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The dataset for this question contains data on number of children, years of education, age, and economic status, for women in Botswana during 1988. Here is a description of the variables in the data set: Variable | Description children number of living children age in years square of age years of education husband's years of education =1, if month born <= 6 =1, if live in urban area =1, if has electricity =1, if has tv =1, if has bicycle =1, if catholic age agesq educ heduc frsthalf urban electric tv bicycle catholic Sample summary children age agesq educ heduc frsthalf urban electric tv bicycle catholic count 1953.000000 1963.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 mean 3.455709 32.040451 1087.239631 5.084997 5.143881 0.568868 0.532002 0.166411 0.119304 0.302099 0.102919 std 2.293346 7.789751 523.421016 4.228725 4.803867 0.495361 0.499103 0.372544 0.324228 0.459286 0.303930 min 0.000000 16.000000 256.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 25% 2.000000 26.000000 676.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 50% 3.000000 31.000000 961.000000 5.000000 6.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 75% 5.000000 38.000000 1444.000000 7.000000 8.000000 1.000000 1.000000 0.000000 0.000000 1.000000 0.000000 49.000000 2401.000000 20.000000 20.000000 1.000000 1.000000 1.000000 max 13.000000 1.000000 1.000000 1.000000 Sample correlation matrix children age agesq educ heduc frsthalf urban electric tv bicycle catholic children 1.000000 0.592830 0.569390 -0.308375 -0.267707 0.061648 -0.211916 -0.128381 -0.123450 0.075452 -0.015139 age 0.592830 1.000000 0.991967 -0.175734 -0.128130 0.021781 -0.128740 0.043577 0.063604 0.014338 0.028534 agesq 0.569390 0.991967 1.000000 -0.176230 -0.133899 0.022417 -0.129817 0.035344 0.054252 0.010605 0.023652 educ -0.308375 -0.175734 -0.176230 1.000000 0.647413 -0.139017 0.306733 0.460587 0.477218 0.090962 0.178140 heduc -0.267707 -0.128130 -0.133899 0.647413 1.000000 -0.121387 0.366976 0.455498 0.459974 0.067361 0.133010 frsthalf 0.061648 0.021781 0.022417 -0.139017 -0.121387 1.000000 -0.041554 -0.107937 -0.084674 0.036855 -0.031790 urban -0.211916 -0.128740 -0.129817 0.306733 0.366976 -0.041554 1.000000 0.328142 0.288223 -0.008674 0.081281 electric -0.128381 0.043577 0.035344 0.460587 0.455498 -0.107937 0.328142 1.000000 0.598972 0.089276 0.124656 tv -0.123450 0.063604 0.054252 0.477218 0.459974 -0.084674 0.288223 0.598972 1.000000 0.112188 0.135270 bicycle 0.075452 0.014338 0.010605 0.090962 0.067361 0.036855 -0.008674 0.089276 0.112188 1.000000 0.026710 catholic -0.015139 0.028534 0.023652 0.178140 0.133010 -0.031790 0.081281 0.124656 0.135270 0.026710 1.000000

The dataset for this question contains data on number of children, years of education, age, and economic status, for women in Botswana during 1988. Here is a description of the variables in the data set: Variable | Description children number of living children age in years square of age years of education husband's years of education =1, if month born <= 6 =1, if live in urban area =1, if has electricity =1, if has tv =1, if has bicycle =1, if catholic age agesq educ heduc frsthalf urban electric tv bicycle catholic Sample summary children age agesq educ heduc frsthalf urban electric tv bicycle catholic count 1953.000000 1963.