Curriculum

  • 1

    Get Started with Practical Regression Analysis in R

  • 2

    Ordinary Least Square Regression Modelling

    • OLS Regression - Theory

    • OLS - Implementation

    • More on Result Interpretations

    • Confidence Interval - Theory

    • Calculate the Confidence Interval in R

    • Confidence Interval and OLS Regressions

    • Linear Regression without Intercept

    • Implement ANOVA on OLS Regression

    • Multiple Linear Regression

    • Multiple Linear regression with Interaction and Dummy Variables

    • Some Basic Conditions that OLS Models Have to Fulfill

    • Conclusions to Section 2
  • 3

    Deal with Multicollinearity in OLS Regression Models

    • Identify Multicollinearity

    • Doing Regression Analyses with Correlated Predictor Variables

    • Principal Component Regression in R

    • Partial Least Square Regression in R

    • Ridge Regression in R

    • LASSO Regression

    • Conclusion to Section 3
  • 4

    Variable & Model Selection

    • Why Do Any Kind of Selection?

    • Select the Most Suitable OLS Regression Model

    • Select Model Subsets

    • Machine Learning Perspective on Evaluate Regression Model Accuracy

    • Evaluate Regression Model Performance

    • LASSO Regression for Variable Selection

    • Identify the Contribution of Predictors in Explaining the Variation in Y

    • Conclusions to Section 4
  • 5

    Dealing With Other Violations of the OLS Regression Models

    • Data Transformations

    • Robust Regression-Deal with Outliers

    • Dealing with Heteroscedasticity

    • Conclusions to Section 5
  • 6

    Generalized Linear Models (GLMs)

    • What are GLMs?

    • Logistic regression

    • Logistic Regression for Binary Response Variable

    • Multinomial Logistic Regression

    • Regression for Count Data

    • Goodness of fit testing

    • Conclusions to Section 6
  • 7

    Working with Non-Parametric and Non-Linear Data

    • Work With Non-Parametric and Non-Linear Data

    • Polynomial and Non-linear regression

    • Generalized Additive Models (GAMs) in R

    • Boosted GAM Regression

    • Multivariate Adaptive Regression Splines (MARS)

    • Machine Learning Regression-Tree Based Methods

    • CART-Regression Trees in R

    • Conditional Inference Trees

    • Random Forest (RF)

    • Gradient Boosting Regression

    • ML Model Selection

    • Conclusions to Section 7

About Your Instructor

Bestselling Instructor & Data Scientist (Cambridge University)

Minerva Singh

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler.

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