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  • Ross Hall 2239
  • Campus Box 122
  • University of Northern Colorado
  • Greeley, CO 80639
  • Phone: (970) 351-2820
  • Fax: (970) 351-1225
  • Dr. Robert Powers
  • Ross Hall 2240-C
  • Campus Box 122
  • University of Northern Colorado
  • Greeley, CO 80639
  • Phone: (970) 351-1157
  • robert.powers@unco.edu

Relating Two Variables

Math 550 at UNC, Summer 2008

The following files include information on covariation, including regression, residual analysis, and correlation. Click on the "Preview" thumbnails to see a slideshow of the documents, or click the "PDF" button to download a handout.

Downloadable Handouts

Document Title Content Preview Download
Relationship between Two Variables Seven data sets relating variables from temperature and cricket chirp rates to sodium and fat content in fast food. preview download as pdf
Return to Old Faithful Use scatterplots and the linear correlation coefficient to calculate correlation. preview download as pdf
Old Faithful Regression Understand the least squares line in the context of geiser eruption intervals. preview download as pdf
A Graphical Interpretation of r Derive a formula for the correlation coefficient using graphical representations of a least-squares regression line. preview download as pdf
The Coefficient of Determination Learn to interpret to coefficient of determination as the percentage of total variation in the response variable that is explained by the least-squares regression line preview download as pdf
Residual Analysis Use a graphing calculator to analyze the errors in predicitions made by a regression line (from Old Faithful). preview download as pdf
Identifying Influential Observations Understand how an influential observation can affect the slope, intercept, and fit of a least squares regression line. preview download as pdf
Big Box-Office Bucks Learn to use a logarithmic transformation and linear regression in a situation of exponential decay (box office sales for Kung Fu Panda). preview download as pdf
Kepler’s Law Use a variety of non-linear regression techniques to model relationships among planetary orbits. preview download as pdf
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