13.1 CW/HW
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Last updated over 5 years ago
18 Nsɛmmisa
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Create a scatter plot of the data using Days Absent as the independent variable. Be sure to also label your x and y axis.
[Use this link to insert the picture of the graph if it doesn't appear: https://i.paste.pics/d6987754da7a2f288d0cd547a16cabd3.png]
Based on the data, how do grades change as the number of days absent increases?
Go back to #1 and draw a trend line on your scatter plot. Identify two points on the trend line and write an equation for the line containing those two points.
What is the meaning of the x and y variables in the equation you wrote?
Interpret the meaning of the slope and the y-intercept of your trend line.
Use your equation from #3 to predict the grade of a student who is absent for 5 days.
Draw a trend line on the scatter plot. Identify two points on the trend line and write a linear equation to model the data containing those two points.
[If the graph does not appear, use this link: https://i.paste.pics/57ab5b85d9ac2ce4eea6a80f8547a7b6.png]
Explain the meaning of x and y in your equation from #7.
Interpret the meaning of the slope and the y-intercept of your trend line.
Given below are seven scatter plots and seven verbal descriptions of relationships. Match each scatter plot with the appropriate description. (Each scatter plot goes with one and only one description.)
| Draggable item | arrow_right_alt | Corresponding Item |
|---|---|---|
| arrow_right_alt | Relatively strong negative linear relationship (r = 0.772). |
| arrow_right_alt | Relatively strong positive linear relationship (r = 0.828) |
| arrow_right_alt | Very slight or no linear relationship (r = 0.043) |
| arrow_right_alt | Relatively weak positive linear relationship (r = 0.310) |
| arrow_right_alt | Very strong negative linear relationship (r = − 0.95) |
| arrow_right_alt | Relatively weak negative linear relationship (r = − 0.238) |
| arrow_right_alt | Very strong positive linear relationship (r = 0.981). |
What feature(s) of the scatter plots did you consider when deciding whether a relationship was positive or negative?
What feature(s) of the scatter plots did you consider when deciding whether a relationship was relatively weak, relatively strong, or very strong?
Make sense of problems. Examine the values of r for each relationship in Item 10. How does the value of r relate to the scatter plots? What makes r increase or decrease?
Reason abstractly. In your own words, describe the similarities and differences between a scatter plot that shows a strong positive relationship and a scatter plot that shows a weak positive relationship.
What type of relationship would you expect to see between height and age? Explain your answer.
Describe a real-world quantities that would have a strong negative relationship.
Describe a real-world quantities that would have no correlation.
Determine whether or not each example is an example of correlation or causation.
The temperature outside and ice cream sales.
Exercising and burning calories
Number of miles driven and the amount of gas used
The annual salary and blood pressure for men ages 20-60
The age of a child and his/her shoe size
The amount of cars a sales person sells and how much commission they make.
The number of cold, snowy days and the amount of hot chocolate sold at a ski resort
The number of cars traveling over a busy holiday weekend and the amount of accidents reported.
Correlation
Causation
