# Graphic Demonstration of Regression Analysis

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Question 1 Slope = 2.097, which means for every unit increase in x, y increases by 2.097. Intercept = -0.5515 is the value of y at x = 0 EMBED Excel.Sheet.12 Question 2 EMBED Excel.Sheet.12 Slope = 5.0443, which means for every unit increase in hours of study, the final exam score increases by 5.0443. Intercept = 56.114, is the final exam score of student for zero (0) hours of study EMBED Excel.Sheet.12 Question 3 EMBED Excel.Sheet.12 EMBED Excel.Sheet.12 Question 4 See graph in Question 1: Coefficient of determination, r2 = 0.9794, hence correlation coefficient, r = 0.989646401 This is approximately 99%, which indicates a strong linear relationship between x and y. And since r is positive, we say there is a strong positive correlation. NB: you can also use the "CORREL" function in excel to calculate your correlation coefficient Question 5 See graph in Question 2: Coefficient of determination, r2 = 0.7166, hence correlation coefficient, r = 0.846522297 This is approximately 85%, which indicates a strong linear relationship between hours of study (x) and final exam scores (y). Again, r is positive, so we say there is a strong positive correlation. NB: you can also use the "CORREL" function in excel to calculate your correlation coefficient The linear regression is limited by the collinearity of the x variables because this can lead to misinterpretation of the coefficients. Another limitation of the linear regression method is that, only one value