CS计算机代考程序代写 Chapter 1

Chapter 1

Marketing Analytics

MKAN1-UC 5103 -001, 4-credits

Dongnanzi (Janet) Zheng

Fall 2021 Session 7

Today’s class

• About Midterm

• Visualizing Your Data Effectively

• Discussion on Graphs

• SAS – Title and Footnote Statements

• SAS – Visualizing Data Tasks

• Midterm Q&A

These two weeks – Due 11/3 6pm

• Assignment 5 – our last graded assignment

• Discussion 4 (1% of final grade in Discussion Score
8%)

– Kleih, A.-K., & Sparke, K. (2021). Visual marketing: The
importance and consumer recognition of fruit
brands in supermarket fruit displays. Food Quality
and Preference, 93. https://doi-
org.proxy.library.nyu.edu/10.1016/j.foodqual.2021.10
4263

• Deadline will be in two weeks: 11/3 6:00 pm after
the midterm

https://doi-org.proxy.library.nyu.edu/10.1016/j.foodqual.2021.104263

About Midterm

• Wednesday 10/27 from 6:30pm to 9:30pm (3 hours)

– No makeup exam

• Open book open internet but if you copy/paste from
an online source you will need to cite the reference for
short answer questions (designed for closed-book
exam originally)

• You should be able to complete the exam within 3
hours if you prepare/review all materials in advance.
Please plan your time to do the exam accordingly.

About Midterm

• Submission: same as Homework. You will receive
instructions, questions, answer sheet and necessary
materials/datasets under NYU-Brightspace Assignments
when opened. You are expected to submit your
answer sheet before the deadline under Assignment.

• No in-person exam/session

• Late submissions receive 5% reduction of the total
points for every 0.01-15 minutes of being late.

Last Semester Midterm

• Exam format: Multiple choices, Fill in the blank, Short
answers, and Assignment-like questions

• Exam content: Anything in Slides, Required Readings
and HW till (including) this session(Session 7)

• Any teamwork found will receive 0 points and be
reported to NYU

Last Semester Midterm

Multiple choices:

• Most (tell/select the following statements wrong/right)

• Textbook (concepts understanding) and Readings

• SAS (concepts and rules)

• SAS (key words and statements)

Filling the blank:

– Most (SAS syntax questions)

Last Semester Midterm

Short Answers:

• Slides (concepts understanding)

• SAS tasks

• SAS syntax

• SAS explanation/why questions

• Assignment-like questions: -Writing code

About Midterm

SAS:
• Concepts/rules

• Point and Click Tasks:

– know what each task does

– know the elements inside the tasks

• Syntax:

– How to write simple ones

– Steps and statements

– Explain the syntax

• HW

• Questions asked in class

About Midterm

Textbook:
• Important examples behind concepts in slides

• Understanding

• Readings

Visualizing Your Data
Effectively

Content partially adopted from a presentation by Kim Unger, 2017

Four questions

• 1. What data is important to show?

• 2. What do I want to emphasize in the
data?

• 3. What options do I have for displaying
this data?

• 4. Which option is most effective in
communicating the data?

What do you want to show with your
data?

Time Series Ranking
Part-To-
Whole

Deviation

Distribution Correlation Comparison

Time series
values display how something changed over time

Bar Graph

(vertical)

To feature

individual values

and support their

comparisons.

Quantitative scale

must begin at zero.

Line Graph

To feature overall

trends and

patterns and

support their

comparisons

Dot Plot (vertical)

When you do not

have a value for

every interval of

time

Strip Plot (multiple)

Only when also

featuring

distributions

Box Plot (vertical)

Ranking
values are ordered by size (descending or ascending)

Bar Graphs

Quantitative scale must begin at zero

Dot Plots

Part-to-whole
values represent parts (ratios) of a whole

Bar Graphs

Quantitative scale must begin at zero

What about pie charts?

