Statistics.com LMS
Available courses
Aim of Course:
This course extends the Bayesian modeling framework to cover hierarchical models, and to add flexibility to standard Bayesian modeling problems.
- Teacher: Peter Congdon
Aim of Course:
This course covers the basic statistical principles in the design and analysis of randomized controlled trials.
- Teacher: Kalyani Kalmankar
Aim of Course:
After successfully completing this course, you will understand the role that MLE plays in statistical models, and be able to assess both the advantages and disadvantages of using a maximum likelihood estimate in a particular situation.
- Teacher: Kuber Deokar
Aim of Course:
This course will explain meta analysis - the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.
- Teacher: Michael Borenstein
- Teacher: Hannah Rothstein
Aim of Course:
To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.
- Teacher: Anuja Kulkarni
Aim of Course:
This online course covers three important modeling techniques. Students will learn how to (1) construct and implement simulations to model the uncertainty in decision input variables (e.g. price, demand, etc.) and supplement the overall estimate of interest by a risk interval of possible other outcomes using risk simulation; (2) model the variability in arrivals over time (customers, cars at a toll plaza, data packets, etc.) and ensuing queues, using queuing theory; (3) how to employ decision trees to incorporate information derived from models to actually make optimal decisions.
- Teacher: Cliff Ragsdale
Aim of Course:
This course will show you how to use R to create statistical models and use them to analyze data.
- Teacher: Madhuri Maddipatla
- Teacher: Takis Tzetzos
Aim of Course:
The aim of this course is to teach R Programming to those with little or no programming knowledge or experience.
- Teacher: Dipanjan Sarkar
Aim of Course:
In this course you will work through a customer analytics project from beginning to end, using R. You will start by gaining an understanding of the problem and the context, and continue to clean, prepare and explore the relevant data. You’ll work on feature engineering, handling dates, summarization, and how to work with the customer lifecycle concept in data analysis. The course culminates with a report that you will write, and a recommendation that you will prepare for a hypothetical company.
- Teacher: Karolis Urbonas
Aim of Course:
This course will show you how to use R to create statistical models and use them to analyze data.
- Teacher: Stats Assistant
- Teacher: Anthony Babinec