MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since
1995.
Year
Retail Sales
85.851
3.
108.426
6.
141.781
9.
169.256
12
202.297
15
222.266
Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is
F(t) =
= 9.44t + 84.182.
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Transcribed Image Text:The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. Year Retail Sales 85.851 3. 108.426 6. 141.781 9. 169.256 12 202.297 15 222.266 Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = = 9.44t + 84.182.
Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is
F(t)
= 9.44t + 84.182.
220-
210
200
190
180
170-
160-
150
140-
130
120
110
100
90
804
3
12
15
Use the above scatter plot to decide whether the linear model fits the data well.
O The function is not a good model for the data
O The function is a good model for the data.
Estimate the retails sales in the U. S. in 2017.
billions of dollars.
Use the model to predict the year in which retails sales will be $243 billion.
expand button
Transcribed Image Text:Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = 9.44t + 84.182. 220- 210 200 190 180 170- 160- 150 140- 130 120 110 100 90 804 3 12 15 Use the above scatter plot to decide whether the linear model fits the data well. O The function is not a good model for the data O The function is a good model for the data. Estimate the retails sales in the U. S. in 2017. billions of dollars. Use the model to predict the year in which retails sales will be $243 billion.
Expert Solution
Check Mark
Step 1

Regression Analysis:

An independent variable is multiplied with a slope and added with an intercept to get the dependent variable is basically known as a regression. The estimation of the dependent variable can a good estimate if the regression model is a good fit model.

In order to know whether the model is a good fit or not, the residual has to be less. 

Residual= observed value - predicted value.

e=y-y^.

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