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5. A group of 5 students scores on Exam 1 and Exam 2 in Math 1153 is recorded bellow: Вс Student 1 3 3 5 N 14 15 Exam 1 86 84
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x y X^2 y^2 xy
86 78 7396 6084 6708
84 68 7056 4624 5712
90 57 8100 3249 5130
74 75 5476 5625 5550
85 80 7225 6400 6800
average 83.8 71.6 7050.6 5196.4 5980

a. Scatter plot :

On X axis is Scores 1 and on Y axis is the scores of 2

90 80 70 60 50 40 Series1 30 20 10 0 0 20 40 60 80 100

The plot clearly shows a negative association among the scores, i.e in a downward direction.

b Correlation coefficient is calculated using the formula

r = \frac{average(xy) - average(x)*average(y)}{{\sqrt{[average(x^2)-(average(x))^{2})]*[average(y^2)-(average(y))^{2})]}}}Substituting the values, we get

r = -0.453,

ie. as shown in the scatter plot the correlation coefficient also agrees with the negative relation.

c. Using the method of least squares, the estimated regression line is given by

Y=a+bX, where a is the intercept and b is the slope estimate.

b = \frac{average(xy) - average(x)*average(y)}{{\sqrt{[average(x^2)-(average(x))^{2})]}}}

and intercept =a = average(y) - b*average(x)

Substituting the values, we get , b= -0.713 and a=131.355

Thus, the regression line is Y= 131.355 -0.713X

When X=80, Y=131.355-0.713*80 = 74.315.

d. The value of r^2, ie the coefficient of determination shows the percentage of variation explained by the model.

here, r^2 = (-0.453)^2 = 0.205

i.e 0.205*100 = 20.5% of the total variation is only explained by the model.

e. Residual of a score is given by the difference between the given y and the estimated Y,,

When X=90, y =57

Estimated Y =131.355-0.713*90 = 67.185

Residual = 57-67.185 = -10.185

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