MB8002 Course Outline PDF

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MBA IN THE MANAGEMENT OF TECHNOLOGY AND INNOVATION MBA GLOBAL Ted Rogers School of Management

MB8002 – QUANTITATIVE METHODS IN MANAGEMENT Contact Information Professor Office Office Hours Email Phone:

Ojelanki Ngwenyama PhD TRS 3-089 By appointment (Virtual on Zoom) [email protected] email preferred

Method of Posting Marks All marks will be communicated via D2L. E-mail Usage & Limits Except for emergencies, please use email for communication with the professors outside of class and office hours. Class Times and Location Thursday 6:30-9:30 Virtually COURSE DESCRIPTION This course is intended to help students build critical competence in statistical reasoning and the use of data analytic techniques for evidence-based management. Emphasis is placed on how to collect data and use specific quantitative modeling functions in EXCEL to analyze business problems; and how to use the results of these analyses to improve evidence-based management decision making. Opportunities will be provided for students to develop their statistical analysis and modeling capabilities for solving common real-world problems that managers will encounter in the course of their careers. The course is problem driven; students will analyze a range of general management problems which requires the analysis of quantitative data in order to generate evidence for decision making. Specific attention will be given to interpreting modeling results and writing evidence-based management recommendations. COURSE OBJECTIVES The learning objectives for students enrolled in this course are: 1. Developing capabilities to apply descriptive and inferential statistical techniques to solve business problems; 2. Developing competence for analyzing business problems and designing solution strategies for them; 3. Developing skills for writing business case reports for evidence-based management decision making. Learning Outcomes: 1. The student must demonstrate an understanding of different types of statistical modeling techniques, and their appropriate application

MT 8600

F2013

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MBA IN THE MANAGEMENT OF TECHNOLOGY AND INNOVATION MBA GLOBAL Ted Rogers School of Management 2. The student must demonstrate basic competence in descriptive and inferential data analysis including (1) hypothesis testing; (2) chi square testing; (3) the analysis of variance, and (4) correlation analysis. 3. The student must demonstrate competence in defining a business problem, formulating an solution strategy and executing the appropriate data analysis using EXCEL 4. The student must demonstrate competence in interpreting the results of specific modeling techniques and writing a business case report for management decision making Teaching Method The pedagogical approach for this course is Outcomes Based Action Learning; consequently, there will be an intensive problem solving component. The course will involve lectures, discussions, short videos and hands-on learning exercises and homework assignments. This course will utilize appropriate statistical methods and techniques and EXCEL software tools. Short case studies and in-class problem solving exercises will be used to reinforce understanding of the concepts. Textbook and Readings/Viewings Textbook: Arthur Glenberg, Matthew Andrzejewski, Learning From Data: An Introduction To Statistical Reasoning, Lawrence Earlbaum Associates, 3rd Edition, 2008. This book can be rented from Amazon for about U$25 for the period of the course. Video Help Week 1: EXCEL Transpose Function https://www.youtube.com/watch?v=ZWu5MetIwlE https://www.youtube.com/watch?v=7zrHZM7aL2U Week 2: Mean, Mode, Standard Deviation https://www.youtube.com/watch?v=2rEhWFhSqnI https://www.youtube.com/watch?v=wJGgZJNYaPA Week 3: Creating Frequency Distributions https://www.youtube.com/watch?v=Pujol1yC1_A https://www.youtube.com/watch?v=asEuFvWGJDs Week 4: Probability Distributions https://www.youtube.com/watch?v=yng9pQQmJUE Week 5: Working with Z scores https://www.youtube.com/watch?v=mai23vW8uFM

Week 6: Student’s T Test https://www.youtube.com/watch?v=QoV_TL0IDGA Week 8: Chi-Square Test https://www.youtube.com/watch?v=ODxEoDyF6RI https://www.youtube.com/watch?v=UPawNLQOv-8 Week 9: ANOVA Test and F test https://www.youtube.com/watch?v=-yQb_ZJnFXw https://www.youtube.com/watch?v=Ke9ttUj7AQc Week 10: Correlation Analysis https://www.youtube.com/watch?v=xGbpuFNR1ME https://www.youtube.com/watch?v=4EXNedimDMs Week 11: Linear Regression https://www.youtube.com/watch?v=ZkjP5RJLQF4 https://www.youtube.com/watch?v=Ut22-WLvEVw

Method of Evaluation Your final grade is based upon your performance in the following course requirements:

Nine Homework Assignments (10 points: 5 for data analysis, 5 for interpretation and report) Final Project (30 points for data analysis, 20 interpretation and report) Total Points

MT 8600

F2013

90 50 140

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MBA IN THE MANAGEMENT OF TECHNOLOGY AND INNOVATION MBA GLOBAL Ted Rogers School of Management Class Schedule Week & Date

Lecture, In-Class Design Exercises and Learning Objective

Week 1 May 7

Lectures 1: Basics of Concepts of Data Measurement, Frequency Distributions and Percentiles In-Class Exercises: Navigating EXCEL, loading the Analysis addin and doing descriptive analysis of sample data using EXCEL Learning Objective: The student should be able to: (1) distinguish between nominal, ordinal, interval and ratio scales; (2) use EXCEL to graph data and interpret the results Lectures 2: Basics of Concepts for Descriptive Data Analysis: Measures of central tendency and Dispersion In-Class Exercises: Descriptive analysis of sample data using EXCEL Learning Objective: The student should be able to analyze and interpret data using the concepts of mean, median and standard deviation using EXCEL, interpret the results and report on the findings Lectures 3: Basics of Descriptive Statistical Analysis: Central Limit Theorem and Standard Normal Distribution. In-Class Exercises: Central limit theorem Learning Objectives: The student should be able to analyze and interpret data using the central limit theorem

