Learn · Predictive Modelling with Applications: Supervised and Unsupervised Learning
Lecture 19 of 39Week 4
Outliers 1
Prof. Sayantee Jana · IIT Hyderabad
Credits and licence
- From
- Predictive Modelling with Applications: Supervised and Unsupervised Learning, IIT Hyderabad (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. Coordinated by IIT Madras.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Introduction to Predictive Modelling
- Correlation coefficient
- Principal Components Analysis 1
- Principal Components Analysis 2
- Principal Components Analysis 3
- Factor Analysis - 1
- Factor Analysis - 2
- Factor Analysis applications with R
- Introduction to Cluster Analysis
- Agglomerative Hierarchical clustering 1
- Hierarchical clustering
- Hierarchical clustering using R examples
- k-means clustering
- Simple linear regression 1
- Simple linear regression 2
- Multiple linear regression 1
- Multiple linear regression 2
- Multiple linear Regression 3
- Outliers 1
- Outliers 2
- Outliers Detection
- Regression Modelling with Categorical predictors using R
- Modelling Non-linear and Interaction effects in Regression using R
- Autocorrelation 1
- Autocorrelation 2
- Tests for Normality 1
- Tests for Normality 2
- Multicollinearity in Regression 1
- Multicollinearity in Regression 2
- Multicollinearity in Regression 3
- Heteroscedasticity 1
- Heteroscedasticity 2
- Heteroscedasticity 3
- Variable Selection and Model Evaluation
- Case study
- Odds Ratio
- Logistic Regression 1
- Logistic Regression 2
- Regression Disconituity Design