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    Quick Facts

    Medium Of InstructionsMode Of LearningMode Of Delivery
    EnglishSelf StudyVideo and Text Based

    Courses and Certificate Fees

    Certificate AvailabilityCertificate Providing Authority
    yesIIT Kanpur

    The Syllabus

    • How to Learn and Follow the Course
    • R Software and its Installation
    • Help, Demonstration, Examples, Packages and Libraries
    • Command line and Data Editor
    • Introduction to R Studio
    • R as a Calculator

    • Calculations with Data Vectors and Builtin Functions
    • Matrix Operations
    • Matrix Operations
    • Univariate Data Central Tendency and Variability

    • Bivariate Data Descriptive Statistics
    • Missing Data Handling
    • Measuring Central Tendency with Missing Data
    • Measuring Variation with Missing Data

    • Coefficient of Variation and Summary
    • Boxplots and Grouped Boxplots
    • Bar Diagram, Subdivided and Multiple Bar Diagrams
    • Pie Diagram, Histogram and Multiple Histogram
    • Scatter Plots, Smooth Scatter Plots and Matrix Plots

    • Three Dimensional Plots, Star Plots and Chernoff Faces
    • Random Variables: Continuous and Discrete
    • Random Variables: Probability Functions
    • Probability Functions for Continuous Bivariate and Multivariate Random Variables
    • Univariate Normal Distribution: Theoretical Properties
    • Univariate Normal Distribution: Application in R Software

    • Bivariate Normal and Multivariate Normal Distributions in R
    • Chi square, t and F Distribution
    • Point and Interval Estimation
    • Maximum Likelihood Estimation

    • Basics of Tests of Hypothesis
    • Test and Confidence Interval for Mean in One Sample with Known Variance in Univariate Data
    • Test and Confidence Interval for Mean in One Sample with Unknown Variance in Univariate Data
    • Tests for Mean in Two Samples with Univariate Data
    • Analysis of Variance and Homogeneity of Variances with Univariate Data

    • Tests for Mean Vector with Multivariate Data in One Sample
    • Tests for Mean Vector with Multivariate Data in Two Samples
    • Scaling of Data :: Centering, Scaling and ZScores
    • Multiple Linear Regression Analysis :: Introduction and Basic Concepts
    • Multiple Linear Regression Analysis :: Estimation of Parameters

    • Model Fitting With R Software
    • Test of Hypothesis and Confidence Interval Estimation on Individual Regression Coefficients
    • Analysis of Variance and Implementation in R Software
    • Goodness of Fit and Testing of Normality
    • Logistic Regression Model
    • Introduction to Classification

    • Bayes Procedure for Classification
    • Classification Procedure for Multivariate Normal Distributions
    • Classification Procedure and Analysis in R
    • Cluster Analysis :: Basic Concepts and Definitions

    • Hierarchical Classification
    • Hierarchical Classification and Analysis with R
    • Hierarchical Classification with Example in R
    • Principle Component Analysis :: Concepts and Theoretical Setup

    • Principle Component and Its Graphical Analysis in R
    • Canonical Variables and Concepts
    • Statistical Analysis of Canonical Variables
    • Canonical Variables Analysis in R

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