1. Build a regression model to forecast the grape yield in your vineyard. Use the first 18 years to build the model and then forecast years 19 through 21. 2. Explain the meaning of the coefficients in your regression model in verbal terms. How well do you think the model fits the data?
1. Build a regression model to forecast the grape yield in your vineyard. Use the first 18 years to build the model and then forecast years 19 through 21. 2. Explain the meaning of the coefficients in your regression model in verbal terms. How well do you think the model fits the data?
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:You are the owner of a vineyard in the Napa Valley of California. The yield from your vineyard, in tons of grapes per acre for the past 21 years, is shown in the last column
of the following table. Degree-days represent a cumulative measure of the number of degrees and days that the average temperature exceeds 50° (A day in which the
average temperature was 74°F would contribute 24 degree-days to the total; a day with 60°F would contribute 10 degree-days; any day with average temperature
below 50°F would contribute zero to the total.) The column labeled Freeze indicates if there are hard frost in that year (value = 1) or not (value - 0).
Degree-Days
Yield (tons per
Rainfall (inches)
Year
(hundreds)
Freeze
acre)
1
14.2
28.57
2.72
16.8
28.83
2.42
3
25.0
29.30
2.99
4
37.4
28.18
2.88
41.2
29.44
2.42
33.9
27.81
2.84
28.2
26.96
2.74
8
16.0
25.76
1.56
9
11.9
26.66
2.19
10
16.3
28.41
2.45
11
28.7
27.32
2.57
Transcribed Image Text:12
38.0
28.66
2.65
13
38.3
27.05
1.96
14
35.8
29.38
3.09
15
29.8
28.24
1.67
16
18.6
27.89
2.63
17
122
26.94
2.38
18
15.4
27.72
2.53
19
24.1
28.55
2.66
20
37.6
28.37
2.76
21
38.2
29.10
2.95
1. Build a regression model to forecast the grape yield in your vineyard. Use the first 18 years to build the model and then forecast years 19 through 21.
2. Explain the meaning of the coefficients in your regression model in verbal terms. How well do you think the model fits the data?
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