An outline of this training course

An essential skill for anyone working with data is the ability to identify relationships and draw effective conclusions. However, the challenge lies in determining whether those relationships are real or coincidental and how to recognize that uncertainty.

In Introduction to Statistics for Data Analysis I, Dr. Andrew Gard introduced key statistical techniques, focusing on how to implement and apply the appropriate tests when analyzing a single variable. In this course, he builds on those techniques, exploring statistical tools used to determine the relationship between two variables.

Data analysts will often want to answer questions about how multiple variables in a dataset are potentially related to one another, or how samples from different populations are potentially related. Throughout the course, he demonstrates how to utilize the appropriate tool to draw valid conclusions. His practical approach guides you through the process of selecting a test, common pitfalls to avoid, and how you can apply the techniques learned to real-life scenarios.

What are needed to take this course 

Participants of this course must have basic knowledge and understanding of variable types, and descriptive and inferential statistics. No other advanced preparation is needed to take this course.

Although the demonstrations in the course are done in R, they are intended to be generalizable to Python or any other statistical software you choose to use. Knowledge of R is not a prerequisite for taking this course.

Who is the course for

Anyone working with data and attempting to draw constructive, generalizable conclusions from data

Details of what you will learn during this course

By the end of this course, you will:

• Possess a toolkit of statistical techniques to compare two potentially different samples of a single variable
  
• Possess a toolkit of statistical techniques to assess relationships between two variables in a single population
  
• Learn common pitfalls of two-sample and two-variable inference including over-generalization
  
• Gain an understanding of the dangers of data dredging
  
• Obtain the ability to apply two-sample and two-variable techniques in real-life data analysis
  

What you get with the course

• A three-hour self-paced video training
  
• Resource pack that includes data source files and R scripts
  
• One final assessment (Quiz)
  

Program Level

Intermediate

Field(s) of Study

Statistics

Instruction Delivery Method

QAS Self-study

***This course was published in April 2023

Enterprise DNA is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org