Plotting Data
Plot data points accurately on coordinate planes and scatter plots: scale axes appropriately, place each ordered pair precisely, and read trends from the resulting visual distribution.
Key Concepts
Press 2nd then Y= to open the [STAT PLOT] menu. Select a plot (e.g., Plot1) and press ENTER to turn it On. Choose the scatter plot icon for Type. Assign L1 to Xlist for your $x$ values and L2 to Ylist for your $y$ values. Finally, press GRAPH to display the plotted data points.
To plot L1 vs L2, turn Plot1 On, set Type to scatter plot, Xlist to L1, and Ylist to L2, then press GRAPH. Before plotting, press Y= and use CLEAR to remove any existing equations to ensure only your data appears. Customize your graph by selecting a different Mark (e.g., a + or dot) to change how data points display.
Now that your numbers are stored, let's make them visual! The STAT PLOT menu is your artist's toolkit, turning raw data into a picture like a scatter plot. You simply tell the calculator which list is for the horizontal ($x$) axis and which is for the vertical ($y$) axis, then hit GRAPH to see it all plotted out.
Common Questions
What are the steps to plot data on a scatter plot?
Choose appropriate scales for both axes that fit all data values. Label each axis with the variable name and units. For each data pair (x,y), locate x on the horizontal axis and y on the vertical axis, then mark the intersection point. Repeat for every data point.
How do you choose an appropriate scale when plotting data?
Find the minimum and maximum values for each variable. Choose an interval that divides evenly into the range and allows all points to fit without crowding. Avoid scales that distort the data by starting at non-zero values without indicating the break.
What patterns should you look for after plotting data?
Look for positive correlation (points rising left to right), negative correlation (points falling left to right), no correlation (random scatter), clusters, outliers, and curved patterns. Identifying these patterns determines which regression model is most appropriate.