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Najib Mozahem
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind logistic regression. The theory is explained in an intuitive way. The math is kept to a minimum. The course starts with an introduction to contingency tables, in which students learn how to calculate and interpret the odds and the odds ratios. From there, the course moves on to the topic of logistic regression, where students will learn when and how to use this regression technique. Topics such as model building, prediction, and assessment of model fit are covered. In addition, the course also covers diagnostics by covering the topics of residuals and influential observations.
Read more
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind logistic regression. The theory is explained in an intuitive way. The math is kept to a minimum. The course starts with an introduction to contingency tables, in which students learn how to calculate and interpret the odds and the odds ratios. From there, the course moves on to the topic of logistic regression, where students will learn when and how to use this regression technique. Topics such as model building, prediction, and assessment of model fit are covered. In addition, the course also covers diagnostics by covering the topics of residuals and influential observations.
Read more
Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind logistic regression. The theory is explained in an intuitive way. The math is kept to a minimum. The course starts with an introduction to contingency tables, in which students learn how to calculate and interpret the odds and the odds ratios. From there, the course moves on to the topic of logistic regression, where students will learn when and how to use this regression technique. Topics such as model building, prediction, and assessment of model fit are covered. In addition, the course also covers diagnostics by covering the topics of residuals and influential observations.
In the second part of the course, students learn how to apply what they learned using Stata. In this part, students will walk through a large project in order to understand the type of questions that are raised throughout the process, and which commands to use in order to address these questions.
Register for this course and see more details by visiting:
OpenCourser.com/course/prmje7/logistic
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What's inside
Learning objectives
- Create contingency tables
- Calculate odds ratio
- Understand what is logistic regression
- Identify when logistic regression is used
- Understand the output produced by logistic regression
- Include categorical variables
- Test for linearity
- Predict probabilities
- Test model fit
- Apply logistic regression using stata
- Visualise the best-fit model
- Show more
- Show less
- Create contingency tables
- Calculate odds ratio
- Understand what is logistic regression
- Identify when logistic regression is used
- Understand the output produced by logistic regression
- Include categorical variables
- Test for linearity
- Predict probabilities
- Test model fit
- Apply logistic regression using stata
- Visualise the best-fit model
- Show more
- Show less
Syllabus
Contingency Tables
Introduction
Two-by-two tables
The odds
Read more
Syllabus
Contingency Tables
Introduction
Two-by-two tables
The odds
Read more
The odds ratio
Two-by-three tables
Logistic Regression
Single independent variable
Examples
Binary variables
Multiple independent variables
Categorical variables
Nonlinearity: Non-graphical test
Nonlinearity: Graphical test
Prediction and Model Fit
Prediction
Goodness of fit: Likelihood ratio test
Goodness of fit: Hosmer-Lemeshow test
Goodness of fit: Classification tables
Goodness of fit: ROC analysis
Residuals
Influential Observations
Application: Fitting the Model
Introduction to the dataset
Continuous variables
Test of linearity: Non-graphical
Test of linearity: Graphical
Quadratic terms
Categorical variables: Part 1
Categorical variables: Part 2
Multivariate analysis
Application: Model Fit
Residual analysis
Influential observations
Combining both residuals and influence in one graph
Application: Visualizing the Model
Non-graphical interpretation
Graphical interpretation: single variable
Graphical interpretation: two variables
Conclusion
Next step
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Ideal for those seeking to improve their analytical skills by applying statistical methods
Suitable for individuals with basic statistical knowledge who want to delve into logistic regression
Instructors have extensive experience in data analysis and teaching statistical methods
Save this course
Save Logistic Regression using Stata to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Logistic Regression using Stata with these
activities:
Practice using statistical software
Show steps
Ensure proficiency in using statistical software, which is essential for applying logistic regression techniques.
Browse courses on
Statistical Software
Show steps
-
Review the software documentation or tutorials.
-
Practice using the software to perform basic statistical analyses.
-
Import a dataset and analyze it using the software.
Review the basics of statistics and probability
Show steps
Strengthen the foundation for understanding logistic regression by refreshing knowledge of statistics and probability concepts.
Browse courses on
Statistics
Show steps
-
Review notes or textbooks on statistics and probability.
-
Solve practice problems to test understanding.
-
Attend a review session or workshop.
Discuss Logistic Regression Concepts
Show steps
Engage in peer discussions to enhance understanding and address misconceptions.
Browse courses on
Logistic Regression
Show steps
-
Join a study group or online forum dedicated to logistic regression.
