on a combination of historical data, estimates, and
projections of future events.Reports focus on the enterprise i n
its entirety. Based almost exclusively on historical transaction
data.
Sheet2
Sheet3

Basic cost management concepts chapter 2mcgraw hillirwin

Basic cost management concepts chapter 2mcgraw hillirwin

Recommended

Recommended

More Related Content

What's hot

What's hot (12)

Similar to Basic cost management concepts chapter 2mcgraw hillirwin

Similar to Basic cost management concepts chapter 2mcgraw hillirwin (20)

More from ssuser454af01

More from ssuser454af01 (20)

Recently uploaded

Recently uploaded (20)

Basic cost management concepts chapter 2mcgraw hillirwin