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What are the Chances? Probability and Uncertainty in Statistics
  • Level Professional
  • المدة 11 ساعات hours
  • الطبع بواسطة Johns Hopkins University
  • Offered by

عن

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.

الوحدات

Welcome!

    • 1 Videos
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1 Videos

  • Welcome Video

Probability Definitions and Axioms

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Probability Definitions and Axioms Practice Problems

1 Videos

  • Probability Definitions and Axioms

2 Readings

  • Axiomatic Probability: Definition, Kolmogorov’s Three Axioms
  • Monty Hall Simulation

Permutations and Combinations

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Permutations and Combinations Practice Problems

1 Videos

  • Permutations and Combinations

1 Readings

  • Combinations and Permutations

Conditional Probability and Independence

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Conditional Probability Practice Problems

1 Videos

  • Conditional Probability and Independence

2 Readings

  • Probability: Joint, Marginal and Conditional Probabilities
  • Dependent Events and Independent Events

Final Assessment on Probability Theory

    • 1 Assignment
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1 Assignment

  • Final Assessment on Probability Theory

Random Variables and Probability Distributions

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Random Variables and Probability Distributions Practice Problems

1 Videos

  • Random Variables and Probability Distributions

2 Readings

  • Random Variables
  • Discrete Random Variables and Continuous Variables

The Normal Distribution

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • The Normal Distribution Practice Problems

1 Videos

  • The Normal Distribution

1 Readings

  • The Normal Distribution

Large Sample Theorems

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Large Sample Theorems Practice Problems

1 Videos

  • Large Sample Theorems

2 Readings

  • Central Limit Theorem: Definition and Examples in Easy Steps
  • The Law of Large Numbers vs. The Central Limit Theorem

Final Assessment on Random Variables and Distributions

    • 1 Assignment
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1 Assignment

  • Final Assessment on Random Variables and Distributions

Bias, Consistency and the Standard Error

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Bias, Consistency and the Standard Error Practice Problems

1 Videos

  • Bias, Consistency and the Standard Error

1 Readings

  • Bias vs. Consistency

Confidence Intervals

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Confidence Intervals Practice Problems

1 Videos

  • Confidence Intervals

1 Readings

  • What are confidence intervals in statistics?

Hypothesis Testing

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Hypothesis Testing Practice Problems

2 Videos

  • Hypothesis Testing: Overview
  • Hypothesis Testing: Implementation

1 Readings

  • What is hypothesis testing?

Final Assessment of Confidence Intervals and Hypothesis Testing

    • 1 Assignment
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1 Assignment

  • Final Assessment on Confidence Intervals and Hypothesis Testing

Testing Regression Coefficients

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Testing Regression Coefficients Practice Problems

1 Videos

  • Testing Regression Coefficients

Pitfalls of Hypothesis Testing

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Pitfalls of Hypothesis Testing Practice Problems

1 Videos

  • Pitfalls of Hypothesis Testing

1 Readings

  • Statistical Hypothesis Testing and Some Pitfalls

The Margin of Error in Polls

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Margin of Error Practice Problems

1 Videos

  • The Margin of Error in Polls

1 Readings

  • Margin of Error: Definition, How to Calculate in Easy Steps

Final Assessment on Quantifying Uncertainty in Regression Analysis and Polling

    • 1 PeerReview
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1 Peer Review

  • Peer Review Assignment on Quantifying Uncertainty

Auto Summary

Unlock the power of probability and uncertainty in statistics with Coursera’s comprehensive course, "What are the Chances? Probability and Uncertainty in Statistics." Tailored for professionals in the Business & Management domain, this course equips analysts with the skills to measure and articulate the confidence in their statistical findings. Guided by expert instructors, participants will start by mastering the foundational probability rules and concepts crucial for calculating uncertainty measures. The curriculum then transitions to applying these concepts to variables and their probability distributions. In the latter part of the course, learners will dive deep into the computation and interpretation of uncertainty, exploring hypothesis testing using test statistics and confidence intervals. The course also covers the role of hypothesis testing within regression analysis, highlighting the insights and limitations of statistical significance. Spanning approximately 660 minutes of engaging content, this course offers a robust learning experience for professionals seeking to enhance their statistical analysis capabilities. Available through Coursera’s Starter subscription, this course is an invaluable resource for anyone aiming to discuss statistical findings in probabilistic terms and accurately interpret uncertainty in estimates.

Jennifer Bachner, PhD
Instructor

Jennifer Bachner, PhD