What You Will 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
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 Should Attend!
- Anyone interested in learning Logistic Regression, whether it is for research, business, or personal use.
- Anyone curious on the outcomes of social surveys