Exploring Scatterplots and Trend Lines

This lesson covers the use of trend lines to make predictions based on data patterns. It includes examples such as predicting baby weights from height, estimating sleep hours from TV watching time, and determining car value over time. Key points include understanding that trend lines may not pass through all data points and do not always start at the origin. Predictions are estimations based on trends and require justification.

What is the predicted weight of a baby who is 27 inches long?

• 12 pounds

• 20 pounds

• 15 pounds

• 10 pounds

Can a 27-inch baby weigh more or less than the predicted 15 pounds?

• Yes, it can vary

• No, it's always 15 pounds

• No, it's more than 15 pounds

• No, it's less than 15 pounds

What does a trend line represent in a scatterplot?

• The outliers in the data

• The general pattern of the data

• The exact predictions

• The errors in data collection

How many hours of sleep is likely if someone watches 5 hours of TV?

• 4 hours

• 6 hours

• 5 hours

• 3.5 hours

What is the approximate value of a car after 10 years?

• $12,000

• $10,000

• $20,000

• $16,000

Can the value of a car after ten years be different from the trend line prediction?

• Only if the car is new

• Only if the car is damaged

• No, it's always exact

• Yes, it's a possibility

Does a trend line always pass through all the data points?

• No, it may not

• Yes, always

• Only if manually adjusted

• Only in perfect conditions

Where do trend lines typically start in a graph?

• At any y-intercept

• Always at the origin

• At the highest data point

• At the lowest data point

What is essential for making valid predictions using trend lines?

• Justifying the reasoning

• Having complete data

• Using complex calculations

• Predicting exact values

What is the role of a trend line in making predictions?

• It replaces actual measurements

• It helps estimate based on patterns

• It serves as a legal document

• It provides exact answers