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64005-001 Guiffrida

M&IS 64005 Section 001 Statistics for Management

Monday and Wednesday 9:15 AM – 10:30 AM

 Room BSA A325

Graduate School of Business Administration

Kent State University

Fall 2015

Instructor       

Dr. Alfred L. Guiffrida

Office:               A-411 Business Administration Building

Office Hours:    Monday 10:30 AM – 11:00 AM; 1:00 PM – 2:00 PM

                          Wednesday 10:30 AM – noon

                          Additional hours available by advance appointment

Telephone:        (716) 954-3504 (do not send texts)

E-mail:              aguiffri@kent.edu

 

Course Objective

The objective of this course is to introduce the student to the basic concepts and techniques of statistics. Upon the completion of the course the student will have learned the basic concepts and techniques of statistical inference and learned how to apply these concepts and techniques to real world situations. These skills will prepare you for more advanced work in your academic major or on the job. 

 

Learning Objectives

After completing this course the student will be:

  1. Able to solve probability problems and problems in statistical distributions including sampling distributions
  2. Able to solve confidence interval problems
  3. Able to solve hypothesis test problems
  4. Able to solve regression and analysis of variance problems
  5. Able to perform statistical analyses using statistical software

Class Materials

i) Lecture Notes in Statistics by Dr. A. L. Guiffrida (provided by instructor to students free of charge in electronic form via Blackboard)

ii) Microsoft Excel (available free of charge in student computer lab)

iii) JMP computer software (available free of charge to all Kent students)

iv) Readings on statistical applications (provided by provided by instructor to students free of charge in electronic form via Blackboard)

v) Sample problems sets by Dr. A. L. Guiffrida (provided by instructor to students free of charge in electronic form via Blackboard)

 

Course Prerequisites and Enrollment Requirements

Prerequisites:

i) MBA student or graduate school standing in the University

ii) Working knowledge of Excel spreadsheets

(Please contact the instructor if you have any concerns regarding the course prerequisites)

 

Enrollment: Students have responsibility to ensure they are properly enrolled in classes.  You are advised to review your official class schedule (using Student Tools on FlashLine) during the first two weeks of the semester to ensure you are properly enrolled in this class and section. Should you find an error in your class schedule, you have until Sunday, September 6, 2015 to correct the error.  If registration errors are not corrected by this date and you continue to attend and participate in classes for which you are not officially enrolled, you are advised now that you will not receive a grade at the conclusion of the semester for any class in which you are not properly registered.

 

Course Withdrawal

For Fall 2015, the course withdrawal deadline is Sunday, November 8, 2015.

 

Grading Policy                         

Evaluation

Weight

Date

Exam I (take home)

30%

Exam distributed Oct 12, 2015

Exam due Oct 19, 2015

Exam II (take home final exam)

40%

Exam distributed Nov 30, 2015

Exam due Dec 15. 2015

Quizzes

30%

Six quizzes

 

Your overall score (OS) for the course is determined by the following equation:

OS = 0.30(Exam I score) + 0.40(Exam II score) + 0.30 (average of quiz scores)

     

Your letter grade for the course will be assigned based on the following scale

 

            OS             Letter Grade           OS          Letter Grade

            93-100             A                     77-79           C+

            90-92               A-                    72-76           C

            87-89               B+                    68-71          C-

            83-86               B                      60-67           D

            80-82               B-                     0-59             F

           

 

Academic Integrity

We will follow the University Policy on Academic Integrity.  Academic honesty: Cheating means to misrepresent the source, nature, or other conditions of your academic work (e.g., tests, quizzes, papers, projects, homework assignments) so as to get undeserved credit. The use of intellectual property of others without giving them appropriate credit is a serious academic offence. It is the University’s policy that cheating or plagiarism result in receiving a failing grade (0 points) for the work or course. Repeat offences may result in dismissal from the University.

Students with disabilities

University policy 3342-3-18 requires that students with disabilities be provided reasonable accommodations to ensure their equal access to course content.  If you have a documented disability and require accommodations, please contact the instructor at the beginning of the semester to make arrangements for necessary classroom adjustments.  Please note, you must first verify your eligibility for these through the Student Accessibility Services (contact 330-672-3391 or visit http:www.registars.kent.edu/disability for more information on registration procedures).

 

Course Topics (subject to revision)

 

Note: Each lecture will be supported with a set of power point slides and a set of readings. It is imperative that students complete the assigned readings for a given lecture prior attending the lecture since the readings are the foundation upon with the lectures and related classroom discussion are based. All power point lecture materials and readings will be made available to the student in electronic form by the instructor.

                           

1.     Data Collection and Presentation.

2.     Probability Concepts.

3.     Random Variables and Distribution Theory.

4.     The Normal Probability Distribution.

5.     Sampling Distributions and Estimation.

6.     Hypothesis Testing.

7.     Correlation and Regression Analysis.

8.     Analysis of Variance.

9      Contingency Tables and Chi Square

10.   Nonparametric models   

 

 

 

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