Answer:
Step-by-step explanation:
Hello!
Given the variables
Y: Number of crimes committed for a city.
X: Number of police officers in a city.
a) You have to estimate the linear regression of the number of crimes in the function of the number of police officers per city.
The population model is
E(Yi)= α + βXi
Where α is the intercept and β is the slope of the population model.
To estimate the linear equation you have to estimate both of these values
The estimate of the intercept is "a" (some Authors call it b₀ and you might find the intercept in the model symbolized as β₀)
a= Y[bar] - b*X[bar]
And the estimate of the slope is "b"
n=8; ∑X=143; ∑X²= 2855; ∑Y= 123; ∑Y²= 2239; ∑XY= 1913
X[bar]= ∑X/n= 143/8= 17.88
Y[bar]= ∑Y/n= 123/8= 15.38
a= 15.38-(-0.96)*17.88= 32.46
The estimated regression equation is ^Y= 32.46 - 0.96Xi
B) To estimate the number of crimes given a determined number of police officers, you have to replace the value of X in the regression equation:
^Y=32.46-0.96*20= 13.26
The estimated number of crimes for a city with 20 police officers is 13.26.
C) See attachment for graph.
The slope of the estimated regression equation is negative this means that the linear regression is also negative. A negative linear regression means that when the variable X: number of police officers, increases, the variable Y: number of crimes committed, decreases.
I hope this helps!