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.

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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 (x1, y1), (x2, y2)---(xn, yn) 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 y = β0+β1X. However when we observe the numerical values of x and y the relation may not be observed perfectly. we assume the model.

    y = β0+β1X+ε

y = Response variable,

x = Explanatory variable.

β0 and β1 are constants.

ε error component.

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