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Comprehensive Linear Modeling with R

Comprehensive Linear Modeling with R provides a wide overview of numerous contemporary linear and non-linear modeling approaches for the analysis of research data. These include basic, conditional and simultaneous inference techniques; analysis of variance (ANOVA); linear regression; survival analysis; generalized linear models (GLMs); parametric and non-parametric smoothers and generalized additive models (GAMs); longitudinal and mixed-effects, split-plot and other nested model designs. The course showcases the use of R Commander in performing these tasks. R Commander is a popular GUI-based "front-end" to the broad range of embedded statistical functionality in R software. R Commander is an 'SPSS-like' GUI that enables the implementation of a large variety of statistical and graphical techniques using both menus and scripts. Please note that the R Commander GUI is written in the RGtk2 R-specific visual language (based on GTK+) which is known to have problems running on a Mac computer.

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Comprehensive Linear Modeling with R provides a wide overview of numerous contemporary linear and non-linear modeling approaches for the analysis of research data. These include basic, conditional and simultaneous inference techniques; analysis of variance (ANOVA); linear regression; survival analysis; generalized linear models (GLMs); parametric and non-parametric smoothers and generalized additive models (GAMs); longitudinal and mixed-effects, split-plot and other nested model designs. The course showcases the use of R Commander in performing these tasks. R Commander is a popular GUI-based "front-end" to the broad range of embedded statistical functionality in R software. R Commander is an 'SPSS-like' GUI that enables the implementation of a large variety of statistical and graphical techniques using both menus and scripts. Please note that the R Commander GUI is written in the RGtk2 R-specific visual language (based on GTK+) which is known to have problems running on a Mac computer.

The course progresses through dozens of statistical techniques by first explaining the concepts and then demonstrating the use of each with concrete examples based on actual studies and research data. Beginning with a quick overview of different graphical plotting techniques, the course then reviews basic approaches to establish inference and conditional inference, followed by a review of analysis of variance (ANOVA). The course then progresses through linear regression and a section on validating linear models. Then generalized linear modeling (GLM) is explained and demonstrated with numerous examples. Also included are sections explaining and demonstrating linear and non-linear models for survival analysis, smoothers and generalized additive models (GAMs), longitudinal models with and without generalized estimating equations (GEE), mixed-effects, split-plot, and nested designs. Also included are detailed examples and explanations of validating linear models using various graphical displays, as well as comparing alternative models to choose the 'best' model. The course concludes with a section on the special considerations and techniques for establishing simultaneous inference in the linear modeling domain.

The rather long course aims for complete coverage of linear (and some non-linear) modeling approaches using R and is suitable for beginning, intermediate and advanced R users who seek to refine these skills. These candidates would include graduate students and/or quantitative and/or data-analytic professionals who perform linear (and non-linear) modeling as part of their professional duties.

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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops critical skills for graduate students and professionals for linear and non-linear modeling using the R programming language
Taught by instructors with expertise in statistical modeling
Provides a comprehensive overview of linear and non-linear modeling approaches
Uses the R Commander GUI, a beginner-friendly interface for statistical analysis in R
Emphasizes not just data analysis, but also model validation and selection
May be less suitable for learners without prior knowledge of statistical concepts

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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 Comprehensive Linear Modeling with R with these activities:
Review high school mathematics concepts
Refreshes prerequisites in mathematics to ensure understanding of statistical concepts in the course.
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  • Practice solving linear equations and inequalities.
  • Review the Pythagorean theorem and its applications.
  • Refresh your knowledge of basic trigonometry, including sine, cosine, and tangent.
Review probability and statistics concepts
Ensures a strong foundation in probability and statistics, essential for understanding linear modeling concepts.
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  • Review the basics of probability, including concepts such as probability distributions, Bayes' theorem, and conditional probability.
  • Refresh your knowledge of statistical inference, including hypothesis testing, confidence intervals, and regression analysis.
Compile reading notes
Improves retention and understanding of course materials by organizing and summarizing key concepts.
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  • Create a dedicated notebook or digital document for course notes.
  • After each lecture or reading assignment, summarize the main points and key concepts.
  • Include examples, diagrams, and any questions you may have.
Six other activities
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Explore online tutorials and resources
Provides additional support and clarification on specific topics or software.
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  • Search for online tutorials or video explanations covering specific concepts or techniques.
  • Follow the tutorials step-by-step, taking notes and practicing the examples.
Practice statistical problem-solving
Strengthens problem-solving skills and deepens understanding of statistical concepts.
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  • Find practice problems or exercises from textbooks, online resources, or past exams.
  • Attempt to solve the problems independently.
  • Check your solutions against provided answer keys or consult with classmates or instructors if needed.
Participate in study groups or discussion forums
Encourages collaboration, knowledge sharing, and different perspectives.
Show steps
  • Join or start a study group with classmates.
  • Meet regularly to discuss course materials, assignments, and concepts.
  • Engage in online discussion forums to ask questions, share insights, and connect with other students.
Develop a data analysis project
Provides hands-on experience in applying statistical techniques to real-world data.
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  • Identify a dataset or research question to explore.
  • Clean and prepare the data using appropriate techniques.
  • Apply statistical models or machine learning algorithms to analyze the data.
  • Visualize the results and draw conclusions based on the analysis.
  • Write a report or presentation summarizing the project findings.
Mentor junior or beginning students
Strengthens understanding of concepts by explaining them to others and provides a valuable service to fellow students.
Show steps
  • Volunteer or sign up to mentor junior or beginning students in the same course or related field.
  • Provide guidance, support, and assistance to mentees on course materials, assignments, and concepts.
  • Answer questions, clarify doubts, and encourage mentees to develop their own understanding.
Build a portfolio of statistical projects
Provides a tangible showcase of skills and demonstrates proficiency in statistical techniques.
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  • Identify a series of projects or datasets to work on.
  • Apply statistical techniques to analyze the data and draw meaningful conclusions.
  • Develop visualizations, reports, or presentations to showcase the results.
  • Compile the projects into a portfolio to demonstrate your skills to potential employers or clients.

