a) P(Person has health insurance) b) P(Person 55–64 has no health insurance) c) P(Person without health insurance is between 25 and 34 years old)
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
The issue of health care coverage in the United States is becoming a critical issue in
American politics. A large-scale study was undertaken to determine who is and is not
covered. From this study, the following table of joint probabilities was produced.
Age
Category
Has Health
Insurance
Does Not Have Health
Insurance
25–34 .167 .085
35–44 .209 .061
45–54 .225 .049
55–64 .177 .026
If one person is selected at random, find the following probabilities.
a) P(Person has health insurance)
b) P(Person 55–64 has no health insurance)
c) P(Person without health insurance is between 25 and 34 years old)
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