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arid ID variable a622 1801 = wants to have a child within 3 years 1802 = wants to have a child >3 years 1803 = does not want to have (more) children aage Age of the respondent in years asex 1 = male 2 = female (ref) ankids Number of children respondent unionstatus 1 = single 2 = LAT 3 = cohabiting (ref) 4 = married employ 1 = employed (ref) 2 = other education 1 = secondary or less 2 = post-secondary (ref) prevdissol If respondent experienced the dissolution of a previous relationship 0 = no (ref) 1 = yes siblings Number of siblings the respondent has mumalive Whether respondent’s mother is still alive 0 = not alive 1 = alive (ref) Which are the categorical variables? Are all categorical variables independent variables?
Variable Obs Mean Std. Dev. Min Max mumalive 3,459 .8722174 .3338958 0 1 siblings 3,459 2.558543 1.986573 0 15 prevdissol 3,459 .2656837 .4417605 0 1 employ 3,459 1.262215 .4399023 1 2 unionstatus 3,459 2.952587 1.231247 1 4 a622 3,459 1802.386 .8112421 1801 1803 ankids 3,459 1.235328 1.251077 0 7 education 3,459 1.349523 .4768883 1 2 asex 3,459 1.559699 .496495 1 2 aage 3,459 34.93033 7.663585 18 50 arid 3,459 81412.45 2378.315 77328 85485 What command was used to report summary statistics?
Variable Obs Mean Std. Dev. Min Max mumalive 3,459 .8722174 .3338958 0 1 siblings 3,459 2.558543 1.986573 0 15 prevdissol 3,459 .2656837 .4417605 0 1 employ 3,459 1.262215 .4399023 1 2 unionstatus 3,459 2.952587 1.231247 1 4 a622 3,459 1802.386 .8112421 1801 1803 ankids 3,459 1.235328 1.251077 0 7 education 3,459 1.349523 .4768883 1 2 asex 3,459 1.559699 .496495 1 2 aage 3,459 34.93033 7.663585 18 50 arid 3,459 81412.45 2378.315 77328 85485 Which variables need to be transformed?
Variable Obs Mean Std. Dev. Min Max mumalive 3,459 .8722174 .3338958 0 1 siblings 3,459 2.558543 1.986573 0 15 prevdissol 3,459 .2656837 .4417605 0 1 employ 3,459 1.262215 .4399023 1 2 unionstatus 3,459 2.952587 1.231247 1 4 a622 3,459 1802.386 .8112421 1801 1803 ankids 3,459 1.235328 1.251077 0 7 education 3,459 1.349523 .4768883 1 2 asex 3,459 1.559699 .496495 1 2 aage 3,459 34.93033 7.663585 18 50 arid 3,459 81412.45 2378.315 77328 85485 Do we need to worry about missing values?
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Given the research question, what kind of dependent variable is needed? Can we work with the data as it is now? If not, make any necessary changes to the variables. Report and explain what you did.
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arid ID variable a622 1801 = wants to have a child within 3 years 1802 = wants to have a child >3 years 1803 = does not want to have (more) children aage Age of the respondent in years asex 1 = male 2 = female (ref) ankids Number of children respondent unionstatus 1 = single 2 = LAT 3 = cohabiting (ref) 4 = married employ 1 = employed (ref) 2 = other education 1 = secondary or less 2 = post-secondary (ref) prevdissol If respondent experienced the dissolution of a previous relationship 0 = no (ref) 1 = yes siblings Number of siblings the respondent has mumalive Whether respondent’s mother is still alive 0 = not alive 1 = alive (ref) Set the reference categories for the independent variable? Why is the command that we use for setting the reference category helpful?
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Run an OLS regression using the dependent variable – which you created in question 2 – and all available socio-demographic characteristics.
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Command: estat hettest Test: Prob > chi2 = 0.0000 chi2(1) = 465.89 Variables: fitted values of chintent Ho: Constant variance Breusch-Pagan / Cook-Weisberg test for heteroskedasticity What does the command test? Try to interpret the test.
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Which of the following violations can lead to biased coefficients and make it difficult to do statistical inferences?
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What is the consequence of violating the assumption of normal distribution of error terms?
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What is the recommended solution to violating the assumption of linearity?
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chintent Coef. Std. Err. t P>|t| [95% Conf. Interval] aage -.0124145 .0011002 -11.28 0.000 -.0145715 -.0102574
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_cons 2.942857 aage -.0910597 male -.330242 ankids -.8212769 single (unionstatus) -1.466456 married (unionstatus) .4667768 LAT (unionstatus) -1.525917 Other (employ) -.6556288 Secondary or l..(education) -.302349 Yes (prevdissol) .488535 siblings (prevdissol) .0556298 What is the fitted regression equation for logit (calculating log-odds)?
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_cons 2.942857 aage -.0910597 male -.330242 ankids -.8212769 single (unionstatus) -1.466456 married (unionstatus) .4667768 LAT (unionstatus) -1.525917 Other (employ) -.6556288 Secondary or l..(education) -.302349 Yes (prevdissol) .488535 siblings (prevdissol) .0556298 What is the fitted regression equation for calculating odds?
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