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 mean 3.455709 32.040451 1087.239631 5.084997 5.143881 0.568868 0.532002 0.166411 0.119304 0.302099 0.102919 std 2.293346 7.789751 523.421016 4.228725 4.803867 0.495361 0.499103 0.372544 0.324228 0.459286 0.303930 min 0.000000 16.000000 256.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 25% 2.000000 26.000000 676.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 50% 3.000000 31.000000 961.000000 5.000000 6.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 75% 5.000000 38.000000 1444.000000 7.000000 8.000000 1.000000 1.000000 0.000000 0.000000 1.000000 0.000000 49.000000 2401.000000 20.000000 20.000000 1.000000 1.000000 1.000000 max 13.000000 1.000000 1.000000 1.000000 Sample correlation matrix children age agesq educ heduc frsthalf urban electric tv bicycle catholic children 1.000000 0.592830 0.569390 -0.308375 -0.267707 0.061648 -0.211916 -0.128381 -0.123450 0.075452 -0.015139 age 0.592830 1.000000 0.991967 -0.175734 -0.128130 0.021781 -0.128740 0.043577 0.063604 0.014338 0.028534 agesq 0.569390 0.991967 1.000000 -0.176230 -0.133899 0.022417 -0.129817 0.035344 0.054252 0.010605 0.023652 educ -0.308375 -0.175734 -0.176230 1.000000 0.647413 -0.139017 0.306733 0.460587 0.477218 0.090962 0.178140 heduc -0.267707 -0.128130 -0.133899 0.647413 1.000000 -0.121387 0.366976 0.455498 0.459974 0.067361 0.133010 frsthalf 0.061648 0.021781 0.022417 -0.139017 -0.121387 1.000000 -0.041554 -0.107937 -0.084674 0.036855 -0.031790 urban -0.211916 -0.128740 -0.129817 0.306733 0.366976 -0.041554 1.000000 0.328142 0.288223 -0.008674 0.081281 electric -0.128381 0.043577 0.035344 0.460587 0.455498 -0.107937 0.328142 1.000000 0.598972 0.089276 0.124656 tv -0.123450 0.063604 0.054252 0.477218 0.459974 -0.084674 0.288223 0.598972 1.000000 0.112188 0.135270 bicycle 0.075452 0.014338 0.010605 0.090962 0.067361 0.036855 -0.008674 0.089276 0.112188 1.000000 0.026710 catholic -0.015139 0.028534 0.023652 0.178140 0.133010 -0.031790 0.081281 0.124656 0.135270 0.026710 1.000000

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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The dataset for this question contains data on number of children, years of education, age, and economic status, for women in Botswana during 1988. Here is a description of the variables in the data set: Variable | Description children number of living children age in years square of age years of education husband's years of education =1, if month born <= 6 =1, if live in urban area =1, if has electricity =1, if has tv =1, if has bicycle =1, if catholic age agesq educ heduc frsthalf urban electric tv bicycle catholic %3D Sample summary children age bsaße educ heduc frsthalf urban electric tv bicycle catholic count 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 mean 3.455709 32.040451 1087.239631 5.084997 5.143881 0.568868 0.532002 0.166411 0.119304 0.302099 0.102919 std 2.293346 7.789751 523.421016 4.228725 4.803867 0.495361 0.499103 0.372544 0.324228 0.459286 0.303930 min 0.000000 16.000000 256.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 25% 2.000000 26.000000 676.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 50% 3.000000 31.000000 961.000000 5.000000 6.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 75% 5.000000 38.000000 1444.000000 7.000000 8.000000 1.000000 1.000000 0.000000 0.000000 1.000000 0.000000 max 13.000000 49.000000 2401.000000 20.000000 20.