SOCR Events May2008 C5 S1

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(Estimate the equation of a line of best fit to make and test conjectures)
 
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===[[SOCR_EduMaterials_Activities_BivariateNormalExperiment | Estimate the equation of a line of best fit to make and test conjectures]]===
===[[SOCR_EduMaterials_Activities_BivariateNormalExperiment | Estimate the equation of a line of best fit to make and test conjectures]]===
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===Interpret the slope and y-intercept of a line through data ===
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===[[SOCR_EduMaterials_Activities_BivariateNormalExperiment | Interpret the slope and y-intercept of a line through data]]===
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===Predict y-values for given x-values when appropriate using a line fitted to bivariate numerical data===
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===[[SOCR_EduMaterials_Activities_BivariateNormalExperiment | Predict y-values for given x-values when appropriate using a line fitted to bivariate numerical data]]===
==References==
==References==

Current revision as of 21:28, 8 February 2008

Contents

SOCR May 2008 Event - Summarize, display, and analyze bivariate data

Collect, record, organize, and display a set of data with at least two variables

Use the Hot dog Sodium Calorie Dataset and explore various bivariate activities.

Determine whether the relationship between two variables is approximately linear or nonlinear by examination of a scatter plot

Characterize the relationship between two linear related variables as having positive, negative, or approximately zero correlation

Try scatter plotting the Weight or the Height (Y-axis) agains the index of the observation for the first 100 subjects in the Human Weight/Height Dataset. Of course, there should be no correlation between the index of the subject and his/her height or weight, as subjects are randomly chosen! On the contrary plotting Weight vs. height will demonstrate a clear positive correlation (i.e., higher weight implier taller individual).

Estimate, interpret, and use lines fit to bivariate data

Estimate the equation of a line of best fit to make and test conjectures

Interpret the slope and y-intercept of a line through data

Predict y-values for given x-values when appropriate using a line fitted to bivariate numerical data

References



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