QMB 6303 Business Analytics Summer 2015 CRN VIRTUAL

Transcription

1 QMB 6303 Business Analytics Summer 2015 CRN VIRTUAL Elias T. Kirche, Ph.D. Associate Professor Department of Information Systems and Operations Management Lutgert College of Business Suite 3304 Phone: (239) Fax: (239) ekirche@fgcu.edu Consultation hours: Online by appointment only About the Course The course is an introduction to Business Analytics covering statistical techniques in descriptive, predictive and prescriptive data analysis. Some of the topics include regression, forecasting, risk analysis, simulation, linear programming, data mining, and decision analysis. This course provides students with the fundamental concepts and knowledge needed to understand the emerging role of business analytics in organizations and shows students how to apply essential tools in a spreadsheet environment. Emphasis is on business applications, concept development and effective interpretation of models and results, rather than theory and calculations. Students use a computer software package for data analysis. Learning Objectives Identify problems, define objectives, analyze information, and evaluate risks and alternatives to enable qualitative and quantitative methods to solve business problems Select and apply appropriate data analysis tools with the support of software in a variety of business scenarios. Understand data gathering and input considerations in model building, validation and testing Be able to analyze and interpret output (graphs, tables, mathematical models, numerical indicators of performance, etc.) and know how to report results in a fair, objective and unbiased manner. Class Format and Policies This course is organized around the Course Schedule (see below) which provides a map of requirements for assignments and corresponding due dates. Each week, closely follow the schedule and complete the requirements as indicated. Course files, such as PowerPoint presentation slides and Excel files referred to in the schedule are posted in Canvas within the respective chapters. 1

2 The Schedule was prepared so you can plan your work accordingly. Pay attention to due dates. This is a compressed 6 weeks course and it is essential that you have the discipline and assiduously dedicate enough time to complete all the required assignments. In general, you should be able to solve the graded assignments (called Projects) after reading the indicated material, watching the instructional videos and solving selected end of chapter problems. Pay attention that these selected problems (referred to as Applications in the course schedule) are already solved (see Required Course Materials section below) but you should try to work on them before looking at the solution. In doing so you will be developing important skills in business analytics and addressing your assignments for grading as well. Should you have problems in solving the assignments or understanding the material, you may contact the instructor via or posting a message in the bulletin board (Discussion option) within Canvas. If you are contacting the instructor by , a few items will help expedite solving the question: a) Describe the issue/question you have as clearly as possible. If there are multiple parts, breakdown the question in multiple parts. I will address them one by one. b) Attach a draft of the work you have done for review c) Expect a return within 24hours but in general, I will return all s on Monday, Wednesday, and Friday. Time permitting, I will also return s on Saturday mornings. Therefore plan your work accordingly. d) We can arrange office hours though SKYPE if necessary. If you call the office, please leave a detailed and clear message on how I can reach you. e) Submit your analysis or questions to the instructor with sufficient time for review and feedback. Assignments Business Analytics involves "learning by doing" assignments. That is, the course and assignments have been designed so you can learn essential skills and concepts from carefully selected applications used in the practice of business analytics. By solving these assignments you will also develop an understanding of widespread issues encountered by the professional in this area. Graded assignments are called Projects which you are required to complete on or before the due date. There are 7 Projects and they involve the analysis of problems and/or cases from the textbook. The analysis for all assignments will be done using Microsoft Excel with the assistance of Frontline Systems Risk Solver Platform and XLMiner. You will need a personal copy installed in your personal computer which can be downloaded from Frontline Systems website. See required Course Material below. Pay attention to the organization and quality of your work file which may reduce your overall grade. NO LATE ASSIGNMENT WILL BE ACCEPTED. It is your responsibility to submit the document on or before the due date. 2

