The following table shows worldwide sales of a certain type of cell phone and their average selling prices in 2012 and 2013. Year 2012 2013 Selling Price ($) 375 335 Sales (millions) 958 1,174 (a) Use the data to obtain a linear demand function for this type of cell phone. (Let p be the price, and let q be the demand). q(p) = Use your demand equation to predict sales if the price is lowered to $275. million phones (b) Fill in the blank. For every $1 increase in price, sales of this type of cell phone decrease by million units.
The following table shows worldwide sales of a certain type of cell phone and their average selling prices in 2012 and 2013. Year 2012 2013 Selling Price ($) 375 335 Sales (millions) 958 1,174 (a) Use the data to obtain a linear demand function for this type of cell phone. (Let p be the price, and let q be the demand). q(p) = Use your demand equation to predict sales if the price is lowered to $275. million phones (b) Fill in the blank. For every $1 increase in price, sales of this type of cell phone decrease by million units.
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
6th Edition
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Bruce Crauder, Benny Evans, Alan Noell
Chapter3: Straight Lines And Linear Functions
Section3.3: Modeling Data With Linear Functions
Problem 17E: Later High School Graduates This is a continuation of Exercise 16. The following table shows the...
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The following table shows worldwide sales of a certain type of cell phone and their average selling prices in 2012 and 2013.
| Year | 2012 | 2013 |
|---|---|---|
| Selling Price ($) | 375 | 335 |
| Sales (millions) | 958 | 1,174 |
(a)
Use the data to obtain a linear demand function for this type of cell phone. (Let p be the price, and let q be the demand).
q(p) =
Use your demand equation to predict sales if the price is lowered to $275.
million phones
(b)
Fill in the blank.
For every $1 increase in price, sales of this type of cell phone decrease by million units.
Expert Solution
Step 1
Linear regression: Suppose are n pairs of observations on variable X and Y.
we assume that Y as dependent variable, which can be expressed in terms of x. The simplest form is the linear relation. Suppose . However when we observe the numerical values of x and y the relation may not be observed perfectly. we assume the model.
y = Response variable,
x = Explanatory variable.
are constants.
error component.
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