Math 2.0

Resources / course

Multivariate Procedures with R (in Hindi Language) ZKKVJEE91J

URIm2://res/ZKKVJEE91J
Kindcourse
CreatorsShalabh (instructor)
Date2025-01-16
Scopein
Idscourse 05pjsgx75:111104612

Expressions

e1: edition

Languagehi
Date2025-01-16
Contents
  • part 1: Week 1
  • session 1: Lecture 0 : How to Learn and Follow the Course
  • session 2: Lecture 1 : R Software and its Installation
  • session 3: Lecture 2 : Help, Demonstration, Examples, Packages and Libraries
  • session 4: Lecture 3 : Command line and Data Editor
  • session 5: Lecture 4 : Introduction to R Studio
  • session 6: Lecture 5 : R as a Calculator
  • part 2: Week 2
  • session 7: Lecture 6 : Calculations with Data Vectors and Built-in Functions
  • session 8: Lecture 7 : Matrix Operations
  • session 9: Lecture 8 : Matrix Operations
  • session 10: Lecture 9 : Univariate Data - Central Tendency and Variability
  • part 3: Week 3
  • session 11: Lecture 10 : Bivariate Data - Descriptive Statistics
  • session 12: Lecture 11 : Missing Data Handling
  • session 13: Lecture 12: Measuring Central Tendency with Missing Data
  • session 14: Lecture 13: Measuring Variation with Missing Data
  • part 4: Week 4
  • session 15: Lecture 14 : Coefficient of Variation and Summary
  • session 16: Lecture 15 : Boxplots and Grouped Boxplots
  • session 17: Lecture 16 : Bar Diagram, Subdivided and Multiple Bar Diagrams
  • session 18: Lecture 17 : Pie Diagram, Histogram and Multiple Histogram
  • session 19: Lecture 18 : Scatter Plots, Smooth Scatter Plots and Matrix Plots
  • part 5: Week 5
  • session 20: Lecture 19 : Three Dimensional Plots, Star Plots and Chernoff Faces
  • session 21: Lecture 20 : Random Variables: Continuous and Discrete
  • session 22: Lecture 21 : Random Variables: Probability Functions
  • session 23: Lecture 22 : Probability Functions for Continuous Bivariate and Multivariate Random Variables
  • session 24: Lecture 23 : Univariate Normal Distribution: Theoretical Properties
  • session 25: Lecture 24 : Univariate Normal Distribution: Application in R Software
  • part 6: Week 6
  • session 26: Lecture 25 : Bivariate Normal and Multivariate Normal Distributions in R
  • session 27: Lecture 26: Chi square, t and F Distribution
  • session 28: Lecture 27: Point and Interval Estimation
  • session 29: Lecture 28: Maximum Likelihood Estimation
  • part 7: Week 7
  • session 30: Lecture 29 : Basics of Tests of Hypothesis
  • session 31: Lecture 30 : Test and Confidence Interval for Mean in One Sample with Known Variance in Univariate Data
  • session 32: Lecture 31 : Test and Confidence Interval for Mean in One Sample with Unknown Variance in Univariate Data
  • session 33: Lecture 32 : Tests for Mean in Two Samples with Univariate Data
  • session 34: Lecture 33 : Analysis of Variance and Homogeneity of Variances with Univariate Data
  • part 8: Week 8
  • session 35: Lecture 34 : Tests for Mean Vector with Multivariate Data in One Sample
  • session 36: Lecture 35 : Tests for Mean Vector with Multivariate Data in Two Samples
  • session 37: Lecture 36 : Scaling of Data - Centering, Scaling and Z-Scores
  • session 38: Lecture 37 : Multiple Linear Regression Analysis - Introduction and Basic Concept
  • session 39: Lecture 38 : Multiple Linear Regression Analysis - Estimation of Parameters
  • part 9: Week 9
  • session 40: Lecture 39 : Model Fitting with R Software
  • session 41: Lecture 40 : Test of Hypothesis and Confidence Interval Estimation on Individual Regression Coefficients
  • session 42: Lecture 41 : Analysis of Variance and Implementation in R Software
  • session 43: Lecture 42 : Goodness of Fit and Testing of Normality
  • session 44: Lecture 43 : Logistic Regression Model
  • session 45: Lecture 44 : Introduction to Classification
  • part 10: Week 10
  • session 46: Lecture 45 : Bayes Procedure for Classification
  • session 47: Lecture 46 : Classification Procedure for Multivariate Normal Distributions
  • session 48: Lecture 47 : Classification Procedure and Analysis in R
  • session 49: Lecture 48 : Cluster Analysis - Basic Concepts and Definitions
  • part 11: Week 11
  • session 50: Lecture 49 : Hierarchical Classification
  • session 51: Lecture 50 : Hierarchical Classification and Analysis with R
  • session 52: Lecture 51 : Hierarchical Classification with Example in R
  • session 53: Lecture 52 : Principal Component Analysis - Concepts and Theoretical Setup
  • part 12: Week 12
  • session 54: Lecture 53 : Principal Component and Its Graphical Analysis in R
  • session 55: Lecture 54 : Canonical Variables and Concepts
  • session 56: Lecture 55 : Statistical Analysis of Canonical Variables
  • session 57: Lecture 56 : Canonical Variables Analysis in R

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