Page 18 - Full Stack Development

 Non-Parametric test continued

 Hypothesis testing using Python and R

 Linear Regression

 Scatter Diagram

 Correlation Analysis

 Principles of Regression

 Introduction to Simple Linear Regression

 R shiny and Python Flask

 Introduction to R shiny and Python Flask (deployment)

 Multiple Linear Regression
Description: Learn about Linear Regression, components of Linear Regression viz regression
line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear
Regression analysis, Multiple Linear Regression and Linear Regression examples.

 Scatter diagram

 Correlation Analysis

 Correlation coefficient

 Ordinary least squares

 Principles of regression

 Splitting the data into training, validation and testing datasets

 Understanding Overfitting (Variance) vs Underfitting (Bias)

 Generalization error and Regularization techniques

 Introduction to Simple Linear Regression

 Heteroscedasticity / Equal Variance
Description: In the second part of the tutorial, you will learn about the Models and
Assumptions for building Linear Regression Models, build Multiple Linear Regression Models
and evaluate the results of the Linear Regression Analysis.

 LINE assumption

 Collinearity (Variance Inflation Factor)

 Linearity

 Normality

 Multiple Linear Regression

 Model Quality metrics

 Deletion diagnostics