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Professional Course

Advanced Statistical Inference and Modelling Using R

edX, Online
Length
6 weeks
Price
249 USD
Next course start
Start anytime See details
Delivery
Self-paced Online
Length
6 weeks
Price
249 USD
Next course start
Start anytime See details
Delivery
Self-paced Online
Visit this course's homepage on the provider's site to learn more or book!

Course description

Advanced Statistical Inference and Modelling Using R

Advanced Statistical Inference and Modelling Using R is part two of the Statistical Analysis in R professional certificate.

This course is directed at people who are already familiar with basic linear regression and fundamentals of statistical inference. It extends the knowledge of linear regression to the situations where the response variable is binary, a count, or categorical as well as to hierarchical experimental set-up. While very practice oriented, it aims to give the students the understanding of why the method works (theory), how to implement it (programming using R) and when to apply it (and where to look if the particular method is not applicable in the specific situation).

Upcoming start dates

1 start date available

Start anytime

  • Self-paced Online
  • Online
  • English

Who should attend?

Prerequisites

None

Course delivery details

This course is offered through University of Canterbury, a partner institute of EdX.

5-10 hours per week

Costs

  • Verified Track -$249
  • Audit Track - Free

Certification / Credits

What you'll learn

  • Exploratory data analysis and data visualisation using R.
  • Multivariate analysis using Generalised Linear Models (GLMs):
    • Binary response (logistic regression) GLM
    • Poisson counts GLM
    • Nominal categorical response (multinomial logistic GLM)
    • Ordinal categorical response (ordinal logistic GLM)
  • Mixed effects linear regression models. Structure, assumptions, diagnostics and interpretation. Model selection.
  • Basics of power analysis (sample size evaluation) and some thoughts on experimental design and missing data.

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