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Marc Huber
This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.
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What's inside
Syllabus
Course Overview and Logistics
Understanding Predictive Modeling
In this module, you review the fundamentals of predictive modeling. Then you explore the business scenario data that is used throughout the course. Finally, you learn about common analytical challenges that you might encounter as a modeler.
Read more
Syllabus
Course Overview and Logistics
Understanding Predictive Modeling
In this module, you review the fundamentals of predictive modeling. Then you explore the business scenario data that is used throughout the course. Finally, you learn about common analytical challenges that you might encounter as a modeler.
Read more
Fitting the Model
In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.
Preparing the Input Variables, Part 1
In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.
Preparing the Input Variables, Part 2
In this module, you learn how to select the most predictive variables to use in your model.
Measuring Model Performance
In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.
SAS Certification Practice Exam - Statistical Business Analysis Using SAS®9: Regression and Modeling
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops predictive modeling skills, which are highly relevant to data analysis
Teaches logistic regression using SAS software, which is standard in statistical modeling
Covers selecting variables and interactions, which are crucial for building effective models
Examines recoding categorical variables based on weight of evidence, which is an advanced technique for improving model accuracy
Assesses models, which is essential for evaluating model performance and making informed decisions
Treats missing values, which is a common challenge in data analysis
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Reviews summary
Sas logistic regression
Learners say this SAS modeling regression course is thorough and a good next step for learners familiar with SAS. Despite the occasional confusion, learners appreciate the progression in content and hands-on demos.
Builds upon previous courses
"This was another great course from SAS and Coursera."
"Further , I recommend that the course "Doing More with SAS Programming" would be a prerequisite fo this course."
"This is a great next step for learners familiar with SAS"
Demonstrates coding from start to finish.
"The coding was also built from beginning to the end with easy flow."
"This is a great course. Most of the commonly occuring problems in real-life situations have been dealt with in great details."
"Awesome course about the predictive modeling with logistic regression using SAS. The instructor is fantastic teaching us from the ground up step by step with excellent explanations."
Can be confusing at times
"This course was far more confusing than the other courses in this specialization."
"The only syntax was in the demos and was already applied to the problem used throughout the course, leaving me to have to figure out why certain (macro)variables or data sets or values were being used in a procedure."
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 Predictive Modeling with Logistic Regression using SAS with these
activities:
Review the fundamentals of regression analysis
Show steps
Strengthens the foundation for learning and enables better understanding by refreshing prior knowledge.
Browse courses on
Regression Analysis
Show steps
-
Review notes or materials from a previous course or textbook.
-
Solve practice problems or exercises related to regression analysis.
-
Identify areas where the review has strengthened understanding.
Review and organize course materials
Show steps
Helps organize and reinforce the concepts covered in the course, improving learning outcomes.
Show steps
-
Collect and organize notes, assignments, quizzes, and exams.
-
Review the materials and identify key concepts.
-
Summarize and condense the materials for easy reference.
Review 'Logistic Regression Models' by Hosmer and Lemeshow
Show steps
Provides additional insights and a deeper understanding of the topic through a well-regarded academic source.
View
Applied Logistic Regression
on Amazon
Show steps
-
Read the book and take notes on key concepts.
-
Identify areas where the book expands on the course material.
-
Apply the concepts from the book to the course assignments.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore SAS/STAT software tutorial
Show steps
Provides hands-on experience with the software and deepens understanding of its capabilities.
Show steps
-
Access the SAS/STAT software tutorial.
-
Follow the instructions to complete the exercises.
-
Review the explanations provided in the tutorial.
Attend a workshop on logistic regression
Show steps
Provides an immersive and interactive learning experience, enhancing comprehension and retention.
Browse courses on
Logistic Regression
Show steps
-
Identify and register for a relevant logistic regression workshop.
-
Attend the workshop and take active participation.
-
Engage with other participants to exchange insights.
Practice logistic regression examples
Show steps
Helps strengthen the understanding of the topic and check for comprehension.
Browse courses on
Logistic Regression
Show steps
-
Review the logistic regression model.
-
Solve practice problems involving logistic regression.
-
Check your answers against provided solutions.
Discuss logistic regression with peers
Show steps
Provides opportunities to clarify concepts, share insights, and enhance understanding through peer interaction.
