Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Kanpur

Course Brief - A brief description of the course content; Downloading and installing R and R Studio
Exploring R - A description of the basic utilities available in R and R Studio
R preliminaries - Using R as a computational tool; Illustrative use of R for data handling
Using Simple Commands - Some examples of R-commands
Working with R - Importing data into R; Simple applications for data handling

Basic Statistical Concepts - Measurement scales; Null hypothesis statistical testing (NHST)
Practical Significance of Results - Statistical power of a test; Effect size; Statistical and practical significance of results
Univariate Data - Univariate statistical analysis: Z-test, t-test, F-test, correlations
Multivariate Techniques - An overview of multivariate techniques: selecting an appropriate multivariate technique for a given dataset
Data Manipulation - Scaling data: Mean-centering and standardizing data

Data Preparation - Visual (graphical) examination of data: box-plot, stem and leaf plot, histogram; Missing values; Outliers
Examining the Data - Examining the data for univariate and multivariate assumptions
Transformations for Non-normal Data - Transformations for skewed data; Selecting between square-root, inverse, and logarithmic transformations; Interpreting results based on the transformed data

Analytical Models - Descriptive, graphical, and mathematical models
Principal Components Analysis (PCA) - Introduction to different factor analysis techniques; Basic concepts; manually doing PCA; Factor structure and rotation of factor structure
Performing Principal Components Analysis - Using R-packages for computing PCA in R; Interpreting the R-output

Exploratory Factor Analysis (EFA) - Introduction to EFA; Comparison of PCA and EFA; Performing EFA; Higher order factor analysis
Application of Factor Analysis - Issues in PCA and EFA; Illustrative applications of PCA and EFA
Confirmatory Factor Analysis (CFA) - Objectives of CFA; Comparison of CFA and EFA; Performing CFA and interpretation of R-output

Regression Analysis - Bivariate and multiple regression analysis; Assumptions of regression analysis; Multicollinearity and its effect on the regression model
Performing regression analysis - Interpretation of R-Output; Multivariate multiple regression analysis
Cluster analysis - Objectives of cluster analysis; Similarity measures; Hierarchical clustering; the concept of dendrogram

Performing Cluster Analysis - Interpreting R-output
Discriminant Analysis - Objectives of discriminant analysis; comparison with regression analysis and clustering
Performing Discriminant Analysis - Two-group discriminant analysis; Multiple group discriminant analysis

Logistic Regression - Objectives of logistic regression; Logistic regression model; Receiver Operating Characteristic (ROC) Curve
Performing Logistic Regression - Interpretation of R-Output
Analysis of Variance (ANOVA) - Univariate ANOVA; Factorial ANOVA designs; Analysis of covariance (ANCOVA)

Computing ANOVA and ANCOVA - Interpretation of R-output
Multivariate analysis of variance (MANOVA) - Analytical approach to MANOVA; Wilks lambda and other statistics to test the statistical significance of MANOVA results
Computing and interpreting MANOVA - Computing MANOVA for two groups and multiple groups; Interpretation of R-output

Canonical Correlation Analysis (CCA) - Introduction to CCA; Statistical tests for significance testing
Performing Canonical Correlation Analysis - Illustrative example and interpretation of R-output
Multidimensional scaling (MDS) - Spatial models; Similarity and distance; multidimensional map

Performing Multidimensional Scaling - Interpretation of R-output
Correspondence Analysis - Basic concepts; Creating perceptual maps
Conjoint Analysis - The concept of utility (worth); Assessing part worth and whole worth based on the product attributes

Structural Equation Modelling (SEM) - Exogenous and endogenous variables; Reflective and formative constructs; inner model and outer model; Statistics related to SEM
Performing Structural Equation Modeling - Interpreting R-output and reporting the results
Partial Least Squares for SEM (SEM-PLS) - Sample size considerations; SEM-PLS as a technique for data that violates multivariate assumptions; Other approaches to the issue of non-normal data in SEM