Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Model Formulas in R: Basics and Applications

In courses (2)

MATH-474: Statistics for genomic data analysis

After a short introduction to basic molecular biology and genomic technologies, this course covers the most useful statistical concepts and methods for the analysis of genomic data. ...

MATH-493: Applied biostatistics

This course covers topics in applied biostatistics, with an emphasis on practical aspects of data analysis using R statistical software. Topics include types of studies and their design and analysis, high dimensional data analysis (genetic/genomic) and oth ...

Description

This lecture covers the basics of model formulas in R, including the generic form, numeric and factor predictors, interactions, and model assessment techniques such as tables of group means and interpreting R output. It also delves into ANOVA, multiple comparisons, and factorial experimental designs, emphasizing the importance of interaction and the advantages of factorial experiments. The lecture concludes with examples like the Tooth Growth study, showcasing the application of model formulas in real-world scenarios.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

Darlene Goldstein

Official source

Related concepts (32)

R (programming language)

R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software. The core R language is augmented by a large number of extension packages containing reusable code and documentation. According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages in data mining.

Factorial experiment

In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors. A full factorial design may also be called a fully crossed design. Such an experiment allows the investigator to study the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable.

C (programming language)

C (pronounced 'siː – like the letter c) is a general-purpose computer programming language. It was created in the 1970s by Dennis Ritchie, and remains very widely used and influential. By design, C's features cleanly reflect the capabilities of the targeted CPUs. It has found lasting use in operating systems, device drivers, protocol stacks, though decreasingly for application software. C is commonly used on computer architectures that range from the largest supercomputers to the smallest microcontrollers and embedded systems.

Analysis of variance

Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation.

The C Programming Language

The C Programming Language (sometimes termed K&R, after its authors' initials) is a computer programming book written by Brian Kernighan and Dennis Ritchie, the latter of whom originally designed and implemented the C programming language, as well as co-designed the Unix operating system with which development of the language was closely intertwined. The book was central to the development and popularization of C and is still widely read and used today.

Related lectures (33)

R Programming: Conditions, Loops, Functions & Graphics

Covers conditions, loops, functions, and graphics in R programming with practical examples.

Introduction to R Programming for Genetics & Genomics

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.

ANOVA & Moderation

Covers ANOVA, mediation, and moderation analyses in R for CS-411 Digital Education.

Factorial Experimental Design

Covers factorial experimental design, 2-way ANOVA, interaction plots, and unbalanced designs.

Statistics essentials: ANOVA