Difference between revisions of "SOCR Events May2008 C9 S1"
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* Copy the first 200 measurements of the [[SOCR_Data_Dinov_020108_HeightsWeights | Human Height and Weight Dataset]] into the [http://socr.ucla.edu/htmls/SOCR_Analyses.html SOCR Analysis (Simple Linear Regression)]. | * Copy the first 200 measurements of the [[SOCR_Data_Dinov_020108_HeightsWeights | Human Height and Weight Dataset]] into the [http://socr.ucla.edu/htmls/SOCR_Analyses.html SOCR Analysis (Simple Linear Regression)]. | ||
| − | <center>[[Image: | + | <center>[[Image:SOCR_Events_May2008_C9_S1_Dinov_020808_Fig7.jpg|500px]]</center> |
| − | |||
| − | * Map the Heights and Weights columns to the X and Y variables. | + | * Paste these in SOCR and Map the Heights and Weights columns to the X and Y variables. |
| − | <center>[[Image:SOCR_Events_May2008_C9_S1_Dinov_020808_Fig3.jpg|500px]]</center> | + | <center>[[Image:SOCR_Events_May2008_C9_S1_Dinov_020808_Fig2.jpg|500px]] |
| + | [[Image:SOCR_Events_May2008_C9_S1_Dinov_020808_Fig3.jpg|500px]]</center> | ||
* Click CALCULATE and see the output numerical results (in the Results tab) and the graphical outputs (in the Graphs tab). | * Click CALCULATE and see the output numerical results (in the Results tab) and the graphical outputs (in the Graphs tab). | ||
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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 | + | The regression line we computed and illustrated on the [http://wiki.stat.ucla.edu/socr/uploads/d/d1/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> | ||
Latest revision as of 20:30, 8 February 2008
Contents
- 1 SOCR May 2008 Event - Analyze bivariate data using linear regression methods
- 1.1 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
- 1.2 Compute predictions of y-values for given x-values using a regression equation, and recognize the limitations of such predictions
- 1.3 Compute and use the standard error for regression
- 2 References
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).
- Paste these in SOCR and 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 illustrated on the image above shows the following linear relation between Heights (inches) and Weights (pounds):
What would be the regression of the Weight on Height (the reverse regression)? You can compute this by hand to be
Or you can simply re-maps (swap) the dependent and independent variables in SOCR Analysis (Simple Linear Regression) and recalculate the linear relation.
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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