SOCR Events May2008 C9 S1
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===Compute predictions of y-values for given x-values using a regression equation, and recognize the limitations of such predictions=== | ===Compute predictions of y-values for given x-values using a regression equation, and recognize the limitations of such predictions=== | ||
| - | The regression line we computed and illustreted on the [ | + | The regression line we computed and illustreted on the [http://wiki.stat.ucla.edu/socr/uploads/thumb/d/d1/SOCR_Events_May2008_C9_S1_Dinov_020808_Fig4.jpg/500px-SOCR_Events_May2008_C9_S1_Dinov_020808_Fig4.jpg image above] shows the following linear relation between Heights (inches) and Weights (pounds): |
<center> <math>Height = 56.457 + 0.09033384003691448\times Weight</math></center> | <center> <math>Height = 56.457 + 0.09033384003691448\times Weight</math></center> | ||
Revision as of 01:04, 9 February 2008
SOCR May 2008 Event - Analyze bivariate data using linear regression methods
Fit regression lines to pairs of numeric variables and calculate the means and standard deviations of the two variables and the correlation coefficient, using technology
- Copy the first 200 measurements of the Human Height and Weight Dataset into the SOCR Analysis (Simple Linear Regression).
- Map the Heights and Weights columns to the X and Y variables.
- Click CALCULATE and see the output numerical results (in the Results tab) and the graphical outputs (in the Graphs tab).
Compute predictions of y-values for given x-values using a regression equation, and recognize the limitations of such predictions
The regression line we computed and illustreted on the image above shows the following linear relation between Heights (inches) and Weights (pounds):
Compute and use the standard error for regression
References
- Utah Secondary Core Curriculum Standards for Statistics
- Interactive Statistics Education EBook
- SOCR Home page: http://www.socr.ucla.edu
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