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 Sample correlation matrix children age agesq educ heduc frsthalf urban electric tv bicycle catholic children 1.000000 0.592830 0.569390 -0.308375 -0.267707 0.061648 -0.211916 -0.128381 -0.123450 0.075452 -0.015139 age 0.592830 1.000000 0.991967 -0.175734 -0.128130 0.021781 -0.128740 0.043577 0.063604 0.014338 0.028534 agesq 0.569390 0.991967 1.000000 -0.176230 -0.133899 0.022417 -0.129817 0.035344 0.054252 0.010605 0.023652 educ -0.308375 -0.175734 -0.176230 1.000000 0.647413 -0.139017 0.306733 0.460587 0.477218 0.090962 0.178140 heduc -0.267707 -0.128130 -0.133899 0.647413 1.000000 -0.121387 0.366976 0.455498 0.459974 0.067361 0.133010 frsthalf 0.061648 0.021781 0.022417 -0.139017 -0.121387 1.000000 -0.041554 -0.107937 -0.084674 0.036855 -0.031790 urban -0.211916 -0.128740 -0.129817 0.306733 0.366976 -0.041554 1.000000 0.328142 0.288223 -0.008674 0.081281 electric -0.128381 0.043577 0.035344 0.460587 0.455498 -0.107937 0.328142 1.000000 0.598972 0.089276 0.124656 tv -0.123450 0.063604 0.054252 0.477218 0.459974 -0.084674 0.288223 0.598972 1.000000 0.112188 0.135270 bicycle 0.075452 0.014338 0.010605 0.090962 0.067361 0.036855 -0.008674 0.089276 0.112188 1.000000 0.026710 catholic -0.015139 0.028534 0.023652 0.178140 0.133010 -0.031790 0.081281 0.124656 0.135270 0.026710 1.000000
Transcribed Image Text:The dataset for this question contains data on number of children, years of education, age, and economic status, for women in Botswana during 1988. Here is a description of the variables in the data set: Variable | Description children number of living children age in years square of age years of education husband's years of education =1, if month born <= 6 =1, if live in urban area =1, if has electricity =1, if has tv =1, if has bicycle =1, if catholic age agesq educ heduc frsthalf urban electric tv bicycle catholic %3D Sample summary children age bsaße educ heduc frsthalf urban electric tv bicycle catholic count 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 1953.000000 mean 3.455709 32.040451 1087.239631 5.084997 5.143881 0.568868 0.532002 0.166411 0.119304 0.302099 0.102919 std 2.293346 7.789751 523.421016 4.228725 4.803867 0.495361 0.499103 0.372544 0.324228 0.459286 0.303930 min 0.000000 16.000000 256.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 25% 2.000000 26.000000 676.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 50% 3.000000 31.000000 961.000000 5.000000 6.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 75% 5.000000 38.000000 1444.000000 7.000000 8.000000 1.000000 1.000000 0.000000 0.000000 1.000000 0.000000 max 13.000000 49.000000 2401.000000 20.000000 20.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 Sample correlation matrix children age agesq educ heduc frsthalf urban electric tv bicycle catholic children 1.000000 0.592830 0.569390 -0.308375 -0.267707 0.061648 -0.211916 -0.128381 -0.123450 0.075452 -0.015139 age 0.592830 1.000000 0.991967 -0.175734 -0.128130 0.021781 -0.128740 0.043577 0.063604 0.014338 0.028534 agesq 0.569390 0.991967 1.000000 -0.176230 -0.133899 0.022417 -0.129817 0.035344 0.054252 0.010605 0.023652 educ -0.308375 -0.175734 -0.176230 1.000000 0.647413 -0.139017 0.306733 0.460587 0.477218 0.090962 0.178140 heduc -0.267707 -0.128130 -0.133899 0.647413 1.000000 -0.121387 0.366976 0.455498 0.459974 0.067361 0.133010 frsthalf 0.061648 0.021781 0.022417 -0.139017 -0.121387 1.000000 -0.041554 -0.107937 -0.084674 0.036855 -0.031790 urban -0.211916 -0.128740 -0.129817 0.306733 0.366976 -0.041554 1.000000 0.328142 0.288223 -0.008674 0.081281 electric -0.128381 0.043577 0.035344 0.460587 0.455498 -0.107937 0.328142 1.000000 0.598972 0.089276 0.124656 tv -0.123450 0.063604 0.054252 0.477218 0.459974 -0.084674 0.288223 0.598972 1.000000 0.112188 0.135270 bicycle 0.075452 0.014338 0.010605 0.090962 0.067361 0.036855 -0.008674 0.089276 0.112188 1.000000 0.026710 catholic -0.015139 0.028534 0.023652 0.178140 0.133010 -0.031790 0.081281 0.124656 0.135270 0.026710 1.000000
(3.2) We next estimate the following model via OLS: children; = Bo + Bieduc; + Bzage; + Bzage? + Baurban; + Bzcatholic; + Becath_educ; + u; where cath_educ; = catholic; · educ; Regression results OLS Estimation Summary =====- Dep. Variable: Estimator: children R-squared: Adj. R-squared: F-statistic: 0.4250 OLS 0.4233 No. Observations: 1953 1443.7 P-value (F-stat) Distribution: Date: Thu, Dec 09 2021 0.0000 Time: 15:30:07 chi2 (6) Cov. Estimator: unadjusted Parameter Estimates Parameter Std. Err. T-stat P-value Lower CI Upper CI const -6.5864 0.6515 -10.110 0.0000 -7.8633 -5.3095 educ -0.0960 0.0105 -9.1105 0.0000 -0.1167 -0.0753 age 0.5151 0.0400 12.875 0.0000 0.4366 0.5935 agesq -0.0053 0.0006 -8.9076 0.0000 -0.0065 -0.0041 urban -0.3932 0.0832 -4.7277 0.0000 -0.5562 -0.2302 catholic 0.4462 0.2522 1.7689 0.0769 -0.0482 0.9406 cath_educ -0.0589 0.0306 -1.9270 0.0540 -0.1189 0.0010 === (i) Interpret the coefficent estimates for catholic and cath_educ. (ii) Given a group of 200 30-year old women who live in an urban area and have 16 years of education, suppose 100 of them are catholic and 100 of them are not catholic. What is the expected effect on fertility for each of these two groups (grouped by catholic or not catholic) if all 200 women were to receive another of education? year (iii) Are the coefficent estimates for catholic and cath.educ (individually) statistically significant (at the 5% level)?
Transcribed Image Text:(3.2) We next estimate the following model via OLS: children; = Bo + Bieduc; + Bzage; + Bzage? + Baurban; + Bzcatholic; + Becath_educ; + u; where cath_educ; = catholic; · educ; Regression results OLS Estimation Summary =====- Dep. Variable: Estimator: children R-squared: Adj. R-squared: F-statistic: 0.4250 OLS 0.4233 No. Observations: 1953 1443.7 P-value (F-stat) Distribution: Date: Thu, Dec 09 2021 0.0000 Time: 15:30:07 chi2 (6) Cov. Estimator: unadjusted Parameter Estimates Parameter Std. Err. T-stat P-value Lower CI Upper CI const -6.5864 0.6515 -10.110 0.0000 -7.8633 -5.3095 educ -0.0960 0.0105 -9.1105 0.0000 -0.1167 -0.0753 age 0.5151 0.0400 12.875 0.0000 0.4366 0.5935 agesq -0.0053 0.0006 -8.9076 0.0000 -0.0065 -0.0041 urban -0.3932 0.0832 -4.7277 0.0000 -0.5562 -0.2302 catholic 0.4462 0.2522 1.7689 0.0769 -0.0482 0.9406 cath_educ -0.0589 0.0306 -1.9270 0.0540 -0.1189 0.0010 === (i) Interpret the coefficent estimates for catholic and cath_educ. (ii) Given a group of 200 30-year old women who live in an urban area and have 16 years of education, suppose 100 of them are catholic and 100 of them are not catholic. What is the expected effect on fertility for each of these two groups (grouped by catholic or not catholic) if all 200 women were to receive another of education? year (iii) Are the coefficent estimates for catholic and cath.educ (individually) statistically significant (at the 5% level)?
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