a) Given that "x=13"
b) The residual is
"e_i=y_i-\\hat{y_i}""e=4-13.118=-9.118"
The residual is negative. The model overestimates the observation (the predicted value is too big).
c)
If the student enrollment x increases by one thousand, then the number of burlaries for this university increases by 1.326
d) R-squared (coefficient of determination) is the percentage of the dependent variable variation that a linear model explains.
This means that the model explains 58.5 % of the variation of the number of burlaries.
The correlation coefficient is
"R=\\sqrt{0.585}\\approx0.7649>0.7"Strong positive correlation (linear relationship).
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