Week 2 May 14

Week 3 May 21

Read Before Lecture For lecture 1 Read Chapters 1 &2

Assignment Due Dates

For lecture 2 Read Chapter 3

Assignment 2: May 18, 11:59 pm (10 points) Late 1 point deducted per day

For lecture 3 Read Chapter 4

Assignment 3: May 26, 11:59 pm (10 points) Late 1 point deducted per day

Assignment 1: May 12, 11:59 pm (10 points) Late Assignments 1 point deducted per day

Week 4 May 28

Lectures 4: Basics of Descriptive Statistical Analysis: Central limit theorem and probability distributions. In-Class Exercises: Using probability distributions to analyze sample data Learning Objectives: The student should be able to analyze and interpret data using probability distributions

For lecture 4 Read Chapter 5 & 6

Assignment 4: June 2, 11:59 pm (10 points) Late 1 point deducted per day

Week 5 June 4

Lectures 5: Basics of Inferential Statistical Analysis: The logic of hypothesis testing and using z scores In-Class Exercises: Constructing hypothesis tests and using Z scores Learning Objectives: The student should able to construct and execute hypotheses test, interpret the results and report on the findings Lectures 6: Basics of Inferential Statistical Analysis: Using the student’s T test In-Class Exercises: Using EXCEL to implement the Student’s t test on sample data Learning Objectives: The student should able to analyze sample data using hypotheses testing techniques in EXCEL, interpret the results and report on the findings Midterm test

For lecture 5 Read Chapter 8 & 11

Assignment 5: June 10, 11:59 pm (10 points) Late 1 point deducted per day

Week 6 June 11

Week 7 June 18

MT 8600

F2013

For lecture 6 Read Chapter 12

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MBA IN THE MANAGEMENT OF TECHNOLOGY AND INNOVATION MBA GLOBAL Ted Rogers School of Management Week & Date

Lecture, In-Class Design Exercises and Learning Objective

Read Before Lecture

Assignment Due Dates

Week 8 June 25

Lectures 7: Basics of Inferential Statistical Analysis: The Chi-Squared Test In-Class Exercises: The application of analysis of Chi-Square Test techniques to sample data Learning Objectives: The student should be able to apply ChiSquare in EXCEL to sample data and make interpretations and report on the findings Lectures 8: Basics of Inferential Statistical Analysis: Analysis of Variance and F test In-Class Exercises: The application of one factor analysis of variance techniques to sample data Learning Objectives: The student should be able to implement ANOVA in EXCEL to sample data and make interpretations and report on the findings Lectures: 9: Basics of Inferential Statistical Analysis: Analysis of Variance In-Class Exercises: The application of two factor analysis of variance techniques to sample data Learning Objectives: The student should be able to implement ANOVA in EXCEL to sample data and make interpretations and report on the findings Lectures 10: Basics of Inferential Statistical Analysis: Pearson’s Correlation Analysis In-Class Exercises: The application of simple linear regression models for predictive analysis in business Learning Objectives: The student should able to apply correlation models in Excel for the analysis of data, interpret the results and report on the findings Lectures 11: Basics of Inferential Statistical Analysis: Simple Linear Regression In-Class Exercises: The application of simple linear regression models for predictive analysis in business Learning Objectives: The student should able to apply regression models in Excel to analyze data, interpret the results and report on the findings TBA

For lecture 8 Read Chapter 22 Teaching note

Assignment 6: June 30, 11:59 pm (10 points) Late 1 point deducted per day

For lecture 8 Read Chapter 17 Teaching Note

Assignment 7: July 7, 11:59 pm (10 points) Late 1 point deducted per day

For lecture 9 Read Chapter 18

Assignment 8: July 14, 11:59 pm (10 points) Late 1 point deducted per day

For lecture 11 Read Chapter 20 & 21

Assignment 9: July 21, 11:59 pm (10 points) Late 1 point deducted per day

Week 9 July 2

Week 10 July 9

Week 11 July 16

Week 12 July 23

Final Project

MT 8600

F2013

For lecture 12 Read Chapter 20 & 21

Final project due August 10 11:59 PM

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MBA IN THE MANAGEMENT OF TECHNOLOGY AND INNOVATION MBA GLOBAL Ted Rogers School of Management Required Reference Format APA Course Assessment Note: Satisfactory performance in a Master’s program requires completion of all courses taken for credit in the graduate program with a grade of at least B- in each course. Any grade below B – will be deemed Unsatisfactory and graded as an F.

Master’s Grading System Letter Grade

Conversion Range Percentage Scale to Letter Grades

Equivalent in Earned Points

A+ A AB+ B BF

90-100 85-89 80-84 77-79 73-76 70-72 0-69 (Master’s Unsatisfactory Performance Level) 0-72 (PhD Unsatisfactory Performance Level)

130-140 118- 129 100 -117 90- 99 80-89 70 - 79...