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  • MI.Math.Content.HSS-ID.B.6a - Fit a function to the data; use functions fitted to data to solve prob...
Star Library: Regression on the Rebound
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This activity is an advanced version of the “Keep your eyes on the ball” activity by Bereska, et al. (1999). Students should gain experience with differentiating between independent and dependent variables, using linear regression to describe the relationship between these variables, and drawing inference about the parameters of the population regression line. Each group of students collects data on the rebound heights of a ball dropped multiple times from each of several different heights. By plotting the data, students quickly recognize the linear relationship. After obtaining the least squares estimate of the population regression line, students can set confidence intervals or test hypotheses on the parameters. Predictions of rebound length can be made for new values of the drop height as well. Data from different groups can be used to test for equality of the intercepts and slopes. By focusing on a particular drop height and multiple types of balls, one can also introduce the concept of analysis of variance.

Subject:
Mathematics
Statistics and Probability
Material Type:
Activity/Lab
Provider:
Consortium for the Advancement of Undergraduate Statistics Education
Provider Set:
Causeweb.org
Author:
Trent D. Buskirk and Linda J. Young, University of Nebraska-Lincoln and University of Nebraska-Lincoln
Date Added:
01/22/2020
Statistical Thinking and Data Analysis, Fall 2011
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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Allison Chang
Cynthia Rudin
Dimitrios Bisias
Date Added:
01/01/2011
Statistics for Applications, Spring 2015
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CC BY-NC-SA
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This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Dr. Peter Kempthorne
Date Added:
01/01/2009