Statistical Analysis


In this course you’ll learn about linear regression and logistic regression, which identify relationships between the variables in your data. You’ll also learn about cluster analysis, which identifies relationships between points in the data set.  You’ll apply key concepts through case-study style learning, with concise lessons and applied assessments.  Created & customized for Chartered Accountants Ireland by

Venue details:  
Online EU, ,
Start date & time:  
01 January 2021 00:00
End date & time:  
01 January 2022 00:00
By registering for this course you have accepted the terms and conditions
Training ticket cost:  
3.00 Training Tickets accepted
CPD hours:  
Speaker details
No speakers have been associated with this event.


Product type:  
CPD online course
Kubicle, Technology and data

Who Should Attend?

This course is suited to advanced learners with a good general knowledge of Alteryx. Some knowledge of statistics is helpful, but we provide a review of the fundamental concepts. This course will be of interest to any business professionals, such as accountants, management consultants and analysts who want to use statistical methods in Alteryx to obtain insights from their data.

Course Overview

This Kubicle course contains 16 lessons, 3 exercises and 1 Exam. It covers the following topics:

  • Review of Fundamental Statistics Concepts
  • Initial Data Investigation
  • Identifying Significant Variables
  • Analyzing Regression Results
  • Forecasting Results with the Score Tool
  • Validating our Regression Model
  • Logistic Regression
  • Segmenting Data into Clusters
  • Cluster Analysis
  • Preparing Cluster Analysis for Data Visualization

Key Outcomes

Once you’ve completed this course, you’ll understand how to create a variety of statistical models. You’ll be able to analyze the relationships between variables in your data set, and understand the clusters within the data itself. By applying the skills you’ll learn in this course, you’ll improve your ability to obtain insight from large and complex datasets.