Analysis of the Distribution of Grades from the Fall 2001 Calculus 152 Final Examination and Nonparametric Estimation of a Linear Relationship for Bivariate Data

Authors

Professor Rudy Gideon, University of Montana, Missoula

Document Type

Presentation Abstract

Presentation Date

2-28-2002

Abstract

The Correlation Principle for the estimation of a parameter theta: A statistical inference or procedure should be consistent with the assumption that any explanation of a set of data should be accompanied by theta-hat, a value of theta, that makes some correlation coefficient zero.

Although extensive methods have been developed for linear models, generalized linear models, non-linear models, and time series models, as well as estimation of parameters for a particular distribution, we only have time to give one result. A nonparametric correlation coefficient measures monotonicity rather than linearity. It will be shown how to measure linearity with any nonparametric correlation coefficient. The Greatest Deviation correlation coefficient (GD) will be used and its robustness demonstrated.

Additional Details

Thursday, 28 February 2002
4:10 p.m. in Math 109
Coffee/treats at 3:30 p.m. Math 104 (Lounge)

Recommended Citation

Gideon, Professor Rudy, "Analysis of the Distribution of Grades from the Fall 2001 Calculus 152 Final Examination and Nonparametric Estimation of a Linear Relationship for Bivariate Data" (2002). Colloquia of the Department of Mathematical Sciences. 113.
https://scholarworks.umt.edu/mathcolloquia/113