What are the Chances? Probability and Uncertainty in Statistics
- Offered byCoursera
What are the Chances? Probability and Uncertainty in Statistics at Coursera Overview
Duration | 11 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
What are the Chances? Probability and Uncertainty in Statistics at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 5 in the Data Literacy Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Familiarity with descriptive statistics and basic regression models
- Approx. 11 hours to complete
- English Subtitles: English
What are the Chances? Probability and Uncertainty in Statistics at Coursera Course details
- 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.
What are the Chances? Probability and Uncertainty in Statistics at Coursera Curriculum
Probability Theory
Welcome Video
Probability Definitions and Axioms
Permutations and Combinations
Conditional Probability and Independence
Axiomatic Probability: Definition, Kolmogorov?s Three Axioms
Monty Hall Simulation
Combinations and Permutations
Probability: Joint, Marginal and Conditional Probabilities
Dependent Events and Independent Events
Probability Definitions and Axioms Practice Problems
Permutations and Combinations Practice Problems
Conditional Probability Practice Problems
Final Assessment on Probability Theory
Random Variables and Distributions
Random Variables and Probability Distributions
The Normal Distribution
Large Sample Theorems
Random Variables
Discrete Random Variables and Continuous Variables
The Normal Distribution
Central Limit Theorem: Definition and Examples in Easy Steps
The Law of Large Numbers vs. The Central Limit Theorem
Random Variables and Probability Distributions Practice Problems
The Normal Distribution Practice Problems
Large Sample Theorems Practice Problems
Final Assessment on Random Variables and Distributions
Confidence Intervals and Hypothesis Testing
Bias, Consistency and the Standard Error
Confidence Intervals
Hypothesis Testing: Overview
Hypothesis Testing: Implementation
Bias vs. Consistency
What are confidence intervals in statistics?
What is hypothesis testing?
Bias, Consistency and the Standard Error Practice Problems
Confidence Intervals Practice Problems
Hypothesis Testing Practice Problems
Final Assessment on Confidence Intervals and Hypothesis Testing
Quantifying Uncertainty in Regression Analysis and Polling
Testing Regression Coefficients
Pitfalls of Hypothesis Testing
The Margin of Error in Polls
Statistical Hypothesis Testing and Some Pitfalls
Margin of Error: Definition, How to Calculate in Easy Steps
Testing Regression Coefficients Practice Problems
Pitfalls of Hypothesis Testing Practice Problems
Margin of Error Practice Problems
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