Question

Question 1 3 pts To what extent is crowding in a neighborhood related to the crime rate in that neighborhood? A city's neighborhoods are rated according to the room occupancy rate (ROR, the number of occupants in a dwelling divided by the number of rooms in that dwelling) and the number of times police were summoned to that neighborhood in the past year. The data are shown in the table. Neighborhood ROR (X) Crimes(Y) 1 1.0 36 2 1.1 39 3 2.4 55 4 0.3 26 5 0.5 39 6 1.0 40 7 0.6 46 8 1.4 39 9 1.6 60 10 1.4 49 11 0.3 37 12 0.5 46 Fit the model $Y_i = \beta_0 + \beta_1 X_i + \epsilon_i$, where $\epsilon_i$ are independent $N(0, \sigma^2)$, $i = 1, 2, \dots, 12$. Obtain the maximum likelihood estimate (mle) of $\beta_1$. Your final answer must be a number. Please round it to 4 decimal places.

          Question 1
3 pts
To what extent is crowding in a neighborhood related to the crime rate in that neighborhood? A city's neighborhoods are rated according to the room occupancy rate (ROR, the number of occupants in
a dwelling divided by the number of rooms in that dwelling) and the number of times police were summoned to that neighborhood in the past year. The data are shown in the table.
Neighborhood
ROR (X)
Crimes(Y)
1
1.0
36
2
1.1
39
3
2.4
55
4
0.3
26
5
0.5
39
6
1.0
40
7
0.6
46
8
1.4
39
9
1.6
60
10
1.4
49
11
0.3
37
12
0.5
46
Fit the model
$Y_i = \beta_0 + \beta_1 X_i + \epsilon_i$, where $\epsilon_i$ are independent $N(0, \sigma^2)$, $i = 1, 2, \dots, 12$.
Obtain the maximum likelihood estimate (mle) of $\beta_1$. Your final answer must be a number. Please round it to 4 decimal places.

Question 1
3 pts
To what extent is crowding in a neighborhood related to the crime rate in that neighborhood? A city's neighborhoods are rated according to the room occupancy rate (ROR, the number of occupants in
a dwelling divided by the number of rooms in that dwelling) and the number of times police were summoned to that neighborhood in the past year. The data are shown in the table.
Neighborhood
ROR (X)
Crimes(Y)
1
1.0
36
2
1.1
39
3
2.4
55
4
0.3
26
5
0.5
39
6
1.0
40
7
0.6
46
8
1.4
39
9
1.6
60
10
1.4
49
11
0.3
37
12
0.5
46
Fit the model
Yi = β0 + β1 Xi +, where are independent N(0, σ^2), i = 1, 2, …, 12.
Obtain the maximum likelihood estimate (mle) of β1. Your final answer must be a number. Please round it to 4 decimal places.

Added by Jordan D.

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