Browse courses on
Logistic Regression
Show steps
-
Find a peer or form a study group.
-
Prepare questions or topics for discussion.
-
Engage in discussions and share perspectives.
-
Summarize the key points and any new insights gained.
Create a visual representation of logistic regression
Show steps
Improves comprehension and aids in retaining information through visual representation.
Browse courses on
Logistic Regression
Show steps
-
Choose a dataset that can be analyzed using logistic regression.
-
Create a logistic regression model for the dataset using SAS/STAT.
-
Plot the fitted logistic regression model using a visualization tool.
-
Interpret the visualization and draw conclusions about the relationship between the variables.
Develop a logistic regression model for a specific business problem
Show steps
Encourages practical application of course concepts, promoting deeper understanding and proficiency.
Browse courses on
Logistic Regression
Show steps
-
Identify a business problem that can be solved using logistic regression.
-
Gather relevant data and prepare it for analysis using SAS/STAT.
-
Develop and fit a logistic regression model to the data.
-
Evaluate the model's performance and interpret the results.
-
Communicate the findings and make recommendations based on the model.
Review the fundamentals of regression analysis
Show steps
Strengthens the foundation for learning and enables better understanding by refreshing prior knowledge.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Review notes or materials from a previous course or textbook.
- Solve practice problems or exercises related to regression analysis.
- Identify areas where the review has strengthened understanding.
Review and organize course materials
Show steps
Helps organize and reinforce the concepts covered in the course, improving learning outcomes.
Show steps
Show steps
Show steps
- Collect and organize notes, assignments, quizzes, and exams.
- Review the materials and identify key concepts.
- Summarize and condense the materials for easy reference.
Review 'Logistic Regression Models' by Hosmer and Lemeshow
Show steps
Provides additional insights and a deeper understanding of the topic through a well-regarded academic source.
View
Applied Logistic Regression
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes on key concepts.
- Identify areas where the book expands on the course material.
- Apply the concepts from the book to the course assignments.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore SAS/STAT software tutorial
Show steps
Provides hands-on experience with the software and deepens understanding of its capabilities.
Show steps
Show steps
Show steps
- Access the SAS/STAT software tutorial.
- Follow the instructions to complete the exercises.
- Review the explanations provided in the tutorial.
Attend a workshop on logistic regression
Show steps
Provides an immersive and interactive learning experience, enhancing comprehension and retention.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify and register for a relevant logistic regression workshop.
- Attend the workshop and take active participation.
- Engage with other participants to exchange insights.
Practice logistic regression examples
Show steps
Helps strengthen the understanding of the topic and check for comprehension.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Review the logistic regression model.
- Solve practice problems involving logistic regression.
- Check your answers against provided solutions.
Discuss logistic regression with peers
Show steps
Provides opportunities to clarify concepts, share insights, and enhance understanding through peer interaction.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find a peer or form a study group.
- Prepare questions or topics for discussion.
- Engage in discussions and share perspectives.
- Summarize the key points and any new insights gained.
Create a visual representation of logistic regression
Show steps
Improves comprehension and aids in retaining information through visual representation.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Choose a dataset that can be analyzed using logistic regression.
- Create a logistic regression model for the dataset using SAS/STAT.
- Plot the fitted logistic regression model using a visualization tool.
- Interpret the visualization and draw conclusions about the relationship between the variables.
Develop a logistic regression model for a specific business problem
Show steps
Encourages practical application of course concepts, promoting deeper understanding and proficiency.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify a business problem that can be solved using logistic regression.
- Gather relevant data and prepare it for analysis using SAS/STAT.
- Develop and fit a logistic regression model to the data.
- Evaluate the model's performance and interpret the results.
- Communicate the findings and make recommendations based on the model.
