Lecture 1 - Introduction to Statistics
Lecture 2 - Introduction to Probability Theory
Lecture 3 - Distribution of a Random Variable - I
Lecture 4 - Distribution of a Random Variable - II
Lecture 5 - Part-I : Interval Estimation - I
Lecture 6 - Part-II : Interval Estimation - II
Lecture 7 - Confidence Interval III and the introduction to Hypothesis Testing
Lecture 8 - Hypothesis Testing
Lecture 9 - The Analysis of Variance (ANOVA) - I
Lecture 10 - The Analysis of Variance (ANOVA) - II
Lecture 11 - The Analysis of Variance (ANOVA) - III
Lecture 12 - The Analysis of Variance (ANOVA) - IV
Lecture 13 - The Analysis of Variance (ANOVA) - V
Lecture 14 - The Analysis of Variance (ANOVA) - VI
Lecture 15 - The Analysis of Variance (ANOVA) - VII and Introduction to Factorial Design
Lecture 16 - Factorial Designs - I
Lecture 17 - Factorial Designs - II
Lecture 18 - Factorial Designs - III
Lecture 19 - Factorial Designs - IV
Lecture 20 - Factorial Designs - V
Lecture 21 - Factorial Designs - VI
Lecture 22 - Factorial Designs - VII
Lecture 23 - Factorial Designs - VIII
Lecture 24 - Two level Fractional Factorial Design - I
Lecture 25 - Two level Fractional Factorial Design - II
Lecture 26 - Two level Fractional Factorial Design - III
Lecture 27 - Two level Fractional Factorial Design - IV
Lecture 28 - Two level Fractional Factorial Design - V
Lecture 29 - Two level Fractional Factorial Design - VI
Lecture 30 - Two level Fractional Factorial Design - VII
Lecture 31 - Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs - I
Lecture 32 - Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs - II
Lecture 33 - Confounding in the 3^k Factorial Design - I
Lecture 34 - Confounding in the 3^k Factorial Design - II
Lecture 35 - Fractional Replication of the 3^k Factorial Design
Lecture 36 - ​Factorials with Mixed Levels
Lecture 37 - Fitting Regression Models - I
Lecture 38 - Fitting Regression Models - II
Lecture 39 - Fitting Regression Models - III
Lecture 40 - Fitting Regression Models - IV
Lecture 41 - Fitting Regression Models - V