3 Grades When preparing your assignments pay attention to the content, cleanliness, and organization of the document. They all contribute to your grade. The final grade is computed as a percentage of the total points earned in 7 projects. Letter grades will be assigned based on the following criteria as a percentage of total points: 92 and above: A 90 to less than 92: A- 87 to less than 90: B+ 82 to less than 87: B 80 to less than 82: B- 75 to less than 80: C+ 70 to less than 75: C Below 70: F Incomplete will be given by exception when a limited portion of the course material has not been completed by the last exam due date, in accordance with University policy published in the Catalog. The instructor on an individual basis will review each case. Required Course Material: 1. Textbook: Essentials of Business Analytics, 1st Edition Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, Dennis J. Sweeney, Thomas A. Williams ISBN-10: X ISBN-13:

4 2. Software: Microsoft Excel and Frontline System s Risk Solver Platform and XLMiner (student version). Follow these instructions to download and install Frontline System s Risk Solver Platform and XLMiner (student version) IMPORTANT NOTES!!!: DO NOT register as Students on This URL is intended for large companies. Most mistakes occur when students do not have or fail to enter the Course Code and Textbook Code (given below). This results in confusion and lost time, which you can mitigate or avoid by following the proper sequence of instructions below. Students must register, download, run SolverSetup, and enter a password and license activation code. The password and activation code are sent to you by . This is all very simple to do, if you read and follow the instructions. Yet when courses are starting up, the software company is handling hundreds of support tickets per day, repairing situations for students who just haven t read the instructions, or have read them, but decided to do something else. This will delay your start and impact your performance in the course. The instructor is not responsible for the proper setup of the software. Make sure you follow these instructions correctly and in advance of the start of the course. The software DOES NOT run under any Excel version for Mac. There is a commercial Premium Solver Platform for Mac, but the company does NOT offer it for academic or textbook use, and it will NOT support students attempting to use it (unless they buy a license for $2,000). Preparation: 1. If you have a Mac, visit and read To use the software for this course, you ll need to install Windows alongside Mac OSX, and install Excel or Office for Windows. This will also allow you to use other Windows software, as well as Mac software on your machine. 2. Check whether you have 32-bit or 64-bit Excel this determines which software version you should download. You probably have 32-bit Excel. You have 64-bit ONLY if (i) in Excel 2010, you click File Help, and you see 64-bit in the lower right, or (ii) in Excel 2013, you click File Account About Excel, and you see 64-bit at the top of the dialog. 4

5 Registration: 1. Point your browser to (Do not attempt to register or download anywhere else on Solver.com this will cause troubles later.) 2. Fill out the form on this page. Enter your address (to ensure you receive your license activation code), enter a login password you can remember, enter your first and last name, and Florida Gulf Coast University for your school. 3. Enter CCFOEBA for the Textbook Code and QMB6303S15 for the Course Code. These are special for our course entering these will give you a 140-day license. (Leaving them blank will give you a 15-day license.) 4. Check the box to acknowledge that you accept the Frontline Systems license agreement. Note: Frontline receives no money from you, or the textbook publisher, or the university; this free 140-day license is a courtesy that they offer to students. 5. Click the button Proceed to Download Page. If everything is OK, this will take you to the Download page. Download: 1. On the Download page, change 32-bit to 64-bit ONLY if you ve confirmed that you have 64-bit Excel (see above). Click the blue Download Now button. 2. In some browsers you will see a dialog "Do you want to run or save this file?" Click Save to save the file, named either SolverSetup.exe or SolverSetup64.exe. 3. Now check your , at the address you entered above, for a message containing an installation password and a license activation code. Frontline sends this twice, from different servers, to ensure that you receive it. If you don t get it, visit and login to request another message. Installation: 1. Make sure that Excel is closed (not running), then run the program SolverSetup.exe (or SolverSetup64.exe). SolverSetup will prompt you to enter the password and activation code from the message above enter them exactly as shown in the (you can copy and paste). 2. The SolverSetup program will prompt you to choose between Analytic Solver Platform, Risk Solver Platform and XLMiner. Choosing Analytic Solver Platform gives you all the features of Risk Solver Platform and XLMiner, so this is usually the best 5

