To predict the number of points scored by a student who spends 45 minutes playing the video game based on the scatterplot, we need to observe the trend in the data.

Analysis:

1. The scatterplot shows a generally upward trend in points scored as time increases.
2. For students who played for around 30 minutes, the points scored seem to be just above 900 (around 950-1000).
3. If the trend continues at a similar rate, the number of points at 45 minutes would be a reasonable extrapolation based on the pattern.

From this pattern:

• The points scored seem to increase by around 200–300 points for every 10–15 minutes played.

Extrapolation:

Looking at this steady increase, it seems that at 45 minutes, the number of points scored would be around 1,200.

Thus, the best prediction for the number of points scored by a student who plays for 45 minutes is:

A. 1,200

Would you like more details on how the prediction was made or have any questions?

Here are 5 related questions:

1. What type of function might best fit this scatterplot (linear, exponential, etc.)?
2. How can we mathematically calculate the rate of increase in points?
3. Could there be a limit to how many points someone could score after a certain time period?
4. How would this scatterplot change if there were more data points for higher times?
5. How would we use a line of best fit to make more accurate predictions?

Tip: Scatterplots are great for identifying trends in data, and a line of best fit can be used to quantify relationships more precisely.