NOC:Statistics for Biomedical Engineers

Lecture 1 - Statistics - Motivation

Lecture 2 - Statistics - Introduction

Lecture 3 - Statistics: Definition and Terminology - Part I

Lecture 4 - Statistics: Definition and Terminology - Part II

Lecture 5 - Data: Primary vs Secondary

Lecture 6 - Data: Quantitative vs Qualitative

Lecture 7 - Data: Presentation

Lecture 8 - Data: Static vs Dynamic

Lecture 9 - Data: Box Plot and Spyder Graphs

Lecture 10 - Data: Summarising Data

Lecture 11 - Probability: Event and Sample space

Lecture 12 - Probability: Mutually exclusive and Independent Events

Lecture 13 - Probability: Random Variables

Lecture 14 - Probability: Expectation of Random Variable

Lecture 15 - Probability: Variance of Random Variable

Lecture 16 - Probability Distribution: Binomial, Poisson, Bernoulli

Lecture 17 - Probability Distribution: Normal Distribution

Lecture 18 - Central Limit Theorem: Statement

Lecture 19 - Central Limit Theorem: Illustration

Lecture 20 - Confidence Interval

Lecture 21 - Determining Sample Size

Lecture 22 - Hypothesis Test: Introduction

Lecture 23 - Hypothesis Test: Example

Lecture 24 - Hypothesis: P value

Lecture 25 - Hypothesis: Type 2 error

Lecture 26 - Hypothesis: Chi square Distribution - Part 1

Lecture 27 - Hypothesis: Chi square Distribution - Part 2

Lecture 28 - Hypothesis: Probability Plots

Lecture 29 - Hypothesis: Contingency Table Test

Lecture 30 - Multivariate Hypothesis: Two Sample Test

Lecture 31 - Multivariate Hypothesis: Paired T test

Lecture 32 - Multivariate Hypothesis: Paired vs Unpaired Testing

Lecture 33 - Multivariate Hypothesis: Two population variances

Lecture 34 - Multivariate Hypothesis: Multiple Random Variables - Part 1

Lecture 35 - Multivariate Hypothesis: Multiple Random Variables - Part 2

Lecture 36 - Multivariate Hypothesis: Covariance and Correlation

Lecture 37 - One Way ANOVA: Motivation and Assumptions

Lecture 38 - One Way ANOVA: Fixed and Random effects Model

Lecture 39 - One Way ANOVA: Derivation and Confidence Interval

Lecture 40 - One Way ANOVA: Confidence Interval

Lecture 41 - One Way ANOVA: Unbalanced Experiment and Residuals

Lecture 42 - One Way ANOVA: Interpretation of Results

Lecture 43 - Statistical Modeling: Introduction

Lecture 44 - Statistical Modeling: Linear Regression Derivation

Lecture 45 - Statistical Modeling: Linear Regression - Assumption and Residuals

Lecture 46 - Statistical Modeling: Multi Linear Regression

Lecture 47 - Statistical Modeling: Logistic Regression

Lecture 48 - Statistical Modeling: Cross Entropy Loss

Lecture 49 - Statistical Modeling: Gradient Descent

Lecture 50 - Statistical Modeling: One Way Anova via Linear Regression

Lecture 51 - Design Of Experiments: Randomised Complete Block Design - Part 1

Lecture 52 - Design Of Experiments: Randomised Complete Block Design - Part 2

Lecture 53 - RCBD: Math Formulation and Derivation

Lecture 54 - RCBD: Necessity and Application

Lecture 55 - Latin Square: Introduction

Lecture 56 - Latin Square: Math and Formulation

Lecture 57 - Graeco - Latin Square

Lecture 58 - Interaction Among Variables

Lecture 59 - Two-Way ANOVA: Introduction - Part 1

Lecture 60 - Two-Way ANOVA: Introduction - Part 2

Lecture 61 - Two-Way ANOVA: Math and Formulation

Lecture 62 - Factorial Design: 2^2 Experiments

Lecture 63 - Factorial Design: 2^k Experiments - Part 1

Lecture 64 - Factorial Design: 2^k Experiments - Part 2

Lecture 65 - Factorial Design: Blocking

Lecture 66 - Introduction to Python Programming for Biomedical Engineers