[size=100]A researcher found an association between a dog’s stride length and its speed: the longer a dog’s steps, the faster it goes. The predicted speed in meters per second, [math]s[/math], as a function of step length in meters, [math]l[/math], is [br][br][math]s=4l-1.6[/math][br][/size][br]What does the rate of change of the function tell you about the association between stride length and speed?
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[/img]
Without including any outliers, does there appear to be an association between body weight and brain weight? Describe the association in a sentence.
Adjust the line by moving the green points, fitting the line to your scatter plot, and estimate its slope. What does this slope mean in the context of brain and body weight?
Does the fitted line help you identify any other outliers?
[list][*]Number of wins vs number of points per game for your favorite sports team in different seasons[/*][*]Amount of money grossed vs critic rating for your favorite movies[/*][*]Price of a ticket vs stadium capacity for popular bands on tour[/*][/list][br]After you have collected the data, create a scatterplot in the applet below.[br]
Are any of the points very far away from the rest of the data?
Would a linear model fit the data in your scatter plot? If so, draw it. If not, explain why a line would be a bad fit.
Is there an association between the two variables? Explain your reasoning.
Sometimes a person’s arm span is the same as their height. Is this true for anyone in the class?
Is the line [math]y=x[/math] a good fit for the data? If so, explain why. If not, find the equation of a line that fits the data better.
Examine the scatter plot. Which person in your class has the [i]largest[/i] ratio between their arm span and their height? Explain or show your reasoning.