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Regression Analysis- Statistics.com

Statistics.com
0-6 Month Online
Intermediate 23-Jan-2015
Online Business Analytics
Online Self Paced 41
DescriptionProgram StructureEligibilityToolsFacultyContact

Regression, perhaps the most widely used statistical technique, estimates relationships between independent (predictor or explanatory) variables and a dependent (response or outcome) variable. Regression models can be used to help understand and explain relationships among variables; they can also be used to predict actual outcomes.

In this online course, you will learn how multiple linear regression models are derived, use software to implement them, learn what assumptions underlie the models, learn how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models.

Course Program:

  • WEEK 1: Foundations and Simple Linear Regression
  • WEEK 2: Multiple Linear Regression
  • WEEK 3: Model Building I
  • WEEK 4: Model Building II

Important Date

January 23, 2015 to February 20, 2015

Duration: – 4 Weeks

Time Requirement: about 15 hours per week, at time of your choosing.

Fees: INR 35,340 (assuming $ = INR 60)

Part time/ Full Time:

Part Time

These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

  • Statistics 1 – Probability and Study Design
  • Statistics 2 – Inference and Association

Who Should Take This Course:

Scientists, business analysts, engineers and researchers who need to model relationships in data in which a single response variable depends on multiple predictor variables. If you were introduced to regression in an introductory statistics course and now find you need a more solid grounding in the subject, this course is for you. If you are planning to learn additional topics in statistics, a good knowledge of regression is often essential.

  • Statistics
  • Dr. Iain Pardoe
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