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The major topics I learned during this course were correlation and regression techniques.

Correlation and linear regression are the most often used approaches for examining the relationship between two quantitative variables. The strength of a linear link between two variables is measured by correlation, whereas regression expresses the relationship as an equation. For example, in patients presenting to an emergency department (ED), we may utilize correlation and regression to see if there is a link between age and urea level, and if urea level can be predicted for a given age.

The key distinction between correlation and regression is that correlation evaluates the degree of a relationship between two variables, let's say x and y. In this case, correlation is a parameter for determining how one variable influence another, whereas regression is a parameter for determining how one variable affects another.

Both measure the degree and direction of a link between two numeric variables. The regression slope (b) will be negative if the correlation (r) is negative. The regression slope will be positive if the correlation is positive.

Business leaders can use correlation and regression analysis to make more accurate forecasts based on data patterns. This strategy can assist lead corporate processes, direction, and performance in the right direction, resulting in better management, customer experience strategies, and operations.

Linear regression is frequently used by health experts to better understand the association between drug dosage and patient blood pressure.

As a clinical coordinator in the medical industry, these two methods can assist me in better understanding any procedure that we need to build or update in our system for our patients.

A physician, for example, might give patients different doses of a medicine and see how their blood pressure responds. They might use dosage as the predictor variable and blood pressure as the response variable in a simple linear regression model.