-
Participate actively in discussions, asking questions and sharing insights.
-
Collaborate with peers to work through complex concepts and case studies.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Create a contingency table and calculate the odds ratio
Show steps
Practice creating contingency tables and calculating odds ratios to build a strong foundation for understanding logistic regression concepts.
Browse courses on
Contingency Tables
Show steps
-
Review the course material on contingency tables and the odds ratio.
-
Find a dataset that contains categorical variables.
-
Create a contingency table using the variables.
-
Calculate the odds ratio for each pair of categories.
-
Interpret the odds ratio to understand the relationship between the variables.
Practice Logistic Regression Problems
Show steps
Reinforce understanding of logistic regression through targeted practice.
Browse courses on
Logistic Regression
Show steps
-
Find online practice problems on logistic regression.
-
Solve the practice problems, taking your time to understand the process.
-
Check your answers and identify areas for improvement.
-
Repeat the process to solidify your understanding.
Compile a glossary of logistic regression terms
Show steps
Creating a glossary will help you familiarize yourself with the key terms and concepts in logistic regression, which will enhance your understanding of the material.
Browse courses on
Logistic Regression
Show steps
-
Gather a list of key terms from the course materials and readings.
-
Define each term in your own words.
-
Organize the terms alphabetically or by topic.
Create Logistic Regression Model in Python
Show steps
Walk through a tutorial to create a logistic regression model in Python, reinforcing concepts learned in the course.
Browse courses on
Logistic Regression
Show steps
-
Find a tutorial on creating a logistic regression model in Python.
-
Gather the necessary data for your model.
-
Follow the tutorial to create your model.
-
Test your model on new data to evaluate its performance.
-
Refine your model as needed to improve its accuracy.
Follow a tutorial on using Stata for logistic regression
Show steps
Gain hands-on experience with Stata, the software used in the course, by following a guided tutorial on logistic regression.
Browse courses on
Stata
Show steps
-
Find a tutorial on using Stata for logistic regression.
-
Follow the tutorial step-by-step.
-
Practice using the commands covered in the tutorial.
-
Apply the commands to analyze a real-world dataset.
-
Troubleshoot any errors encountered.
Join a study group to discuss and solve logistic regression problems
Show steps
Enhance learning through collaboration by joining a study group where you can discuss and solve logistic regression problems with peers.
Browse courses on
Logistic Regression
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss the course material.
-
Work together to solve logistic regression problems.
-
Share knowledge and insights with each other.
-
Provide support and encouragement to fellow group members.
Solve practice problems on logistic regression
Show steps
Solving practice problems will reinforce your understanding of the concepts and help you develop proficiency in applying logistic regression techniques.
Browse courses on
Logistic Regression
Show steps
-
Find practice problems in the course materials, textbooks, or online resources.
-
Solve the problems using the techniques learned in the course.
-
Check your solutions against provided answer keys or consult with the instructor for feedback.
Create a presentation on the key concepts of logistic regression
Show steps
Deepen understanding of logistic regression concepts by creating a presentation that explains the material in a clear and organized manner.
Browse courses on
Logistic Regression
Show steps
-
Review the course material on logistic regression.
-
Identify the key concepts of logistic regression.
-
Organize the concepts into a logical flow.
-
Create visual aids to illustrate the concepts.
-
Rehearse the presentation.
Develop a logistic regression model for a real-world dataset
Show steps
Apply logistic regression knowledge to a practical problem by developing a model for a real-world dataset, solidifying understanding and enhancing problem-solving skills.
Browse courses on
Logistic Regression
Show steps
-
Identify a suitable dataset.
-
Clean and prepare the data.
-
Build a logistic regression model.
-
Evaluate the model's performance.
-
Interpret the results and write a report.
Create a presentation on a logistic regression case study
Show steps
Working on a case study will provide you with hands-on experience in applying logistic regression to real-world problems, deepening your understanding and critical thinking skills.
Browse courses on
Logistic Regression
Show steps
-
Identify a suitable case study from the course materials or other sources.
-
Analyze the data and develop a logistic regression model.
-
Interpret the results and draw conclusions.
-
Create a presentation to showcase your findings.
Attend a workshop on advanced logistic regression techniques
Show steps
Expand knowledge and learn advanced techniques by attending a workshop focused on advanced logistic regression concepts.
Browse courses on
Logistic Regression
Show steps
-
Find a suitable workshop.
-
Register and attend the workshop.