6 choice. You can CHANGE this choice later in Excel, by choosing a menu option Help Change Product on the Ribbon. 3. When the SolverSetup program finishes, start Excel (the last Setup dialog prompts you to do this). You should see new tabs on the Ribbon for Analytic Solver Platform or Risk Solver Platform, and XLMiner. Click the Solver Platform tab you should see a Welcome dialog with various links. Use the Help dropdown menu to open Help text, the User Guide and Reference Guide, and load example workbooks. If all has gone well, you re ready for our class exercises. If you have problems, the best avenues to get help are to support@solver.com (this creates a support ticket in Frontline s Help Desk) or start a Live Chat from any page on or from within Excel (Help Support Live Chat). 3. Data files (free): Access the companion website to download important course resources: data files, solution to even numbered chapter problems. No need to log in. Just insert your ISBN number and select free material tab. Download WEBfile data file and the Answers_to_even-numbered_exercises files. The textbook companion website can be found at: 4. Optional: You may also want to read a good book about how companies are using what we cover in this course to gain a competitive edge in the business world: Competing on Analytics - The New Science of Winning (Hardcover) by Thomas H. Davenport and Jeanne G. Harris, Harvard Business School Press. 6

7 Class Schedule The Schedule provides a map of the course. It is organized by Week Number and Week Start-End Dates so you can plan your studies accordingly. The critical item to remember is that assignments cannot be delayed. Generally, the week starts on Monday and ends on Saturday, except where indicated otherwise. Assignment due time is scheduled to 11:59PM of the indicated due date. IMPORTANT: Go to Files option in Canvas and access course files through the specified chapter folder. For the instructional videos, copy and paste the link to your web browser address bar. Week 1: 6/22 to 6/27 In this first week you need to cover two topics related to business analytics. You have to review some important concepts in descriptive statistics, and then finish the week by working on simple regression analysis (SLR). But first you have to get familiar with our course structure and requirements. Become familiar with the course, syllabus and course requirements. Set up required software: Frontline Systems Analytical Solver Platform for Education. Carefully follow instruction in this syllabus. Mac users pay attention to instructions. Answer Roll Call in Canvas by Saturday 6/27 (look it up in the Discussions option) Scan chapter 1 key points: o Categorization o Business Analytics in practice o Glossary Graded assignment for the week: Download and complete Project 1 (see Files option in Canvas). Due date is 6/27 (Saturday). Upload one Excel file to corresponding drop box in Canvas. Topic 1: Descriptive Statistics Scope: chapter 2. Focus on: Histograms and frequency distribution Measures of location Measures of variability Measures of association 7

8 Watch instructional videos in Descriptive Measures: Video D1: Video D2: Video D3: Video D4: Optional: You may want to download and scan the Excel files worked in the instructional videos Purchase Order.xlsx Closing Stock Prices.xlsx Colleges and Universities.xlsx Application: Solve problem 26 chapter 2. Download JoblessRate file from Webfile (Note: Webfile is a free database accessed through the companion web site. See Required Course Material section above) Topic 2: Simple Linear Regression. Scope: chapter 4 Simple linear regression model: sections 4.1 and 4.2 Assessing fit model utility: section 4.3 Scan PowerPoint slides for chapter 4 (see Files option and then Chapter 4 folder): Chap 4 linear regression.pptx Watch instructional video in Simple Linear Regression (SLR): Video SLR: Optional: You may want to download and scan accompanying PowerPoint and Excel files presented on instructional videos: Home Market Value regression analysis.xlsx (Excel) Simple Regression Video PP.pptx (PowerPoint) 8

9 Application: Solve problems 4 and problem 8 End of Week 1: make sure you upload required course assignments on or before due date. Week 2: 6/29-7/6 (pay attention to assignment due dates for this week) In this second week you need to cover two topics. Multiple linear regression (MLR) which is a continuation of simple linear regression covered in chapter 4, and then finish the week with forecasting presented in chapter 5. Pay attention that MLR has an assignment due on 7/1 (Wednesday) and the forecasting assignment is due on 7/6, Monday (so it is not due on a national holiday) Graded assignments for the week: Download and complete Project 2 (MLR). Due date is 7/1 (Wednesday). Upload one Excel file to corresponding drop box in Canvas. Download and complete Project 3 (forecasting). Due date is 7/6 (Monday). Upload one Excel file to corresponding drop box in Canvas. Topic 1: Multiple linear regression (MLR) Scope: chapter 4 Multiple linear regression: section 4.4 and 4.5 Categorical independent variables: section 4.6 Model fitting: section 4.8 Scan PowerPoint slides for chapter 4: Chap 4 linear regression.pptx Watch instructional videos in MLR: Video MLR1: Model building: Video MLR2: Modeling with qualitative/categorical variables: Video MLR3: Non-linear regression: 9

