How would a manager use a regression equation for decision-making? For example:

Question

How would a manager use a regression equation for decision-making? For example: Q = -6,500 - 100PA + 50PB + .3I + .2A; R2 =.12, (2,500) (50) (30) (.1) (.08)
where Q is the quantity demanded of good A; PA = $10, price of good A; PB = $8, price of good B; I = $12,000, per capita income; and A = $20,000, monthly advertising expenditures. How would a manager use a regression equation for decision-making? For example: Q = -6,500 - 100PA + 50PB + .3I + .2A; R2 =.12, (2,500) (50) (30) (.1) (.08)
where Q is the quantity demanded of good A; PA = $10, price of good A; PB = $8, price of good B; I = $12,000, per capita income; and A = $20,000, monthly advertising expenditures. How would a manager use a regression equation for decision-making? For example: Q = -6,500 - 100PA + 50PB + .3I + .2A; R2 =.12, (2,500) (50) (30) (.1) (.08)
where Q is the quantity demanded of good A; PA = $10, price of good A; PB = $8, price of good B; I = $12,000, per capita income; and A = $20,000, monthly advertising expenditures.

Explanation / Answer

The R-Squared value (0.12) is very small in this case. So, this regression model is not good for predicting future responses. However, if you have a R-squared value greater than 0.8 then you can use the values of independent variables like 'PA', 'PB', 'I' and 'A' to predict the quantity demanded of good A.

This would help the manager to forecast the future demand.

For the given values -

Q = -6,500 - 100(10) + 50(8) + 0.3(12000) + 0.2(20000)

= -6500 -1000 +400 + 3600 + 4000

= 500

Hence, the quantity demanded of good for the given data is estimated to be 500.

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