"\\bar{X}={1\\over n}\\displaystyle\\sum_{i=1}^nX_i={360\\over 10}=36"
"\\bar{Y}={1\\over n}\\displaystyle\\sum_{i=1}^nY_i={1005\\over 10}=100.5"
"S_{XX}=\\displaystyle\\sum_{i=1}^nX_i^2-{1\\over n}(\\displaystyle\\sum_{i=1}^nX_i)^2=""=16500-{1\\over 10}(360)^2=3540"
"S_{YY}=\\displaystyle\\sum_{i=1}^nY_i^2-{1\\over n}(\\displaystyle\\sum_{i=1}^nY_i)^2=""=106475-{1\\over 10}(1005)^2=5472.5"
"S_{XY}=\\displaystyle\\sum_{i=1}^nX_iY_i-{1\\over n}(\\displaystyle\\sum_{i=1}^nX_i)(\\displaystyle\\sum_{i=1}^nY_i)=""=40425-{1\\over 10}(360)(1005)=4245"
Correlation coefficient
"r={4245\\over \\sqrt{3540}\\sqrt{5472.5}}\\approx0.9645"
This is a strong positive correlation, which means that high X variable scores go with high Y variable scores (and vice versa).
"A=\\bar{Y}-B\\bar{X}=100.5-{4245\\over 3540}(36)\\approx57.3305"
"y=57.3305+1.1992x"
(ii) Old adults should be given priority. Older adults suffer from high blood pressure.
(iii) R-squared is a goodness-of-fit measure for linear regression models. R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination
High coefficient of determination gives a reason to support fitting a regression model of the form
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