TERM: | 2021-22 Winter |
COURSE NUMBER: |
BUSI 391 |
COURSE TITLE: |
Statistics for Business II |
NAME OF
INSTRUCTOR: |
Dr
Tetyana Khramova |
CREDIT WEIGHT
AND WEEKLY TIME DISTRIBUTION: |
credits 3 (hrs lect 3 - hrs sem 0 - hrs lab 1.5) |
COURSE
DESCRIPTION: |
Students will deepen their skills in data analysis and
decision making under uncertainty using quantitative methods.
Regression analysis, modeling, and time series forecasting are applied
to real data and business examples. The course also provides a basic
understanding of optimization modelling, simulation modeling, and data
mining. Students will learn to interpret output from statistical
spreadsheets.
Prerequisites: BUSI 320, 396 |
REQUIRED TEXTS: |
Lecture notes and handouts: The lectures make use of presentation
software; PDFs of slides will be available for downloading and printing
from the Moodle site. There will be other handouts/manuals/tutorials
for you on Moodle with examples and business cases.
Textbooks: Students are expected to have one of the following textbooks; either physical or digital format is acceptable.
- Essentials
of Business Statistics, 5th Edition, Bruce L. Bowerman, Richard T.
O’Connell, Emily S. Murphree, J.B. Orris. Published by McGraw-Hill
Education, 2015 (ISBN 978-0078020537);
- 4th edition works as well
- Statistics
for Business and Economics, 13th Edition, David R. Anderson, Dennis J.
, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran. Published by
South-Western College Pub, 2016 (ISBN 978-1305585317). This text is
reserved for the stats students at the King’s library.
Free Online Resources:
- Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition; Ch. 6 - 8
- Principles of Business Statistics; Ch. 6
- Business Statistics: Revealing Facts from Figures; Ch. 9, 10
- Intro to Statistics; Ch. 10, 11
|
MARK
DISTRIBUTION IN PERCENT: |
|
Attendance and participation | 5% |
Laboratory | 20% |
Assignments | 20% |
Midterm Exams |
25% |
Final Exam |
30% |
|
|
|
100% |
|
COURSE
OBJECTIVES: |
Upon completion of this course,
the student will be able to:
- Demonstrate knowledge of regression analysis, model
building, time series forecasting, and statistical methods for quality
control
- Understand the main concepts of decision analysis,
optimization and simulation modeling, and data mining
- Analyze the advantages and drawbacks of different
quantitative methods; demonstrate ability to apply statistical methods
and write reports
- Show knowledge of computer-based statistical analysis
and Excel spreadsheets, be able to choose and apply right tools while
provide their statistical analysis
- Interpret the results of statistical analyses and
spreadsheets outputs, draw conclusions to make decisions under
uncertainty
|
COURSE OUTLINE: |
Introduction to Statistics for Business 2
Part 1: Experimental Design
- Experimental Design & Analysis of Variance
(ANOVA)
- Basic Concepts of Experimental Design
- One-Way Analysis of Variance
- The Randomized Block Design
- Two-Way Analysis of Variance Review
- Multinomial Data & Chi-Square Tests
- The Multinomial Experiment
- Chi-Square Goodness of Fit Tests
- A Chi-Square Test for Independence
Part 2: Regression Analysis: Simple Regression
- Using Simple Regression to Describe a Linear
Relationship
- Model Assumptions and the Standard Error
- Testing the Significance
- Confidence and Prediction Intervals
- Simple Coefficients of Determination and Correlation
- Residual Analysis
Part 3: Regression Analysis: Multiple Regression
- Using Multiple Regression to Describe a Linear
Relationship
- Model Assumptions and the Standard Error
- Testing the Significance
- Confidence and Prediction Intervals
- Multiple Coefficients of Determination and Correlation
- Multiple Regression and Model Building
Part 4: Time Series Analysis and Forecasting
- Time Series Patterns
- Forecast Accuracy
- Time Series Analysis and Forecasting Modeling:
- Time Series Decomposition
Part 5: Special Topics
- Statistical Methods for Quality Control
- Quality: Philosophies and Frameworks
- Statistical Process Control
- Introduction to Decision Analysis
- Optimization and Simulation Modeling
|
LAB OUTLINE: |
- One-Way ANOVA
- Two-Way ANOVA
- Chi-Square Tests
- Simple Linear Regression
- Multiple Regression
- Multiple Regression with Indicator Variables
- Forecasting Modeling
- Seasonality with Trend Forecasting
|