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Assess and explain whether any of the violations are present and how they may be corrected in a new regression model. After estimating a corrected

Assess and explain whether any of the violations are present and how they may be corrected in a new regression model. After estimating a corrected model, report the final results and make recommendations for policies that your organization could promote to increase female life expectancy.

Pearson's Partial Correlations
Variableurbangdpradiophonehospbeddocs
1. urbanPearson's r
p-value
2. gdpPearson's r0.271
p-value0.003
3. radioPearson's r0.2380.473
p-value0.009<.001>
4. phonePearson's r0.2610.9030.388
p-value0.005<.001><.001>
5. hospbedPearson's r0.1160.4000.2270.418
p-value0.217<.001>0.015<.001>
6. docsPearson's r0.3110.4280.1840.4990.579
p-value<.001><.001>0.044<.001><.001>
Note. Conditioned on variables: lifeexpf.

image text in transcribed
Percent urban GDP Per capita Radios per thousand people Phones per thousand people>.>/ Female life expectancy Hospital beds per 10,000 people Doctors per 10,000 people

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