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# Data Collection

• ### Penn State STAT 503: Design of Experiments

Statistics is often taught as though the design of the data collection and the data cleaning have already been done in advance.  However, as most practicing statisticians quickly learn, typically problems that arise at the analysis stage, could have been avoided if the experimenter had consulted a statistician before the experiment was done and the data were conducted.  This course is created to provide an understanding of how experiments should be designed so that when the data are collected, these shortcomings are avoided.  Perfect for students and teachers wanting to learn/acquire materials for this topic.

• ### Penn State STAT 502: Analysis of Variance and Design of Experiments

This is a graduate level course/collection of lessons in analysis of variance (ANOVA), including randomization and blocking, single and multiple factor designs, crossed and nested factors, quantitative and qualitative factors, random and fixed effects, split plot and repeated measures designs, crossover designs and analysis of covariance (ANCOVA). Perfect for students and teachers alike looking to learn/acquire materials on ANOVA.

• ### Online Statistics Education: An Interactive Multimedia Course of Study

Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.

• ### Rice Virtual Lab Case Studies

Examples of real data/studies and their analyses and interpretation.

• ### Song: Throw That Out?

A song for use in helping students identify factors to consider when deciding how outliers should be treated, as well as factors for deciding if a study is worthwhile.  Lyrics and music © 2016 by Greg Crowther.This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

• ### Song: The Enlightened Teacher

A song for use in helping students identify factors that allow a sample of data to be representative of the population and distinguish between random and convenience samples.  Lyrics © 2016 by Amy Adler, may be sung to the tune of “Miss Susie had a steamboat.”This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

• ### Song: Levels of Measurement

A song for use in helping students to learn the four levels of measurement (nominal, ordinal, interval, ratio) in appropriate hierarchical order and to identify examples of each in context.  Lyrics by Larry Lesser and music by Larry Lesser and Dominic Dousa copyright 2015.  This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).

• ### Data Science for Undergraduates: Opportunities and Options

As our economy, society, and daily life become increasingly dependent on data, work across nearly all fields is becoming more data driven, affecting both the jobs that are available and the skills that are required. At the request of the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine were asked to set forth a vision for the emerging discipline of data science at the undergraduate level. The study committee considered the core principles and skills undergraduates should learn and discussed the pedagogical issues that must be addressed to build effective data science education programs. Data Science for Undergraduates: Opportunities and Options underscores the importance of preparing undergraduates for a data-enabled world and recommends that academic institutions and other stakeholders take steps to meet the evolving data science needs of students.

Watch the report release webinar here:  https://vimeo.com/269033724

• ### Analysis Tool: RStudio Cloud

RStudio Cloud makes it easy for professionals, hobbyists, trainers, teachers and students to do, share, teach and learn data science using R.  Create analyses using RStudio directly from your browser - there is no software to install and nothing to configure on your computer.  Share your projects - and access those of others - without worrying about data transfer or package installation. Each project defines its own environment, and RStudio Cloud automatically reproduces that environment whenever anyone accesses the project.  It’s easy to share analyses with the world - but it’s also simple to collaborate with a select group in a private space. You control who can enter a space - and via roles, you have fine grained control over what each user can do.  There are also many learning materials available: interactive tutorials covering the basics of data science, cheatsheets for working with popular R packages, links to Datacamp courses, and a guide to using RStudio Cloud.

• ### swirl - Learning R

swirl is a software package for the R programming language that turns the R console into an interactive learning environment. Users receive immediate feedback as they are guided through self-paced lessons in data science and R programming.