The scatter plot shown compares the number of items i several suitcases on an airplane with the weight of the suitcase. Use the data to answer \( 4-5 \). 4. Kylie has 20 items in her suitcase. Which is the mos reasonable prediction of the weight of her suitcase? \( \begin{array}{llll}\text { a. } 17 \mathrm{lbs} & \text { b. } 36 \mathrm{lbs} & \text { c. } 20 \mathrm{lbs} & \text { d. } 26 \mathrm{lbs}\end{array} \) 5. Beau's suitcase weighs 40 pounds. Which is NOT reasonable prediction for the number of items he po \( \begin{array}{llll}\text { a. } 39 & \text { b. } 45 & \text { c. } 31 & \text { d. } 42\end{array} \)

When it comes to predicting suitcase weights based on the number of items, we often rely on trends observed in scatter plots. If the scatter plot indicates a positive correlation, we can infer that more items generally mean a heavier suitcase. If Kylie has 20 items, a reasonable prediction for her suitcase weight would likely be around 26 lbs, given that weights typically increase with more items. As for Beau's situation, if his suitcase weighs 40 pounds, we should look at the data range for the number of items corresponding to that weight. If the higher number of items exceeds a reasonable account based on scatter plot trends, choices like 45 may seem out of place. Graphs often reveal that adding too many items disproportionately increases weight, so skepticism about unreasonably high counts is justified!