Learn · Multivariate Procedures with R (in Hindi Language)
Lecture 20 of 57Week 5
Lecture 19 : Three Dimensional Plots, Star Plots and Chernoff Faces
Prof. Shalabh · IIT Kanpur
Credits and licence
- From
- Multivariate Procedures with R (in Hindi Language), 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.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Lecture 0 : How to Learn and Follow the Course
- Lecture 1 : R Software and its Installation
- Lecture 2 : Help, Demonstration, Examples, Packages and Libraries
- Lecture 3 : Command line and Data Editor
- Lecture 4 : Introduction to R Studio
- Lecture 5 : R as a Calculator
- Lecture 6 : Calculations with Data Vectors and Built-in Functions
- Lecture 7 : Matrix Operations
- Lecture 8 : Matrix Operations
- Lecture 9 : Univariate Data - Central Tendency and Variability
- Lecture 10 : Bivariate Data - Descriptive Statistics
- Lecture 11 : Missing Data Handling
- Lecture 12: Measuring Central Tendency with Missing Data
- Lecture 13: Measuring Variation with Missing Data
- Lecture 14 : Coefficient of Variation and Summary
- Lecture 15 : Boxplots and Grouped Boxplots
- Lecture 16 : Bar Diagram, Subdivided and Multiple Bar Diagrams
- Lecture 17 : Pie Diagram, Histogram and Multiple Histogram
- Lecture 18 : Scatter Plots, Smooth Scatter Plots and Matrix Plots
- Lecture 19 : Three Dimensional Plots, Star Plots and Chernoff Faces
- Lecture 20 : Random Variables: Continuous and Discrete
- Lecture 21 : Random Variables: Probability Functions
- Lecture 22 : Probability Functions for Continuous Bivariate and Multivariate Random Variables
- Lecture 23 : Univariate Normal Distribution: Theoretical Properties
- Lecture 24 : Univariate Normal Distribution: Application in R Software
- Lecture 25 : Bivariate Normal and Multivariate Normal Distributions in R
- Lecture 26: Chi square, t and F Distribution
- Lecture 27: Point and Interval Estimation
- Lecture 28: Maximum Likelihood Estimation
- Lecture 29 : Basics of Tests of Hypothesis
- Lecture 30 : Test and Confidence Interval for Mean in One Sample with Known Variance in Univariate Data
- Lecture 31 : Test and Confidence Interval for Mean in One Sample with Unknown Variance in Univariate Data
- Lecture 32 : Tests for Mean in Two Samples with Univariate Data
- Lecture 33 : Analysis of Variance and Homogeneity of Variances with Univariate Data
- Lecture 34 : Tests for Mean Vector with Multivariate Data in One Sample
- Lecture 35 : Tests for Mean Vector with Multivariate Data in Two Samples
- Lecture 36 : Scaling of Data - Centering, Scaling and Z-Scores
- Lecture 37 : Multiple Linear Regression Analysis - Introduction and Basic Concept
- Lecture 38 : Multiple Linear Regression Analysis - Estimation of Parameters
- Lecture 39 : Model Fitting with R Software
- Lecture 40 : Test of Hypothesis and Confidence Interval Estimation on Individual Regression Coefficients
- Lecture 41 : Analysis of Variance and Implementation in R Software
- Lecture 42 : Goodness of Fit and Testing of Normality
- Lecture 43 : Logistic Regression Model
- Lecture 44 : Introduction to Classification
- Lecture 45 : Bayes Procedure for Classification
- Lecture 46 : Classification Procedure for Multivariate Normal Distributions
- Lecture 47 : Classification Procedure and Analysis in R
- Lecture 48 : Cluster Analysis - Basic Concepts and Definitions
- Lecture 49 : Hierarchical Classification
- Lecture 50 : Hierarchical Classification and Analysis with R
- Lecture 51 : Hierarchical Classification with Example in R
- Lecture 52 : Principal Component Analysis - Concepts and Theoretical Setup
- Lecture 53 : Principal Component and Its Graphical Analysis in R
- Lecture 54 : Canonical Variables and Concepts
- Lecture 55 : Statistical Analysis of Canonical Variables
- Lecture 56 : Canonical Variables Analysis in R