Learn · Advanced R Programming for Data Analytics in Business
Lecture 29 of 45Week 8
Lesson 29: Introduction to Panel Data Modelling
Prof. Abhinava Tripathi · IIT Kanpur
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- Advanced R Programming for Data Analytics in Business, IIT Kanpur (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
- Lesson 1: Fundamentals of R
- Lesson 2: Data cleaning
- Lesson 3: Data Visualization- Part 1
- Lesson 4: Data Visualization- Part 2
- Lesson 5: Introduction to Probability Theory
- Lesson 6: Conditional Probabilities and Bayes Theorem
- Lesson 7: Random Variables and Probability Distributions
- Lesson 8: Binomial Distribution
- Lesson 9: Continuous Random Variables and Normal Distribution
- Lesson 10: Descriptive Analytics: Measures of Central Tendency
- Lesson 11: Descriptive Analytics: Measures of Variability and Shape
- Lesson 12: Statistical Inference: Sampling
- Lesson 13: Statistical Inference: Central Limit Theorem and Confidence Intervals
- Lesson 14: Statistical Inference: Hypothesis testing
- Lesson 15: Introduction to R
- Lesson 16: Inferential statistics: R Implementation
- Lesson 17: Statistical Inference: Sampling
- Lesson 18: Hypothesis Testing: R Implementation
- Lesson 19: Regression Modelling-Part 1
- Lesson 20: Regression Modelling-Part 2
- Lesson 21: Regression Algorithm: Application (Part 1)
- Lesson 22: Regression Algorithm: Application (Part 2)
- Lesson 23: Classification Algorithms: Logit/Probit Regression (Part 1)
- Lesson 24: Classification Algorithms: Logit/Probit Regression (Part 2)
- Lesson 25: Classification Algorithms: Application (Part 1)
- Lesson 26: Classification Algorithms: Application (Part 2)
- Lesson 27: Advanced Data Visualization- Part 1
- Lesson 28: Advanced Data Visualization- Part 2
- Lesson 29: Introduction to Panel Data Modelling
- Lesson 30: Panel data application and implementation with R
- Lesson 31: Advanced Time-Series Models
- Lesson 32: Introduction to ARMA process
- Lesson 33: Forecasting with ARMA models
- Lesson 34: Non-stationarity, Cointegration, and Error correction Models
- Lesson 35: Return and Volatility Modelling and Forecasting
- Lesson 36: Maximum Likelihood Estimation (MLE)
- Lesson 37: Quantile Regression - Part A
- Lesson 38: Quantile Regression - Part B
- Lesson 39: Solar Panel Marketing Case Study
- Lesson 40: PCA and Clustering- Part 1
- Lesson 41: PCA and clustering- Part 2
- Lesson 42: Tutorial- HR Analytics Part 1
- Lesson 43: Tutorial - HR Analytics- Part 2
- Lesson 44: Application of Logistic Regression -Part 1
- Lesson 45: Application of logistic regression- Part 2