# Multivariate Procedures with R (in Hindi Language)

- Institution: IIT Kanpur
- Department: Mathematics
- Teacher: Prof. Shalabh
- Level: Undergraduate
- Language: English
- Subjects: Mathematics
- Source: [NPTEL](https://nptel.ac.in/courses/111104612)
- Licence: [CC BY-NC-SA](https://nptel.ac.in/faq) (Non-commercial use only.)
- Syllabus: [at the source](https://nptel.ac.in/courses/111104612)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

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

1. [Lecture 0 : How to Learn and Follow the Course](https://invisiblenetwork.ai/learn/nptel/111104612/0f4d1c4d644c) (YouTube)
2. [Lecture 1 : R Software and its Installation](https://invisiblenetwork.ai/learn/nptel/111104612/37aba867595b) (YouTube)
3. [Lecture 2 : Help, Demonstration, Examples, Packages and Libraries](https://invisiblenetwork.ai/learn/nptel/111104612/8f8671d363a8) (YouTube)
4. [Lecture 3 : Command line and Data Editor](https://invisiblenetwork.ai/learn/nptel/111104612/88ac7b470c5b) (YouTube)
5. [Lecture 4 : Introduction to R Studio](https://invisiblenetwork.ai/learn/nptel/111104612/9a3e221df972) (YouTube)
6. [Lecture 5 : R as a Calculator](https://invisiblenetwork.ai/learn/nptel/111104612/1a30c0a8d43f) (YouTube)
7. [Lecture 6 : Calculations with Data Vectors and Built-in Functions](https://invisiblenetwork.ai/learn/nptel/111104612/71919766fb44) (YouTube)
8. [Lecture 7 : Matrix Operations](https://invisiblenetwork.ai/learn/nptel/111104612/098c9d0bea41) (YouTube)
9. [Lecture 8 : Matrix Operations](https://invisiblenetwork.ai/learn/nptel/111104612/56635343c612) (YouTube)
10. [Lecture 9 : Univariate Data - Central Tendency and Variability](https://invisiblenetwork.ai/learn/nptel/111104612/c6c8464456e8) (YouTube)
11. [Lecture 10 : Bivariate Data - Descriptive Statistics](https://invisiblenetwork.ai/learn/nptel/111104612/001def82c8b1) (YouTube)
12. [Lecture 11 : Missing Data Handling](https://invisiblenetwork.ai/learn/nptel/111104612/6109aa182c24) (YouTube)
13. [Lecture 12: Measuring Central Tendency with Missing Data](https://invisiblenetwork.ai/learn/nptel/111104612/e61dd200a8b7) (YouTube)
14. [Lecture 13: Measuring Variation with Missing Data](https://invisiblenetwork.ai/learn/nptel/111104612/6d1120f9ccc0) (YouTube)
15. [Lecture 14 : Coefficient of Variation and Summary](https://invisiblenetwork.ai/learn/nptel/111104612/ef5d70a4c2f9) (YouTube)
16. [Lecture 15 : Boxplots and Grouped Boxplots](https://invisiblenetwork.ai/learn/nptel/111104612/cd6e46904684) (YouTube)
17. [Lecture 16 : Bar Diagram, Subdivided and Multiple Bar Diagrams](https://invisiblenetwork.ai/learn/nptel/111104612/692459005c20) (YouTube)
18. [Lecture 17 : Pie Diagram, Histogram and Multiple Histogram](https://invisiblenetwork.ai/learn/nptel/111104612/03e59fd81058) (YouTube)
19. [Lecture 18 : Scatter Plots, Smooth Scatter Plots and Matrix Plots](https://invisiblenetwork.ai/learn/nptel/111104612/9f90a206dae4) (YouTube)
20. [Lecture 19 : Three Dimensional Plots, Star Plots and Chernoff Faces](https://invisiblenetwork.ai/learn/nptel/111104612/5ad5521e2e61) (YouTube)
21. [Lecture 20 : Random Variables: Continuous and Discrete](https://invisiblenetwork.ai/learn/nptel/111104612/9c2d120ea2f7) (YouTube)
22. [Lecture 21 : Random Variables: Probability Functions](https://invisiblenetwork.ai/learn/nptel/111104612/f579479e1a82) (YouTube)
23. [Lecture 22 : Probability Functions for Continuous Bivariate and Multivariate Random Variables](https://invisiblenetwork.ai/learn/nptel/111104612/4d111808e7e5) (YouTube)
24. [Lecture 23 : Univariate Normal Distribution: Theoretical Properties](https://invisiblenetwork.ai/learn/nptel/111104612/cdfdc9191478) (YouTube)
25. [Lecture 24 : Univariate Normal Distribution: Application in R Software](https://invisiblenetwork.ai/learn/nptel/111104612/e6453f379e02) (YouTube)
26. [Lecture 25 : Bivariate Normal and Multivariate Normal Distributions in R](https://invisiblenetwork.ai/learn/nptel/111104612/5d54d771ef02) (YouTube)
27. [Lecture 26: Chi square, t and F Distribution](https://invisiblenetwork.ai/learn/nptel/111104612/90d82561b1e7) (YouTube)
28. [Lecture 27: Point and Interval Estimation](https://invisiblenetwork.ai/learn/nptel/111104612/60c7b6951027) (YouTube)
