Statistical Inference and Regression Models

This course is an introduction to using statistical inference and regression modeling in the data science field. Statistical inference is the process by which we draw conclusions about a particular data set. These conclusions may lead us to actionable tasks for our business. Regression modeling allows us to predict events based on the relationship between variables included in the data set that we are working with. In this class, we will use R Programming language to walk through how to do regression modeling on a data set and how to interpret the results. You will understand the mathematics and be able to rely on R to do the heavy lifting.

Course Outline:

  • Definition of Statistical Inference
  • Benefits and usage
  • Statistics
  • Estimation
  • Hypothesis testing
  • Definition of regression modeling
  • Benefits and usage
  • Regression models
  • Residuals
  • Prediction
  • How to select a model
  • Confounding variables

Unique Value of Course:

Objectives:

Who Should Attend:

  • Chief Analytics Officer
  • Chief Data Officer
  • Senior Managers in Operations and IT
  • Business Process Managers
  • Business Process Analysts
  • Business Analysts
  • Business Architects
  • Project managers
  • Data Analyst
  • Data Scientist
  • Data Specialist
  • Data Stewards
  • Information Architects

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Course Availability

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Events

BrainStorm DC - 09/26/2019 - 09:00

Live Online

N/A

On Demand

N/A

Custom

Available In-House or Private Live Online

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This training course is part of an eight-course Learning Path.

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Use the Learning Paths guide for easy course selection. When you complete eight courses, you will earn your BAIPSM.