Suppose a national survey of women was conducted in the years 1972, 1974, 1976, 1978, 1980, 1982, and 1984. Suppose the survey data from each year is pooled to create a pooled cross-sectional data set consisting of 13,000 observations. A researcher wants to use these data to estimate a multiple linear regression model using OLS to explain the number of children born to a woman durir this time period. To do this, she regressed number of kids born to a woman on education, age, ethnicity, and dummy variables for the years 1974, 1976 1978, 1980, 1982, and 1984. For example, if an observation comes from the year 1974, y74 would equal 1, while the remaining dummy variables for other years would equal 0. The regression output is provided below with corresponding standard errors in parentheses: kids = - 7.731+0.19y74 – 0.09y76 –0.06y78 –0.08y80 – 0.42y82 – 0.65y84 –0.135educ + 0.277age – 0.0055age? + 0.88black (0.177) (3.101) (0.171) (0.171) (0.182) (0.188) (0.175) (0.173) (0.20) (0.111) (0.001) R2 = 0.114 Given the below critical values fill in the blanks. Round to two decimals if necessary. Significance Critical level value 1% 2.58 5% 1.96 10% 1.64 The coefficient of y84 is This implies, holding education, age and race constant, 100 women in 1984 are predicted to hav children compared to 100 women in 1972. (Hint: For the second blank of this sentence make a choice between "more" and "fewer".) t-value of y84 is equal to So, the difference between number of children born to a woman in 1984 compared to a woma having the same characteristics in the base year statistically significant at 1% significance level.
Suppose a national survey of women was conducted in the years 1972, 1974, 1976, 1978, 1980, 1982, and 1984. Suppose the survey data from each year is pooled to create a pooled cross-sectional data set consisting of 13,000 observations. A researcher wants to use these data to estimate a multiple linear regression model using OLS to explain the number of children born to a woman durir this time period. To do this, she regressed number of kids born to a woman on education, age, ethnicity, and dummy variables for the years 1974, 1976 1978, 1980, 1982, and 1984. For example, if an observation comes from the year 1974, y74 would equal 1, while the remaining dummy variables for other years would equal 0. The regression output is provided below with corresponding standard errors in parentheses: kids = - 7.731+0.19y74 – 0.09y76 –0.06y78 –0.08y80 – 0.42y82 – 0.65y84 –0.135educ + 0.277age – 0.0055age? + 0.88black (0.177) (3.101) (0.171) (0.171) (0.182) (0.188) (0.175) (0.173) (0.20) (0.111) (0.001) R2 = 0.114 Given the below critical values fill in the blanks. Round to two decimals if necessary. Significance Critical level value 1% 2.58 5% 1.96 10% 1.64 The coefficient of y84 is This implies, holding education, age and race constant, 100 women in 1984 are predicted to hav children compared to 100 women in 1972. (Hint: For the second blank of this sentence make a choice between "more" and "fewer".) t-value of y84 is equal to So, the difference between number of children born to a woman in 1984 compared to a woma having the same characteristics in the base year statistically significant at 1% significance level.
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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Transcribed Image Text:Suppose a national survey of women was conducted in the years 1972, 1974, 1976, 1978, 1980, 1982, and 1984. Suppose the survey data from each year is pooled
to create a pooled cross-sectional data set consisting of 13,000 observations.
A researcher wants to use these data to estimate a multiple linear regression model using OLS to explain the number of children born to a woman during
this time period. To do this, she regressed number of kids born to a woman on education, age, ethnicity, and dummy variables for the years 1974, 1976,
1978, 1980, 1982, and 1984. For example, if an observation comes from the year 1974, y74 would equal 1, while the remaining dummy variables for
other years would equal 0.
The regression output is provided below with corresponding standard errors in parentheses:
kids = -7.731+ 0.19y74 - 0.09y76 - 0.06y78 – 0.08y80 – 0.42y82 – 0.65y84 – 0.135educ + 0.277age - 0.0055age? +0.88black
(3.101)
(0.171)
(0.171)
(0.182)
(0.188)
(0.175)
(0.173)
(0.20)
(0.111)
(0.001)
(0.177)
R2 = 0.114
Given the below critical values fill in the blanks. Round to two decimals if necessary.
Significance
Critical
level
value
1%
2.58
5%
1.96
10%
1.64
The coefficient of y84 is
This implies, holding education, age and race constant, 100 women in 1984 are predicted to have
children compared to 100 women in 1972. (Hint: For the second blank of this sentence make a
choice between "more" and "fewer".)
t-value of y84 is equal to
So, the difference between number of children born to a woman in 1984 compared to a woman
having the same characteristics in the base year
statistically significant at 1% significance level.
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