Importance Of Correlation In Health Care
Correlation is a statistical technique used by statistics experts and researchers to determine if and to what extent are two variables related in a research. For instance, being in a relationship is correlated to being married. In this care correlation technique will determine if indeed being in a relationship has any relationship to being married. In this case it will also determine how likely or unlikely a person in a relationship will marry. Covariance on the other hand measures to what extent a change in on variable is associated with a change in another variable. For instance, what expenditure increase in a household can be associated with increase in household income? Therefore, for correlation is to measure if there is a relationship
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between two variables and if it is there to what extent. On the other hand, covariance goes beyond establishing if there is a relationship to establish the degree of change in one variable that is associated with change in the other variable.
The two variables I have chosen are the subsidization of health care services by the government and the number of people turning up in hospitals.
When government subsidizes health care, it removes the burden of cost and therefore those who could not afford troop to hospitals. In this case as the cost of health care falls due to subsidization, the number of people going to hospital increases. Therefore, I expect the relationship to be inverse since one variable increases with reduction of the other.
Health care subsidization by the government-this refers to the government support of health care through covering some of the costs incurred in provision of health care services. It covers public health facilities and it is aimed at helping the poor people who are deemed incapable of affording basic health care.
Number of people going to hospital-this refers to the total number of people going to hospital at any particular time
For this exercise I will use Excel program, which is a more accurate tool for finding correlation and covariance between two sets of data. It is also convenient in that it can produce images such as graphs to demonstrate the relationship between the two variables. The program is also readily available to me unlike R and SPSS software, which are very hard to
get.
between two variables and if it is there to what extent. On the other hand, covariance goes beyond establishing if there is a relationship to establish the degree of change in one variable that is associated with change in the other variable.
The two variables I have chosen are the subsidization of health care services by the government and the number of people turning up in hospitals.
When government subsidizes health care, it removes the burden of cost and therefore those who could not afford troop to hospitals. In this case as the cost of health care falls due to subsidization, the number of people going to hospital increases. Therefore, I expect the relationship to be inverse since one variable increases with reduction of the other.
Health care subsidization by the government-this refers to the government support of health care through covering some of the costs incurred in provision of health care services. It covers public health facilities and it is aimed at helping the poor people who are deemed incapable of affording basic health care.
Number of people going to hospital-this refers to the total number of people going to hospital at any particular time
For this exercise I will use Excel program, which is a more accurate tool for finding correlation and covariance between two sets of data. It is also convenient in that it can produce images such as graphs to demonstrate the relationship between the two variables. The program is also readily available to me unlike R and SPSS software, which are very hard to
get.