10 Optional: Download and scan accompanying PowerPoint and Excel files presented on instructional videos Multiple linear regression video PP (PowerPoint) Banking data MLR analysis 1 (Excel) College and Universities MLR analysis (Excel) Employee salary analysis (Excel) Surface finish analysis (Excel) Curvature with beverage sales (Excel) Application: Solve problem 10. Part c of the problem calls for t-test to determine the significance of the independent variables but you can use the p-value approach to answer this part (see video MRL1 for the approach). Solve problems 14 and 18 Pay attention Project 2 is due on 7/1. Topic 2: Time series analysis and forecasting. Focus on forecasting methods: Moving averages, Exponential Smoothing, Adjusted Exponential smoothing and linear trend line, Seasonal patterns and Forecast accuracy. Pay attention to Forecast Accuracy to measure how good your forecast really is: MAE, MSE, and MAPE. You should understand these acronyms. Scope: chapter 5 Time series patterns section 5.1 Forecasting reliability section 5.2 Forecasting models: o moving averages and exponential smoothing o linear trend projection o seasonality o determining best forecasting model to use Scan PowerPoint slides for chapter 5: Chap 5 forecasting.pptx Watch instructional videos in forecasting: Video FOR1: Video FOR2: 10

11 Video FOR3: Video FOR4: Optional: Download and scan accompanying Excel files presented on instructional videos Gasoline-video.xlsx Bicycle-video.xlsx SmartPhoneSales-video.xlsx Application: Solve problem 8, 12, 20 and 24 (solved in video FOR4) End of Week 2 is on Monday 7/6: make sure you upload required course assignments on or before due date. Week 3: 7/6-7/11 In this third week you need to cover one topic only. Chapter 6 is an introduction to data mining concepts and applications. Graded assignment for the week: Download and complete Project 4 (Data Mining). Due date is 7/11 (Saturday). Upload one Excel file to corresponding drop box in Canvas. Topic 1: Data mining. Start with sampling and the need for data preparation. Pay attention that in unsupervised learning applications the goal is to use the variable values to identify relationships between observations, and in supervise learning techniques, the goal is to develop a model that predicts a value for a continuous outcome or classifies a categorical outcome, and therefore the need for partitioning the data set in this latter case. Scope: chapter 6 Data sampling (6.1) Data preparation (6.2) Unsupervised learning application (6.3): Cluster Analysis only (up to page 265) Supervised Learning (6.4): Pages 269 to 283 o Partitioning data o Classification accuracy o K-nearest neighbors only. Skip Regression trees and Logistic regression techniques. 11

12 Scan accompanying PowerPoint slides for chapter 6: Chap 6 Data mining.pptx (pay attention we are not covering the entire chapter) Watch instructional videos in Data mining: Video DM1: Sampling and unsupervised applications - Cluster Analysis: Video DM2: Partitioning the data set and KNN for classification of categorical variable: Video DM3: KNN for prediction of a continuous variable: Optional: Download and scan accompanying Excel files from videos KTC-small-video.xlsx Application: Solve problem 4, 6 and 10 End of Week 3 is on Saturday 7/11: make sure you upload required course assignments on or before the due date. Week 4: 7/13-7/18 In this fourth week you need to cover two important topics but there is only one assignment for the week. We will cover building good spreadsheet models presented in chapter 7 and then finish the week with simulation topic from chapter 11. The assignment is for the simulation topic only, but you will need the concepts developed in chapter 7. Graded assignment for the week: Download and complete Project 5 (Monte Carlo Simulation). Pay attention that the due date is 7/18 (Saturday). Upload one Excel file to corresponding drop box in Canvas. Topic 1: Spreadsheet models chapter 7. The PowerPoint slides and the instructional video will give a good overview of this chapter and important Excel functions (remember that there is no assigned project for this topic/chapter). This chapter will provide important skills to be further developed in the next topic of the week. Focus on building clear and organized spreadsheet 12

