The response variable here is the number of processed requests "(y)," and we attempt to predict it from the size of a data set "(x)."
"S_{xx}=\\sum_i x_i^2-n\\cdot\\bar{x}^2=623-7\\cdot(9)^2=56"
"S_{xy}=\\sum_i x_iy_i-n\\cdot\\bar{x}\\bar{y}=1973-7\\cdot(9)(35)=-232"
"S_{yy}=\\sum_i y_i^2-n\\cdot\\bar{y}^2=10027-7\\cdot(35)^2=1452"
Therefore, based on the above calculations, the regression coefficients (the slope "m," and the "y-" intercept "n") are obtained as follows:
"n=\\bar{y}-m\\bar{x}=35-(-{29\\over 7})(9)={506\\over 7}\\approx72.285714"
Therefore, we find that the regression equation is:
ii. Is there any correlation between the processing request and the size of incoming data?
What is the correlation coefficient?
Correlation cofficient
Strong correlation
iii. By what percentage is the processing time dependent on the size of incoming data?
The coefficient of determination
"66.19\\ \\%"
The proportion of Y variance explained by the linear relationship between X and Y is "66.19\\ \\%."
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