A developer who specializes in summer cottage properties is considering purchasing a large tract of land adjoining a lake. The current owner of the tract has already subdivided the land into separate building lots and has prepared the lots by removing some of the trees. The developer wants to forecast the value of each lot. From previous experience, she knows that the most important factors affecting the price of a lot are size, number of mature trees, and distance to the lake. From a nearby area, she gathers the relevant data for 60 recently sold lots. Use a 5% significance level for all tests. What is tri. standard error of estimate? a) 0.55 b) None of the answers are correct O c) 5.97 O d) 0.19 e) 40.24
A developer who specializes in summer cottage properties is considering purchasing a large tract of land adjoining a lake. The current owner of the tract has already subdivided the land into separate building lots and has prepared the lots by removing some of the trees. The developer wants to forecast the value of each lot. From previous experience, she knows that the most important factors affecting the price of a lot are size, number of mature trees, and distance to the lake. From a nearby area, she gathers the relevant data for 60 recently sold lots. Use a 5% significance level for all tests. What is tri. standard error of estimate? a) 0.55 b) None of the answers are correct O c) 5.97 O d) 0.19 e) 40.24
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Price | Lot size | Trees | Distance |
105.4 | 41.2 | 24 | 42 |
91.2 | 44.8 | 5 | 71 |
183.3 | 21.3 | 72 | 43 |
93.8 | 43.9 | 58 | 14 |
207.5 | 57.7 | 52 | 12 |
130.9 | 33.4 | 78 | 26 |
162.3 | 31.4 | 65 | 51 |
18.8 | 27.4 | 22 | 0 |
80.5 | 26.2 | 68 | 83 |
38.3 | 40 | 57 | 76 |
71.3 | 47.6 | 53 | 35 |
55.5 | 31.6 | 36 | 26 |
85.7 | 21.6 | 23 | 24 |
110.5 | 36.3 | 48 | 98 |
85.1 | 47.2 | 61 | 59 |
78.3 | 30.5 | 41 | 55 |
27.2 | 41.8 | 1 | 60 |
70.9 | 20.6 | 20 | 33 |
101.4 | 35.3 | 38 | 75 |
133.3 | 40.1 | 68 | 0 |
117.7 | 35.6 | 24 | 41 |
49.7 | 20.6 | 16 | 77 |
49.6 | 22.4 | 32 | 86 |
83.2 | 45.8 | 77 | 19 |
81.3 | 29.4 | 40 | 0 |
152.5 | 51.7 | 60 | 34 |
112.2 | 27.2 | 0 | 16 |
37.1 | 37 | 50 | 49 |
130.2 | 38.9 | 48 | 63 |
39.1 | 32.5 | 25 | 45 |
81.9 | 34 | 12 | 0 |
24.6 | 35.8 | 16 | 34 |
101.9 | 32.9 | 44 | 42 |
117.6 | 46.4 | 62 | 48 |
148.8 | 51.9 | 59 | 39 |
60.2 | 28.9 | 0 | 66 |
43.7 | 35.2 | 57 | 77 |
113.1 | 30.4 | 70 | 78 |
38.1 | 38.3 | 24 | 62 |
89.2 | 49.2 | 61 | 0 |
3 | 21.5 | 46 | 83 |
55.8 | 41.9 | 10 | 69 |
89.7 | 21.8 | 79 | 62 |
136.1 | 66.3 | 56 | 34 |
44.7 | 28.2 | 73 | 77 |
63.2 | 41.9 | 64 | 65 |
163.4 | 46.7 | 69 | 27 |
64.1 | 32.1 | 12 | 0 |
98.7 | 38.5 | 59 | 77 |
139.9 | 27.6 | 0 | 0 |
92 | 47 | 65 | 37 |
66.6 | 20.7 | 24 | 51 |
16.4 | 34 | 12 | 75 |
131.9 | 31.9 | 76 | 63 |
11 | 28 | 2 | 42 |
27.9 | 40 | 52 | 84 |
103.5 | 46.6 | 26 | 70 |
107 | 23.2 | 11 | 83 |
51.6 | 46.4 | 53 | 44 |
133.4 | 32.1 | 55 | 98 |
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