What are the Chances? Probability and Uncertainty in Statistics
About this course
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.
90/100
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- What the provider tells you
- 39/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Understand key probability rules and concepts
- Apply probability concepts to statistical variables
- Conduct hypothesis tests using test statistics and confidence intervals
- Interpret uncertainty in statistical estimates
Course objectives
- Measure and describe confidence in statistical findings
- Discuss statistical significance in the context of regression analysis
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