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Applying the Mathematical MASS Model with R

Janani Ravi
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This course is no longer available. Find something similar by browsing:
R MASS Model Statistical Analysis Machine Learning Linear Discriminant Analysis (LDA) Quadratic Discriminant Analysis (QDA) Robust Regression

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Read about what's good
what should give you pause
and possible dealbreakers
Suits statisticians who want to extend their knowledge and skills
Offers a thorough overview of the MASS package in R, suitable for seasoned learners
In-depth exploration of statistical techniques like multi-state models, discriminant analysis, and robust regression
Taught by Janani Ravi, an experienced instructor in applied statistics
Suitable for those with a strong foundation in statistics seeking advanced knowledge in specialized statistical techniques

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Career center

Learners who complete Applying the Mathematical MASS Model with R will develop knowledge and skills that may be useful to these careers:
Statistician
Statisticians collect, analyze, and interpret data to provide insights in various fields, including healthcare, education, and finance. This course may be helpful for building a foundation in statistical analysis and modeling techniques, such as linear discriminant analysis and robust regression.
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Survival Analyst
Survival Analysts use statistical models to analyze data related to survival times. This course may be particularly useful for developing expertise in survival models, which are widely used in medical research and other fields.
See salaries and explore the career path for Survival Analyst
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. Understanding statistical analysis and modeling techniques, such as linear discriminant analysis and robust regression, may be useful for developing and evaluating machine learning models.
See salaries and explore the career path for Machine Learning Engineer
Operations Research Analyst
Operations Research Analysts develop mathematical models to solve business problems. This course may be helpful for building a foundation in statistical modeling and applying techniques like linear discriminant analysis and robust regression.
See salaries and explore the career path for Operations Research Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to assess financial risk. T-tests and ANOVA are common statistical techniques used in quantitative analysis, and this course may be useful for developing foundational knowledge in these areas.
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Data Engineer
Data Engineers build and maintain data pipelines and infrastructure. Understanding statistical modeling and analysis, such as linear discriminant analysis and robust regression, may be useful in designing data processing systems and ensuring data integrity.
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Biostatistician
A Biostatistician applies statistical techniques to multiple biological fields. A background in mathematical models with R may be useful in building a foundation for applying statistical principles to biology. An understanding of survival models, which are widely used in medical research, could also be relevant.
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Underwriter
Underwriters assess and manage risk in insurance and finance. Understanding statistical analysis and modeling techniques, such as survival models, may be useful for evaluating risk and making informed decisions.
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Risk Analyst
Risk Analysts identify and assess risks in various industries, including finance, insurance, and healthcare. Understanding statistical analysis and modeling techniques, such as survival models, may be useful for evaluating risk and making informed decisions.
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Web Analyst
Web Analysts use statistical analysis to track and analyze website traffic and user behavior. Understanding statistical analysis techniques, such as t-tests and ANOVA, may be useful for evaluating website performance and making data-driven decisions.
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Product Manager
Product Managers oversee the development and launch of new products. Understanding statistical analysis and modeling techniques may be useful in evaluating market research, conducting user testing, and making data-driven decisions.
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User Experience (UX) Researcher
UX Researchers study user behavior and feedback to improve the user experience of products and services. Understanding statistical analysis and modeling techniques may be useful for analyzing user data and making data-driven decisions.
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Market Researcher
Market Researchers study market conditions and analyze data to provide insights to businesses. Understanding how to apply statistical analysis to data, including techniques like t-tests and ANOVA, may be useful in this role.
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Data Scientist
Data Scientists use both classical statistical analysis and machine learning to extract insights from data. This course may be useful for developing R skills and reinforcing understanding of statistical principles used in data science, such as t-tests and ANOVA.
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Teacher
Teachers instruct students in various subjects, including mathematics, science, and social studies. Understanding statistical analysis and modeling techniques may be useful for developing lesson plans and teaching students about data analysis.
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Reading list

We've selected five 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 Applying the Mathematical MASS Model with R.
Cover image
Modern Applied Statistics with S-PLUS
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Is the inspiration for the MASS package. It is an excellent reference for understanding the statistical methods implemented in the package.
Modern Applied Statistics with S-PLUS
Paperback
Check
Modern Applied Statistics with S-PLUS
Kindle Edition
Check
Cover image
Statistical Modeling with R
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Provides a good overview of statistical modeling in R, including many of the techniques covered in the MASS package.
Statistical Modeling With R: a dual frequentist and...
Paperback
$$
Statistical Modeling With R: a dual frequentist and...
Kindle Edition
$$
Cover image
The R Book
Save
Provides a good introduction to data analysis in R, including many of the techniques covered in the MASS package.
The R Book
Hardcover
$$$
The R Book
Kindle Edition
$$$
Cover image
Linear Regression Models
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Provides a good overview of regression analysis and generalized linear models, including many of the techniques covered in the MASS package.
Linear Regression Models: Applications in R...
Paperback
$$$
Linear Regression Models: Applications in R...
Kindle Edition
$$$
Cover image
Survival Analysis
Save
Provides a good overview of survival analysis, including many of the techniques covered in the MASS package.
Survival Analysis: Techniques for Censored and...
Hardcover
$$$$
Survival Analysis: Techniques for Censored and...
Kindle Edition
$$$

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Time
2 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a collection called Building Statistical and Mathematical Models with R. It's comprised of nine courses:
  1. Understanding Statistical Models and Mathematical Models
  2. Solving Problems with Numerical Methods
  3. Applying the Mathematical MASS Model with R (viewing)
  4. Applying Differential Equations and Inverse Models with R
  5. Performing Dimension Analysis with R
  6. Applying Linear Algebra with R
  7. Building Statistical Summaries with R
  8. Implementing Bootstrap Methods in R
  9. Implementing Monte Carlo Method in R
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Suits statisticians who want to extend their knowledge and skills
Offers a thorough overview of the MASS package in R, suitable for seasoned learners
In-depth exploration of statistical techniques like multi-state models, discriminant analysis, and robust regression
Taught by Janani Ravi, an experienced instructor in applied statistics
Suitable for those with a strong foundation in statistics seeking advanced knowledge in specialized statistical techniques
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