Applied Data Analysis 593 (Graduate class)

Introduction

The significant advances in computing and communication technologies over the past two decades have resulted in a business environment today that increasingly requires managers who can use the principles of statistical analysis to make effective decisions. Managers with these capabilities will be able to 1) consider richer sets of alternatives, 2) more deeply understand and question assumptions, and 3) consider more diverse measures of performance. This course will improve a student’s capabilities in each of these areas.

Catalog Course Description

This course covers the basic techniques of applied statistical analysis beginning with an exploration of the meaning of data. Methods of describing data on individual variables and relationships between variables are covered. Sampling and probability are introduced as a basis for understanding how to infer results from samples to the populations from which they are drawn. These techniques include estimation, tests of mean differences, differences in distributions, and regression.

Student Learning

Summary: This course is designed to introduce students to the practice of introductory statistical analysis. Coverage will include examining data distributions, examining relationships between variables, probability theory including standard probability distributions such as normal and binomial; sampling distributions, the basic statistical inference methods, and applied linear regression techniques/forecasting. This course will emphasize statistical thinking. While the foundations of statistical analysis are rooted in math theory, the practice of statistics today involves learning a process of scientific inquiry. Statistics is the science of data. In this course, students will work hands-on with real data and develop questions they seek to answer with appropriate data analysis.

Learning Outcomes

Goal 1: Given a description and demonstration of statistical concepts, techniques, and calculations, students will be able to perform and interpret statistical analyses within the scope of topics listed in the Course Plan.

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Goal 2: Given demonstrations of the use of Microsoft Excel tools for statistical analysis, students will be able to match appropriate tools to types of data and analysis goals, and to perform the targeted analysis with the Excel tool.

Goal 3: Given a survey of statistical techniques outlined in the course plan, students will be able to name and explain all techniques, including matching appropriate techniques to specified analysis goals.

Goal 4: Given a description and examples of basic principles of experimental design, including the production of data, analysis and findings, students will be able to: 1. Define key terms and concepts associated with experimental design, 2. Recognize various designs when given in a case problem, and 3. Determine appropriate statistical analysis to use within the experiment.

Goal 5: Given examples of statistical reporting, students will be able to generate appropriate statistical reports, including description of the research problem, formulation of research questions, selection of statistical methods, analysis of data/testing, and a summary of key findings.

Total Directed Instruction Hours: 30.

Suggested Course Textbook

The Practice of Statistics for Business and Economics by Moore and McCabe (any edition may be used, 3rd or 4th is recommended).

ISBN-10: 1-4292-4253-1 ISBN-13: 978-1-4292-4253-0

Note: eBook available at substantial savings. If you search our book title and eBook, you will find the Freeman Publisher website, which lists all “student buying options.” The textbook is not required but may be preferred by some students.

A scheduled reading list, with suggested/required exercises and cases will be posted within our Sakai course, Courses.pepperdine.edu.

Video Instruction

Each unit in our Sakai course contains instructional videos that supplement the material we cover in class. You may use the videos to complete example analysis as many times as you like. Video activities align with the course schedule, and completing the activity is part of weekly class preparation. Additional videos are provided to support and direct your progress on assigned case projects.

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Online Instruction

Scheduled online sessions (two sessions) will be offered. The link within our Sakai course will direct you to an Adobe Connect virtual classroom. Log in as “Guest” and enter your first name. Headphones are helpful to hear me. A microphone will allow you speak in the room. Most students who attend will use the chat app to type questions. If you can’t attend the online session, meetings will be recorded and links to the recordings will be made available to all students. Online sessions will be used for interactive discussion, webinar material, and exam review/project workshops.

Course Computing: Business Statistics

Excel Tools for Statistical Analysis, a set of excel workbooks designed to assist students with statistical analysis, are provided by the instructor to students via Sakai download.

NOTE: Excel 2010 (Windows) or 2011 (Mac), at least, is required.

Grading: Additional Details for Each Assignment at course Sakai site. 10% Class Participation

•   Attendance: Much of our learning occurs in class. Attendance is expected at all class meetings.

•   Practice Quizzes: Practice quizzes are taken using Sakai. Quiz completion is graded credit/no credit. Quizzes may be submitted unlimited times.

•   Data Discussion: Using the Data Discussion link in our Sakai course, complete two discussion assignments according to posted instruction details.

•   Assigned textbook exercise/cases. : text exercises will be posted within our Sakai course as covered in class. A team homework report will be collected twice during the term. Detailed instructions and due dates will be available in our Sakai course.

30% Midterm Exam

 The purpose of the midterm is to assess progress and readiness for the remaining course material. The midterm will consist of problems that require the use of data and course Excel tools. The midterm also has a section of multiple- choice items.

