Decision Sciences Data Analysis for Managers

Transcription

1 Spring 2013 CALIFORNIA STATE UNIVERSITY, SACRAMENTO School of Business Administration Decision Sciences Data Analysis for Managers COURSE OUTLINE INSTRUCTOR: Dr. Stanley A. Taylor OFFICE: TAH sataylor@csus.edu OFFICE PHONE: (Voice Mail) OFFICE HOURS: T TH 6:45 7:15 AM ; 12:00-1:00 PM or by appointment REQUIRED TEXT Download from. Blackboard Additional Resources --- for review material Online Statistics Education: An Interactive Multimedia Course of Study Kahn Academy (KA) PREREQUISITES MIS 1,2,3 Stat 1 and Math 24. COURSE OBJECTIVES The objectives of this course are to prepare the students to:understand variation. Be able to differentiate between specific variation and common variation, as applied to managerial decision-making situations. 1. Develop an intuitive feel for statistical techniques. 2. Use statistical software [STATGRAPHICS] and interpret computer outputs. 3. Communicate, verbally and in written form, statistical results. 4. Understand the important characteristics of forecasting. Remember "Some people predict the future. You can intelligently compute it." 5. Create and manipulate data files in order to conduct appropriate statistical analysis. CATALOG DESCRIPTION The current catalog description is as follows: A second course in business statistics that focuses on the application of statistical methods to business problems. Emphasis is placed on case studies, data analysis, model building techniques, statistical reasoning, and communications of statistical results. A statistical computer package [StatGraphics Centurion XVI] will be used in the course. Prerequisites:[MIS1,2,3 ] MATH 24 and STAT 1. 3 units.

2 COURSE OVERVIEW This course is designed to provide students with the background to execute data analysis in a managerial capacity. The key factors to accomplish this are obtaining an intuitive feel for statistical modeling and the ability to utilize the computer via statistical software [StatGraphics Centurion XVI]. Emphasis is placed on statistical thinking, not memorization of statistical facts. To be successful in this course, you should be: 1. PREPARED -- You should study the assigned pages before coming to class. 2. PRESENT -- You are expected to attend class. [If you miss class, you are responsible for obtaining class assignments, handouts, and a copy of another student s notes.] 3. PAY ATTENTION and PARTICIPATE - ask questions and put your mobile phone away ASSIGNMENTS DUE DATE POLICY Assignments are due at the beginning of class. Once class starts on the due date, no more assignments will be accepted. NO EXCEPTIONS. Frequently, assignments will be discussed in class on the due date, hence to make the discussion meaningful, it is suggested that you make an extra copy of your assignment so that you have a copy during the discussion. Assignments will be given via SacCt (blackboard). EXAMS The exams will require using the computer to analyze data. Make-up exams are not given. Check the course outlines for the specific dates. Unless there is a justifiable excuse,* missing an exam will result in a score of 0. *Dr. Taylor determines what is justifiable. Practice exams (copies of exams from previous semesters) will be posted on Blackboard. CSUS student IDs will be required at time of each exam. PROJECT AND CLASS PRESENTATION Students will be assigned a term project. Written projects will be due April 25. Written proposals are due no later than March 21. If one fails to submit a proposal on, or before March 21, they will receive a score of 0 for the project, No exceptions RECOMMENDED MATERIALS STATGRAPHICS Centurion software. Details regarding the distribution of the software is provided via Blackboard..

3 GRADING POLICY Your final grade will be based on your performance on exams, quizzes, project, and homework assignments. The course components are indicated below: Exam 1 Exam II Exam III (Final- optional) Quizzes* Written Project** Homework Assignments / Cases 100 Points 150 Points 200 points 10 Points Each 60 Points Varies By Assignment * Lowest quiz will be dropped ** Abstract must be approved in order to receive any credit for the project or status report. Minimal grades are assigned in accordance with the following percentages: A = % C = 73-76% A- = 90-92% C- = 70-72% B+ = 87-89% D+ = 67-69% B = 83-86% D = 63-66% B- = 80-82% D- = 60-62% C+ = 77-79% F = < 60% *The scale may be lowered but not raised. Incomplete (I) is not awarded in this class. Extra credit exercises, or projects, are not assigned nor will they be accepted for credit.

4 Who will fail the course? Those who: 1. Do not turn in homework. 2. Memorize facts, and fail to see "the big picutre". 3. Write down everything said in class without thinking about "the big picture" 4. Are afraid to ask questions 5. Are over committed 6. Don't experiment with the software (Statgraphics) until an assignment is to be turned in for credit. 7. Only come to some of the classes ACADEMIC DISHONESTY The attempt by a student to cheat on an exam or other academic assignments or engage in plagiarism is a violation of a fundamental principle of academic honesty and integrity and will not be tolerated in the University. Penalties will be imposed on students who are found guilty of academic dishonesty. The Dean of Students will be notified and, at a minimum, a student guilty of academic dishonesty will be awarded the letter grade of "F" in this course. TENTATIVE SCHEDULE Date Topic Text Pages Assignment /Comments Jan 29 Introduction Review Mean and Variance Parameters vs Statistics Syllabus Pg 5-7 Download and install Statgraphics Centurion if you desire to use it outside the CSUS computer labs Jan 31 Review: Random Variables Sampling Sampling Distributions Normal Distribution Standardization Pg 5-7 Work on Math Concepts for Feb 7 Feb 5 Confidence Intervals Hypothesis Testing P Value Feb7 Review Math Concepts Feb 12 Go over HW #1.. Quiz #1 Feb 14 Go over Quiz 1 Continue HW #1 Pg 8-16 Case #1 assigned

5 Feb 19 Red Bead Experiment Variation: Specific vs Common Data Types: Time series vs cross sectional Feb 21 Go over case #1 Statistical Quality Control Pg Pg Work through the series in HW.SF3. See pg for assistance Feb 26 Quality Control Pg Transformations Random Walk Feb 28 Experimental Design Pg ANOVA 1 and 2 way Mar 5 ANOCOVA Pg 162 Go over ANOVA problems Mar 7 Review Mar 12 Exam #1 Mar 14 Model Building -- 3 Pg Phases Simple Linear regression Market Model Project Discussed Mar 19 Go over Exam #1 Pg Forecasting SLR Introduction to Multiple Regression Project Discussed Mar 21 Go over proposals Project Proposals Due March Spring Break April 2 Dummy Variables April 4 Residuals Pg April 9 Correlation Autocorrelation Interventions Prediction Pg Pg April 11 Crosscorrelation Pg Leading indicators. Fred Case. April 16 JKA Case -- go over Provided via SacCt April 18 Outliers Pg

6 Multicollinearity Stepwise April 23 Crosstabulation Pg April 25 Sampling Projects due April 30 Sampling May 2 TBD May 7 Review for Exam 2 May 9 Exam #2 May 14 Go Over Exam #2 May 16 Review for Final Exam Final Exams: All final exams will be given in ALPINE 224 Section Date Time 7:30 Thurs., May 23 8:00 AM - 10:00 AM 9:00 Tues., May 21 10:15 AM 12:15 PM 10:30 Thurs., May 23 10:15 AM 12:15 PM Last updated (schedule) January 21, 2013