Career center
Learners who complete Predictive Modeling with Logistic Regression using SAS will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians apply mathematical and statistical principles to collect, analyze, and interpret data, providing insights for decision-making and problem-solving. The Predictive Modeling with Logistic Regression using SAS course delves into the fundamentals of logistic regression, a widely used statistical technique for modeling binary outcomes. Through hands-on exercises, you'll learn to build and evaluate logistic regression models, addressing common challenges in data preparation, variable selection, and model assessment. This course is highly relevant to statisticians seeking to advance their expertise in regression modeling and data analysis.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage financial risks and uncertainties. The Predictive Modeling with Logistic Regression using SAS course provides a solid foundation in logistic regression, a key technique for modeling the probability of events in insurance, finance, and other risk-related fields. By mastering logistic regression, you'll gain valuable skills in data analysis, model building, and risk assessment, enhancing your ability to develop innovative solutions to complex financial problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and implement mathematical and statistical models to analyze financial data and make investment decisions. The Predictive Modeling with Logistic Regression using SAS course provides a comprehensive introduction to logistic regression, a powerful technique for modeling the probability of events and outcomes. By gaining proficiency in logistic regression, you'll enhance your ability to build and evaluate predictive models for risk assessment, portfolio optimization, and trading strategies.
Data Scientist
Data Scientist
Data Scientists leverage advanced statistical and computational techniques to extract insights from data and solve business problems. The Predictive Modeling with Logistic Regression using SAS course provides a solid foundation in logistic regression, a widely used technique for modeling binary outcomes. By mastering logistic regression, you'll gain valuable skills in data exploration, model development, and predictive analytics, enabling you to make data-driven decisions and contribute to innovative solutions in various industries.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage financial and operational risks within organizations. The Predictive Modeling with Logistic Regression using SAS course offers a comprehensive introduction to logistic regression, a powerful technique for modeling the probability of events and outcomes. By gaining proficiency in logistic regression, you'll enhance your ability to develop and implement predictive models to forecast risk, optimize risk management strategies, and make informed decisions under uncertainty.
Data Analyst
Data Analyst
Data Analysts leverage advanced statistical and data models to solve complex business problems and drive data-driven decision-making. The Predictive Modeling with Logistic Regression using SAS course provides a solid foundation in logistic regression, a powerful technique for modeling and understanding the relationship between a binary dependent variable and a set of independent variables. By mastering logistic regression, you'll gain valuable skills in variable selection, model assessment, and data transformation, which are essential for success in data analysis.
Biostatistician
Biostatistician
Biostatisticians apply statistical principles to design and analyze studies in medical and health sciences. The Predictive Modeling with Logistic Regression using SAS course offers a comprehensive introduction to logistic regression, a widely used technique for modeling binary outcomes in medical research. By mastering logistic regression, you'll enhance your ability to analyze clinical data, develop predictive models for disease diagnosis and prognosis, and contribute to the advancement of medical knowledge.
Epidemiologist
Epidemiologist
Epidemiologists investigate the distribution and patterns of health-related states and events in populations. The Predictive Modeling with Logistic Regression using SAS course provides a valuable toolkit for Epidemiologists seeking to develop predictive models to identify risk factors for diseases, evaluate the effectiveness of interventions, and inform public health policy. By mastering logistic regression, you'll gain skills in data analysis, model building, and statistical inference, enabling you to contribute to the prevention and control of diseases.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and effectiveness of systems and processes. The Predictive Modeling with Logistic Regression using SAS course provides a valuable toolkit for Operations Research Analysts seeking to develop predictive models to optimize resource allocation, improve logistics, and enhance operational performance. By mastering logistic regression, you'll gain skills in data analysis, model building, and optimization, enabling you to drive data-driven decision-making and improve operational outcomes.
Business Analyst
Business Analyst
Business Analysts help organizations improve their performance by analyzing business processes, identifying inefficiencies, and proposing solutions. The Predictive Modeling with Logistic Regression using SAS course equips you with the skills to develop and implement predictive models to forecast business outcomes, optimize operations, and make informed decisions based on data. By leveraging logistic regression, you'll gain a competitive edge in understanding customer behavior, market trends, and risk factors, enabling you to drive profitable outcomes for your organization.
Market Researcher
Market Researcher
Market Researchers conduct studies to gather and analyze data about consumer behavior, market trends, and competitor activity. The Predictive Modeling with Logistic Regression using SAS course provides a valuable toolkit for market researchers seeking to develop predictive models to understand customer preferences, forecast demand, and optimize marketing campaigns. By mastering logistic regression, you'll gain the ability to identify key factors influencing consumer behavior and develop data-driven strategies to drive business growth.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts leverage statistical and analytical techniques to transform healthcare data into actionable insights for improving patient care and optimizing healthcare operations. The Predictive Modeling with Logistic Regression using SAS course provides a solid foundation in logistic regression, a powerful technique for modeling the probability of health-related events and outcomes. By mastering logistic regression, you'll gain valuable skills in data analysis, model development, and predictive analytics, enabling you to contribute to data-driven decision-making and improve healthcare outcomes.
