Statistical Foundation for ML in R

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Section 1: Course Overview

  1. Course Overview
  2. How to Get Queries Resolved
  3. Download Resources

Section 2: Introduction to Statistical Analyses

  1. Introduction to Statistical Analysis
  2. Descriptive vs Prescriptive Analysis
  3. Types of Statistical Analysis
  4. Statistical Testing
  5. Panel Data and Types
  6. Cross Sectional and Pooled Cross Sectional
  7. Types of Variables

Section 3: Fundamental Statistical Concepts

  1. Measures of Central Tendency and Dispersion
  2. Demo - Measures of Central Tendency Dispersion
  3. Law of Large Numbers
  4. Gamblers Fallacy
  5. Normal Distribution
  6. Standard Normal Distribution
  7. Central Limit Theorem
  8. Demo - Central Limit Theorem
  9. Standard Error
  10. Confidence Intervals Formula
  11. Confidence Intervals with Bootstrapping
  12. Correlation
  13. Demo - Correlation Test
  14. Course Review

Section 4: Statistical T-tests

  1. Introduction to T-Tests
  2. Introduction to T-Tests
  3. One Sample T-Test
  4. One Sample T-Test - R Demo
  5. One Sample T-Test - Hand Computation
  6. What is Two Sample Paired T-Test
  7. Two Sample Dependent T test - Examples of when to use
  8. Two Sample Dependent T test - Hand Computation
  9. Two Sample Dependent T test - R Demo
  10. What is Independent Two Sample T-test
  11. Independent Two Sample T test - Examples of when to use
  12. Independent Two Sample T test - Hand Computation
  13. Independent Two Sample T test - R Demo

Section 5: Chi-Squared Test

  1. What is Chi-Squared Test
  2. Chi-Squared Test - Examples of When to Use
  3. Chi Square Test - Hand Computation
  4. Chi-Squared Test - R Demo
  5. Course Review

Section 6: ANOVA

  1. What is ANOVA
  2. When to Use ANOVA
  3. When to Use ANOVA
  4. ANOVA - Hand Computation
  5. ANOVA - R Demo
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