The scatterplot represents data collected about how often students between the ages of 2 and 18 play with toys. Each point represents a piece of data collected from each student. For example, (8,7)%0D%0A(%0D%0A8%0D%0A,%0D%0A7%0D%0A)%0D%0A represents an 8-year-old who plays with toys for 7 hours a day. Which model is the best fit for this data?%0D%0A%0D%0AOption #1: linear%0D%0A%0D%0AOption #2: quadratic%0D%0A%0D%0AOption #3: exponential%0D%0A%0D%0A(1 point)
Without seeing the scatterplot, it is difficult to determine which model is the best fit for the data. To determine the best fit model, you would need to analyze the pattern of the data points and see if they form a straight line (linear), a curved line (quadratic), or an exponential curve.
If the data points are evenly distributed and form a straight line, a linear model would be the best fit. If the data points follow a curved pattern, a quadratic model may be the best fit. If the data points show rapid growth or decay over time, an exponential model may be the best fit.
It is recommended to analyze the scatterplot and see which model best represents the data pattern.