In order to compute the regression coefficients, the following table needs to be used:
"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c:c:c:c}\n & X & Y & XY & X^2 & Y^2 \\\\ \\hline\n & 10 & 14 & 140 & 100 & 196 \\\\\n \\hdashline\n & 12 & 17 & 204 & 144 & 289 \\\\\n \\hdashline\n & 15 & 23 & 345 & 225 & 529 \\\\\n \\hdashline\n & 23 & 25 & 575 & 529 & 625 \\\\\n \\hdashline\n & 20 & 21 & 420 & 400 & 441 \\\\\n \\hdashline\nSum= & 80 & 100 & 1684 & 1398 & 2080 \\\\\n \\hdashline\n\\end{array}"
"\\bar{X}=\\dfrac{1}{n}\\sum _{i}X_i=\\dfrac{80}{5}=16"
"\\bar{Y}=\\dfrac{1}{n}\\sum _{i}Y_i=\\dfrac{100}{5}=20"
"SS_{XX}=\\sum_iX_i^2-\\dfrac{1}{n}(\\sum _{i}X_i)^2""=1398-\\dfrac{80^2}{5}=118"
"SS_{YY}=\\sum_iY_i^2-\\dfrac{1}{n}(\\sum _{i}Y_i)^2""=2080-\\dfrac{(100)^2}{5}=80"
"SS_{XY}=\\sum_iX_iY_i-\\dfrac{1}{n}(\\sum _{i}X_i)(\\sum _{i}Y_i)""=1684-\\dfrac{80(100)}{5}=84"
"r=\\dfrac{SS_{XY}}{\\sqrt{SS_{XX}SS_{YY}}}=\\dfrac{84}{\\sqrt{118(80)}}"
"=0.8646>0.7"
Strong positive correlation
"m=slope=\\dfrac{SS_{XY}}{SS_{XX}}=\\dfrac{84}{118}=0.7119""n=\\bar{Y}-m\\bar{X}=20-\\dfrac{84}{118}(16)=8.6102"
i) The regression equation is:
"y=8.6102+0.7119x"
ii)
"x=30"
"y=8.6102+0.7119(30)"
"y=29.9672\\ Shs. millions"
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