# Secondary Data Analysis Workshop (Introductory/AP Statistics)

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(830 KB|10 pages)
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This handout is intended for those interested in teaching/learning basic introductory statistics concepts to analyze real data. Students will learn how to tell a story using secondary data analysis. The handout is for SAS (email for R, SPSS, Stata, & Python), a free cloud based system and the US Longitudinal Study of Adolescent Health data set. For handouts on setting up a free SAS account, the data set code book and raw data file, a PowerPoint to accompany the handout, or questions please email kristin.flaming@gmail.com or ldierker@wesleyan.edu.

This handout was developed as a condensed version of PassionDrivenStatistics.com course model. It is an NSF funded project that is a supportive, multidisciplinary, project-based, introductory course. Please visit the website to access published articles, some resources, promotional videos of students and instructors discussing their experiences with the course.

All course resources are FREE! Those interested in assignments, handouts, exams, etc. should email to gain access as those are not on the main website. The lecture videos and course resources are developed for SAS, R, SPSS, Stata, and Python.

Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations. For example, compare the chance of having lung cancer if you are a smoker with the chance of being a smoker if you have lung cancer.
Construct and interpret two-way frequency tables of data when two categories are associated with each object being classified. Use the two-way table as a sample space to decide if events are independent and to approximate conditional probabilities. For example, collect data from a random sample of students in your school on their favorite subject among math, science, and English. Estimate the probability that a randomly selected student from your school will favor science given that the student is in tenth grade. Do the same for other subjects and compare the results.
Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling.
Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
Represent data with plots on the real number line (dot plots, histograms, and box plots).
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10 pages
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