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previous -- view my teaching portfolio here

Business Analytics II -- BUS 310

Course Descriptions (from the University):  This course introduces the concepts of modeling relationships contained in data and the use of linear models to make predictions in business. Topics include estimation, hypotheses testing, statistical inference, analysis of variance and linear regression techniques. The course also introduces students to fundamentals of linear programming to solve optimization problems in business.   Lecture: 3 hours

Prerequisites:  Essential and expected knowledge: Proficiency in elementary algebra and geometry. Familiarity with recent versions of MS Word, PowerPoint, and Excel. Deficiencies in any of these areas should be self-remediated.  And completion of BUS 210 with a grade of C or higher.

Syllabus -- in Word format
           

Important Dates (traditional section)
Exam I a/b Data a/b Part I Data -- Monday, July 8th; key/key/key/key/key/study guide/formula sheet
Exam II a/b Part 1 Data-- Friday, July 19th; key/key/ key/study guide
Final Exam a/b/ DataA/DataB Part I/Data -- Friday, July 26th 7:00 p.m. key/key/key/key/key/study guide/formula sheet
 

see the syllabus for a more detailed calendar
 

Email List

via Blackboard

Announcements:

Business Office:
My GMU voicemail:
My GMU email: bmccall6@gmu.edu
Office Hours: After class, and Friday 4-7

Problem Sets

Homework #1
Homework #1 -- Data
Homework #3 -- Data
Homework #4 -- Data
Homework #5 -- Data
Homework #6 -- Data
Homework #7 -- Data
Homework #8 -- Data
Homework #9 -- Data
Homework #10 -- Data
Homework #11 -- Data
Homework #12 -- Data

Projects

Case Study #1 -- Data -- Rubric
Case Study #2 -- Data -- Rubric
Case Study #3 -- Rubric
Case Study #4 -- Data -- Rubric
Case Study #5 -- Data -- Rubric

Notes

6/24 N E	6/26 N E	6/28 N E
7/1 N E	7/3 N E
	7/10 N E	7/12 N E
7/15 N E	7/17 N
7/22 N E	7/24

Answer Keys

Quiz #1 Data -- key/key
Quiz #2 Data -- key/key
Quiz #3/Data -- key/key

 

Links!

Excel Tutorials
Statistics Tutorials
Linear Programming Videos
Fall 2017
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Fall 2018
Spring 2019