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How well the regression line fits the data

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Smart Technology is now developing demand equation for its ireader 'Smart', which targets for primary students. It gathered average monthly sales figures (in number of units sold) and other data it believed that they are affecting their sales (in US dollars). When these data are entered into  spreadsheet, it looks like the following table.

Coefficients Tstatistic P-value 

Intercept 125 4.25 0.03 

Price of 'Smart' (PS) -19 5.25 0.008

 Price of ireader from another brand (PI) 25 3.01 0.02 

Price of 'Smart's' accessory produced by Smart Technology (PA) -12 2.20 0.042 

Monthly dollar expenditures on advertising (A) 20 1.82 0.067 R2 = 0.79 F=35.8 

Significance F = 0.02 

The assumed values of the variables are: PS = $100, PI = $85, PA = $35 and A = $80

a) Based on these estimates, write an equation that summarises the demand for 'Smart'.

b) Comment on how well the regression line fits the data, its significance as well as the significance of each coefficient. 

c) Based on your answer in part (a), compute the own price elasticity of 'Smart' and explain ONE reason for obtaining the own price elasticity you calculated. Suggest ONE way that Smart Technology can decrease its own price elasticity. Explain. 

d) Suppose Smart Technology earns US$80,000 per month in revenues from the sale of 'Smart' and US$10,000 per month from the sale of Smart's accessories. What would happen to the firm's total revenues if it reduced the price of 'Smart' by 2%?

Reference no: EM133164907

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