Predicting Outcomes Using Scatter Plots and Best Fit Lines

The video tutorial explains two types of predictions using scatter plots: interpolation and extrapolation. Interpolation involves making predictions between existing data points, offering more accuracy. Extrapolation involves predictions beyond the data range, which are less reliable. The video provides examples using a candle's height over time to illustrate both concepts.

What are the two types of predictions that can be made using scatter plots?

• Analysis and Synthesis

• Calculation and Approximation

• Prediction and Estimation

• Interpolation and Extrapolation

What is interpolation in the context of scatter plots?

• Predicting without data points

• Predicting beyond the data points

• Predicting the exact data points

• Predicting between the data points

Why are interpolations generally more accurate than extrapolations?

• They use more data points

• They occur between known data points

• They are based on future predictions

• They rely on past data

What is extrapolation in the context of scatter plots?

• Predicting without data points

• Predicting the exact data points

• Predicting between the data points

• Predicting beyond the data points

Why are extrapolations considered less reliable?

• They are based on fewer data points

• They predict beyond the known data range

• They use outdated data

• They are always incorrect

In the example given, what is the height of the candle after 120 minutes?

• 4.5 centimeters

• 11 centimeters

• 15 centimeters

• 8 centimeters

In the example given, what is the height of the candle after 260 minutes?

• 15 centimeters

• 4.5 centimeters

• 8 centimeters

• 11 centimeters