# Weekly Learning Update: Expected Value, Variance & Correlation

**URL:** <https://discourse.pymc.io/t/weekly-learning-update-expected-value-variance-correlation/17976>\
**Category:** Sharing\
**Created:** [October 23, 2026, 8:05am UTC](https://discourse.pymc.io/t/weekly-learning-update-expected-value-variance-correlation/17976 "2026-10-23T08:05:25Z")\
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**Author:** ![Yashveer](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/yashveer/32/9744_2.png) [@Yashveer](https://discourse.pymc.io/u/Yashveer)\
**Post date:** [October 23, 2026, 8:05am UTC](https://discourse.pymc.io/t/weekly-learning-update-expected-value-variance-correlation/17976/1 "2026-10-23T08:05:25Z")

</div>

Over the past week, study Statistics 2 and covered several fundamental topics on expectations and relationships between random variables. Here is a summary of what I went through:

**Expected Value & Its Properties:**

- Explored the core concept of the expected value of a random variable.

- Studied the key algebraic properties of expectation (linearity of expectation).

- Implemented simulations in Python to visualize and verify expected values numerically.

**Variance & Standard Deviation:**

- Analyzed measures of dispersion to understand how random variables spread around their mean.

**Relationships Between Variables:**

- **Covariance:** Investigated joint variability and key properties of covariance between two random variables.

- **Correlation:** Learned how to compute and interpret the correlation coefficient to measure the strength and direction of linear relationships.

🤪