Probability and Statistics: To p or not to p?
- Offered byCoursera
Probability and Statistics: To p or not to p? at Coursera Overview
Duration | 16 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Probability and Statistics: To p or not to p? at Coursera Highlights
- 33% started a new career after completing these courses.
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
Probability and Statistics: To p or not to p? at Coursera Course details
- We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for "black swans" - low-probability high-impact events.
- To study, or not to study? To invest, or not to invest? To marry, or not to marry?
- While uncertainty makes decision-making difficult, it does at least make life exciting! If the entire future was known in advance, there would never be an element of surprise. Whether a good future or a bad future, it would be a known future.
- In this course we consider many useful tools to deal with uncertainty and help us to make informed (and hence better) decisions - essential skills for a lifetime of good decision-making.
- Key topics include quantifying uncertainty with probability, descriptive statistics, point and interval estimation of means and proportions, the basics of hypothesis testing, and a selection of multivariate applications of key terms and concepts seen throughout the course.
Probability and Statistics: To p or not to p? at Coursera Curriculum
Dealing with Uncertainty and Complexity in a Chaotic World
Welcome!
1.1 The Monty Hall Problem
1.2 Decision Making Under Uncertainty
1.3 Uncertainty in the News
1.4 Simplicity vs. Complexity - The Need for Models
1.5 Safe to Assume? Beware, When Model Assumptions Go Wrong!
1.6 Roadmap of the Course
Week One Summary and Key Takeaways
1.1 The Monty Hall Problem
1.2 Decision Making Under Uncertainty
1.3 Uncertainty in the News
1.4 Simplicity vs. Complexity - The Need for Models
1.5 Safe to Assume? Beware, When Model Assumptions Go Wrong!
1.6 Roadmap of the Course
Week One Quiz
Quantifying Uncertainty With Probability
2.1 Probability Principles
2.2 Simple Probability Distributions
2.3 Expectation of Random Variables
2.4 Bayesian Updating
2.5 Parameters
2.6 The Distribution Zoo
Week Two Summary and Key Takeaways
2.1 Probability Principles
2.2 Simple Probability Distributions
2.3 Expectation of Random Variables
2.4 Bayesian Updating
2.5 Parameters
2.6 The Distribution Zoo
Week Two Quiz
Describing The World The Statistical Way
3.1 Classify Your Variables!
3.2 Data Visualisation
3.3 Descriptive Statistics - Measures of Central Tendency
3.4 Descriptive Statistics - Measures of Spread
3.5 The Normal Distribution
3.6 Variance of Random Variables
Week Three Summary and Key Takeaways
3.1 Classify Your Variables!
3.2 Data Visualisation
3.3 Descriptive Statistics - Measures of Central Tendency
3.4 Descriptive Statistics - Measures of Spread
3.5 The Normal Distribution
3.6 Variance of Random Variables
Week Three Quiz
On Your Marks, Get Set, Infer!
4.1 Introduction to Sampling
4.2 Random Sampling
4.3 Further Random Sampling
4.4 Sampling Distributions
4.5 Sampling Distribution of the Sample Mean
4.6 Confidence Intervals
Week Four Summary and Key Takeaways
4.1 Introduction to Sampling
4.2 Random Sampling
4.3 Further Random Sampling
4.4 Sampling Distributions
4.5 Sampling Distribution of the Sample Mean
4.6 Confidence Intervals
Week Four Quiz
To p Or Not To p?
5.1 Statistical Juries
5.2 Type I and Type II errors
5.3 P-values, Effect Size and Sample Size Influences
5.4 Testing a Population Mean Claim
5.5 The Central Limit Theorem
5.6 Proportions: Confidence Intervals and Hypothesis Testing
Week Five Summary and Key Takeaways
5.1Statistical Juries
5.2 Type I and Type II errors
5.3 P-values, Effect Size and Sample Size Influences
5.4 Testing a Population Mean Claim
5.5 The Central Limit Theorem
5.6 Proportions: Confidence Intervals and Hypothesis Testing
Week Five Quiz
Applications
6.1 Decision Tree Analysis
6.2 Risk
6.3 Linear Regression
6.4 Linear Programming
6.5 Monte Carlo Simulation
6.6 Overview of the Course and Next Steps
6.1 Decision Tree Analysis
6.2 Risk
6.3 Linear regression
6.4 Linear Programming
6.5 Monte Carlo Simulation
Week Six Quiz
Probability and Statistics: To p or not to p? at Coursera Admission Process
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