Statistical and Machine Learning For Regression Modelling in R
Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R
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1
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Segment - 01 - Introduction
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Segment - 02 - Getting Started with R and R Studio
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Segment - 03 - Reading in Data with R
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Segment - 04 - Data Cleaning with R
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Segment - 05 - Some More Data Cleaning with R
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Segment - 06 - Basic Exploratory Data Analysis in R
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Segment - 07 - Conclusion
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2
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Segment - 08 - OLS Regression - Theory
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Segment - 09 - OLS - Implementation
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Segment - 10 - More on Result Interpretations
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Segment - 11 - Confidence Interval - Theory
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Segment - 12 - Calculate the Confidence Interval in R
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Segment - 13 - Confidence Interval and OLS Regressions
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Segment - 14 - Linear Regression without Intercept
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Segment - 15 - Implement ANOVA on OLS Regression
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Segment - 16 - Multiple Linear Regression
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Segment - 17 - Multiple Linear regression with Interaction and Dummy Variables
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Segment - 18 - Some Basic Conditions that OLS Models Have to Fulfill
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Segment - 19 - Conclusion
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3
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Segment - 20 - Identify Multicollinearity
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Segment - 21 - Doing Regression Analyses with Correlated Predictor Variables
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Segment - 22 - Principal Component Regression in R
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Segment - 23 - Partial Least Square Regression in R
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Segment - 24 - Ridge Regression in R
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Segment - 25 - LASSO Regression
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Segment - 26 - Conclusion
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4
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Segment - 27 - Why Do Any Kind of Selection?
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Segment - 28 - Select the Most Suitable OLS Regression Model
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Segment - 29 - Select Model Subsets
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Segment - 30 - Machine Learning Perspective on Evaluate Regression Model Accuracy
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Segment - 31 - Evaluate Regression Model Performance
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Segment - 32 - LASSO Regression for Variable Selection
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Segment - 33 - Identify the Contribution of Predictors in Explaining the Variation in Y
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Segment - 34 - Conclusion
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5
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Segment - 35 - Robust Regression-Deal with Outliers
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Segment - 36 - Dealing with Heteroscedasticity
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Segment - 37 - Conclusion
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6
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Segment - 38 - What are GLMs?
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Segment - 39 - Logistic regression
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Segment - 40 - Logistic Regression for Binary Response Variable
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Segment - 41 -Multinomial Logistic Regression
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Segment - 42 - Regression for Count Data
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Segment - 43 - Conclusion
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7
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Segment - 44 - Generalized Additive Models (GAMs) in R
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Segment - 45 - Boosted GAM Regression
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Segment - 46 - Multivariate Adaptive Regression Splines (MARS)
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Segment - 47 - CART-Regression Trees in R
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Segment - 48 - Conditional Inference Trees
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Segment - 49 - Random Forest (RF)
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Segment - 50 - Gradient Boosting Regression
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Segment - 51 - ML Model Selection