Marketing Analytics Manager
Marketing Analytics Manager
Marketing Analytics Managers oversee the collection, analysis, and interpretation of marketing data to drive data-driven decision-making. The Predictive Modeling with Logistic Regression using SAS course provides a valuable toolkit for Marketing Analytics Managers seeking to develop predictive models to understand customer behavior, optimize marketing campaigns, and improve ROI. By mastering logistic regression, you'll gain skills in data analysis, model building, and predictive analytics, enabling you to make data-driven recommendations that drive business growth.
Insurance Analyst
Insurance Analyst
Insurance Analysts evaluate risks and develop insurance products and policies. The Predictive Modeling with Logistic Regression using SAS course provides a solid foundation in logistic regression, a technique widely used in insurance to model the probability of events and outcomes. By mastering logistic regression, you'll gain valuable skills in data analysis, model building, and risk assessment, enabling you to contribute to the development of innovative insurance solutions and optimize underwriting practices.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investments and financial products. The Predictive Modeling with Logistic Regression using SAS course provides a valuable toolkit for Financial Analysts seeking to develop predictive models to assess risk, forecast financial performance, and make informed investment decisions. By mastering logistic regression, you'll gain skills in data analysis, model building, and financial modeling, enabling you to make data-driven recommendations that optimize financial outcomes.
For more career information including salaries, visit:
OpenCourser.com/course/f0mw5e/predictive
Reading list
We've selected 12 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
Predictive Modeling with Logistic Regression using SAS .
Provides a comprehensive overview of logistic regression, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this powerful statistical technique.
Provides a comprehensive overview of statistical learning, covering a wide range of topics, including logistic regression. It valuable resource for anyone who wants to learn more about the theory and practice of statistical learning.
More accessible introduction to statistical learning than The Elements of Statistical Learning, and it covers logistic regression in more detail. It is an excellent resource for anyone who wants to learn the basics of logistic regression.
Provides a detailed overview of logistic regression, with a focus on SAS implementation. It valuable resource for anyone who wants to learn more about logistic regression using SAS.
Provides a comprehensive overview of regression analysis, including logistic regression. It valuable resource for anyone who wants to learn more about the theory and practice of regression analysis.
Provides a comprehensive overview of regression modeling strategies, including logistic regression. It valuable resource for anyone who wants to learn more about the theory and practice of regression modeling.
Save
Provides a detailed overview of logistic regression models for ordinal response variables. It valuable resource for anyone who wants to learn more about the theory and practice of logistic regression models for ordinal response variables.
Provides a comprehensive overview of logistic regression using R, a popular statistical software package. It valuable resource for anyone who wants to learn more about logistic regression using R.
Provides a comprehensive overview of generalized linear models, which includes logistic regression. It valuable resource for anyone who wants to learn more about the theory and practice of generalized linear models.
Provides a comprehensive overview of logistic regression using Stata, a popular statistical software package. It valuable resource for anyone who wants to learn more about logistic regression using Stata.
Provides a detailed overview of logistic regression using SAS, a popular statistical software package. It valuable resource for anyone who wants to learn more about logistic regression using SAS.
Save
Provides a comprehensive overview of logistic regression analysis, with a focus on sociological applications. It valuable resource for anyone who wants to learn more about the theory and practice of logistic regression analysis in a sociological context
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f0mw5e/predictive
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Level
Intermediate
Via
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Institution
SAS
Instructor
Marc Huber
Language
English
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This course belongs to a
collection
called
SAS Statistical Business Analyst. It's comprised of three courses:
- Regression Modeling Fundamentals
- Predictive Modeling with Logistic Regression using SAS (viewing)
- Introduction to Statistical Analysis: Hypothesis Testing
Good to know
Develops predictive modeling skills, which are highly relevant to data analysis
Teaches logistic regression using SAS software, which is standard in statistical modeling
Covers selecting variables and interactions, which are crucial for building effective models
Examines recoding categorical variables based on weight of evidence, which is an advanced technique for improving model accuracy
Assesses models, which is essential for evaluating model performance and making informed decisions
Treats missing values, which is a common challenge in data analysis
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