The Importance Of Correlation In Research

Significance: With the knowledge of correlation analysis, we can measure in one figure the degree of relationship existing between heights and weight, income and expenditure, demand and supply etc. If the variables are closely related, we can equally finds the value of one variable provided the value of another variable is given. Correlation helps in the understanding of behavior and natural events.

Application in the real world: Correlation is basically use when quantifying how one variable affects another; it is also used for extracting second and higher order statistics from any random signal. In research, correlation is the only promising method to find significant relationships among alerts that have been triggered by multiple intrusion

Higher nutrition knowledge with healthy dietary behavior in our clients is mostly associated with some factors like: contact and frequency of contact with dietitians and nutritionists, clients’ field of study especially nutrition-related qualifications, female gender, being or not being on a diet and health status of the client.

5) Univariate regression: This is the simplest regression analysis between two variables with an independent (explanatory) and a dependent (response) variable. Where a linear relationship exists between independent and dependent variables, the general equation is presented as: Wi = α + βXi + ϵi.

Significance: Univariate regression is an important biostatistical method for the analysis of data in the medical field. It is use for the characterization and identification of relationships existing among multiple

For example, an individual would like to know not only whether patients have hypertension, but having hypertension influenced by factors such as weight and age. The dependent variable is the variable to be explained alternatively, while the variable (weight, age) explaining it is the independent or predictor variable. Measures of association give an initial impression of the level of statistical dependence between variables. If the variables are continuous, as in the case for hypertension and weight, then a measure of the strength of the relationship between them can be calculated as correlation coefficient. Regression analysis uses a model that explains the relationships existing between the dependent and the independent variables in a simplified statistical form. The regression coefficient gives a measure of the contribution of the independent variable toward describing the dependent