Study Guide

I created a PDF with step-by-step solutions to a range of problems in statistics and statistics for data science. These problems originally came out of tutoring sessions I have been having with students in the Introduction to Statistcs for Data Science course at SFSU. However, this guide should be helpful to many students in a wide range of statistics and data science courses at many universities and programs.

Check out my Github repo for access to this PDF and other study guides.

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Topics

The material in this guide includes Hypothesis Testing, Joint and Marginal Probability Density Functions (PDF) and Probability Mass Functions (PMF), Maximum Likelihood Estimation (MLE), Confidence Intervals, and more. These topics show up in statistcs and statistics for data science courses at UC Berkeley, Stanford, MIT, SFSU, and other universities around the world.

The problems in this guide are inspired by exercises from the book Introduction to Mathematical Statistics, 8th Edition by Robert Hogg, Joseph McKean, and Allen Craig.

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Example Problems

I offer a down-to-earth teaching style that helps make complex material less intimidating and more digestible. Each section of problems is broken down into technical solutions and conceptual explanations.

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Technical Solutions

Image 1. Hypothesis Testing: Technical Solution Sample

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Conceptual Explanations

Image 2. Hypothesis Testing: Conceptual Explanation Sample

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Master AI and Data Science!

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