(a)
X Y X Y X 2 Y 2 40 35 1400 1600 1225 20 40 800 400 1600 25 40 1000 625 1600 20 35 700 400 1225 30 45 1350 900 2025 50 50 2500 2500 2500 40 45 1800 1600 2025 20 40 800 400 1600 50 55 2750 2500 3025 40 55 1100 1600 3025 S u m = 335 440 15300 12525 19850 \def\arraystretch{1.5}
\begin{array}{c:c:c:c:c:c}
& X & Y & XY & X^2 & Y^2 \\ \hline
& 40 & 35 & 1400 & 1600 & 1225 \\
\hdashline
& 20 & 40 & 800 & 400 & 1600 \\
\hdashline
& 25 & 40 & 1000 & 625 & 1600 \\
\hdashline
& 20 & 35 & 700 & 400 & 1225 \\
\hdashline
& 30 & 45 & 1350 & 900 & 2025 \\
\hdashline
& 50 & 50 & 2500 & 2500 & 2500 \\
\hdashline
& 40 & 45 & 1800 & 1600 & 2025 \\
\hdashline
& 20 & 40 & 800 & 400 & 1600 \\
\hdashline
& 50 & 55 & 2750 & 2500 & 3025 \\
\hdashline
& 40 & 55 & 1100 & 1600 & 3025 \\
\hdashline
Sum= & 335 & 440 & 15300 & 12525 & 19850 \\
\hdashline
\end{array} S u m = X 40 20 25 20 30 50 40 20 50 40 335 Y 35 40 40 35 45 50 45 40 55 55 440 X Y 1400 800 1000 700 1350 2500 1800 800 2750 1100 15300 X 2 1600 400 625 400 900 2500 1600 400 2500 1600 12525 Y 2 1225 1600 1600 1225 2025 2500 2025 1600 3025 3025 19850
X ˉ = 1 n ∑ i X i = 335 10 = 33.5 \bar{X}=\dfrac{1}{n}\sum _iX_i=\dfrac{335}{10}=33.5 X ˉ = n 1 i ∑ X i = 10 335 = 33.5
Y ˉ = 1 n ∑ i Y i = 440 10 = 44 \bar{Y}=\dfrac{1}{n}\sum _iY_i=\dfrac{440}{10}=44 Y ˉ = n 1 i ∑ Y i = 10 440 = 44
S S X X = ∑ i X i 2 − 1 n ( ∑ i X i ) 2 = 12525 − 33 5 2 10 SS_{XX}=\sum _iX_i^2-\dfrac{1}{n}(\sum _iX_i)^2=12525-\dfrac{335^2}{10} S S XX = i ∑ X i 2 − n 1 ( i ∑ X i ) 2 = 12525 − 10 33 5 2
= 1302.5 =1302.5 = 1302.5
S S Y Y = ∑ i Y i 2 − 1 n ( ∑ i Y i ) 2 = 19850 − 44 0 2 10 SS_{YY}=\sum _iY_i^2-\dfrac{1}{n}(\sum _iY_i)^2=19850-\dfrac{440^2}{10} S S YY = i ∑ Y i 2 − n 1 ( i ∑ Y i ) 2 = 19850 − 10 44 0 2
= 490 =490 = 490
S S X Y = ∑ i X i Y i − 1 n ( ∑ i X i ) ( ∑ i Y i ) SS_{XY}=\sum _iX_iY_i-\dfrac{1}{n}(\sum _iX_i)(\sum _iY_i) S S X Y = i ∑ X i Y i − n 1 ( i ∑ X i ) ( i ∑ Y i )
= 15300 − 335 ( 440 ) 10 = 560 =15300-\dfrac{335(440)}{10}=560 = 15300 − 10 335 ( 440 ) = 560
s l o p e = m = S S X Y S S X X = 560 1302.5 = 0.42994242 slope=m=\dfrac{SS_{XY}}{SS_{XX}}=\dfrac{560}{1302.5}=0.42994242 s l o p e = m = S S XX S S X Y = 1302.5 560 = 0.42994242
n = Y ˉ − m X ˉ = 44 − 0.42994242 ( 33.5 ) n=\bar{Y}-m\bar{X}=44-0.42994242(33.5) n = Y ˉ − m X ˉ = 44 − 0.42994242 ( 33.5 )
= 29.59692893 =29.59692893 = 29.59692893 We find that the regression equation is:
y = 29.596929 + 0.429942 x y=29.596929+0.429942x y = 29.596929 + 0.429942 x
(b)
R 2 = ( S S X Y ) 2 S S X X S S Y Y = 56 0 2 1302.5 ( 490 ) = 0.49136276 R^2=\dfrac{(SS_{XY})^2}{SS_{XX}SS_{YY}}=\dfrac{560^2}{1302.5(490)}=0.49136276 R 2 = S S XX S S YY ( S S X Y ) 2 = 1302.5 ( 490 ) 56 0 2 = 0.49136276
r = R 2 = 0.49136276 = 0.7010 > 0.7 r=\sqrt{R^2}=\sqrt{0.49136276}=0.7010>0.7 r = R 2 = 0.49136276 = 0.7010 > 0.7 Strong positive correlation.
(c)
x = 35 x=35 x = 35
y = 29.596929 + 0.429942 ( 35 ) = 45 y=29.596929+0.429942(35)=45 y = 29.596929 + 0.429942 ( 35 ) = 45 Weekly advertising expenditure 35000 rupees will give 45000 rupees sales.
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