Demand for Cell Phones. Letting Y = number of cell phonesubscribers and X =purchasing-power-adjusted per capita income, weobtained the following regression.

ˆYi = 14.4773 + 0.0022Xi (3.7.3)

se (ˆ?1) = 6.1523; se (ˆ?2) = 0.00032

r2= 0.6023

Demand for Cell Phones. Letting Y = number of cell phonesubscribers and X =purchasing-power-adjusted per capita income, weobtained the following regression.

ˆYi = 14.4773 + 0.0022Xi (3.7.3)

se (ˆ?1) = 6.1523; se (ˆ?2) = 0.00032

r2= 0.6023

Demand for Cell Phones. Letting Y = number of cell phonesubscribers and X =purchasing-power-adjusted per capita income, weobtained the following regression.

ˆYi = 14.4773 + 0.0022Xi (3.7.3)

se (ˆ?1) = 6.1523; se (ˆ?2) = 0.00032

r2= 0.6023

Demand for Cell Phones. Letting Y = number of cell phonesubscribers and X =purchasing-power-adjusted per capita income, weobtained the following regression.

ˆYi = 14.4773 + 0.0022Xi (3.7.3)

se (ˆ?1) = 6.1523; se (ˆ?2) = 0.00032

r2= 0.6023

(i) Is the estimated intercept coefficient significant atthe 5 percent level of significance? What is the null hypothesisyou are testing?

(ii) Is the estimated slope coefficient significant at the5 percent level? What is the underlying null hypothesis