Learning Outcomes What you will know

Applying Statistics to Business Forecasting

You will be able to perform a multiple regression analysis using real-world data working on a real-world business problem.

You will be able to make a confident revenues forecast.

You will gain an appreciation of the information required to make such a forecast, forecasting methodologies, and the strength and weaknesses of such forecasts.

Referencing modules: Forecasting Project

Assessed by: FP1, FP2, FP3

Understand course structure

You understand the goals, structures, and procedures for learning in BUS 619.

Referencing modules: Syllabus

Assessed by: Statistics Self Test

Introduction to Business Statistics and Analytics

Understand the purpose and objectives of Business Statistics and Analytics, how it is organized, and learn about the basics of working with data.

Referencing modules: Overview of Business Analytics

Assessed by: Session 1 Exercises

Understanding regression model validity

After this session you should be able to:

• Check whether regression assumptions are satisfied
• Check to see if the data contains unusual values

Referencing modules: Residual Analysis

Assessed by: Session 10 Exercises

Regression Model Building

After this module you should be able to:

• Develop a multiple regression model
• Interpret regression coefficients for multiple independent variables
• Determine which independent variables to include in the regression model
• Use categorical variables in a regression model

Referencing modules: Model Building

Assessed by: Session 11 Exercises

Describing Catagorical Data

Understand visual and statistical anaytics for describing categorial data. Key concepts: categorial data as groups, qualitative distribution, one-way tables, bar chart, pie chart, pareto chart, mode, discreet probability, conditional probability, two-way tables, emperical vs. a priori probability, excpected value, payoff matrix, decision tree

Referencing modules: Categorical Data

Assessed by: Session 2 Exercises

Describing Numerical Data

Understand visual and statistical analytics for describing numerical data. Key concepts: numerical data as measures, quantitative distribution, histogram, box plot, distribution central tendency, variability, shape, bi-variate data, correlation

Referencing modules: Numerical Data

Assessed by: Session 2 Exercises

Time Series Data

Understand describing time series data, naive time series forecasting, Forecasting project Part 1

Referencing modules: Time Series Data

Assessed by: Session 4 Exercises

Working with distributions

Understand working with emperical and a priori distributions as data models and inferences from sample data, outliers, and emperical validaton of Normal distibution assumption.

Referencing modules: Working with Distributions

Assessed by: Session 5 Exercises

Use Basic Inferential Statistics

The student will be able to identify data and apply appropriate confidence interval estimates and hypothesis testing based on need and data type using methods as indicated in Choosing Situation by Data Type.

• Estimating population mean, median, proportion (confidence intervals)
• Numerical/categorical variable vs. hypothesized value
• Comparing means, medians of two paired/unpaired numerical variables
• Relationship between two categorical variables

Referencing modules: Basics of Inferential Statistics

Assessed by: Session 7 Exercises

Relationships between one numerical and and one categorical variable

After completing this module you will:

• Know how to recognize relationships between one numerical and and one categorical variable
• Use ANOVA to test for differences between means of multiple groups
• Use the Tukey-Kramer procedure to analyze differences between groups

Referencing modules: ANOVA

Assessed by: Session 8 Exercises

Simple Linear Regression

After this module you should be able to:

• Interpret the simple linear regression equation for a set of data
• Use descriptive statistics to describe the relationship between X and Y
• Determine whether a regression model is significant
• Interpret confidence intervals for the regression coefficients
• Interpret confidence intervals for a predicted value of Y
• Check whether regression assumptions are satisfied
• Check to see if the data contains leverage or residual outliers

Referencing modules: Simple Linear Regression

Assessed by: Session 9 Exercises

Six Sigma Certification

Obtain a Six Sigma White Belt Certification.

Referencing modules: Six Sigma Certification

Assessed by: Six Sigma Exercises

Masters Econ Tutorial

Understand fundamental economics reasoning and concepts common to Masters coursework and how they apply to business problems and how to implement them in a spreadsheet for quantitative analysis.

Referencing modules: Tutorials

Assessed by: Masters Econ Tutorial

Masters Excel Tutorial

Obtain or refresh fundamental spreadsheet skills needed for your courses and for business Understand how to construct a structured spreadsheet models that are ready for performing and communicating quantitative analysis.

Referencing modules: Tutorials

Assessed by: Masters Excel Tutorial

Masters Math Tutorial

Obtain or relearn fundamental quantitative reasoning and math concepts and how to apply them within spreadsheets to perform quantitative analysis useful in business.

Referencing modules: Tutorials

Assessed by: Masters Math Tutorial

Understand Control Charts

After this module you will understand:

• What control charts are and how they relate to process quality management

• How to identify different control charts and when they apply

• Statistical basis for in-control or out of control processess

• Recognizing common out of control patterns

Referencing modules: Statistical Process Control

Assessed by: Session 6 Exercises