29. [Lecture 28: Maximum Likelihood Estimation](https://invisiblenetwork.ai/learn/nptel/111104612/99286294cc11) (YouTube)
30. [Lecture 29 : Basics of Tests of Hypothesis](https://invisiblenetwork.ai/learn/nptel/111104612/37c36d229b3d) (YouTube)
31. [Lecture 30 : Test and Confidence Interval for Mean in One Sample with Known Variance in Univariate Data](https://invisiblenetwork.ai/learn/nptel/111104612/b651ccc76d6a) (YouTube)
32. [Lecture 31 : Test and Confidence Interval for Mean in One Sample with Unknown Variance in Univariate Data](https://invisiblenetwork.ai/learn/nptel/111104612/b9edd376a5f7) (YouTube)
33. [Lecture 32 : Tests for Mean in Two Samples with Univariate Data](https://invisiblenetwork.ai/learn/nptel/111104612/ec8ae959ca8b) (YouTube)
34. [Lecture 33 : Analysis of Variance and Homogeneity of Variances with Univariate Data](https://invisiblenetwork.ai/learn/nptel/111104612/e77800190f8a) (YouTube)
35. [Lecture 34 : Tests for Mean Vector with Multivariate Data in One Sample](https://invisiblenetwork.ai/learn/nptel/111104612/3531bae3d88f) (YouTube)
36. [Lecture 35 : Tests for Mean Vector with Multivariate Data in Two Samples](https://invisiblenetwork.ai/learn/nptel/111104612/49d69834929e) (YouTube)
37. [Lecture 36 : Scaling of Data - Centering, Scaling and Z-Scores](https://invisiblenetwork.ai/learn/nptel/111104612/3806a88f6fd9) (YouTube)
38. [Lecture 37 : Multiple Linear Regression Analysis - Introduction and Basic Concept](https://invisiblenetwork.ai/learn/nptel/111104612/8714712f292c) (YouTube)
39. [Lecture 38 : Multiple Linear Regression Analysis - Estimation of Parameters](https://invisiblenetwork.ai/learn/nptel/111104612/e0ca966223fb) (YouTube)
40. [Lecture 39 : Model Fitting with R Software](https://invisiblenetwork.ai/learn/nptel/111104612/8448975e3351) (YouTube)
41. [Lecture 40 : Test of Hypothesis and Confidence Interval Estimation on Individual Regression Coefficients](https://invisiblenetwork.ai/learn/nptel/111104612/a721e4c55437) (YouTube)
42. [Lecture 41 : Analysis of Variance and Implementation in R Software](https://invisiblenetwork.ai/learn/nptel/111104612/dfebe1fc8ba0) (YouTube)
43. [Lecture 42 : Goodness of Fit and Testing of Normality](https://invisiblenetwork.ai/learn/nptel/111104612/0a780b02eab1) (YouTube)
44. [Lecture 43 : Logistic Regression Model](https://invisiblenetwork.ai/learn/nptel/111104612/1346ad8f8ee8) (YouTube)
45. [Lecture 44 : Introduction to Classification](https://invisiblenetwork.ai/learn/nptel/111104612/cb6049755ef4) (YouTube)
46. [Lecture 45 : Bayes Procedure for Classification](https://invisiblenetwork.ai/learn/nptel/111104612/7bfec710a18a) (YouTube)
47. [Lecture 46 : Classification Procedure for Multivariate Normal Distributions](https://invisiblenetwork.ai/learn/nptel/111104612/084141721fd7) (YouTube)
48. [Lecture 47 : Classification Procedure and Analysis in R](https://invisiblenetwork.ai/learn/nptel/111104612/a2a81abea886) (YouTube)
49. [Lecture 48 : Cluster Analysis - Basic Concepts and Definitions](https://invisiblenetwork.ai/learn/nptel/111104612/deec34ea9e3e) (YouTube)
50. [Lecture 49 : Hierarchical Classification](https://invisiblenetwork.ai/learn/nptel/111104612/71cded22ce7c) (YouTube)
51. [Lecture 50 : Hierarchical Classification and Analysis with R](https://invisiblenetwork.ai/learn/nptel/111104612/81ab4cf22258) (YouTube)
52. [Lecture 51 : Hierarchical Classification with Example in R](https://invisiblenetwork.ai/learn/nptel/111104612/47d641b75a2e) (YouTube)
53. [Lecture 52 : Principal Component Analysis - Concepts and Theoretical Setup](https://invisiblenetwork.ai/learn/nptel/111104612/fa0cf199a8f2) (YouTube)
54. [Lecture 53 : Principal Component and Its Graphical Analysis in R](https://invisiblenetwork.ai/learn/nptel/111104612/5faece853745) (YouTube)
55. [Lecture 54 : Canonical Variables and Concepts](https://invisiblenetwork.ai/learn/nptel/111104612/ba103e3a652d) (YouTube)
56. [Lecture 55 : Statistical Analysis of Canonical Variables](https://invisiblenetwork.ai/learn/nptel/111104612/72188450827d) (YouTube)
57. [Lecture 56 : Canonical Variables Analysis in R](https://invisiblenetwork.ai/learn/nptel/111104612/5cf35288c198) (YouTube)