Career center

Learners who complete Comprehensive Linear Modeling with R will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Data Analysts. By learning how to use these techniques, you will be able to extract meaningful insights from data and make accurate predictions. This course is especially relevant for Data Analysts who want to build a career in research, consulting, or finance.
See salaries and explore the career path for Data Analyst
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Statisticians. By learning how to use these techniques, you will be able to design and conduct statistical studies, analyze data, and draw conclusions from data. This course is especially relevant for Statisticians who want to work in research, government, or academia.
See salaries and explore the career path for Statistician
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make investment decisions. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Quantitative Analysts. By learning how to use these techniques, you will be able to develop and implement trading strategies, risk models, and portfolio optimization models. This course is especially relevant for Quantitative Analysts who want to work in investment banks, hedge funds, or asset management firms.
See salaries and explore the career path for Quantitative Analyst
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Machine Learning Engineers. By learning how to use these techniques, you will be able to develop and deploy machine learning models that can solve real-world problems. This course is especially relevant for Machine Learning Engineers who want to work in technology companies, research labs, or academia.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data Scientists use data to solve business problems. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Data Scientists. By learning how to use these techniques, you will be able to extract meaningful insights from data and develop data-driven solutions to business problems. This course is especially relevant for Data Scientists who want to work in technology companies, consulting firms, or research labs.
See salaries and explore the career path for Data Scientist
Biostatistician
Biostatisticians apply statistical methods to data in the field of biology. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Biostatisticians. By learning how to use these techniques, you will be able to design and conduct statistical studies, analyze data, and draw conclusions from data in the field of biology. This course is especially relevant for Biostatisticians who want to work in research, government, or academia.
See salaries and explore the career path for Biostatistician
Epidemiologist
Epidemiologists investigate the causes of disease and other health problems in populations. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Epidemiologists. By learning how to use these techniques, you will be able to design and conduct epidemiological studies, analyze data, and draw conclusions from data. This course is especially relevant for Epidemiologists who want to work in research, government, or academia.
See salaries and explore the career path for Epidemiologist
Survey Researcher
Survey Researchers design, conduct, and analyze surveys. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Survey Researchers. By learning how to use these techniques, you will be able to design and conduct surveys, analyze data, and draw conclusions from data. This course is especially relevant for Survey Researchers who want to work in research, government, or academia.
See salaries and explore the career path for Survey Researcher
Market Researcher
Market Researchers collect and analyze data about markets and consumers. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Market Researchers. By learning how to use these techniques, you will be able to design and conduct market research studies, analyze data, and draw conclusions from data. This course is especially relevant for Market Researchers who want to work in marketing firms, consulting firms, or research labs.
See salaries and explore the career path for Market Researcher
Financial Analyst
Financial Analysts provide investment advice to individuals and institutions. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Financial Analysts. By learning how to use these techniques, you will be able to analyze financial data, make investment recommendations, and manage portfolios. This course is especially relevant for Financial Analysts who want to work in investment banks, hedge funds, or asset management firms.
See salaries and explore the career path for Financial Analyst
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Actuaries. By learning how to use these techniques, you will be able to develop and implement risk management strategies, and advise clients on financial matters. This course is especially relevant for Actuaries who want to work in insurance companies, pension funds, or consulting firms.
See salaries and explore the career path for Actuary
Economist
Economists study the production, distribution, and consumption of goods and services. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Economists. By learning how to use these techniques, you will be able to analyze economic data, make economic forecasts, and develop economic policies. This course is especially relevant for Economists who want to work in government, academia, or research labs.
See salaries and explore the career path for Economist
Sociologist
Sociologists study human society and social behavior. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Sociologists. By learning how to use these techniques, you will be able to analyze social data, make social predictions, and develop social policies. This course is especially relevant for Sociologists who want to work in government, academia, or research labs.
See salaries and explore the career path for Sociologist
Political Scientist
Political Scientists study politics and government. This course provides a comprehensive overview of linear and non-linear modeling approaches, which are essential skills for Political Scientists. By learning how to use these techniques, you will be able to analyze political data, make political predictions, and develop political policies. This course is especially relevant for Political Scientists who want to work in government, academia, or research labs.
See salaries and explore the career path for Political Scientist
Anthropologist
Anthropologists study human beings and their cultures. This course provides a comprehensive overview of linear and non-linear modeling approaches, which may be useful for Anthropologists. By learning how to use these techniques, you will be able to analyze anthropological data, make anthropological predictions, and develop anthropological theories. This course may be especially relevant for Anthropologists who want to work in academia or research labs.
See salaries and explore the career path for Anthropologist