13 models which are easy to understand and to make changes if necessary. Important!!!!! Build the spreadsheet models indicated in the Applications section since they will be used in the next topic. Scope: chapter 7 Just scan: Building good spreadsheet models (7.1) Focus on: What if analysis (7.2) and watch related instructional video below Just scan: Useful Excel functions (7.3) and Auditing spreadsheet models (7.4) Scan accompanying PowerPoint slides and handout for chapter 7: Chap 7 Spreadsheet models.pptx Chap-7-modeling-handout.pptx (VERY IMPORTANT!!!!!) Watch instructional videos in spreadsheet modeling: Video SM1: Sensitivity analysis with Excel What-if: the Newlin outsourcing problem: Video SM2: The profit problem Sanotronics: Optional: Download and scan accompanying Excel files presented in videos Nowlin-solved.xlsx Sanotronics-solved.xlsx Applications: Build models and reproduce the What-if Analysis applications presented in the videos and in chap-7-modeling-handout.pptx slides. Save the work to be used in the next topic. Topic 2: Monte Carlo Simulation, chapter 11. The chapter has important concepts in simulation but unfortunately the subject is demonstrated through complex examples and, therefore, I have uploaded additional handout to facilitate the understanding of this chapter. Scan some of the material in the text and go through the PowerPoint slides (chap 11 simulation) which is part of the textbook material, and then go through and understand material in the PowerPoint handout (chap_11_handout_simulation). Watch the instructional video and finally work the applications, which apply simulation techniques to models developed in previous topic. Scope: chapter 11 - Monte Carlo Simulation Scan: What-if analysis (11.1) and Simulation with native Excel functions (11.2) Scan: Simulation modeling with Analytical Solver Platform (11.3) Scan: Simulation considerations (11.5). Skip section (11.4) 13

14 Scan accompanying PowerPoint slides and handout for chapter 11: Chap 11 simulation.pptx Chap 11 simulation handout.pptx (important!!!) Watch instructional videos in simulation: Video SIM1: Simulation of Newlin (the outsourcing model): Video SIM2: Sanotronics (profit model) Optional: Download and scan accompanying Excel files presented in videos Newlin-sim.xlsx Sanotronics-sim.xlsx Applications: reproduce the simulation applications for the Outsourcing model presented in the instructional video. Additionally, solve problem 10, chapter 11. End of Week 4 is on Saturday 7/18: make sure you upload required course assignments on or before the due date. Week 5: 7/20-7/28 In this week you need to cover two topics but the good news is that the assignment is due in the following week (7/28). Chapter 8 is an introduction to linear optimization concepts and applications and chapter 9 extends these concepts to integer optimization models. Therefore the assignment includes problems related to both chapters. Graded assignment for the week: Download and complete Project 6 (linear optimization models). Pay attention that the due date is 7/28 (Tuesday). Upload one Excel file to corresponding drop box in Canvas. Topic 1: Linear programming (LP) chapter 8. The PowerPoint slides and the instructional video will give a good overview of this chapter and important requirements to solve LP models. This chapter will provide important skills to be further developed in the next topic of the week (chapter 9, integer programing). Focus on building clear and organized models which are easy to understand, interpret and to make changes if necessary. 14

