MGTSC 312 Course Outline Winter Term 2017
1
Alberta School of Business
Department of Finance and Statistical Analysis Lecture Section B1: Tuesdays and Thursdays, 2:00 - 3:20 PM, Bus 3-6 Lab Section H1: Thursdays, 12:30 - 1:20 PM, Bus B-28
Instructor: Phil Davidson Email: [email protected]
Office: BUS 1-23B Phone: 780-492-7818
Office hours: e-mail for appointment, or try your luck
The various topics in this course build on previous topics, so don’t get behind. If you are having difficulty please discuss with your instructor immediately.
Introduction: This course will help you learn how to access the wealth of data available online, how to analyse that data using Microsoft Excel, and how to interpret the results. We will look first at descriptive statistics, then at one-sample inference for the population mean and proportion. The bulk of the course will focus on regression. Developing facility in the use of Excel to manipulate and analyze data will prove to be a very useful skill in later courses and in your working life. The ability to interpret results and explain them to others is a valuable business skill.
Software: We will use Office 2010 for Windows in classrooms and labs. Office 2007 or 2013 should also work without significant problems. If you use a Mac and have problems, we are not in a position to help with those and do not have Mac versions of the course materials that are in electronic form. We also recommend that you use a Windows machine for practice materials since that is what you will use in the labs and for exams.
Course pack: There is a course pack, customized to the needs of our program and that is far less costly than the commercial textbooks for this sort of course. It is sold by the OM Club for $30 on a cash-only, no- refund basis in the first week of the term.
Notes and Schedule: I will post slides, data sets, and other materials on uLearn. It is helpful to review the notes before class. Dates for exams will not normally change from those listed below, but dates for assignments are tentative until an assignment is made available to work on.
Tutorial: An online self-paced tutorial covering the earlier material of the course is available at
http://apps.business.ualberta.ca/mgtsc312. It is new and still incomplete, but has a lot of useful material.
Practice Problems: I will post practice materials and solutions on uLearn.
What you should be able to do at the end of the course:
• Understand applied articles, reports, textbook materials, and lectures in other courses that make use of descriptive statistics, correlation and regression analysis, and statistical inference for the mean, correlations and standard regression parameters.
• Locate, manipulate and analyse real data from existing online sources using Microsoft Excel.
• Compute descriptive statistics, correlations and regressions; carry out statistical inference for parameters of interest.
• Use Excel formulas, functions, tools and wizards, including pivot table, auto filter, the data analysis toolbox, etc.
• Create effective graphs.
• Find and interpret values in computer output: Values of test statistics, p-values, parameter estimates, confidence intervals, etc.
• Write up the results of statistical analyses of data.
Class participation: Participation in the lectures, including class discussions, and labs is essential. If you must miss a lecture or lab, it is your responsibility to get the materials from someone who did attend.
MGTSC 312 Course Outline Winter Term 2017
2 Evaluation:
• Three exams: worth 20%, 20% and 35% (75%), all in the lab
• Two assignments: assignment 1 worth 10%, assignment 2 worth 15% (25%)
• If you miss an exam with a valid excuse then its weight will be transferred to the other exams
Students who require accommodations in this course due to a disability affecting mobility, vision, hearing, learning, or mental or physical health are advised to discuss their needs with Student Accessibility Services, 1-80 Students' Union Building, [email protected], 780-492-3381.
Policy about course outlines can be found in §23.4(2) of the University Calendar.
The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at
http://www.governance.ualberta.ca/CodesofConductandResidenceCommunityStandards/CodeofStudentBeh aviour.aspx) and avoid any behaviour which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.
The assignments are individual assignments. You are expected to do your own computing and write your own report. Evidence of cooperation will result in a score of zero for the assignment to the cooperating students. Both the giver and receiver will be assigned the score of zero. Additional sanctions are possible.
