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