Reading list

We've selected 11 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 Comprehensive Linear Modeling with R.
Cover image
An Introduction to Statistical Learning
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An Introduction to Statistical Learning widely-used textbook for introductory courses on statistical learning. It provides a comprehensive overview of a variety of supervised and unsupervised learning methods. While the book is not specific to the R programming language, it covers many of the concepts and techniques that are used in the course.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
Applied Linear Statistical Models
Save
Applied Linear Statistical Models widely-used textbook for courses on linear regression and generalized linear models. It provides a balanced approach between theory and applications, and it includes a wealth of examples and exercises.
Applied Linear Statistical Models
Paperback
Check
Applied Linear Statistical Models
Kindle Edition
Check
Cover image
Modern Applied Statistics with S-PLUS
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Modern Applied Statistics with S-PLUS classic textbook that provides a comprehensive overview of modern statistical methods. It covers a wide range of topics, including linear and nonlinear regression, analysis of variance, and time series analysis.
Modern Applied Statistics with S-PLUS
Paperback
Check
Modern Applied Statistics with S-PLUS
Kindle Edition
Check
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Survival Analysis
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Survival Analysis textbook that provides a comprehensive overview of survival analysis methods. It covers topics such as Kaplan-Meier estimation, the Cox proportional hazards model, and accelerated failure time models.
Survival Analysis: Techniques for Censored and...
Hardcover
$$$$
Survival Analysis: Techniques for Censored and...
Kindle Edition
$$$
Cover image
Generalized Linear Models, Second Edition
Save
Generalized Linear Models classic textbook on generalized linear models. It provides a thorough treatment of the theory and methods of generalized linear models, and it includes a wealth of examples and applications.
Generalized Linear Models with Random Effects:...
Kindle Edition
$$$
Cover image
Bayesian Data Analysis, Third Edition
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Bayesian Data Analysis textbook that provides a comprehensive overview of Bayesian data analysis methods. It covers a wide range of topics, including Bayesian inference, model selection, and computational methods.
Bayesian Data Analysis, Third Edition
Paperback
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Bayesian Data Analysis, Third Edition
Kindle Edition
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Cover image
The Elements of Statistical Learning
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The Elements of Statistical Learning textbook that provides a comprehensive overview of modern statistical learning methods. It covers a wide range of topics, including linear and nonlinear regression, classification, and clustering.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
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Data Mining with R
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Data Mining with R textbook that provides a comprehensive overview of data mining methods. It covers a wide range of topics, including data preprocessing, feature selection, and clustering.
Data Mining with R: Learning with Case Studies,...
Hardcover
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Data Mining with R: Learning with Case Studies...
Hardcover
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Data Mining with R: Learning with Case Studies,...
Kindle Edition
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Machine Learning with R
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Machine Learning with R textbook that provides a comprehensive overview of machine learning methods. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Machine Learning with R - Fourth Edition: Learn...
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Machine Learning with R - Second Edition: Expert...
Kindle Edition
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Machine Learning with R - Third Edition: Expert...
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(Italiano) Machine Learning con R: Conoscere le tecniche per...
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R: Unleash Machine Learning Techniques
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Machine Learning with R: Learn How to Use R to...
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(中文) Machine learning and R language (3rd edition of the...
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Deep Learning with R, Second Edition
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Deep Learning with R textbook that provides a comprehensive overview of deep learning methods. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Deep Learning with R, Second Edition
Paperback
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Deep Learning with R, Second Edition
Kindle Edition
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Time Series Analysis
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Time Series Analysis with R textbook that provides a comprehensive overview of time series analysis methods. It covers a wide range of topics, including time series forecasting, seasonality, and趋势分析.
Time Series Analysis: With Applications in R...
Hardcover
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Good to know
Develops critical skills for graduate students and professionals for linear and non-linear modeling using the R programming language
Taught by instructors with expertise in statistical modeling
Provides a comprehensive overview of linear and non-linear modeling approaches
Uses the R Commander GUI, a beginner-friendly interface for statistical analysis in R
Emphasizes not just data analysis, but also model validation and selection
May be less suitable for learners without prior knowledge of statistical concepts
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