O n l i n e M o n t h l y E − O n l i n e A d v e r t i s i n g S t o r e c o m m e r s e S a l e s D o l l a r s ( i n 1000 s ) ( 1000 s ) 1 368 1.7 2 340 1.5 3 665 2.8 4 954 5 5 331 1.3 6 556 2.2 7 376 1.3 \def\arraystretch{1.5}
\begin{array}{c:c:c}
Online & Monthly\ E- & Online\ Advertising \\
Store & commerse\ Sales & Dollars \\
& \ (in\ 1000s) & (1000s)\\
\hline
1 & 368 & 1.7\\
2 & 340 & 1.5\\
3 & 665 & 2.8\\
4 & 954 & 5\\
5 & 331 & 1.3\\
6 & 556 & 2.2\\
7 & 376 & 1.3\\
\end{array} O n l in e St ore 1 2 3 4 5 6 7 M o n t h l y E − co mm erse S a l es ( in 1000 s ) 368 340 665 954 331 556 376 O n l in e A d v er t i s in g Do ll a rs ( 1000 s ) 1.7 1.5 2.8 5 1.3 2.2 1.3 a) Online Advertising Dollars is independent variable ( X ) . (X). ( X ) .
Monthly E-commerse Sales is dependent variable ( Y ) . (Y). ( Y ) .
X ˉ = 1 n ∑ i X i = 15.8 7 = 2.257143 \bar{X}=\dfrac{1}{n}\sum_iX_i=\dfrac{15.8}{7}=2.257143 X ˉ = n 1 i ∑ X i = 7 15.8 = 2.257143
Y ˉ = 1 n ∑ i Y i = 3590 7 = 512.857143 \bar{Y}=\dfrac{1}{n}\sum_iY_i=\dfrac{3590}{7}=512.857143 Y ˉ = n 1 i ∑ Y i = 7 3590 = 512.857143
S S X X = ∑ i ( X i − X ˉ ) 2 = 10.537143 SS_{XX}=\sum_i(X_i-\bar{X})^2=10.537143 S S XX = i ∑ ( X i − X ˉ ) 2 = 10.537143
S S Y Y = ∑ i ( Y i − Y ˉ ) 2 = 322280.857143 SS_{YY}=\sum_i(Y_i-\bar{Y})^2=322280.857143 S S YY = i ∑ ( Y i − Y ˉ ) 2 = 322280.857143
S S X Y = ∑ i ( X i − X ˉ ) ( Y i − Y ˉ ) = 1806.757143 SS_{XY}=\sum_i(X_i-\bar{X})(Y_i-\bar{Y})=1806.757143 S S X Y = i ∑ ( X i − X ˉ ) ( Y i − Y ˉ ) = 1806.757143
m = s l o p e = S S X Y S S X X = 1806.757143 10.537143 = 171.465564 m=slope=\dfrac{SS_{XY}}{SS_{XX}}=\dfrac{1806.757143}{10.537143}=171.465564 m = s l o p e = S S XX S S X Y = 10.537143 1806.757143 = 171.465564
n = Y ˉ − m X ˉ n=\bar{Y}-m\bar{X} n = Y ˉ − m X ˉ
= 512.857143 − 171.465564 ( 2.257143 ) =512.857143-171.465564(2.257143) = 512.857143 − 171.465564 ( 2.257143 )
= 125.834870 =125.834870 = 125.834870 Therefore, we find that the regression equation is:
Y = 125.834870 + 171.465564 X Y=125.834870+171.465564X Y = 125.834870 + 171.465564 X
b) Correlation coefficient:
r = S S X Y S S X X S S Y Y r=\dfrac{SS_{XY}}{\sqrt{SS_{XX}}\sqrt{SS_{YY}}} r = S S XX S S YY S S X Y
= 1806.757143 10.537143 322280.857143 =\dfrac{1806.757143}{\sqrt{10.537143}\sqrt{322280.857143}} = 10.537143 322280.857143 1806.757143
= 0.980440 =0.980440 = 0.980440 We have strong positive correlation.
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