Business Applications of Statistics and Data Analytics


Business Applications of Statistics and Data Analytics covers the latest and most trusted techniques for analyzing data with instruction in data cleaning, multivariate analysis, hypothesis testing, survey design and analysis, data visualization and regression diagnostics.

This course will cover data analysis, statistics and quantitative methods with a focus on hands-on, applied problems. It will discuss the collection and analysis of data, including survey and sampling methods, cleaning and coding data, and structuring datasets. It will also cover statistical techniques including basic probability, probability distributions, descriptive statistics, confidence intervals, and hypothesis testing. In addition, the course will cover various quantitative methods including analysis of variance, simple and multivariate regression, discrete models, diagnostics, and an introduction to basic big data analysis. The course also covers basic project planning and an overview of how to communicate technical concepts such as elasticity, regression estimates, and forecasting.

This offering is the latest element added to NABE's Certified Business Economist program, the certification in applied economics and data analytics. More information

See below for more information on the course, including pricing and future offerings.

Course Topics

  • Basic probability

  • Sampling and Survey methods

  • Probability distributions

  • Cleaning and Coding Data

  • Descriptive Statistics

  • Introduction to Basic Big Data

  • Confidence Intervals

  • Hypothesis Testing

  • Basic Correlation and Regression Analysis


  • Discrete Methods

  • Project Planning

  • Communicating Technical Results

  • Diagnostics


Questions to be Addressed

  • How do I decide who to survey?

  • How can I tell if a distribution is normal and what do I do if it isn't?

  • What do I do with missing data?

  • What are the consequences of using unweighted data?

  • How do I start working with really large datasets and is there any advantage of doing so?

  • How do I work with uncertainty? Can I place any confidence in my results?

  • How "good" are my regression estimates?

  • Which functional form is best?

  • How do I transform my data?

  • How do I create an index, and why are they useful?

  • Can I really use Excel to produce "reliable" estimates?

  • Have I checked and fixed common problems like heteroskedasticity and multicollinearity?

  • Can I provide useful statistical insight to problems without fancy software?

  • What is statistical significance? How can I make the results "useful?"


 Registration Rates* 

Member Early-Bird: $1275

Government Early-Bird: $1350

Non-Member Early-Bird: $1425

Future Offerings

May 16-18 - Federal Reserve Bank of Atlanta - Birmingham Branch

For future offerings of NABE's other 
Certified Business Economist programs, check the CBE calendar.




About the Instructor

Dr. Heather Luea is an economics instructor and academic department chair, with expertise in quantitative methods. She earned her Bachelors degree in Finance from Fort Hays State University and her Master’s in Business Administration from Wichita State University. She worked in corporate finance prior to completing her PhD in Economics from Kansas State University with fields in econometrics and public and regional economics.

Dr. Luea has extensive teaching experience that includes positions at Vanderbilt University, Tennessee State University, and University of California, Berkeley. She has taught across the business curriculum and has won numerous teaching awards.

Her research interests include housing, healthcare, and fringe banking services. Her most recent research focuses on the impact of payday lending on neighborhood crime. Her work has been published in Contemporary Economic Policy, Journal of Housing Economics, and the Nashville Area Business and Economic Review. She has an active research agenda and her papers have been accepted at numerous national conferences, including the American Economics Association annual meetings.