Learning Resources: We want to provide you with as many resources as possible to help you succeed in this course. We have lectures and normally post the lecture slides before we begin each topic in class. We have labs and normally post both lab slides and solution Excel files. We have a custom course pack. We have an online, self-paced tutorial which covers much of the basic material you need to know. We post additional practice materials on uLearn.
Classroom and Lab Etiquette
Attendance: It is your responsibility to attend lectures and labs. If you miss class for any reason, you are still responsible for all materials covered and announcements made.
Be on time for lectures and labs and remain for the entire period. Arriving late or leaving early is
inconsiderate of other students. If you have a valid reason for coming late or leaving early, please discuss that with your instructor.
Conversations: Do participate in class discussions and active learning exercises, as prompted by your instructor. Do not engage in sidebar conversations at other times, to avoid distracting other students or the instructor.
Breaks: You should not normally leave or re-enter the classroom or lab during the class period. Doing this is disruptive to fellow students and to the instructor. If you are affected by illness or medication such that you realize it may be necessary for you to leave during the class period, then please arrive early enough to sit close to the door so that you can leave and return with a minimum of disturbance.
Cell Phones: All cell phones, pagers, Blackberries, iPods, and similar communication devices are to be used in class only for course-related activities like reading/taking notes.
Laptops: Some students find it useful to use a laptop during lectures to take notes or perform analysis that the instructor is demonstrating. Laptop usage during class for this purpose, and for viewing PowerPoint slides, is permitted, but with the following caveats:
• Laptops are not permitted during exams;
• If you use a laptop, then be considerate of students around you and behind you;
• Using a laptop for email or other non-class purposes during lectures or labs is not permitted.
Recording of Lectures or Labs: Recording or taking pictures in lectures or labs is permitted only with the prior written consent of the instructor or if recording is part of an approved accommodation plan.
MGTSC 312 Course Outline Winter Term 2017
3 Approximate Schedule
Week Lecture Topics Reading Lab Evaluation
1
Jan. 9 - 13
Datasets
Descriptive Statistics
Topic 1 slides Crs Pk Ch 1, 2, 3
Excel basics Manipulating data 2
Jan. 16 – 20 Probability and Sampling Crs Pk Ch 4 Descriptive statistics Probability Functions 3
Jan. 23 - 27
Confidence intervals for one mean or proportion
Ch 5
Topic 2 slides One-sample Inference 4
Jn. 30 – Fb. 3
Hypothesis tests for one mean or proportion
Ch 6
Topic 3 slides Exam Exam 1
5
Feb. 6 – 10 Covariance and Correlation Ch 8
Topic 4 slides
Covariance &
Correlation 6
Feb. 13 – 17 Simple Linear Regression Ch 9
Graphs
Accessing online data
Feb. 20 - 24 Reading Week No Classes
7
Fb 27 – Mr 3
Simple Linear Regression Multiple Regression
Ch 10
Topic 5 slides
SLR “by hand”
Regression tool
Assignment 1 due 8
Mar. 6 – 10
Multiple Regression Multicollinearity
Violations of Assumptions
Ch 12
Topic 6 slides Exam Exam 2
9
Mar. 13 – 17
Diagnostics for regression Comparing Models – Partial F
Ch 14
Topic 7 slides
Multicollinearity, Residual Plots 10
Mar. 20 – 24
Categorical variables in regression
Ch 11 Topic 8 & 9 slides
Partial F
Categorical Variables 11
Mar. 27 – 31
Transformations Time Series Analysis Model Selection
Ch 13
Topic 10 slides
Transformations Time Series Model Validation 12
Apr. 3 – 7 Exam & Term Paper questions Exam Exam 3
13
Apr. 10 – 12 Term Paper questions Term paper
See uLearn Lectures and Labs for details on topics to be covered each week. The schedule for when each topic will be covered is tentative and may be updated throughout the term.
Dates for exams will not normally change. Any necessary changes will be posted as soon as possible on uLearn.