Basic Statistics in R

The module provides an overview of statistics and also covers how to conduct the best test and exploratory analysis.

• Covariance & Correlation
• Central Limit Theorem
• Z Score
• Normal Distributions
• Hypothesis

Ordinary Least Square Regression 1

This module explains how to present the basic regression and multiple regression graphically.

• Bivariate Data
• Quantifying Association
• Least Squares Method
• The Regressions
• Simple Linear Regression
• Deletion Diagnostics
• Influential Observations
• Regularization

Ordinary Least Square Regression 2

In this module, the delegates will learn about linear regression and check for Heteroscedasticity.

• Collinearity
• Using Linear Regression, Model fitting
• Performing Under Fitting and Over Fitting
• What is Heteroscedasticity?

Logistic Regression

This module introduces delegates to Linear Probability Model and logistic regression.

• Understand Binary Response Regression Model
• Logistic Function
• Logistic Curve
• Linear regression as Linear Probability Model
• Goodness of fit matrix
• Linear Probability Model drawback
• All Interactions Logistic Regression
• Multinomial Logit
• Interpretation
• Ordered Categorical Variable

Advanced Regression

This module covers logistic regression in detail and explains how logistic regression used on the various dataset.

• Poisson Regression
• Model Fit Test
• Offset Regression
• Negative Binomial
• Dual Models
• Hurdle Models
• Poisson Model with Offset
• Zero-Inflated Poisson Models
• Variables used in the Analysis
• Poisson Regression Parameter Estimates
• Zero-Inflated Negative Binomial

Imputation

In this module, the delegates will learn about addressing missing values.

• Why is Missing Data a Problem?
• Missing Values are Common
• Types of Missing Values
• Problems with Pairwise Deletion
• No Treatment Option
  • Complete Case Method
  • Available Case Method
• Mean Substitution Method
• Imputation
• Regression Substitution Method
• K-Nearest Neighbour Approach
• Maximum Likelihood Estimation
• EM Algorithm
• Little’s Test for MCAR
• Single and Multiple Imputation

Forecasting 1

This module explains forecasting and time series data.

• Need for Forecasting
• Types of Forecast
• Forecasting Steps
• Autocorrelation
• Correlogram
• Time Series Components
• Variations in Time Series
• Seasonality
• Forecast Error
• Mean Error (ME)
• MPE and MAPE---Unit free measure
• Difference between Additive and Multiplicative Seasonality
• Curve Fitting
• Simple Exponential Smoothing
• Decomposition with R
• Generating Forecasts
• Explicit Modeling
• Modeling of Trend
• Seasonal Components
• Smoothing Methods
• ARIMA Model-building

Forecasting 2

This module covers Seasonality and Trend Analysis.

• Analysis of Log-transformed Data
• How to Formulate the Model
• Partial Regression Plot
• Normal Probability Plot
• Tests for Normality
• Box-Cox Transformation
• Box-Tidwell Transformation
• Growth Curves
• Logistic Regression: Binary
• Neural Network
• Network Architectures
• Neural Network Mathematics

Dimensionality Reduction

The module introduces delegates to the Dimensionality Reduction.

• Factor Analysis
• Principal Component Analysis
• Mechanism of finding PCA
• Linear Discriminant Analysis
• Use LDA for determining the maximum separable line
• Implement Dimensionality Reduction algorithm in R

Survival Analysis

This module covers Churn analysis and Regression.

• Time-to-Event Data
• Censoring
• Survival Analysis
• Types of Censoring
• Survival Analysis Techniques
• PreProcessing
• Elastic Net