Learn · Statistics for Biomedical Engineers
Lecture 15 of 66Week 3
Probability: Variance of Random Variable
Prof. Babji Srinivasan · IIT Madras
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- From
- Statistics for Biomedical Engineers, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Statistics - Motivation
- Statistics - Introduction
- Statistics - Definition and Terminology I
- Statistics - Definition and Terminology II
- Data: Primary vs Secondary
- Data: Quantitative vs Qualitative
- Data: Presentation
- Data: Static vs Dynamic
- Data: Box Plot and Spyder Graphs
- Data: Summarising Data
- Probability: Event & Sample space
- Probability: Mutually exclusive and Independent Events
- Probability: Random Variables
- Probability: Expectation of Random Variable
- Probability: Variance of Random Variable
- Probability Distribution: Binomial, Poisson, Bernoulli
- Probability Distribution: Normal Distribution
- Central Limit Theorem: Statement
- Central Limit Theorem: Illustration
- Confidence Interval
- Determining Sample Size
- Hypothesis Test: Introduction
- Hypothesis Test: Example
- Hypothesis: P value
- Hypothesis: Type 2 error
- Hypothesis: Chi square Distribution - Part 1
- Hypothesis: Chi square Distribution - Part 2
- Hypothesis: Probability Plots
- Hypothesis: Contingency Table Test
- Multivariate Hypothesis: Two Sample Test
- Multivariate Hypothesis: Paired T test
- Multivariate Hypothesis: Paired vs Unpaired Testing
- Multivariate Hypothesis: Two population variances
- Multivariate Hypothesis: Multiple Random Variables - Part 1
- Multivariate Hypothesis: Multiple Random Variables - Part 2
- Multivariate Hypothesis: Covariance and Correlation
- One Way ANOVA: Motivation and Assumptions
- One Way ANOVA: Fixed and Random effects Model
- One Way ANOVA: Derivation and Confidence Interval
- One Way ANOVA: Confidence Interval
- One Way ANOVA: Unbalanced Experiment and Residuals
- One Way ANOVA: Interpretation of Results
- Statistical Modeling: Introduction
- Statistical Modeling: Linear Regression Derivation
- Statistical Modeling: Linear Regression - Assumption and Residuals
- Statistical Modeling: Multi Linear Regression
- Statistical Modeling: Logistic Regression
- Statistical Modeling: Cross Entropy Loss
- Statistical Modeling: Gradient Descent
- Statistical Modeling: One Way Anova via Linear Regression
- Design Of Experiments: Randomised Complete Block Design - Part 1
- Design Of Experiments: Randomised Complete Block Design - Part 2
- RCBD: Math Formulation and Derivation
- RCBD: Necessity and Application
- Latin Square: Introduction
- Latin Square: Math and Formulation
- Graeco - Latin Square
- Interaction Among Variables
- Two-Way ANOVA: Introduction
- Two-Way ANOVA: Introduction - Part 2
- Two-Way ANOVA: Math and Formulation
- Factorial Design: 2^2 Experiments
- Factorial Design: 2^k Experiments - Part 1
- Factorial Design: 2^k Experiments - Part 2
- Factorial Design: Blocking
- Introduction to Python Programming for Biomedical Engineers