For the initial comparison between her students' years of programming experience and their class average, the teacher should create a scatter plot. A scatter plot is ideal for visualizing the relationship between two quantitative variables—in this case, "years of programming experience" on the x-axis and "class average" on the y-axis. This type of chart will allow her to see if there's a trend or correlation between the students' experience levels and their performance in class.
If she observes a trend (e.g., students with more years of experience tend to have higher class averages), it could suggest that programming experience positively influences class performance.
To visually represent student test grades and further enhance her predictions, the teacher can leverage a few additional techniques:
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Box Plot: This can be used to show the distribution of test grades, providing insight into the median, quartiles, and potential outliers. It allows her to see how students’ test grades compare overall.
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Violin Plot: Similar to a box plot but also shows the density of the test grades at different score levels, which helps in understanding how grades are spread out.
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Line Graph or Bar Chart: If she wants to track changes over time (for instance, test grades over multiple assessments), these types of charts can visualize the trend in grades effectively.
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Regression Line: If she includes a regression line in her scatter plot of years of programming experience versus class average, it can help model the relationship and make predictions about test grades based on years of experience.
Using these visualizations together will provide the teacher with a comprehensive view of how programming experience relates to class performance and test grades, allowing her to make informed predictions about future assessments.
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