Scatterplots - Causation/Correlation and Correlation Coefficient

Which example shows CAUSATION? (Which one CAUSED the other to happen.)

• High social media usage and reduced grades.

• Car ran out of gas and being stranded on the side of the road.

• Recess time and number of friends.

Tags

CCSS.HSS.ID.C.9

Which example shows CAUSATION? (Which one CAUSED the other to happen.)

• Being happy and being a better student.

• An insect ate poison and it died.

Tags

CCSS.HSS.ID.C.9

Which example shows CORRELATION? (The events show correlation, but one thing does NOT cause the other thing to happen.)

• I put water in the freezer and now I have ice.

• The alarm clock went off and I woke up.

• Temperature and the amount of people at the beach.

Tags

CCSS.HSF-LE.A.1B

Which example shows a CORRELATION? (The events show correlation, but one thing does NOT cause the other thing to happen.)

• It rained and the ground is wet.

• Ice cream sales increase as temperature increases.

Tags

CCSS.HSF-LE.A.1B

Data is collected comparing minutes of exercise each day and blood pressure. It is determined that the correlation coefficient is -0.87. What does this mean?

• There is a strong negative correlation between exercise and blood pressure.

• There is a weak negative correlation between exercise and blood pressure.

• There is a strong positive correlation between exercise and blood pressure.

• There is no correlation between exercise and blood pressure.

Tags

CCSS.HSF.LE.B.5

Data is analyzed comparing hours studied and grades. It is determined that the correlation coefficient is 0.92. What does this mean?

• There is a strong positive correlation between studying and grades.

• There is a strong negative correlation between studying and grades.

• There is a weak positive correlation between studying and grades.

• There is a weak negative correlation between studying and grades.

Tags

CCSS.HSF.LE.B.5

Data is collected comparing sales of cars and the age of the salesperson. After analyzing the data, it is determined that the correlation coefficient is 0.19. What does this mean?

• There is no correlation between sales and age.

• There is a strong positive correlation between sales and age.

• There is a strong negative correlation between sales and age.

• There is a weak negative correlation between sales and age.

Tags

CCSS.HSF.LE.B.5

Data is collected comparing minutes of exercise each day and blood pressure. It is determined that the correlation coefficient is -0.87. What does this mean?

• There is a strong negative correlation between exercise and blood pressure.

• There is a weak negative correlation between exercise and blood pressure.

• There is a strong positive correlation between exercise and blood pressure.

• There is no correlation between exercise and blood pressure.

Tags

CCSS.HSF.LE.B.5

Describe the relationship between the variables.

• no correlation
• strong positive correlation
• strong negative correlation
• weak positive correlation

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

Estimate the correlation coefficient

• r= -0.9

• r= 0.9

• r=-0.5

• r=0.5

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8