201q_syllabus.pdf

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QBA201 Quantitative Business Analysis

(3 credits)

COURSE SYLLABUS

COURSE DESCRIPTIONFROM CATALOG

An applications-oriented course that covers descriptive and inferential statistics. Introduces students to the use of statistical software. Includes the following topics: descriptive statistics, probability distributions, estimation and hypothesis testing, correlation, and simple and multiple linear regression.

COURSE OBJECTIVES

The objectives of this course are to introduce the students to the basic and advanced statistical concepts and methods of decision making, develop and enhance qualitative and quantitative skills, foster critical thinking through identifying and applying the appropriate statistical methodologies to the decision-making process, and develop information processing skills that contribute to the analysis of data using statistical software packages. QBA 201 is a prerequisite for QBA 202, FIN 201, MGT 310, MKT 302, MIS 302, and ECO 351. To prepare students for these courses, QBA 201 introduces the topic of descriptive statistics as preparation for similar concepts that are important in FIN 201, MKT 302, and ECO 351. The general framework of probabilities is presented for understanding the role of uncertainty in business decision making, discussed in courses such as QBA 202, FIN 201, MKT 302, and MKT 302. The importance and application of statistical distributions, hypothesis testing and the application of regression analysis are treated in the course as they are important concepts utilized in courses such QBA 201, FIN 201, MKT 302, and ECO 351 where the building and testing of statistical models are important. Additionally, students will learn how to explain mathematical and statistical concepts and interpret the results of the calculations that they conduct with relevant formula. This skill is vital for numerous business courses, including courses beyond those for which QBA 201 is a prerequisite.

PRE/CO-REQUISITE(S)

MTH 101

LEARNING OUTCOMES

Upon completion of the course, students will be able to:

Apply basic and advanced statistical concepts and methods of decision making 1.

Apply both quantitative and qualitative analysis to business problems 2.

Show critical thinking through identifying and applying appropriate statistical methodologies to the decision-making process

3.

Demonstrate information processing skills that contribute to the analysis of data using statistical software packages 4.

REQUIRED TEXTBOOK

Keller, G. (2012). Managerial Statistics (9th Edition). South-Western Cengage Learning, 5191 Natorp Boulevard, Mason, OH 45040, USA.

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I verify that I have reviewed and approved this syllabus for use in QBA201 course.

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A Faculty Name Michael Malcolm

B Term/ Year Summer 2013

C Sections

CRN Course Days Time Location

30375 QBA 201‐01 UMTWR 11:00 – 12:15 SBM009

 Location subject to change

D Instructor

Information

Instructor Office Telephone Email

Michael Malcolm SBM 2120 515‐2592 [email protected]

Office Hours:

 UTMWR: 1:30‐2:30, MW: 5:00‐6:00

 Or by appointment

 Office Hours will be posted on the office door as well as on iLearn.

E Other

Instructional

Material and

Resources

My lecture notes will comprise the main material for the course. An electronic copy is posted to iLearn, and you can also purchase a hard copy at the copy center. Practice problems and old exams are also posted to iLearn. These will be useful as you study and review the material.

Email and iLearn are the main ways in which I will communicate with you, including to transmit course materials. You are responsible for checking your email at least daily.

F Teaching and

Learning

Methodologies

The primary method of content delivery is lecture. Lectures are generally informal, and I invite your participation and questions. It is important to interject if something is unclear or if I am moving too quickly.

Overall, this is a skills‐based course and not a content‐based course. That is, the emphasis is not on memorizing a large body of facts, but on understanding and applying core principles to a variety of different settings. I am more interested in developing your critical thinking skills and analytical problem solving ability than I am in recall of facts.

It is also important for you to review the material regularly as it is taught in class. Ideas are cumulative, and so understanding each topic is critical to making progress. You simply cannot fall behind and catch up the night before the exam.

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G Grading Scale,

Grading

Distribution, and

Due Dates

Grading Scale

[95 , 100] 4.0 A [77 , 80) 2.3 C+

[90 , 95) 3.7 A‐ [73 , 77) 2.0 C

[87 , 90) 3.3 B+ [70 , 73) 1.7 C‐

[83 , 87) 3.0 B [60 , 70) 1.0 D

[80 , 83) 2.7 B‐ Less Than 60 0 F

Grading Distribution

Assessment Weight Date

Attendance 10% Every class

Midterm 1 20% June 20

Midterm 2 20% July 4

Midterm 3 20% July 18

Final Exam 30% July 23 (2:00‐4:00)

Total 100%

H Explanation of

Assessments

 Attendance is taken at the beginning of each class. Students who arrive on time receive 1 point. Students who arrive late receive 1/2 point and students who are absent receive 0 points. You have four “free” points to use across the semester, which is designed to account for emergencies. There are no excused absences for any reason whatsoever.

 The midterms and the final exam consist of problem‐solving exercises related to the material covered in class.

I Student

Academic

Integrity Code

Statement

Students are advised that violations of the Student Academic Integrity Code will be treated seriously and can lead to suspension or dismissal from the university. A notation of the academic integrity code violation can become part of the student’s permanent record.

Academic violations include but are not limited to:

 Plagiarism

 Inappropriate Collaboration

 Inappropriate Proxy

 Dishonesty in Examinations and Submitted Work

 Work Completed for One Course and Submitted to Another

 Deliberate Falsification of Data

 Interference with Other Students’ Work

 Copyright Violations

 Complicity in Academic Dishonesty

Students MUST read the Student Academic Integrity Code outlined in the AUS Catalog and

agree to abide by the standards for academic conduct, students’ rights and responsibilities and

procedures for handling allegations of academic dishonesty.

J Student

Responsibilities/

Behavioral

Expectations

 There are no excused absences for any reason. Your four free attendance points are designed to account for emergencies that may arise over the course of the semester.

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 You may not use your mobile phone or your laptop in class. This includes text messaging.

 You may be withdrawn from the course if you are absent for more than 15% of the class

meetings.

K Student –

Instructor

Interaction /

Feedback

Students are encouraged to contact the instructor during office hours or via email. The instructor is willing and able to provide additional tutorials to a student who is struggling with the class material. In order to gather feedback for future course improvements, suggestions, comments and concerns regarding any aspect of this course will gladly be accepted, at any time during the semester. In addition, a formal course evaluation will be administered at the end of the semester.

SCHEDULE

DATE Topic Text

June 11 Describing data Chapters 1‐5

June 16 Probability Chapter 6

June 20 Midterm 1 Chapters 1‐6

June 23 Discrete distributions Chapter 7

June 26 Continuous distributions Chapter 8

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July 2 Sampling distributions and estimation Chapters 9‐10

July 3 Sampling distributions and estimation Chapters 9‐10

July 4 Midterm 2 Chapters 7‐10

July 7 Hypothesis testing and confidence intervals Chapters 11‐13

July 15 Single‐variable regression Chapter 16

July 16 Single ‐variable regression Chapter 16

July 17 Single‐variable regression Chapter 16

July 18 Midterm 3 Chapters 11‐13, 16

July 21 Multiple regression Chapter 17

July 22 Multiple regression Chapter 17

July 23 Final exam (12:00‐2:00) Cumulative