What are the Chances? Probability and Uncertainty in Statistics
$49
ENROLL NOWCourse Overview
What You'll Learn
- We’ll then apply these ideas to variables (which are the building blocks of statistics) and their associated probability distributions.
- Finally, we’ll consider the role of hypothesis testing in a regression context, including what we can and cannot learn from the statistical significance of a coefficient.
This course focuses on how analysts can measure and describe the confidence they have in their findings. The course begins with an overview of the key probability rules and concepts that govern the calculation of uncertainty measures. We’ll then apply these ideas to variables (which are the building blocks of statistics) and their associated probability distributions. The second half of the course will delve into the computation and interpretation of uncertainty. We’ll discuss how to conduct a hypothesis test using both test statistics and confidence intervals. Finally, we’ll consider the role of hypothesis testing in a regression context, including what we can and cannot learn from the statistical significance of a coefficient. By the end of the course, you should be able to discuss statistical findings in probabilistic terms and interpret the uncertainty of a particular estimate.
Course FAQs
Is this an accredited online course?
Accreditation for 'What are the Chances? Probability and Uncertainty in Statistics' is determined by the provider, Johns Hopkins University. For online college courses or degree programs, we strongly recommend you verify the accreditation status directly on the provider's website to ensure it meets your requirements.
Can this course be used for continuing education credits?
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How do I enroll in this online school program?
To enroll, click the 'ENROLL NOW' button on this page. You will be taken to the official page for 'What are the Chances? Probability and Uncertainty in Statistics' on the Johns Hopkins University online class platform, where you can complete your registration.
