Statistical Foundation for ML in R
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Section 1: Course Overview
- Course Overview
- How to Get Queries Resolved
- Download Resources
Section 2: Introduction to Statistical Analyses
- Introduction to Statistical Analysis
- Descriptive vs Prescriptive Analysis
- Types of Statistical Analysis
- Statistical Testing
- Panel Data and Types
- Cross Sectional and Pooled Cross Sectional
- Types of Variables
Section 3: Fundamental Statistical Concepts
- Measures of Central Tendency and Dispersion
- Demo - Measures of Central Tendency Dispersion
- Law of Large Numbers
- Gamblers Fallacy
- Normal Distribution
- Standard Normal Distribution
- Central Limit Theorem
- Demo - Central Limit Theorem
- Standard Error
- Confidence Intervals Formula
- Confidence Intervals with Bootstrapping
- Correlation
- Demo - Correlation Test
- Course Review
Section 4: Statistical T-tests
- Introduction to T-Tests
- Introduction to T-Tests
- One Sample T-Test
- One Sample T-Test - R Demo
- One Sample T-Test - Hand Computation
- What is Two Sample Paired T-Test
- Two Sample Dependent T test - Examples of when to use
- Two Sample Dependent T test - Hand Computation
- Two Sample Dependent T test - R Demo
- What is Independent Two Sample T-test
- Independent Two Sample T test - Examples of when to use
- Independent Two Sample T test - Hand Computation
- Independent Two Sample T test - R Demo
Section 5: Chi-Squared Test
- What is Chi-Squared Test
- Chi-Squared Test - Examples of When to Use
- Chi Square Test - Hand Computation
- Chi-Squared Test - R Demo
- Course Review
Section 6: ANOVA
- What is ANOVA
- When to Use ANOVA
- When to Use ANOVA
- ANOVA - Hand Computation
- ANOVA - R Demo