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Logistic Regression Analysis of ER Website Survey Data - Prof. Valente, Assignments of Geriatrics

University of Southern California (USC)Geriatrics
Prof. Valente

The results of a logistic regression analysis conducted on data collected from an er website survey. The analysis examines the relationship between various demographic and behavioral variables and the likelihood of respondents having certain attitudes towards brca and er. Descriptive statistics, logit estimates, and factor analysis results.

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Pre 2010

Uploaded on 11/08/2009

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Class Exercise on Analysis
PM 536
December 6, 2020
Attached are data collected from the ER website survey. Create a table reporting the results:
d dk10f dk10g age educ income married divorced nonusres q4b erexp
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
dk10f float %9.0g Dont Know if BRCA increases
risk of Breast Cancer
dk10g float %9.0g Dont Know if BRCA increases
risk Ovarian Cancer
age float %9.0g
educ float %9.0g
income float %9.0g
married float %9.0g
divorced float %9.0g
nonusres float %9.0g Non US Resident
q4b float %9.0g Freq watch ER
erexp float %9.0g Saw ER Episodes with BRCA
. summ dk10f dk10g age educ income married divorced nonusres q4b erexp
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
dk10f | 2216 .392148 .4883396 0 1
dk10g | 2216 .4720217 .4993293 0 1
age | 2108 27.49241 11.76165 2 89
educ | 1433 3.441731 1.120961 1 6
income | 1432 3.183659 1.459133 1 5
-------------+--------------------------------------------------------
married | 2216 .2572202 .4372004 0 1
divorced | 2216 .0293321 .1687738 0 1
nonusres | 2216 .1173285 .321884 0 1
q4b | 1664 3.823317 .5061589 1 4
erexp | 2216 .7197653 .9341706 0 2
.
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Download Logistic Regression Analysis of ER Website Survey Data - Prof. Valente and more Assignments Geriatrics in PDF only on Docsity!

Class Exercise on Analysis

PM 536

December 6, 2020

Attached are data collected from the ER website survey. Create a table reporting the results:

d dk10f dk10g age educ income married divorced nonusres q4b erexp storage display value variable name type format label variable label

dk10f float %9.0g Dont Know if BRCA increases risk of Breast Cancer dk10g float %9.0g Dont Know if BRCA increases risk Ovarian Cancer age float %9.0g educ float %9.0g income float %9.0g married float %9.0g divorced float %9.0g nonusres float %9.0g Non US Resident q4b float %9.0g Freq watch ER erexp float %9.0g Saw ER Episodes with BRCA

. summ dk10f dk10g age educ income married divorced nonusres q4b erexp Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- dk10f | 2216 .392148 .4883396 0 1 dk10g | 2216 .4720217 .4993293 0 1 age | 2108 27.49241 11.76165 2 89 educ | 1433 3.441731 1.120961 1 6 income | 1432 3.183659 1.459133 1 5 -------------+-------------------------------------------------------- married | 2216 .2572202 .4372004 0 1 divorced | 2216 .0293321 .1687738 0 1 nonusres | 2216 .1173285 .321884 0 1 q4b | 1664 3.823317 .5061589 1 4 erexp | 2216 .7197653 .9341706 0 2 .

logit dk10f age educ income married divorced nonusres q4b erexp, or

dk10f | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

dk10g | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

  • Iteration 0: log likelihood = -969.
  • Iteration 1: log likelihood = -948.
  • Iteration 2: log likelihood = -948.
  • Iteration 3: log likelihood = -948.
  • Logit estimates Number of obs = - LR chi2(8) = 42. - Prob > chi2 = 0.
  • Log likelihood = -948.31264 Pseudo R2 = 0. - age | .993919 .0057305 -1.06 0.290 .9827506 1. -------------+---------------------------------------------------------------- - educ | .7799899 .0394513 -4.91 0.000 .7063759. - income | 1.016505 .0383339 0.43 0.664 .9440814 1. - married | 1.24739 .1633818 1.69 0.091 .964968 1.
    • divorced | 1.049551 .2993863 0.17 0.865 .6000627 1.
    • nonusres | 1.31367 .1910244 1.88 0.061 .9878951 1. - q4b | 1.118596 .1218769 1.03 0.304 .9035043 1. - erexp | .8417274 .0488467 -2.97 0.003 .7512335.
  • Iteration 0: log likelihood = -871. logit dk10g age educ income married divorced nonusres q4b erexp, or
  • Iteration 1: log likelihood = -858.
  • Iteration 2: log likelihood = -858.
  • Iteration 3: log likelihood = -858.
  • Logit estimates Number of obs = - LR chi2(8) = 26. - Prob > chi2 = 0.
  • Log likelihood = -858.64756 Pseudo R2 = 0. - age | .9989216 .0061886 -0.17 0.862 .9868656 1. -------------+---------------------------------------------------------------- - educ | .8380596 .044852 -3.30 0.001 .7546047. - income | .9957346 .040021 -0.11 0.915 .9203049 1. - married | 1.422055 .201509 2.48 0.013 1.077206 1.
    • divorced | .7862202 .2303002 -0.82 0.412 .4428021 1.
    • nonusres | 1.267588 .1993882 1.51 0.132 .931293 1. - q4b | 1.203318 .1344325 1.66 0.098 .9666869 1. - erexp | .9964153 .0619712 -0.06 0.954 .882065 1.

. reg bc_attit age educ income married divorced nonusres q4b erexp, b Source | SS df MS Number of obs = 1415 -------------+------------------------------ F( 8, 1406) = 3. Model | 5.8785949 8 .734824363 Prob > F = 0. Residual | 325.345775 1406 .231398133 R-squared = 0. -------------+------------------------------ Adj R-squared = 0. Total | 331.22437 1414 .234246372 Root MSE =.

bc_attit | Coef. Std. Err. t P>|t| Beta

-------------+---------------------------------------------------------------- age | .0021815 .0013824 1.58 0.115. educ | .0229752 .0117631 1.95 0.051. income | -.0048845 .0088329 -0.55 0.580 -. married | .0397054 .0309081 1.28 0.199. divorced | -.0948746 .0684051 -1.39 0.166 -. nonusres | -.0744606 .0336326 -2.21 0.027 -. q4b | .057836 .0259538 2.23 0.026. erexp | .002593 .0136031 0.19 0.849. _cons | 3.112361 .1194823 26.05 0..