Welcome to Business
Statistics Made Easy in SAS by Professor Gregory
John Lee. This book is a breakthrough in business statistics learning,
with a fresh new approach to explaining and teaching the exciting
area of data analysis.
This book is designed
as a user-friendly, practice-oriented text to teach businesspeople,
students and others core statistics concepts and applications.
Business
Statistics Made Easy in SAS steers away from complex
mathematical-based explanations, and also avoids basing the explanations
on traditional concepts such as distributions, probability theory
and the like, which tend to lose the practice-oriented reader.
Instead of these traditional
approaches, this book employs many features that have proved successful
in a great number of MBA and other classes, some of which are completely
innovative. These features include the following:
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Unique
templates for understanding statistics: There
are several chapters that present a process template overview of statistical
thinking, in other words, they attempt to show the reader how the
general process of statistical thinking works. These chapters are
a completely fresh way to teach people how statistics works as a whole.
Chapter 2 gives a process overview of statistics in general; Chapter
11, an overview of statistical analysis as a process; and Chapter
12, an overview of how to analyze a given statistic after it is generated.
These chapters are mostly innovative, and enable readers to grasp
the statistical method as a whole rather than learning this in the
traditional way, in which a person is expected to learn individual
techniques and concepts, and then piece them together to form the
bigger concept of the statistical method.
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Extrapolation
of statistics to business strategy, financial impact, and problem
solving: I believe that one of the weaknesses
of traditional statistics texts, from the point of view of the average
business reader, is the lack of application beyond the statistics
itself. I attempt to overcome this through two completely innovative
chapters that take statistical outcomes and extrapolate findings to
much broader implications, such as the profitability of business cases.
This allows readers to answer the question “Why should I care?”
that so often plagues statistics courses. I also take this approach
extensively in practice questions.
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Binding
case: At the beginning, in Chapter 1, I build
a central, illustrative statistical case that most of the rest of
the text builds on and uses throughout for illustration. This helps
to focus the discussion and ground readers in a well-understood context.
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Practicality
through SAS® Studio or SAS®9: The core
idea behind the text is to focus on practical implementation through
a specific statistics package, in this case SAS. The book has been
written to work well for both the exciting new SAS Studio as well
as SAS 9 (specifically I use SAS 9.4 or SAS 9.3). Extensive screenshots
of each major step are provided to guide the user carefully through
using the package, and the data and prewritten code used for every
example are given with the textbook, which the reader can simply open
and run to get the output. The reader can then usually change dataset
and variable names to run the same programs on other data situations.
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Non-technical
exposition: There is very little mathematical
development, aside from some limited development in appendices for
the interested reader. The text is developed verbally in a logical
and clear way, often using metaphorical analogies that help the reader
to connect the statistics concepts to life examples with which he
or she is familiar. I avoid starting off with hard-to-understand terminology;
instead I ease the reader into it from the perspective of what the
terminology is really trying to achieve.
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Pictorial
and metaphorical explanations: There is extensive
use of unique pictorial and metaphorical explanations – not
just diagrams but various different figures that help to explain the
concepts. This proves exceptionally useful for teaching the less technical
user, and adds completely new pedagogical features to many difficult
sections, such as the one on the concept of power. I also include
decision-making flowcharts for some techniques that have been hugely
popular with readers.
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Case
vignettes: I also have other illustrative cases
of actual statistics applications in real life to help readers see
the applicability of the techniques.
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Practice
questions and datasets: I include a large number
of practice questions and datasets. I also run online assessments
that readers can take to self-assess or instructors might use to assess;
these may be adjusted for the text.
There are many several analysis and statistics programs
available, each with substantial merit, including but certainly not
limited to SAS, SPSS, STATA, EViews, R, STATISTICA, and NCSS. Different
disciplines tend to develop their own favorites. This book specifically
uses SAS because I believe it to have enormous and world-leading merit.
A Note on Learning Statistics through SAS Programming
There are two predominant
ways to run SAS:
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You can use various point-and-click
windows to perform tasks in SAS. This is relatively simple to use,
and favored by most non-technical people. If you were using the point-and-click
options, you could, for instance, open and use SAS Enterprise Guide
instead of programming in SAS.
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SAS – notably in Base SAS®
- often uses programming code to input keywords that tell it what
dataset to analyze, which variables to analyze, and what statistical
analysis to do on these variables.
While point-and-click
options are easier at first and more popular, there are several reasons
why I base this book on the programming code of SAS:
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Point-and-click
is very limiting: All point-and-click programs,
from SAS Enterprise Guide to SPSS, are very limited in what they have
been programmed to do, with a large amount of the statistical universe
either left out altogether or constrained to simple versions. While
this may seem alright to a beginner for whom the starter topics are
well covered in point-and-click programs, as soon as you or your organizational
colleagues start to get more advanced, the limited versions become
constricting and problematic. The simple fact is that users with expanded
needs often then have to start accessing the programming back-ends
of such programs anyway! While this may change in the future, starting
your journey in SAS with easy-to-use programming code that has been
written for you is the best way to make sure that later you are ready
to go further on your own.
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Programming
is efficient: The programming code input method
is very efficient and advantageous. It is far quicker than using point-and-click
and takes far less memory, and also you can save programming code
for later use. Finally, point-and-click takes a lot of time to go
through if you are in a classroom teaching situation, whereas opening
and running a programming code file is quick.
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Saving
and re-using programming code: You can save the
keywords you used in a programming code file and re-use them time
and time again (see for instance the programming code files in the
“SAS Code” folder). Generally, once you have the keywords
you like to use, the only thing you have to do is change the names
of the variables.
This book mostly uses
programming code. Because of the advantages of programming code, it
is this type of input method I will mostly use and teach in this book.
You will not have to learn what keywords to use, I will give you programming
code files (see the “Textbook SAS Materials” folder),
and each time we run an analysis you will be directed to open and
run a pre-existing file.
If these reasons for
using the programming-based SAS do not seem as attractive as using
point-and-click, then consider looking at SAS Enterprise Guide as
an alternative option.
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Microsoft
PowerPoint slides: Well-formulated and animated
slides are provided for each chapter.
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Textbook
datasets and code files and programs: The textbook
comes with pre-prepared datasets and program files. Each chapter is
accompanied with prewritten code files which the reader can simply
open and run to get the output. The reader can then usually change
dataset and variable names to run the same programs on other data
situations. In addition, there are two type of programs provided:
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Bespoke programs and macros written for the
book: Some of the code files provided are an exciting
set of programs written exclusively by myself to help readers get
directly to the types of outputs you really want. For instance, in
the regression chapters I provide macros in which you have to put
only the names of the dataset and variables and a few other details
to get advanced, formatted regression outputs (including nifty additions
like p-value stars), robust regression including comparisons to the
normal model, and bootstrapped regressions including again comparisons
to the normal model.
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Usual SAS programs in
which the reader can simply manipulate the dataset and variable names.
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Extensive
exercise and exam questions: There is a large
bank of extensive and mixed-format questions for most chapters, including
multiple choice, theory questions, longer and shorter questions based
on SAS outputs, and longer or shorter questions based on datasets
and instructions for SAS analysis.
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Additional
online chapters: There are several extra chapters
that are not printed with the book but that are available online with
the book materials, including advanced topics on regression (hierarchical
regression, mediation, moderation, and non-linear regression).