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Analysis of Low Birth Weight Factors Using Logistic Regression

This study delves into various factors affecting low birth weight in infants through logistic regression. We explore the significance of maternal smoking status, race, age, weight at the last menstrual period, and demographic details. Key analyses include estimating and interpreting coefficients, assessing model fit, and checking regression assumptions using methods like the Likelihood Ratio Test and Hosmer & Lemeshow Test. We highlight the impact of maternal smoking on low birth weight, with findings indicating a significant increase in risk. Guidelines for post-estimation, including residual analysis and multi-collinearity checks, are also provided.

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Analysis of Low Birth Weight Factors Using Logistic Regression

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  1. Gerrit Rooks 22-02-10 AMMBR III

  2. Description of data Variable Description Codes/Values Name 1 Identification Code ID Number ID 2 Birth Number 1-4 BIRTH 3 Smoking Status 0 = No, 1 = Yes SMOKE During Pregnancy 4 Race 1 = White, 2 = Black RACE 3 = Other 5 Age of Mother Years AGE 6 Weight of Mother at Pounds LWT Last Menstrual Period 7 Birth Weight Grams BWT 8 Low Birth Weight 1 = BWT <=2500g, LOW 0 = BWT >2500g

  3. Summary of the data

  4. Estimate the coefficients • Assess model fit • Interpret coefficients • Check regression assumptions Logistics of logistic regression

  5. Empty model

  6. Classificationtableempty model

  7. Full model

  8. Estimate the coefficients • Assess model fit • Interpret coefficients • Check regression assumptions Logistics of logistic regression

  9. Model fit: Likelihood Ratio test

  10. Classificationtable full model

  11. Hosmer & Lemeshow test

  12. Estimate the coefficients • Assess model fit • Interpret coefficients • Check regression assumptions Logistics of logistic regression

  13. Significance and direction

  14. Magnitude (Exponentiatedcoefficienti- 1.0) * 100 = 125 -> a smoker has 125% higher odds of have a lowweight baby.

  15. Isolatepointsforwhich the model fits poorly Isolateinfluential data points Examiningresiduals in lr

  16. Residualstatistics

  17. Samanthas tips In stataafterestimation of the model the predictcommandcan beused to calculateresiduals etc. Type help logitpostestimationfor details

  18. Predictedprobabilities

  19. Histogram of standardizedresiduals

  20. Standardizedresidual

  21. Index plot st. residuals

  22. Cooksdistance . predict cook, dbeta

  23. Index plot Cooksdistance

  24. Field recommendsobtaining VIF byusing a OLS regression to estimate the same model Checking the correlation matrix of the independent variables is oftenenough. Ifyoufind high correlations (say >.6), then check VIFs Multi-collinearity

  25. Incomplete information Complete seperation Finally 2 causesfortrouble

  26. Incomplete information

  27. Complete separation

  28. Complete separation

  29. Open ammbr.dta Analyse entrepreneurship Practical

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