### 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:
• 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.