#### Linear regression and Correlation

Linear regression and Correlation

Linear Regression and Correlation are two important Statistical parameters which are utilized when there are two sets of data, which are somehow related to one another and for which further, the extent of relationship is established through the Linear Regression and Correlation Analysis. The graph has two variables, x and y, out of which one could be kept constant and with respect to the standard values of one variable, the other could be varied, for example, the age of participants could be kept standard, say 20 years could be chosen as the standard value, and the other variable, say their hear beat could be measured. This could be plotted, such that for the same value of one variable there would be varying values of another variable. In the second type, both the variables could be varied, say for example, in case of dosage of drug and blood pressure or blood sugar values in a patient. Here, though it is from the same person, there are two different variables involved.

What are difficulties encountered in linear regression and correlation problems?

The students should frame a good hypothesis which is called the null hypothesis before venturing on to carry out the research process. This hypothesis will either be rejected or accepted based on the basis of the Linear Regression and Correlation so values should be carefully handled and plotted. Mistakes might happen either in recording the values, or in plotting them against corresponding variables of the other set of data. Care should be taken by students to avoid mistakes while plotting the graph as values gone haphazard will ultimately affect the result. Also whether a significant relationship exists between two values should be determined, which is tedious for the students as they are novice to Linear Regression and Correlation. The value of the relationship between the two variables, if not accurately calculated by the students, would give incorrect significance values.

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