Without the context of the actual scatter plot, it's difficult to provide an exact step-by-step solution. However, we need just the general steps you can take to analyze the scatter plot:
1. Gather Scatter Plot Data: To start with, collect the X and Y coordinates of the scatter plot. X represents the time spent eating while Y stands for the time spent exercising by each of the 25 students.
2. Plot the Data: Use a graphing tool or software to plot the data. Each (X,Y) coordinate will be a point. 25 students will give you 25 individual points in the scatter plot.
3. Look for a Correlation: See if there's a pattern or trend in the data. Is it upward or downward? This shows a positive or negative correlation (respectively) between time spent eating and exercising. A pattern might indicate that students who eat more spend more or less time exercising, or there might be no relationship at all.
4. Calculate the Correlation Coefficient: If there seems to be a linear trend in the data, you can calculate a correlation coefficient, a numerical measure of the strength and direction of a linear relationship between two variables.
5. Create the Best-Fit Line: If you find a linear correlation, you can create a line of best fit, or a "trend line". This is the line that most closely models your data.
6. Analysis: Once you have your correlation and trend line, you can analyze what this tells you about the students' time spent eating and exercising. For example, if there's a strong positive correlation, it suggests that the more time a student spends eating, the more time they also spend exercising.
Remember, in the absence of the actual scatter plot data, all these procedures are just hypothetical examples and are meant to give you guidance on how to generally address such problems. Please refer to the actual scatter plot and data for the precise steps and analysis.