Logistic Regression Made Simple

Statistics in R Series

What you'll learn

Improved Data Analysis Skills: One of the key outcomes of this regression course is the development of improved data analysis skills in R

Better Decision-Making: Statistics provide a framework for making informed decisions based on data rather than intuition or guesswork.

Increased Statistical Literacy: This course aims to improve statistical literacy, enabling students to understand and critically evaluate logistic regression

Preparation for Further Studies and Careers: This courses provides a solid foundation for students pursuing further studies or careers in data analysis field.

Proficiency in Statistical Software: This course solely uses R as the analysis platform. Students will have a solid understanding of logistic regression in R

Requirements

Basic understanding of Statistics and R programming language

Description

Logistic regression is a statistical technique that has become increasingly important in the field of data analysis and machine learning. Various disciplines, including economics, biology, social sciences, and engineering, use it to model and analyze binary and categorical data.This course introduces logistic regression and its applications in application in socioeconomic case studies. In this course, a wide range of audiences is addressed, from students and practitioners with a basic knowledge of statistics to researchers in the field of machine learning. Fewer equations and more concepts are the two dominating ideas behind developing this course.Initially, the course provides a brief overview of regression analysis, followed by an explanation of the various logistic regression models in detail. Assumptions and limitations of the model are discussed, as well as methods for selecting and validating the model.Additionally, the course provides a practical guide to the use of logistic regression in data analysis. Topics covered include data preparation, model construction, interpretation of results, and model evaluation. In this course, there are examples and case studies that illustrate how logistic regression is used in a variety of fields.The course  also introduces advanced topics such as generalized linear models and partial proportional odd model. In general, this course  aims to provide a comprehensive overview of logistic regression, starting with the basics and progressing to more advanced topics. To aid readers in understanding the concepts and applications of logistic regression, the course  is managed in a clear and concise manner, with examples and illustrations.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 My background and course topics

Section 2: Basic Statistical R Commands

Lecture 3 Introduction

Lecture 4 Why R?

Lecture 5 Dataset that we will use

Lecture 6 A quick look at the data set and codebook

Lecture 7 More understanding on the dataset

Lecture 8 Data Types in the dataset

Lecture 9 Useful R commands (part 1)

Lecture 10 Useful R commands (part 2)

Lecture 11 Useful R commands (part 3)

Lecture 12 Generalized Linear and Cumulative Link Model Function

Lecture 13 Conclusion

Section 3: Logistic Regression: Statistics for Goodness-of-Fit

Lecture 14 Introduction

Lecture 15 Bernoulli distribution

Lecture 16 Statistics for Goodness-of-Fit

Lecture 17 Conclusion

Section 4: Simple Logistic Regression for Binary Variables

Lecture 18 Introduction

Lecture 19 Understanding simple one predictor logistic regression

Lecture 20 Violations of Assumptions of Linear Regression

Lecture 21 Implementation in R and interpretation (part1)

Lecture 22 Implementation in R and interpretation (part2)

Lecture 23 Resource Link

Lecture 24 Conclusion

Section 5: Multiple Logistic Regression for Binary Variables

Lecture 25 Introduction

Lecture 26 Understanding Multiple Predictor Logistic Regression

Lecture 27 Implementation in R and interpretation (part1)

Lecture 28 Implementation in R and interpretation (part2)

Lecture 29 Resource Link

Lecture 30 Conclusion

Section 6: Simple Logistic Regression for Ordinal Variables

Lecture 31 Introduction

Lecture 32 The new dataset for ordinal regression

Lecture 33 Research Question

Lecture 34 Implementation in R and interpretation (part1)

Lecture 35 Implementation in R and interpretation (part2)

Lecture 36 Implementation in R and interpretation (part3)

Lecture 37 Implementation in R and interpretation (part4)

Lecture 38 Implementation in R and interpretation (part5)

Lecture 39 Resource Link

Lecture 40 Conclusion

Section 7: Multiple Logistic Regression for Ordinal Variable

Lecture 41 Introduction

Lecture 42 Implementation in R and interpretation (part1)

Lecture 43 Implementation in R and interpretation (part2)

Lecture 44 Implementation in R and interpretation (part3)

Lecture 45 Implementation in R and interpretation (part4)

Lecture 46 Resource Link

Lecture 47 Conclusion

Section 8: Logistic Regression Models Comparison

Lecture 48 Introduction

Lecture 49 Implementation in R and interpretation (part1)

Lecture 50 Implementation in R and interpretation (part2)

Lecture 51 Implementation in R and interpretation (part3)

Lecture 52 Conclusion

Lecture 53 Resource Link

Section 9: Generalized Ordinal Regression Model

Lecture 54 Introduction

Lecture 55 Simplification of the idea

Lecture 56 Difference between generalized and PPO model

Lecture 57 Implementation in R and interpretation (part1)

Lecture 58 Implementation in R and interpretation (part2)

Lecture 59 Implementation in R and interpretation (part3)

Lecture 60 Implementation in R and interpretation (part4)

Lecture 61 Implementation in R and interpretation (part5)

Lecture 62 Resource Link

Lecture 63 More explanation on the result

Lecture 64 Conclusion

Section 10: Partial Proportional Odd Model

Lecture 65 Introduction

Lecture 66 Simplification of the idea

Lecture 67 Implementation in R and interpretation (part1)

Lecture 68 Implementation in R and interpretation (part2)

Lecture 69 Implementation in R and interpretation (part3)

Lecture 70 Resource Link

Lecture 71 Conclusion

Section 11: Multinomial Logistic Regression Model

Lecture 72 Introduction

Lecture 73 A quick recap

Lecture 74 What is multinomial logistic regression?

Lecture 75 Implementation in R and interpretation (part1)

Lecture 76 Implementation in R and interpretation (part2)

Lecture 77 Implementation in R and interpretation (part3)

Lecture 78 Implementation in R and interpretation (part4)

Lecture 79 Resource Link

Lecture 80 Conclusion

Section 12: Poisson Regression Model

Lecture 81 Introduction

Lecture 82 What is Poisson Regression?

Lecture 83 Note

Lecture 84 Implementation in R and interpretation (part1)

Lecture 85 Implementation in R and interpretation (part2)

Lecture 86 Implementation in R and interpretation (part3)

Lecture 87 Implementation in R and interpretation (part4)

Lecture 88 Resource Link

Lecture 89 Conclusion

Section 13: Datasets and codes

Lecture 90 Github Resource

Section 14: Final Conclusion

Lecture 91 Conclusion

Anyone interested in learning Logistic Regression, whether it is for research, business, or personal use.,Anyone curious on the outcomes of social surveys