30% Cases (team assignment)

 Parts of a comprehensive applied statistical analysis will be submitted in week 3, week 5 and at the course conclusion. Students will work in teams of approximately 5, depending upon number of students in a particular class section.

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30% Final Exam

 The final exam will be in two parts: 1. Using personal laptops, students will conduct analysis of data provided by the instructor. Solutions will be recorded on a hard copy exam. 2. Multiple-choice and short answer items will examine key terms, concepts, and theory.

Grading Scale

94-100=A; 90-93 = A- 80-82=B-; 83-86=B; 87-89=B+ 70-72=C-; 73-76=C; 77-79=C+ 60-62=D-; 63-66=D; 67-69=D+

Policies

Student Channels of Communication

1. Questions about the center (room, copying facilities, etc.) should be directed to the front desk at your center, and if still a problem, to the Center Director.

2. Questions about your program, future offerings, and so forth should be directed to your Program Director at your center.

3. Concerns about the class and feedback should first be directed to the professor. Students have a right to request of a professor clarification of grading. If classroom or grading concerns are still not resolved, please contact the Academic Chair of Decision Sciences with your concerns in writing. The process then moves up to the Associate Dean of Academic Affairs and finally the Dean of GSBM. Any attempt to bypass this chain of command will result in delays, as the message will be sent back down to the appropriate level.

Conduct

The University expects from all of its students and employees the highest standard of moral and ethical behavior in harmony with its Christian philosophy and purposes. Engaging in or promoting conduct or lifestyles inconsistent with traditional Christian values is not acceptable.

The following regulations apply to any person, graduate or undergraduate, who is enrolled as a Pepperdine University student. These rules are not to be interpreted as all-inclusive as to situations in which discipline will be invoked. They are illustrative, and the University reserves the right to take disciplinary action in appropriate circumstances not set out in this catalog. It is understood that each student who enrolls at Pepperdine University will assume the responsibilities involved by adhering to the regulations of the University. Students are expected to respect order, morality, personal honor, and the rights and property of others at all times. Examples of improper conduct for which students are subject to discipline are as follows:

•  Dishonesty in any form, including plagiarism, illegal copying of software, and knowingly furnishing false information to the University.

•  Forgery, alteration, or misuse of University documents, records, or Page 5 of 7

identification.

•  Failure to comply with written or verbal directives of duly authorized University

officials who are acting in the performance of assigned duties.

•  Interference with the academic or administrative process of the University or

any of the approved activities.

•  Otherwise unprotected behavior that disrupts the classroom environment.

•  Theft or damage to property.

•  Violation of civil or criminal codes of local, state, or federal governments.

•  Unauthorized use of or entry into University facilities.

•  Violation of any stated policies or regulations governing student relationships to

the University.

Disciplinary action may involve, but is not limited to, one or a combination of the alternatives listed below:
Dismissal – separation of the student from the University on a permanent basis. Suspension – separation of the student from the University for a specified length of time.

Probation – status of the student indicating that the relationship with the University is tenuous and that the student’s records will be reviewed periodically to determine suitability to remain enrolled. Specific limitations to and restrictions of the student’s privileges may accompany probation.”

Originality of Work

This course may require electronic submission of essays, papers, or other written projects through the plagiarism detection service Turnitin (http://www.turnitin.com). Turnitin is an online plagiarism detection service that conducts textual similarity reviews of submitted papers. When papers are submitted to Turnitin, the service will retain a copy of the submitted work in the Turnitin database for the sole purpose of detecting plagiarism in future submitted works. Students retain copyright on their original course work. The use of Turnitin is subject to the Terms of Use agreement posted on the Turnitin.com website. You may request, in writing, to not have your papers submitted through Turnitin. If you choose to opt-out of the Turnitin submission process, you will need to provide additional research documentation and attach additional materials (to be clarified by the instructor) to help the instructor assess the originality of your work.

Policy on Disabilities

Assistance for Students with Disabilities

The Disability Services Office (DSO) offers a variety of services and accommodations to students with disabilities based on appropriate documentation, nature of disability, and academic need. In order to initiate services, students should meet with the Director of the DSO at the beginning of the semester to discuss reasonable accommodation. If a student does not request accommodation or provide documentation, the faculty member is under no obligation to provide accommodations. You may contact the Director of Disability Services at (310) 506-6500. For further information, visit the DSO Web site at: http://www.pepperdine.edu/disabilityservices/.

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Class Meeting Schedule

Meeting 1: Examining Data Distributions & Relationships; Visual Display of Data Meeting 2: Random Variables, Probability & Statistical Inference
Meeting 3: Testing and Estimation
Meeting 4: Midterm Exam and Case Workshop

Meeting 5: Inference for Regression Meeting 6: Inference for Regression Meeting 7: Final Exam

Summary of Directed Instruction Elements