Regression. Population Covariance and Correlation. Sample Correlation. Sample Correlation. -.04. .98. -.79. Linear Model. DATA. REGRESSION LINE. (Still) Linear Model. DATA. REGRESSION CURVE. Parameter Estimation. Minimize SSE over possible parameter values.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Minimize SSE over possible parameter values
Intercept parameter is significant at .0623 level
Slope parameter is significant at .001 level, so reject
Residual Standard Error:
R-squared is the correlation squared, also % of variation
explained by the linear regression
Example: we could try to predict change in diameter
using both change in height as well as starting height
Timbre (90 attributes)