Unit 6 Bivariate Data Review

Fill in the two-way table based on the information on the Venn Diagram.

Rylee is making a relative frequency table from this data.

What operation should she perform to determine the relative frequency of a person over 30 years old who prefers to run outdoors?

• Subtract 299 from 1027, then divide by 1027

• Add 681 to 1027, then divide by 1027

• Divide 299 by 1027

• Divide 681 by 1027

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CCSS.8.SP.A.4

The ratio of those who do not like skateboards to those who do not like snowmobiles is ​ (a)   to ​ (b)   . Remember to simplify your ratio.

• 5
• 9
• 25
• 45
• 10
• 80
• 8
• 1

The scatter plot shows the number of cars and trucks sold by 10 different employees at a car and truck dealership during a month.

Which points represent employees who sold more trucks than cars?

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CCSS.HSA.CED.A.2

Find the correlation coefficient (r) for the data. Round to the nearest hundredth.

Hint: use the online Correlation Coefficient Calculator

(a)  

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CCSS.HSS.ID.C.8

What kind of correlation exists in this scatter plot?​

Pay attention to the labels! If unsure, consider using a calculator.

​ (a)  

• No
• Perfect
• Moderate
• Strong

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CCSS.HSS.ID.B.5

A study found that there's a positive correlation between the number of umbrellas sold and the pairs of boots sold. Umbrellas don't cause people to buy boots and boots don't cause people to buy umbrellas.

What could be the lurking variable?

• Weather conditions
• Seasonal discounts
• Advertising campaigns
• Sales promotions

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CCSS.HSS.ID.C.9

Which correlation is most likely causation? Select all that are correct.

• The positive correlation of eating junk food and physical fitness

• The negative correlation of sleep and concentration.

• The positive correlation between exercise and physical fitness

• The negative correlation between a car's age and it's value.

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CCSS.HSS.ID.C.9

What is a linear function equation that best fits the data set?

• y^ = 10x + 30

• y^ = 3x + 40

• y^ = 2x + 70

• y^ = 5x + 55

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CCSS.8.EE.C.8C

Data was collected on the weight of a male laboratory rat for the first 25 weeks after its birth. The linear regression equation is 
y^= 40x+100, where x is number of weeks and y is weight in grams. 
What does the y-intercept mean in context of the problem?

• The predicted weight of the rat in year 0

• The predicted weight of the rat at birth

• The current weight of the rat

• The average increase in the rat's weight

Tags

CCSS.HSF.LE.B.5