(a)
Sum=X40202520305040205040335Y35404035455045405555440XY140080010007001350250018008002750110015300X21600400625400900250016004002500160012525Y2122516001600122520252500202516003025302519850
Xˉ=n1i∑Xi=10335=33.5
Yˉ=n1i∑Yi=10440=44
SSXX=i∑Xi2−n1(i∑Xi)2=12525−103352
=1302.5
SSYY=i∑Yi2−n1(i∑Yi)2=19850−104402
=490
SSXY=i∑XiYi−n1(i∑Xi)(i∑Yi)
=15300−10335(440)=560
slope=m=SSXXSSXY=1302.5560=0.42994242
n=Yˉ−mXˉ=44−0.42994242(33.5)
=29.59692893We find that the regression equation is:
y=29.596929+0.429942x
(b)
R2=SSXXSSYY(SSXY)2=1302.5(490)5602=0.49136276
r=R2=0.49136276=0.7010>0.7 Strong positive correlation.
(c)
x=35
y=29.596929+0.429942(35)=45 Weekly advertising expenditure 35000 rupees will give 45000 rupees sales.
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