BUSI 391 Outline

THE KING'S UNIVERSITY

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:

Moving Averages

Exponential Smoothing

Trend Projection

Seasonality and Trend

Time Series Decomposition

Part 5: Special Topics

Statistical Methods for Quality Control

Quality: Philosophies and Frameworks

Statistical Process Control

Control Charts

Acceptance Sampling

Introduction to Decision Analysis

Optimization and Simulation Modeling

Data Mining

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

Required texts, assignments, and grade distributions may vary from one offering of this course to the next. Please consult the course instructor for up to date details.