Logistic Regression Made Simple

Name: Logistic Regression Made Simple
Rating: 4.6 (12 reviews)

Statistics in R Series

Highest Rated

Created byMd Sohel Mahmood

Last updated 7/2023

English

English [Auto],

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

Course content

14 sections • 91 lectures • 4h 38m total length

Introduction2:21
My background and course topics1:57

Introduction1:22
Why R?0:59
Dataset that we will use0:47
A quick look at the data set and codebook1:50
More understanding on the dataset2:37
Explore how the dataset is structured in Excel, with year, respondent IDs, and coded survey data. Learn how to decode variables such as working status and gender from the codebook.
Data Types in the dataset1:25
Useful R commands (part 1)13:41
Useful R commands (part 2)8:18
Useful R commands (part 3)13:24
Generalized Linear and Cumulative Link Model Function4:08
Explore generalized linear models in R, handling binary, ordinal, nominal, and count responses with GLM, cumulative link models, and regression using vglm and vgam packages.
Conclusion0:32
Review key logistic regression functions in R and essential statistical commands, and prepare to compare models using goodness of fit statistics.

Introduction0:53
Explore simple binary logistic regression with one predictor and one binary response variable in RStudio, forming a binary logistic equation for outcomes 0 or 1.
Understanding simple one predictor logistic regression1:25
Violations of Assumptions of Linear Regression5:38
Explore why linear regression fails with binary outcomes and how logistic regression uses logit odds to model a binary response, with an R example.
Implementation in R and interpretation (part1)6:59
Implement binary logistic regression in R to predict a child's bachelor's degree from the mother's bachelor's degree, and interpret the logit coefficient as indicating higher odds with significance (p<0.05).
Implementation in R and interpretation (part2)5:35
Compute Nagelkerke and Macfadden pseudo R-squared values for a simple logistic model in R, and interpret the log-likelihood ratio test with AIC and Bic for nested and non-nested models.
Resource Link0:02
Quiz
Conclusion0:42

Introduction0:36
Understanding Multiple Predictor Logistic Regression2:49
Implementation in R and interpretation (part1)4:32
Implementation in R and interpretation (part2)4:06
Resource Link0:02
Quiz
Conclusion0:55
Explore implementing multiple predictor logistic regression in R and prepare for ordinal logistic regression with ordered predictors and levels, noting real-world complexity beyond simple models.

Introduction1:20
The new dataset for ordinal regression5:59
Research Question1:17
Implementation in R and interpretation (part1)5:22
Implementation in R and interpretation (part2)3:17
Model Klemtu uses a binary bachelor’s predictor to model an ordinal income outcome with the CLM function. The bachelor’s degree increases the log odds of earning over 50k, with p<0.05.
Implementation in R and interpretation (part3)3:00
examine the third logistic regression model using education years as a continuous predictor for income above 50k, with a positive coefficient and a very small p-value.
Implementation in R and interpretation (part4)3:56
Implementation in R and interpretation (part5)11:00
Resource Link0:02
Conclusion0:26

Introduction0:58
Implementation in R and interpretation (part1)9:59
Implementation in R and interpretation (part2)2:21
Implementation in R and interpretation (part3)3:17
Implementation in R and interpretation (part4)7:19
Resource Link0:02
Quiz 1
Conclusion1:00
Conclude the section on implementing ordinal logistic regression in R and outline upcoming discussions on model comparisons using pseudo r square and goodness of fit statistics.

Introduction2:51
Simplification of the idea2:58
Explore how education levels influence family income using the generalized ordinal regression model, contrasting proportional odds with level-dependent coefficients to reveal varying transition effects on income beyond 50k.
Difference between generalized and PPO model1:33
Implementation in R and interpretation (part1)13:01
Implementation in R and interpretation (part2)4:59
Implementation in R and interpretation (part3)3:18
Compare model one and model two in R, interpret odds ratios 0.667 and 0.886 for married versus unmarried health status, and assess fit with Macfadden's pseudo R-square, AIC, and BIC.
Implementation in R and interpretation (part4)3:49
Implementation in R and interpretation (part5)2:09
Resource Link0:01
More explanation on the result3:50
Interpret the 1.772 odds ratio for married versus unmarried status in generalized ordinal regression, and compare models with and without family income using log likelihood and LR test.
Conclusion0:35

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.

Who this course is for:

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

Logistic Regression Made Simple

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 4min

Basic Statistical R Commands11 lectures • 49min

Logistic Regression: Statistics for Goodness-of-Fit4 lectures • 14min

Simple Logistic Regression for Binary Variables7 lectures • 21min

Multiple Logistic Regression for Binary Variables6 lectures • 13min

Simple Logistic Regression for Ordinal Variables10 lectures • 36min

Multiple Logistic Regression for Ordinal Variable7 lectures • 25min

Logistic Regression Models Comparison6 lectures • 13min

Generalized Ordinal Regression Model11 lectures • 39min

Partial Proportional Odd Model7 lectures • 25min

Requirements

Description

Who this course is for: