# QMB Business Analytics CRN Fall 2015 T & R 9.30 to AM -- Lutgert Hall 2209

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6 D. Optional: There are several helpful web tutorials, example models, and YouTube channel which could be useful to students through the course E. 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. Class Schedule* The Schedule provides a map of the course. The schedule is organized by Week Number and Week Start Date so you can organize and plan your studies accordingly. The critical item to remember is that project assignment and exam due dates cannot be delayed. Week Topic Description number and date 1 8/19 Chapter 1 Course overview Introduction to Analytics and Excel Decision Models Problem Solving and Decision Making Spreadsheet Add-ins for Business Analytics Software requirement: Solver Platform 6

7 2 8/25 & 8/27 3 9/1 & 9/3 Chapter 4 Chapter 5 Chapter 6 Review Online material: instructional videos in Descriptive Measures: D-1, D-2, D-3, and D-4 In-class applications: Descriptive Statistical Measures (Chap. 4): Populations and Samples Measures of Location, Dispersion and Shape Measures of Association Excel functions Review online instructional videos in probability distribution: Dist-1 In-class review: probability distribution and applications (Chap. 5) Random variable and probability distribution Continuous and discrete distributions Random Sampling Due: Assignment 1 8/25: Course expectations (upload to Canvas drop box) Assignment 2 handout Last day to drop 8/25 (100% refund) Review online material: Sampling and estimation: confidence-r1 In class applications: Sampling and estimation (Chap 6) Estimating population parameters Sampling error (margin of error) Confidence interval for decision making 4 9/8 & 9/10 Chapter 7 Review online material: Hypothesis Testing HT-1s, HT-2s Optional: Download (in Canvas Files) PowerPoint slides (PP-1s, PP2s) In class applications: Statistical inference (Chap 7) Hypothesis Testing One-Sample Hypothesis Tests Two-Sample Hypothesis Tests ANOVA 5 9/15 & 9/17 Chapter 7 In class applications: Statistical inference Hypothesis Testing One-Sample Hypothesis Tests Two-Sample Hypothesis Tests ANOVA 6 9/22 & 9/24 Exam week EXAMS 1&2 (theory & practice) Due: Assignment 2-9/24 7

8 7 9/29 & 10/1 8 10/6 & 10/8 9 10/13 & 10/15 Chapter 8 Chapter 8 Chapter 8 Chapter 9 Regression Analysis Simple Linear Regression Evaluating Good Regression Models Assignment 3 handout Regression Analysis Simple and Multiple Linear Regression Building Good Regression Models Regression with Categorical Independent Variables Regression Analysis (Chap 8) Simple and Multiple Linear Regression Building Good Regression Models Regression with Categorical Independent Variables Forecasting (Chap 9) Forecasting Techniques Qualitative and Judgmental Forecasting Forecasting Models for Time Series with a Linear Trend Forecasting Time Series with Seasonality Measures of Forecasting Accuracy 10 10/20 & 10/22 Chapter 9 Forecasting (Chap 9) Forecasting Techniques Qualitative and Judgmental Forecasting Forecasting Models for Time Series with a Linear Trend Forecasting Time Series with Seasonality Measures of Forecasting Accuracy 11 10/27 & 10/ /3 & 11/ /10 & 11/12 Exam week Chapter 10 Chapter 10 Exam 3&4: 10/27 & 10/29 (theory and practice) Due: Assignment 3-10/29 Introduction to data mining The Scope of Data Mining Data Exploration and Reduction Classification Techniques Association Rule Mining Assignment 4 handout Last day to drop w/o penalty (11/3) Introduction to data mining The Scope of Data Mining Data Exploration and Reduction Classification Techniques Association Rule Mining 8

10 * This is a planned course structure and may change if necessary to meet learning goals 10

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