Question

In: Economics

The variable smokes is a binary variable equal to one if a person smokes and zero...

The variable smokes is a binary variable equal to one if a person smokes and zero otherwise. Cigprice is the price of cigarettes, white is a dummy variable that takes the value 1 if the person is white and 0 otherwise and restaurn is a dummy that takes the value 1 if the person lives in a state with restaurant smoking restrictions.

The estimated linear probability model is

smokes = .656 - .069 log(cigpric) + .012 log(income) - .029 educ + .02 age - .00026 age^2 - .101 restaurn - .026 white

If education increases by four years, what is the effect on the estimated probability of smoking?

The probability of smoking increases by .116 (or 11.6 percentage points)

The probability of smoking decreases by .116 (or 11.6 percentage points)

The probability of smoking decreases by 11.6 (or 11.6 percentage points)

The probability of smoking decreases by .413 (or 41.3 percentage points)

The variable smokes is a binary variable equal to one if a person smokes and zero otherwise. Cigprice is the price of cigarettes, white is a dummy variable that takes the value 1 if the person is white and 0 otherwise and restaurn is a dummy that takes the value 1 if the person lives in a state with restaurant smoking restrictions.

The estimated linear probability model is

smokes = .656 - .069 log(cigpric) + .012 log(income) - .029 educ + .02 age - .00026 age^2 - .101 restaurn - .026 white

At what point does another year of age reduce the probability of smoking?

19

21.93

38.46

51.62

The variable smokes is a binary variable equal to one if a person smokes and zero otherwise. Cigprice is the price of cigarettes, white is a dummy variable that takes the value 1 if the person is white and 0 otherwise and restaurn is a dummy that takes the value 1 if the person lives in a state with restaurant smoking restrictions.

The estimated linear probability model is

smokes = .656 - .069 log(cigpric) + .012 log(income) - .029 educ + .02 age - .00026 age^2 - .101 restaurn - .026 white

Interpret the coefficient of the binary variable “restaurn”

A person who lives in a state with restaurant smoking restrictions has a probability of smoking 10.1 percentage points lower than somebody living in a state without restaurant smoking restrictions

A person who lives in a state with restaurant smoking restrictions has a probability of smoking 10.1 percentage points higher than somebody living in a state without restaurant smoking restrictions

A person who lives in a state with restaurant smoking restrictions has a probability of smoking .101 percentage points lower than somebody living in a state without restaurant smoking restrictions

A person who lives in a state with restaurant smoking restrictions has a probability of smoking 1.01 percentage points lower than somebody living in a state without restaurant smoking restrictions

The variable smokes is a binary variable equal to one if a person smokes and zero otherwise. Cigprice is the price of cigarettes, white is a dummy variable that takes the value 1 if the person is white and 0 otherwise and restaurn is a dummy that takes the value 1 if the person lives in a state with restaurant smoking restrictions.

The estimated linear probability model is

smokes = .656 - .069 log(cigpric) + .012 log(income) - .029 educ + .02 age - .00026 age^2 - .101 restaurn - .026 white

Person number 206 in the datset has cigpric = 67.44, income = 6500, educ =16, age =77, restaurn = 0 and white = 0. What is the predicted probability of smoking?

.12

.65

.053

.0052

The variable smokes is a binary variable equal to one if a person smokes and zero otherwise. Cigprice is the price of cigarettes, white is a dummy variable that takes the value 1 if the person is white and 0 otherwise and restaurn is a dummy that takes the value 1 if the person lives in a state with restaurant smoking restrictions.

The estimated linear probability model is

smokes = .656 - .069 log(cigpric) + .012 log(income) - .029 educ + .02 age - .00026 age^2 - .101 restaurn - .026 white

What is the interpretation of the coefficient for log(cigpric)?

If cigarette prices go up 1% then the probability of smoking decreases by .00069 (or .069 percentage points)

If cigarette prices go up 1% then the probability of smoking increases by .00069 (or .069 percentage points)

If cigarette prices go up 1% then the probability of smoking decreases by .69 (or 6.9 percentage points)

If cigarette prices go up 1% then the probability of smoking decreases by 6.9 (or 69 percentage points)

The variable smokes is a binary variable equal to one if a person smokes and zero otherwise. Cigprice is the price of cigarettes, white is a dummy variable that takes the value 1 if the person is white and 0 otherwise and restaurn is a dummy that takes the value 1 if the person lives in a state with restaurant smoking restrictions.

The estimated linear probability model is

smokes = .656 - .069 log(cigpric) + .012 log(income) - .029 educ + .02 age - .00026 age^2 - .101 restaurn - .026 white

What is the interpretation of the coefficient for log(income)?

If income goes up 1% then the probability of smoking increases by .12 (or 12 percentage points)

If income goes up 1% then the probability of smoking decreases by .00012 (or .012 percentage points)

If income goes up 1% then the probability of smoking increases by .00012 (or .012 percentage points)

If income goes up 1% then the probability of smoking increases by 1.2 (or 12 percentage points)

Solutions

Expert Solution

ANswer for 1)

Change in smoking with respect to change in education can be measured with the help of coefficient of education i.e. 0.029 units. It says every year increase in education smoking decreases by 0.029 units therefore 4years of increase in education will decrease smoking by 0.029*4=0.116 units

Hence option B is correct response

Answer for 2)

Its asking for change in smoke with respect to change in age should be negative and that is possible if 0.02-2*0.00026qge<0

0.02/0.00052<age

38.46<age i.e age>38.46we have negative effect on smoking by age.

Hence Option C is correct response

Answer for 3)

0.101 less propbability is correct response here Hence Option C is correct here

Answer for 4)

smokes = .656 - .069 log(67.44) + .012 log(6500) - .029 16 + .02 77 - .00026 77^2 - .101 *(0) - .026 *(0)

We will use all the above values to find probability of smoking

smokes = .656 - .069 log(67.44) + .012 log(6500) - .029 16 + .02 77 - .00026 77^2 - .101 *(0) - .026 *(0)

=0.0052

Rahul Sunny answered 1 year ago
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