-
Participate actively in the workshop.
-
Apply the learned techniques to practical problems.
Practice using statistical software
Show steps
Ensure proficiency in using statistical software, which is essential for applying logistic regression techniques.
Browse courses on
Statistical Software
Show steps
Show steps
Show steps
- Review the software documentation or tutorials.
- Practice using the software to perform basic statistical analyses.
- Import a dataset and analyze it using the software.
Review the basics of statistics and probability
Show steps
Strengthen the foundation for understanding logistic regression by refreshing knowledge of statistics and probability concepts.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks on statistics and probability.
- Solve practice problems to test understanding.
- Attend a review session or workshop.
Discuss Logistic Regression Concepts
Show steps
Engage in peer discussions to enhance understanding and address misconceptions.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Join a study group or online forum dedicated to logistic regression.
- Participate actively in discussions, asking questions and sharing insights.
- Collaborate with peers to work through complex concepts and case studies.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Create a contingency table and calculate the odds ratio
Show steps
Practice creating contingency tables and calculating odds ratios to build a strong foundation for understanding logistic regression concepts.
Browse courses on
Contingency Tables
Show steps
Show steps
Show steps
- Review the course material on contingency tables and the odds ratio.
- Find a dataset that contains categorical variables.
- Create a contingency table using the variables.
- Calculate the odds ratio for each pair of categories.
- Interpret the odds ratio to understand the relationship between the variables.
Practice Logistic Regression Problems
Show steps
Reinforce understanding of logistic regression through targeted practice.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find online practice problems on logistic regression.
- Solve the practice problems, taking your time to understand the process.
- Check your answers and identify areas for improvement.
- Repeat the process to solidify your understanding.
Compile a glossary of logistic regression terms
Show steps
Creating a glossary will help you familiarize yourself with the key terms and concepts in logistic regression, which will enhance your understanding of the material.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Gather a list of key terms from the course materials and readings.
- Define each term in your own words.
- Organize the terms alphabetically or by topic.
Create Logistic Regression Model in Python
Show steps
Walk through a tutorial to create a logistic regression model in Python, reinforcing concepts learned in the course.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find a tutorial on creating a logistic regression model in Python.
- Gather the necessary data for your model.
- Follow the tutorial to create your model.
- Test your model on new data to evaluate its performance.
- Refine your model as needed to improve its accuracy.
Follow a tutorial on using Stata for logistic regression
Show steps
Gain hands-on experience with Stata, the software used in the course, by following a guided tutorial on logistic regression.
Browse courses on
Stata
Show steps
Show steps
Show steps
- Find a tutorial on using Stata for logistic regression.
- Follow the tutorial step-by-step.
- Practice using the commands covered in the tutorial.
- Apply the commands to analyze a real-world dataset.
- Troubleshoot any errors encountered.
Join a study group to discuss and solve logistic regression problems
Show steps
Enhance learning through collaboration by joining a study group where you can discuss and solve logistic regression problems with peers.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss the course material.
- Work together to solve logistic regression problems.
- Share knowledge and insights with each other.
- Provide support and encouragement to fellow group members.
Solve practice problems on logistic regression
Show steps
Solving practice problems will reinforce your understanding of the concepts and help you develop proficiency in applying logistic regression techniques.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find practice problems in the course materials, textbooks, or online resources.
- Solve the problems using the techniques learned in the course.
- Check your solutions against provided answer keys or consult with the instructor for feedback.
Create a presentation on the key concepts of logistic regression
Show steps
Deepen understanding of logistic regression concepts by creating a presentation that explains the material in a clear and organized manner.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Review the course material on logistic regression.
- Identify the key concepts of logistic regression.
- Organize the concepts into a logical flow.
- Create visual aids to illustrate the concepts.
- Rehearse the presentation.
Develop a logistic regression model for a real-world dataset
Show steps
Apply logistic regression knowledge to a practical problem by developing a model for a real-world dataset, solidifying understanding and enhancing problem-solving skills.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify a suitable dataset.
- Clean and prepare the data.
- Build a logistic regression model.
- Evaluate the model's performance.
- Interpret the results and write a report.
Create a presentation on a logistic regression case study
Show steps
Working on a case study will provide you with hands-on experience in applying logistic regression to real-world problems, deepening your understanding and critical thinking skills.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify a suitable case study from the course materials or other sources.
- Analyze the data and develop a logistic regression model.
- Interpret the results and draw conclusions.
- Create a presentation to showcase your findings.
