![]() ![]() A student with a really good grade on the midterm might be overconfident going into the final, and as a result doesn’t prepare adequately. Of course, that relationship isn’t set in stone a student’s performance on a midterm exam doesn’t cement their performance on the final! A student might use a poor result on the midterm as motivation to study more for the final. Similarly, if a student did poorly on the midterm, they probably also did poorly on the final exam. It seems reasonable to expect that there is a relationship between those two datasets: If a student did well on the midterm, they were probably more likely to do well on the final than the average student. For example, a student who wants to know how well they can expect to score on an upcoming final exam may consider reviewing the data on midterm and final exam scores for students who have previously taken the class. One of the most powerful tools statistics gives us is the ability to explore relationships between two datasets containing quantitative values, and then use that relationship to make predictions. Estimate and interpret regression lines.Distinguish among positive, negative and no correlation.Construct a scatter plot for a dataset. ![]() Learning ObjectivesĪfter completing this section, you should be able to: Figure 8.65 A scatter plot is a visualization of the relationship between quantitative dataset. ![]()
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