NOC:Multivariate Procedures with R (Hindi)

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

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 - Continuous and Discrete

Lecture 21 - Probability Functions

Lecture 22 - Probability Functions for Continuous Bivariate and Multivariate Random Variables

Lecture 23 - Theoretical Properties

Lecture 24 - Application in R Software

Lecture 25 - Bivariate Normal and Multivariate Normal Distributions in R

Lecture 26 - Chi square (𝝌^𝟐), 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 - Centering, Scaling and Z-Scores

Lecture 37 - Introduction and Basic Concepts

Lecture 38 - 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 - 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 - Concepts and Theoretical Setup

Lecture 53 - Principle 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