Learn · Data Analytics with Python
Lecture 27 of 60Unit 6
Two Way ANOVA
Prof. A Ramesh · IIT Roorkee
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- Data Analytics with Python, IIT Roorkee (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
- Introduction to data analytics
- Python Fundamentals - I
- Python Fundamentals - II
- Central Tendency and Dispersion - I
- Central Tendency and Dispersion - II
- Introduction to Probability- I
- Introduction to Probability- II
- Probability Distributions - I
- Probability Distributions - II
- Probability Distributions - III
- Python Demo for Distributions
- Sampling and Sampling Distribution
- Distribution of Sample Means, population, and variance
- Confidence interval estimation: Single population - I
- Confidence interval estimation: Single population - II
- Hypothesis Testing- I
- Hypothesis Testing- II
- Hypothesis Testing- III
- Errors in Hypothesis Testing
- Hypothesis Testing: Two sample test- I
- Hypothesis Testing: Two sample test- II
- Hypothesis Testing: Two sample test- III
- ANOVA - I
- ANOVA - II
- Post Hoc Analysis(Tukey’s test)
- Randomize block design (RBD)
- Two Way ANOVA
- Linear Regression - I
- Linear Regression - II
- Linear Regression - III
- Estimation, Prediction of Regression Model Residual Analysis
- Estimation, Prediction of Regression Model Residual Analysis - II
- MULTIPLE REGRESSION MODEL - I
- MULTIPLE REGRESSION MODEL-II
- Categorical variable regression
- Maximum Likelihood Estimation- I
- Maximum Likelihood Estimation-II
- LOGISTIC REGRESSION- I
- LOGISTIC REGRESSION-II
- Linear Regression Model Vs Logistic Regression Model
- Confusion matrix and ROC- I
- Confusion Matrix and ROC-II
- Performance of Logistic Model-III
- Regression Analysis Model Building - I
- Regression Analysis Model Building (Interaction)- II
- Chi - Square Test of Independence - I
- Chi-Square Test of Independence - II
- Chi-Square Goodness of Fit Test
- Cluster analysis: Introduction- I
- Clustering analysis: part II
- Clustering analysis: Part III
- Cluster analysis: Part IV
- Cluster analysis: Part V
- K- Means Clustering
- Hierarchical method of clustering -I
- Hierarchical method of clustering- II
- Classification and Regression Trees (CART : I)
- Measures of attribute selection
- Attribute selection Measures in CART : II
- Classification and Regression Trees (CART) - III