Exploring Scatter Plots and Lines of Best Fit

The video tutorial introduces scatter plots as a tool for visualizing data trends. It covers examples such as tree heights measured by a park ranger, restaurant numbers in Kansas towns, and video game sales. The tutorial emphasizes using a line of best fit for making future estimates based on data trends.

What do scatterplots help us to understand?

• Data without any patterns

• Historical data without trends

• Exact values only

• Trends in data

Tags

CCSS.HSF.IF.B.4

What is true about the relationship between money and problems according to the scatterplot shown?

• No relationship

• More money, more problems

• Data is inconclusive

• More money, fewer problems

Tags

CCSS.HSF-LE.A.1B

Which type of scatterplot was not covered in the tutorial?

• Crowded scatterplots

• Multiple sets, crowded, and 3D

• 3D scatterplots

• Multiple sets and crowded

Tags

CCSS.8.SP.A.1

For future estimates in the tree height scatterplot, which line should we use?

• Line of symmetry

• Horizontal line

• Line of best fit

• Vertical line

Tags

CCSS.8.EE.B.5

What does the line of best fit represent in a scatterplot?

• Symmetry in data

• A division between data sets

• A trend in the data

• The exact path of data points

Tags

CCSS.8.SP.A.2

What is the primary purpose of using a line of best fit in scatterplots?

• To create symmetry in the data

• To connect all the data points

• To estimate future data points

• To draw attention to outliers

Tags

CCSS.8.SP.A.2

In the new restaurants scatterplot, what is the estimated number of restaurants in 2008?

• 18

• 16

• 15

• 13

Tags

CCSS.HSF.LE.B.5

How many years after 1996 is the 2008 estimate for new restaurants?

• 20 years

• 15 years

• 12 years

• 10 years

Tags

CCSS.HSF.LE.B.5

What is the estimated weekly video game sales in week 25?

• $6,000

• $5,500

• $5,000

• $4,500

What trend is observed in the video game sales scatterplot?

• Decreasing sales over time

• Stable sales over time

• Increasing sales over time

• Random fluctuations in sales