Final answer:
A scatter plot visually represents the relationship between two variables by plotting data points on a graph. The pattern in the scatter plot indicates whether the variables are good candidates for linear regression, which can then be used to fit a regression line to the data.
Step-by-step explanation:
To create a scatter plot, you need to plot data points on a graph where each point represents a pair of values. For example, Figure 12.5 could be illustrating the relationship between the scores of the third exam and the final exam, with the third exam scores on the x-axis and the final exam scores on the y-axis. The function signature (x_column, y_column) suggests plotting each pair of x and y values directly from a table of data.
The pattern observed in the scatter plot helps to determine the relationship between the two variables. If there is a clear pattern, such as a linear trend, the data might be suitable for linear regression, which is a statistical method to model and analyze the relationships between variables. Linear regression can help to predict the value of the dependent variable (final exam score) based on the value of the independent variable (third exam score).
Tools like a calculator's regression function or software like LinRegTTest can be used to compute the regression line that best fits the data. This line is added to the scatter plot to visually represent the estimated relationship between x and y.