Difference between revisions of "SOCR Events July2008 C5 S1"
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| − | == [[SOCR_Events_July2008 | SOCR CensusAtSchool International Workshop]] - | + | == [[SOCR_Events_July2008 | SOCR CensusAtSchool International Workshop]] - Summarizing, displaying, and analyzing ''bivariate'' data== |
| + | ===Collect, record, organize, and display a set of data with at least two variables=== | ||
| + | Use the [[SOCR_012708_ID_Data_HotDogs | Hot dog Sodium Calorie Dataset]] and explore [[SOCR_EduMaterials_Activities_BivariteUniformExperiment | various bivariate activities]]. | ||
| + | ===Determine whether the relationship between two variables is approximately [[SOCR_EduMaterials_Activities_ScatterChart#Example_.28Human_Heights_and_Weights.29 | 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 [[SOCR_Data_Dinov_020108_HeightsWeights | 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). | ||
| + | |||
| + | <center>[[Image:SOCR_Activities_ScatterCharts_Dinov_020808_Fig7.jpg|500px]]</center> | ||
| + | |||
| + | ===[[SOCR_EduMaterials_Activities_BivariteUniformExperiment | Estimate, interpret, and use lines fit to bivariate data]]=== | ||
| + | |||
| + | ===[[SOCR_EduMaterials_Activities_BivariateNormalExperiment | Estimate the equation of a line of best fit to make and test conjectures]]=== | ||
| + | |||
| + | ===[[SOCR_EduMaterials_Activities_BivariateNormalExperiment | Interpret the slope and y-intercept of a line through data]]=== | ||
| + | |||
| + | ===[[SOCR_EduMaterials_Activities_BivariateNormalExperiment | Predict y-values for given x-values when appropriate using a line fitted to bivariate numerical data]]=== | ||
==References== | ==References== | ||
* [[EBook | Interactive Statistics Education EBook]] | * [[EBook | Interactive Statistics Education EBook]] | ||
| − | * SOCR Home page: http://www.socr. | + | * SOCR Home page: http://www.socr.umich.edu |
| − | {{translate|pageName=http://wiki. | + | {{translate|pageName=http://wiki.socr.umich.edu/index.php?title=SOCR_Events_July2008_C5_S1}} |
Latest revision as of 12:15, 6 March 2014
Contents
- 1 SOCR CensusAtSchool International Workshop - Summarizing, displaying, and analyzing bivariate data
- 1.1 Collect, record, organize, and display a set of data with at least two variables
- 1.2 Determine whether the relationship between two variables is approximately linear or nonlinear by examination of a scatter plot
- 1.3 Characterize the relationship between two linear related variables as having positive, negative, or approximately zero correlation
- 1.4 Estimate, interpret, and use lines fit to bivariate data
- 1.5 Estimate the equation of a line of best fit to make and test conjectures
- 1.6 Interpret the slope and y-intercept of a line through data
- 1.7 Predict y-values for given x-values when appropriate using a line fitted to bivariate numerical data
- 2 References
SOCR CensusAtSchool International Workshop - Summarizing, displaying, and analyzing 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
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
- Interactive Statistics Education EBook
- SOCR Home page: http://www.socr.umich.edu
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