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Course11110461257 lectures

Multivariate Procedures with R (in Hindi Language)

Prof. Shalabh · IIT Kanpur

Start with lecture 1 At NPTEL

About

Multivariate Procedures with R is designed to equip the learners with the necessary theory and application of multivariate procedures in academic, research, and business domains with appropriate examples. The comprehensive content is designed to manually compute various multivariate statistics for learning and R-based computations for application.

Intended audience: UG students of Science and Engineering. Students of Science, management, Engineering with basic mathematical background can do it. Working professionals in analytics and statistical computations, simulations and data analysis can also do it.

Prerequisites: Mathematics background up to class 12 is needed. Having some preliminary knowledge will be helpful but not necessarily mandatory.

Lectures

Week 1

  1. Lecture 0 : How to Learn and Follow the Course YouTube
  2. Lecture 1 : R Software and its Installation YouTube
  3. Lecture 2 : Help, Demonstration, Examples, Packages and Libraries YouTube
  4. Lecture 3 : Command line and Data Editor YouTube
  5. Lecture 4 : Introduction to R Studio YouTube
  6. Lecture 5 : R as a Calculator YouTube

Week 2

  1. Lecture 6 : Calculations with Data Vectors and Built-in Functions YouTube
  2. Lecture 7 : Matrix Operations YouTube
  3. Lecture 8 : Matrix Operations YouTube
  4. Lecture 9 : Univariate Data - Central Tendency and Variability YouTube

Week 3

  1. Lecture 10 : Bivariate Data - Descriptive Statistics YouTube
  2. Lecture 11 : Missing Data Handling YouTube
  3. Lecture 12: Measuring Central Tendency with Missing Data YouTube
  4. Lecture 13: Measuring Variation with Missing Data YouTube

Week 4

  1. Lecture 14 : Coefficient of Variation and Summary YouTube
  2. Lecture 15 : Boxplots and Grouped Boxplots YouTube
  3. Lecture 16 : Bar Diagram, Subdivided and Multiple Bar Diagrams YouTube
  4. Lecture 17 : Pie Diagram, Histogram and Multiple Histogram YouTube
  5. Lecture 18 : Scatter Plots, Smooth Scatter Plots and Matrix Plots YouTube

Week 5

  1. Lecture 19 : Three Dimensional Plots, Star Plots and Chernoff Faces YouTube
  2. Lecture 20 : Random Variables: Continuous and Discrete YouTube
  3. Lecture 21 : Random Variables: Probability Functions YouTube
  4. Lecture 22 : Probability Functions for Continuous Bivariate and Multivariate Random Variables YouTube
  5. Lecture 23 : Univariate Normal Distribution: Theoretical Properties YouTube
  6. Lecture 24 : Univariate Normal Distribution: Application in R Software YouTube

Week 6

  1. Lecture 25 : Bivariate Normal and Multivariate Normal Distributions in R YouTube
  2. Lecture 26: Chi square, t and F Distribution YouTube
  3. Lecture 27: Point and Interval Estimation YouTube
  4. Lecture 28: Maximum Likelihood Estimation YouTube

Week 7

  1. Lecture 29 : Basics of Tests of Hypothesis YouTube
  2. Lecture 30 : Test and Confidence Interval for Mean in One Sample with Known Variance in Univariate Data YouTube
  3. Lecture 31 : Test and Confidence Interval for Mean in One Sample with Unknown Variance in Univariate Data YouTube
  4. Lecture 32 : Tests for Mean in Two Samples with Univariate Data YouTube
  5. Lecture 33 : Analysis of Variance and Homogeneity of Variances with Univariate Data YouTube

Week 8

  1. Lecture 34 : Tests for Mean Vector with Multivariate Data in One Sample YouTube
  2. Lecture 35 : Tests for Mean Vector with Multivariate Data in Two Samples YouTube
  3. Lecture 36 : Scaling of Data - Centering, Scaling and Z-Scores YouTube
  4. Lecture 37 : Multiple Linear Regression Analysis - Introduction and Basic Concept YouTube
  5. Lecture 38 : Multiple Linear Regression Analysis - Estimation of Parameters YouTube

Week 9

  1. Lecture 39 : Model Fitting with R Software YouTube
  2. Lecture 40 : Test of Hypothesis and Confidence Interval Estimation on Individual Regression Coefficients YouTube
  3. Lecture 41 : Analysis of Variance and Implementation in R Software YouTube
  4. Lecture 42 : Goodness of Fit and Testing of Normality YouTube
  5. Lecture 43 : Logistic Regression Model YouTube
  6. Lecture 44 : Introduction to Classification YouTube

Week 10

  1. Lecture 45 : Bayes Procedure for Classification YouTube
  2. Lecture 46 : Classification Procedure for Multivariate Normal Distributions YouTube
  3. Lecture 47 : Classification Procedure and Analysis in R YouTube
  4. Lecture 48 : Cluster Analysis - Basic Concepts and Definitions YouTube

Week 11

  1. Lecture 49 : Hierarchical Classification YouTube
  2. Lecture 50 : Hierarchical Classification and Analysis with R YouTube
  3. Lecture 51 : Hierarchical Classification with Example in R YouTube
  4. Lecture 52 : Principal Component Analysis - Concepts and Theoretical Setup YouTube

Week 12

  1. Lecture 53 : Principal Component and Its Graphical Analysis in R YouTube
  2. Lecture 54 : Canonical Variables and Concepts YouTube
  3. Lecture 55 : Statistical Analysis of Canonical Variables YouTube
  4. Lecture 56 : Canonical Variables Analysis in R YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Kanpur
Department
Mathematics
Teacher
Prof. Shalabh
Level
Undergraduate
Language
English
Subjects
Mathematics
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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