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Translation Code
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Description

As part of our NSF-funded passion-driven statistics project, we have just started to share more widely our “translation code” aimed at supporting folks in learning code-based software and in moving more easily between them. The pdf includes all of the basic syntax for managing, displaying and analyzing data, translated across SAS, R, Python, Stata and SPSS. http://bit.ly/PDSTranslationCode.

For more information about our warm and welcoming data-driven curriculum, check out https://passiondrivenstatistics.com/ or reach out to Kristin Flaming at kristin.flaming@gmail.com or Lisa Dierker at ldierker@wesleyan.edu.

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Translation Code

Kristin Flaming
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6th - 12th, Adult Education, Higher Education
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Description

As part of our NSF-funded passion-driven statistics project, we have just started to share more widely our “translation code” aimed at supporting folks in learning code-based software and in moving more easily between them. The pdf includes all of the basic syntax for managing, displaying and analyzing data, translated across SAS, R, Python, Stata and SPSS. http://bit.ly/PDSTranslationCode.

For more information about our warm and welcoming data-driven curriculum, check out https://passiondrivenstatistics.com/ or reach out to Kristin Flaming at kristin.flaming@gmail.com or Lisa Dierker at ldierker@wesleyan.edu.

Report this resource to TPT
Reported resources will be reviewed by our team. Report this resource to let us know if this resource violates TPT's content guidelines.

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Standards

to see state-specific standards (only available in the US).
Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers. For example, “How old am I?” is not a statistical question, but “How old are the students in my school?” is a statistical question because one anticipates variability in students’ ages.
Understand that a set of data collected to answer a statistical question has a distribution which can be described by its center, spread, and overall shape.
Recognize that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation describes how its values vary with a single number.
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