*-1. Estimate the linear probability model. Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. A B U 1 y x1 x2 Constant x1 ×2 Predictor Variable Linear Model 0.42 0.01 -0.03 20 14.44 13.93 30 18.45 3.16 40 5.45 8.51 50 10.77 10.04 60 |11.21 10.02 70 8.31 9.50 81 13.09 2.66 e-2. Use the above estimates to compute ŷ for x₁ = 12, and x2 = 8. 91 19.72 9.61 Note: Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places. Report the probability between 0 and 1 (not in %). 10 1 18.06 9.24 11 0 8.13 9.32 120 6.26 4.19 13 0 5.31 10.89 140 19.83 3.33 15 1 7.00 7.27 b-1. Estimate the logistic regression model. 16 1 9.77 5.66 Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. 17 0 19.83 13.03 18 0 9.87 8.06 Predictor Variable Logistic Model 19 0 6.31 8.99 Constant 201 17.01 10.60 21 1 ×2 4.59 8.08 22 23 24 25 b-2. Use the above estimates to compute ŷ for x₁ = 12, and x2 = 8. Note: Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places. Report the probability between 0 and 1 (not in %). + B
*-1. Estimate the linear probability model. Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. A B U 1 y x1 x2 Constant x1 ×2 Predictor Variable Linear Model 0.42 0.01 -0.03 20 14.44 13.93 30 18.45 3.16 40 5.45 8.51 50 10.77 10.04 60 |11.21 10.02 70 8.31 9.50 81 13.09 2.66 e-2. Use the above estimates to compute ŷ for x₁ = 12, and x2 = 8. 91 19.72 9.61 Note: Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places. Report the probability between 0 and 1 (not in %). 10 1 18.06 9.24 11 0 8.13 9.32 120 6.26 4.19 13 0 5.31 10.89 140 19.83 3.33 15 1 7.00 7.27 b-1. Estimate the logistic regression model. 16 1 9.77 5.66 Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. 17 0 19.83 13.03 18 0 9.87 8.06 Predictor Variable Logistic Model 19 0 6.31 8.99 Constant 201 17.01 10.60 21 1 ×2 4.59 8.08 22 23 24 25 b-2. Use the above estimates to compute ŷ for x₁ = 12, and x2 = 8. Note: Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places. Report the probability between 0 and 1 (not in %). + B
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:*-1. Estimate the linear probability model.
Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.
A
B
U
1 y
x1
x2
Constant
x1
×2
Predictor Variable
Linear Model
0.42
0.01
-0.03
20
14.44
13.93
30
18.45
3.16
40
5.45
8.51
50
10.77
10.04
60
|11.21
10.02
70
8.31
9.50
81
13.09
2.66
e-2. Use the above estimates to compute ŷ for x₁ = 12, and x2 = 8.
91
19.72
9.61
Note: Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places. Report the probability
between 0 and 1 (not in %).
10 1
18.06
9.24
11 0
8.13
9.32
120
6.26
4.19
13 0
5.31
10.89
140
19.83
3.33
15 1
7.00
7.27
b-1. Estimate the logistic regression model.
16 1
9.77
5.66
Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.
17 0
19.83
13.03
18 0
9.87
8.06
Predictor Variable
Logistic Model
19 0
6.31
8.99
Constant
201
17.01
10.60
21 1
×2
4.59
8.08
22
23
24
25
b-2. Use the above estimates to compute ŷ for x₁ = 12, and x2 = 8.
Note: Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places. Report the probability
between 0 and 1 (not in %).
+
B
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