• Commonly used to show parts of a whole

• However…

➢ Hard to judge relative size of pie slices –
better at differentiating length

➢ Take up a lot of space to present little
information

➢Require labels and good color contrast to
even be usable (often difficult)

Best use is when one overwhelmingly larger value
than the rest – no need to focus on actual values

deviation
difference between two sets of values

Bar Graphs

Quantitative scale must being at zero

Line Graph

Only when also

featuring time series or

single distribution

Distribution
count of values per interval along quantitative scale

Bar Graphs

Quantitative Scale, must begin at

zero

Line Graph

To feature overall

shape of

distribution

Box Plots

When Comparing Multiple

Distributions

Strip Plot (single)

When you want

to see each value
Strip Plot

(multiple)

When comparing

multiple

distributions AND

you want to see

each value

Correlation
Comparison of two paired sets of values to determine if

there is a relationship between them

Scatter Plot

Normal comparison
simple comparison of values for a set of ordered items

Bar Graphs

Quantitative scale must begin at zero
Dot Plots

Other
visualizations
a picture is worth a

thousand words

Schematics

Illustrations

Flow Charts

Explain how

experiment was

conducted or

design concepts

for engineering

project

Tables

Photographs

Raw data or

statistical

summaries in well-

organized manner.

Convey important

details.
Great to show

experimental

setup, or examples

of actual results

Adhere to data presentation standards
in your field

• Judged by those often familiar with research field

• Expected presentations of data in that field

• Review scientific articles – how is data presented?

➢ Are there graphs?

➢ What kind?

➢ What statistics are used?

➢ Review schematics – are there specific icons?

➢ Does the journal have a style guide?

Visual best
practices

most important
data

Empha
size

graph/table
Organi

overloading graphsAvoid

# of colors and
shapes

Limit

through important
text

Inform

Discussion on Graphs

Discussion 1

Broman, Karl, “The top ten worst graphs”
https://www.biostat.wisc.edu/~kbroman/topten_worstgr
aphs/

Why they don’t look good?

• Confusing, misleading

• Don’t provide information from the graphs

• Not necessary, show stats # is better

• Other ways of presenting are better

• Give wrong information

https://www.biostat.wisc.edu/~kbroman/topten_worstgraphs/

Discussion 1

Question: good or bad? Why?

Discussion 1

Question: good or bad? Why?

• Two Y-Axes are misleading. People need to figure out
the line with its corresponding y-axis.

• Hard to see the data from the graph according to
“14,000 people becoming uninsured every day”

• The trends of two lines look similar, however the graph
is wrong. $ and % scale are different. Uninsured people
increase slightly (transfer $ to %), compared with more
rapidly growing unemployment rate

Discussion 2

Kaiser Fung, “Junk Charts trifecta checkup: The definitive
guide” https://junkcharts.typepad.com/junk_charts/junk-
charts-trifecta-checkup-the-definitive-guide.html

What are the three key questions?

https://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html

Discussion 2

It is a fine case of using a great design to reveal interesting
spatial patterns in a set of good data that addresses an
interesting question.

Discussion 2

Arctic Ice Death
Spiral concerns an
existential problem,
and as far as I know,
the data are
standard fare but the
chart fails because of
the spiral design.

Discussion 2

Question: What are the characteristics of good graphs?

• Clean and clear

• Right and good amount of information

• Help better understand data/patterns (better than
only #)

SAS – Visualizing Data

• Graph Tasks

– Bar Chart

– Bar-Line Chart

– Box Plot

– Bubble Plot

– Heat Map

– Histogram

– Line Chart

– Mosaic Plot

– Pie Chart

– Scatter Plot

– Series Plot

Types of Graphs

The graphs are displayed

under Tasks  Graph.

Graphs: Task Roles

Each graph has roles to fill in specific to that task.

Graphs: Task Options

On the OPTIONS tab, each graph task has options that

enable you to customize the graph.

Graphs: Task Appearance

On the APPEARANCE tab, each graph task enables you to

customize the appearance of the graph.

Bar Chart Task
• The Bar Chart task creates

horizontal/vertical bar charts

that compare numeric values

or statistics between

different values of a chart

variable.

• Bar charts show the relative

magnitude of data by

displaying bars of varying

height. Each bar represents

a category of data.

Bar Chart Task – Example 1

• In the Tasks section, expand the Graph folder, and

then double-click Bar Chart. The user interface for the

Bar Chart task opens.

• On the Data tab, select

the SASHELP.PRICEDATA data set.

• Assign regionName to the Category role.

• Assign productLine to the Subcategory role.

• From the Measure drop-down list, select Variable.

Assign sale to the Variable role. From

the Statistic drop-down list, select Sum (default).

• Run the task

Bar Chart Task – Example 1

How do you describe?

Bar Chart Task – Example 2

• In the Tasks section, expand the Graph folder, and

then double-click Bar Chart. The user interface for the

Bar Chart task opens.

