Answer:
The critical region is t ≥ t(0.025, 7) = 2.365
Since the calculated value of t= 18.50249 falls in the critical region we reject the null hypothesis and conclude that there is sufficient reason to support the claim of a linear relationship between the two variables.
Step-by-step explanation:
We set up our hypotheses as
H0: β= 0 the two variable X and Y are not related
Ha: β ≠ 0. the two variables X and Y are related.
The significance level is set at α =0.05
The test statistic if, H0 is true, is t= b/s_b
Where Sb =S_yx/√(∑(X-X`)^2 )
Syx = √((∑(Y-Y`)^2 )/(n-2))
In the given question we have the estimated regression line as y= 0.449x - 30.27
X Y X2 Y2 XY
72 3 5184 9 216
85 7 7225 49 595
91 10 8281 100 910
90 10 8100 100 900
88 8 7744 64 704
98 15 9604 225 1470
75 4 5625 16 300
100 15 10000 225 1500
<u>80 5 6400 25 400 </u>
<u>∑779 77 68163 813 6995</u>
Now finding the variances
∑(Y-Y`)^2 = ∑〖Y^2- a〗 ∑Y- b∑XY
= 813 – (- 30.27)77 - 0.449(6995)
= 813+2330.79 – 3140.755
= 3.035
∑(X-X`)^2 = ∑X^2 – (∑〖X)〗^2 /n
= 68163 – (779)2/9
= 736.22
Syx = √((∑(Y-Y`)^2 )/(n-2)) = √(3.035/7) = 0.65846 and
Sb =S_yx/√(∑(X-X`)^2 ) = (0.65846 )/27.13337 = 0.024267
t= b/s_b = 0.449/ 0.024267 = 18.50249
The critical region is t ≥ t(0.025, 7) = 2.365
Since the calculated value of t= 18.50249 falls in the critical region we reject the null hypothesis and conclude that there is sufficient reason to support the claim of a linear relationship between the two variables.
