Download Basic Statistical Terms, Concepts, and Methods

What Statistical Knowledge Serves as a
Foundation for a Business Analytics Course?
Abstract: This session examines the statistical principles and
techniques that would prepare students to take a business
analytics course. Presenters seek, through discussion with
attendees, to formulate the required knowledge as a series of
review questions that could help students prepare for such a
course.
DSI 2016 DASI Session, Saturday, November 19, 2016, 4:30-6 PM,
as presented on that date, with special formatting removed
What Statistical Knowledge Serves as a
Foundation for a Business Analytics Course?
David Levine,
30+ years teaching business statistics and writing statistics textbooks
David Stephan
20+ years teaching CIS, 40+ years facilitating IT use,
30+ designing instructional tools
Kathy Szabat,
30+ years teaching business statistics,
5 years developing business analytics courses
DSI 2016 DASI Session, Saturday, November 19, 2016, 4:30-6 PM
Why That Question?
• Rise in Business Analytics
• Writing a book
• Developing a course and curriculum
Define Business Analytics
• Differentiated from Data Science and Data Analytics
• Not just advanced OR/MS, but elements of Statistics and
Information Systems
Define Business Analytics
“Business analytics are the methods that combine advances
in statistics, operations research and management science,
and information technology to reveal meaningful information
from data….Business analytics forms a basis for fact-based
management decision-making.”
Determining the Foundational Knowledge
• What concepts and methods are necessary to knowingly use
business analytics?
• Which concepts and knowledge from an introductory
statistics course would you want students to remember
several years later?
Issues Discussing the Foundational
Knowledge
• Range of student preparations
AP Statistics, Business Statistics, some general statistics; one-semester, twosemester
• What might an introductory business statistics course omit
that is important to understanding business analytics?
• Calendar tensions
Must avoid re-teaching introductory statistics
Presenting the Foundational Knowledge
• How much time to spend?
• What needs to be taught as new material?
• What should be the method/format of presentation?
Presenting the Foundational Knowledge
Using a Q&A format to present
Foundational Knowledge
• Sequencing issue: what question comes first?
• What concepts do not lend themselves to questions?
Our solution: Three Units, 2 of which are
Q&As
• Q&A: Basic Statistical Terms, Concepts, and Methods
• Q&A: Statistical Inference Basics
• Review Chapter: Linear Regression Basics
TBD: The amount of “practical skills” reviewed for these topics.
Basic Statistical Terms, Concepts, and Methods
1.1 What’s “statistics” and why should I bother studying
statistics?
Statistics is one of the tools of a modern information system.
Businesses use information systems to generate useful
information for management decision-making. Statistics provides
the means to analyze data that may affect decision-making. In an
information system, statistics has the unique ability to organize
and summarize large volumes of data and to demonstrate
relationships among related data.
Basic Statistical Terms, Concepts, and Methods
1.2 What is data??
Data are the facts that a business collects or generates as part of
its ongoing activities as well as facts that a business may collect
from other sources to facilitate decision-making.
Basic Statistical Terms, Concepts, and Methods
1.3 What’s the distinction meant by using “business statistics”
instead of “statistics?”
Business statistics emphasizes the application of statistics to
management decision making. Using business statistics always
begins with the statement of an organizational problem or goal
that fact-based, rational decision-making can help solve or
achieve....Business statistics users need to understand the logic
behind the statistical methods they use…(and) need to know the
implications and assumptions about the data they use.
Basic Statistical Terms, Concepts, and Methods
1.4 What kinds of data do statistical methods use?
Statistical methods use data that are the results of a counting,
measuring, or classifying activity. Counting things leads to discrete
counts…
Measuring things leads to continuous values that may be any
value within an interval…
Classifying activities results in assigning categories to something….
Statistical Inference Basics
2.7 What is sampling error? Why is it important?
Sample statistics almost always vary from sample to sample. This
expected variation is called the sampling error. The size of the
sampling error is primarily based on the variation in the
population itself and on the size of the sample selected. Larger
samples will have less sampling error….
Statistical Inference Basics
2.8 What is a confidence interval?
A confidence interval estimate is a range of numbers, called an
interval, constructed around a point estimate, the value that
represents the “best guess” of a particular sample statistic. One
constructs confidence intervals in a way that ensures that the
probability that the interval includes the population parameter
will be known….
Statistical Inference Basics
2.15 How do you perform hypothesis testing using the
value approach?
p-
Most modern statistical and spreadsheet software, including the
functions found in spreadsheet programs, can calculate the
probability value known as the p-value that you can use to
determine whether to reject the null hypothesis.
Statistical Inference Basics
2.16 More specifically, what is a p-value?
The p-value is the probability of computing a test statistic equal to
or more extreme than the sample results, given that the null
hypothesis H0 is true. The p-value is the smallest level at which H0
can be rejected for a given set of data. The p-value represents the
actual risk of having a Type I error for a given set of data.
(Type I error is defined in an earlier Q&A.)
Our solution:
Regression Basics Chapter Topics
• Simple linear regression models
• Evaluating goodness of fit
• Nonlinear relationships
• Assumptions in regression
• Residual analysis
• Inferences in regression
• Pitfalls in regression
• Multiple regression models
Mapping the Foundational Knowledge to
Business Analytics/Justifying Choices
• Foundational knowledge is the knowledge that unlocks
understanding.
• Prediction/predictive: How can one understand “predictive
analytics” if one does not understand what “predict” means in
this sense.
• With foundational knowledge can answer such insight
questions as “Can predictive models be wrong?”
Further Considerations
If the Business Analytics course follows as a second course
after Business Statistics, what foundational concepts from
other disciplines need to be reviewed/discussed?
“Other Discipline” Example
A “Data” concepts component?
(Come back tomorrow to further consider this.)
Thank You From the Two Davids and
Kathy!
Contact us at:
[email protected]
2016 DSI DASI session, Saturday, November 19, 2016, 4:30-6 PM