Logistic regression extends ordinary least squares (OLS) methods to model data with binary (yes/no, success/failure) outcomes. Rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.
This course will explain meta analysis - the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.
This course is designed to give you an introduction to the algorithms, techniques and software used in natural language processing (NLP).
This course will teach you how to choose an appropriate time series
forecasting method, fit the model, evaluate its performance, and
use it for forecasting.
This course is about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software.
This course will show you how to use R to create statistical models and use them to analyze data.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
This course covers key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis and classification.
This course covers the issues that need to be addressed in order for a study to produce statistically valid conclusions. The first session covers different study designs, with an emphasis on designs that can be used with observational data (clinical trials are reviewed briefly, but those seeking a more in depth treatment should take the Institute's sequence of courses in clinical trial design and analysis). Students will learn what types of bias and extraneous factors can endanger studies, and how to avoid or adjust for them.
This course will introduce you to the basic ideas of Bayesian Statistics. You will learn how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.
This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.
To provide an easy introduction to statistical inference for a single variable.
Continues the exploration of Rasch theory and its application in the Winsteps software, begun in "Practical Rasch Measurement-Core Topics." The course introduces exciting new topics and delves into earlier topics more deeply. Participants are encouraged to analyze their own datasets in parallel to the course datasets.
This course will show you how to use R to create statistical models and use them to analyze data.
This course teaches participants how to model financial events that have uncertainties associated with them.
This course will introduce the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text.