Explain using supportive arguments from your analyses. Write a general summary paragraph identifying groups lacking in coverage by gender, age and income and commenting on the headline. Reference the observed numbers and percentages in your summary paragraph to see how the groups trend in identifying potentially more at-risk.
In 2010, the U.S. Congress passed the historic health care reform bill that will provide some type of coverage for the 32 million Americans currently without health care insurance. Just how widespread is the lack of medical coverage? The media claim that the segments of the population most at risk for disease and thus needing healthcare are women, children, the elderly and the poor. The following tables were generated from the U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplement (2011).
- Explain using supportive arguments from your analyses. Write a general summary paragraph identifying groups lacking in coverage by gender, age and income and commenting on the headline. Reference the observed numbers and percentages in your summary paragraph to see how the groups trend in identifying potentially more at-risk.
Analysises for reference:
Is being insured or not dependent on gender? Fill in the degrees of freedom and p-value in the output below and draw a clear conclusion indicating which gender is more at risk of not being insured if there is a significant difference.
DF: 1
Value: 307.40941
P-value: 0.000000
H0: There is no significant relationship between being insured and gender.
Ha: There is a significant relationship between being insured and gender.
p-value < alpha
Because the p-value is smaller than alpha (0.05), we reject the null hypothesis. We can safely assume that there is a significant relationship between being insured and gender. Males are more at risk of not being insured.
Is being insured or not dependent on the age groups identified in the analysis? Fill in the degrees of freedom and p-value in the output below and draw a clear conclusion indicating which age-groups are more at risk of not being insured if there is a significant relationship.
DF: 4
Value: 8776
P-value: 0.000001
H0: There is no significant relationship between being insured and the age group.
Ha: There is a significant relationship between being insured and the age group.
p-value < alpha
Because the p-value is smaller than alpha (0.05), we reject the null hypothesis. We can safely assume that there is a significant relationship between being insured and the age group. Age group 25-44 are more at risk of not being insured. Followed by 0-17, 45-64, 18-24 and then 65-80.
Is being insured or not dependent on the income groups identified in the analysis? Fill in the degrees of freedom and p-value in the output below and draw a clear conclusion indicating which income-groups are more at risk of not being insured if there is a significant relationship.
DF: 3
Value: 15274.268
P-value: 0.000000
H0: There is no significant relationship between being insured and the income group.
Ha: There is a significant relationship between being insured and the income group.
Because the p-value is smaller than alpha (0.05), we reject the null hypothesis. We can safely assume that there is a significant relationship between being insured and the income group. The higher income groups are more at risk of not being insured.
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