Introduction to Data Analysis Using R

Learn to load, clean, obtain summary statistics and visualize dataset. 

Explore some multivariate statistics like MANOVA, Cluster Analysis, Structural Equation Modeling and Discriminant Analysis.

The aim of this section is to demonstrate how to compute performance metrics such as the:

MSE, MAE, MAPE and RMSE for regression analysis.

We discuss performing PCA and FA on a dataset and extracting the factors for further analysis.

Understand how to model your data using various regression analysis techniques.

The violation of regression model assumptions can lead to misleading model results, learn how to identify and handle them.

ANOVA is a powerful tool for testing hypotheses about the effects of independent variables on the dependent variable especially in experimental designs.

Time series analysis can be used to identify trends, patterns, and anomalies in the data, and to make predictions about future values of the variable.

A statistical distribution is a mathematical function that describes the probability of different values occurring for a random variable.

Survival analysis is used to estimate the probability of surviving for a certain period of time, and to compare the survival rates of different groups.