• On the Data tab, select

the SASHELP.PRICEDATA data set.

• Assign regionName to the Category role.

• Assign productLine to the Subcategory role.

• From the Measure drop-down list, select Frequency

count (default).

• Run the task

Bar Chart Task – Example 2

Bar-Line Chart Task
The Bar-Line Chart task creates

a vertical bar chart with a line

chart overlay.

You can use the Bar-Line Chart

task to perform these tasks:

• display and compare exact

and relative magnitudes

• examine the contribution of

each part to the whole

• determine trends and

patterns in the data

Bar-Line Chart Task – Example

• In the Tasks section, expand the Graph folder, and

then double-click Bar-Line Chart. The user interface

for the Bar-Line Chart task opens.

• On the Data tab, select the SASHELP.CARS data set.

• Assign columns to these roles:

• Then run the task

Bar-Line Chart Task – Example

How do you describe?

Box Plot Task – Example

How do you describe?

Bubble Plot Task
• The Bubble Plot task

explores the relationship

between three or more

variables.

• In a bubble plot, two

variables determine the

location of the bubble

centers, and a third variable

specifies the size of each

bubble.

• A fourth variable can be

used to determine the colors

of the bubbles.

Bubble Plot Task – Example

• In the Tasks section, expand the Graph folder, and

then double-click Bubble Plot. The user interface for

the Bubble Plot task opens.

• On the Data tab, select the SASHELP.CLASS data

set.

• Assign columns to these roles:

• Then run the task

Bubble Plot Task – Example

How do you describe?

Heat Map Task
The Heat Map task displays the

magnitude of the response

based on two variables. The

response is represented as a

color value from a color

gradient.

Heat Map Task – Example

• In the Tasks and Utilities section, expand

the Graph folder, and then double-click Heat Map. The

user interface for the Heat Map task opens.

• On the Data tab, select the SASHELP.HEART data

set.

• Assign columns to these roles:

• Then run the task

Heat Map Task – Example

How do you describe?

Line Chart Task
The Line Chart task shows the

mathematical relationships

between variables by revealing

trends or patterns of data

points.

Line Chart Task – Example

• In the Tasks section, expand the Graph folder, and

then double-click Line Chart. The user interface for the

Line Chart task opens.

• On the Data tab, select the SASHELP.CARS data set.

• Assign columns to these roles:

• From the Measure drop-down list, select Variable.

Assign MPG_City to the Variable role. From

the Statistic drop-down list, select Mean.

• Then run the task

Line Chart Task – Example

How do you describe?

Mosaic Plot Task
• Mosaic plots display tiles that

correspond to the

crosstabulation table cells.

• The areas of the tiles are

proportional to the frequencies

of the table cells.

• The column variable is

displayed on the X axis, and the

tile widths are proportional to

the relative frequencies of the

column variable levels.

• The row variable is displayed on

the Y axis, and the tile heights

are proportional to the relative

frequencies of the row levels

within column levels.

Mosaic Plot Task – Example

• In the Tasks section, expand the Graph folder, and

then double-click Mosaic Plot. The user interface for

the Mosaic Plot task opens.

• On the Data tab, select the SASHELP.CARS data set.

• Assign columns to these roles:

• Then run the task

Mosaic Plot Task – Example

How do you describe?

Pie Chart Task

• The Pie Chart task creates pie charts that represent

the relative contribution of the parts to the whole by

displaying data as wedge-shaped “slices” of a circle.

• Each slice represents a category of data. The size of a

slice represents the contribution of the data to the total

chart statistic.

Pie Chart Task – Example

• In the Tasks section, expand the Graph folder, and

then double-click Pie Chart. The user interface for the

Pie Chart task opens.

• On the Data tab, select the SASHELP.CARS data set.

• Assign Origin to the Category role.

• From the Measure drop-down list, select Variable.

Assign MSRP to the Variable role. From

the Statistic drop-down list, select Sum (default).

• Run the task.

Pie Chart Task – Example

How do you describe?

Series Plot Task

The Series Plot task

creates plots that display a

series of line segments

that connect observations

of input data.

Series Plot Task – Example

• In the Tasks section, expand the Graph folder, and

then double-click Series Plot. The user interface for

the Series Plot task opens.

• On the Data tab, select the SASHELP.STOCKS data

set.

• Assign columns to these roles:

• Then run the task

Series Plot Task – Example

How do you describe?

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