Aim of Course:
To provide an easy introduction to statistical inference for a single variable.
Aim of Course:
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.
Aim of Course:
This course will show you how to use R to create statistical models and use them to analyze data.
Aim of Course:
The goal of this course is to introduce the basics of programming in
Python, on either Windows or Mac. You will use both Jupyter notebooks
and standard script editors, and work through simple arithmetic
operations, statistical operations, variables, keywords, lists, arrays,
and dictionaries. You'll use conda to install modules and close with
some data visualizations.
Aim of Course:
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.
Aim of Course:
This course will show you how to use R to create statistical models and use them to analyze data.
Aim of Course:
This course covers how to apply linear programming to complex systems to make better decisions – decisions that increase revenue, decrease costs, or improve efficiency of operations.
Aim of Course:
This course will show you how to use R to create statistical models and use them to analyze data.
Aim of Course:
After completing this course, students will be able to use R to summarize and graph data, calculate confidence intervals, test hypotheses, assess goodness-of-fit, and perform linear regression. This is a course to "Learn R via your existing knowledge of basic statistics" and does not treat statistical concepts in depth. Be sure to read the prerequisites.