## Using XY Scatter Chart to Predict Student Scores

The file P02_10.xlsx contains midterm and final exam scores for 96 students in a corporate finance course.

Do the students’ scores for the two exams tend to go together, so that those who do poorly on the midterm tend to do poorly on the final, and those who do well on the midterm tend to do well on the final? Create a scatterplot, along with a correlation, to answer this question.
Superimpose a (linear) trend line on the scatterplot, along with the equation of the line. Based on this equation, what would you expect a student with a 75 on the midterm to score on the final exam? (Albright, 2017, p. 105).

In the discussion area, answer both questions in Parts a and b. Attach the Excel document that shows the scatterplot, correlation, and trend line.

### Using XY Scatter Chart to Predict Student Scores

According to Albright & Winston (2017, p. 95), the primary purpose of a scatterplot is to make the relationship of variables (X and Y) apparent. In this case, the scatterplot is showing the relationship between a student’s scores in the midterm exam (x variable) and the final exam (y-variable).  From the graph, we can observe that the two variables have a positive correlation (that is when one value of the x variable increases, the value of the y variable also increases.

However, we cannot tell the strength of this relationship without relying on the correlation coefficient (Multiple R)-that ranges between -1 and 1 (p.103) and the coefficient of determination (R square)-that ranges between 0 to 1 or 0% to 100%. From the regression ANOVA output, the correlation coefficient is 0.7612 and this tells us that the two variables have a moderate positive correlation (p.429). Coefficient of determination, on the other hand, shows us how this model explains future outcomes (in this case, final score). From the AVOVA output,… read more

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Using XY Scatter Chart to Predict Student Scores