15 Scope: chapter 8. Pay attention to the relationship between business problem and model formulation. Additionally, focus on how to formulate linear models and the interpretation of the solution. Pay attention to key words such as objective function allowable increase/decrease, sensitivity analysis, range of optimality, dual value, reduced cost, range of feasibility, sunk costs, relevant costs, binding and non-binding constraints. Finally, use Excel to build and solve the models in the examples. Scan accompanying PowerPoint slides for chapter 8: Chap 8 linear programing.pptx Watch instructional videos in optimization (see sections 8.1 through 8.6 for additional references to the problems presented in the videos): Video MaxLP: Maximization problem for Par Inc.: Video MinLP: Minimization problem for M&D Chemicals: Video TDP: Transportation/Distribution problem for Foster Generators: Optional: Download and scan accompanying Excel files presented in videos ParInc-solved.xlsx M&D-solved.xlsx Foster-solved.xlsx The following is a series of older videos in LP (nevertheless important). They refer to a different textbook but you may want to watch them to gain additional insights. The accompanying spreadsheet can be found in chapter 8 folder in Canvas: Profit maximization in manufacturing (Beaver Creek Pottery Company): Video BC1: Video BC2: Video BC3: Video BC4: Video BC5: Cost minimization in agriculture (Fertilizer mixing problem): Video FE1: Video FE2: Video FE3: 15

16 Video FE4: The investment problem (maximizing return on investment) Video INV1: Video INV2: Video INV3: Optional: Download Excel spreadsheets for the additional LP videos above (see chapter 8 folder): Investments.xlsx Beaver_creek.xlsx Fertilizer.xlsx Applications: Solve problems 2, 4 and 16 Topic 2: Integer Linear Optimization Models, Chapter 9. This topic continues what you have learned in previous topic and requires some adjustment to account for integer requirements in the model. For example you want to produce a whole number of cars or planes, or serve a whole number of customers etc. Therefore additional constraints will be added to the model to account for this requirement. Scope: chapter 9 sections 9.1 through 9.3 (solving integer programing with Excel) Scan section : using binary variables Scan accompanying PowerPoint slides for chapter 9: Chap 9 integer programing.pptx Watch instructional videos in integer programing (see sections 9.1, through 9.3 for additional references to problems presented in the videos): Video IP: Integer programing for the Eastborne Realty problem: Video BV: Binary variable programing/capital budgeting for the Ice-Cold Refrigeration: Optional: Download and scan accompanying Excel files used in videos: Eastborne-solved.xlsx IceCold-solved.xlsx 16

17 Applications: Solve problem 6 and 8 (parts a and b only) End of Week 5 is on Tuesday 7/28: make sure you upload required course assignments on or before the due date. Week 6: 7/27-8/1 Our final week!! (Do I hear applauses?). You have to work on only one topic, decision analysis. Graded assignment for the week: Download and complete Project 7. Pay attention that the due date is 8/1 (Saturday). Upload one Excel file to corresponding drop box in Canvas. Topic 1: Decision Analysis Scope: Chapter 12. Focus on sections 12.1 through 12.4 only which includes payoff tables, decisions without probabilities (optimistic and conservative approaches, and minimax with regret) and decisions with probabilities (Expected Value and Expected Opportunity Loss, including expected value of perfect information - EVPI), risk and sensitivity analysis. Pay attention to decision tree diagrams. Read up to page 567. Scan PowerPoint handout slides for chapter 12 Chap 12-decisionanalysis-handout.pptx (pay attention we are not covering the entire chapter) Watch instructional videos on decision analysis applied to PDC condominium project (see chapter section 12.1 for additional information on the PDC problem) Video DA: Video Dtree: Application: Solve problems 2, 4, and 8 End of Week 6 is on Saturday 8/1: make sure you upload required course assignments on or before the due date. The End!! 17

18 Florida Gulf Coast University, in accordance with the Americans with Disabilities Act and the University s guiding principles, will provide classroom and academic accommodations to students with documented disabilities. If you need to request an accommodation in this class due to a disability, or you suspect that your academic performance is affected by a disability, please see me or contact the Office of Adaptive Services. The Office of Adaptive Services is located in Howard Hall, room 137. The phone number is or TTY Additional assistance: The Center for Academic Achievement (CAA) offers academic support services for any FGCU student. The services are at no extra charge to students and include: peer tutoring, Supplemental Instruction, Student Success Workshops, and individualized academic coaching. If you would like to participate in or learn more about these services, please visit the CAA in Library 103. You may also the CAA at caa@fgcu.edu or call at (239) The CAA website is * This is a planned course structure and may change if necessary to meet learning goals 18