Attend a workshop on advanced logistic regression techniques
Show steps
Expand knowledge and learn advanced techniques by attending a workshop focused on advanced logistic regression concepts.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find a suitable workshop.
- Register and attend the workshop.
- Participate actively in the workshop.
- Apply the learned techniques to practical problems.
Career center
Learners who complete Logistic Regression using Stata will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of statistical methods to develop and implement trading strategies. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the rapidly growing field of quantitative finance.
Statistician
Statistician
Statisticians use their knowledge of statistical methods to collect, analyze, interpret, and present data. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in various industries, including healthcare, finance, and market research.
Biostatistician
Biostatistician
Biostatisticians use their knowledge of statistical methods to design and analyze studies in the field of medicine. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the rapidly growing field of biostatistics.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistical methods to extract insights from data. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the rapidly growing field of data science.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of statistical methods to analyze financial data and make investment recommendations. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the rapidly growing field of financial analysis.
Actuary
Actuary
Actuaries use their knowledge of statistical methods to assess risk and uncertainty. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the insurance and finance industries.
Risk Manager
Risk Manager
Risk Managers use their knowledge of statistical methods to assess and manage risk. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in various industries, including finance, insurance, and healthcare.
Epidemiologist
Epidemiologist
Epidemiologists use their knowledge of statistical methods to investigate the causes and patterns of disease. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the field of public health.
Market Researcher
Market Researcher
Market Researchers use their knowledge of statistical methods to collect, analyze, and interpret data about consumer behavior. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the rapidly growing field of market research.
Public Health Analyst
Public Health Analyst
Public Health Analysts use their knowledge of statistical methods to analyze public health data and make recommendations for improvement. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the field of public health.
Survey Researcher
Survey Researcher
Survey Researchers use their knowledge of statistical methods to design and analyze surveys. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in various industries, including market research, public opinion polling, and social science research.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use their knowledge of statistical methods to analyze healthcare data and make recommendations for improvement. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the rapidly growing field of healthcare analytics.
Business Analyst
Business Analyst
Business Analysts use their knowledge of statistical methods to analyze business data and make recommendations for improvement. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in various industries, including consulting, finance, and healthcare.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of statistical methods to improve the efficiency of operations. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in various industries, including manufacturing, healthcare, and logistics.
Data Analyst
Data Analyst
Data Analysts use their expertise in statistical analysis, machine learning, and data visualization to help businesses make informed decisions. This course in Logistic Regression using Stata provides a strong foundation in statistical modeling, which is essential for success in this role. The course covers topics such as creating contingency tables, calculating odds ratios, and understanding the output produced by logistic regression. These skills are in high demand in the rapidly growing field of data analytics.
For more career information including salaries, visit:
OpenCourser.com/course/prmje7/logistic
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Logistic Regression using Stata.
Provides a comprehensive overview of logistic regression, covering topics such as model building, prediction, and assessment of model fit. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Comprehensive and accessible introduction to logistic regression. It covers all the essential concepts, from the basics to more advanced topics such as model building and assessment. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Practical guide to using logistic regression in Stata. It covers all the essential steps, from data preparation to model building and assessment. It valuable resource for students and practitioners who want to learn more about using this statistical technique in Stata.
Save
Provides a comprehensive overview of logistic regression theory and practice. It covers all the essential concepts, from the basics to more advanced topics such as model building and assessment. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Provides a comprehensive overview of logistic regression analysis. It covers all the essential concepts, from the basics to more advanced topics such as model building and assessment. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Provides a comprehensive overview of statistical learning, including logistic regression. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Provides a comprehensive overview of statistical learning, including logistic regression. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Provides a comprehensive overview of machine learning, including logistic regression. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Save
Provides a comprehensive overview of data mining, including logistic regression. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Save
Provides a comprehensive overview of predictive modeling, including logistic regression. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Provides a comprehensive overview of regression analysis, including logistic regression. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Provides a comprehensive overview of Bayesian statistics, including logistic regression. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
Provides a comprehensive overview of Bayesian data analysis, including logistic regression. It valuable resource for students and practitioners who want to learn more about this important statistical technique.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/prmje7/logistic
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Effort
3.5 total hours
Via
Udemy
Instructor
Najib Mozahem
Language
English
Good to know
Ideal for those seeking to improve their analytical skills by applying statistical methods
Suitable for individuals with basic statistical knowledge who want to delve into logistic regression
Instructors have extensive experience in data analysis and teaching statistical methods
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