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Correlation and regression are used in research heavily for determining the relationship between variables. Many people think the two terms to be the same though they are different. The difference between the two analyses lies mainly in the objective.

Correlation analysis helps you know the relationship between variables and regression analysis depicts the value of a dependent variable depending on the independent variables’ value. Students pursuing Statistics often face difficulty in this topic and thus they look for Correlation and Regression assignment help from BookMyEssay.

## What are Correlation and Regression?

Correlation and Regression are the two methods of analysis that are based on multivariate distribution. Correlation is an analysis that helps you know the absence of the association of the relationship between x and y two variables. Regression analysis, on the other hand, predicts the value of a dependent variable depending on the known value of an independent variable.

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Regression is used for examining the relationship between a dependent and an independent variable. Regression analysis is used for predicting the dependent variable if you know the independent variable. Correlation analysis is measuring the relationship between the two variables. These variables are not independent or dependent. The popular Correlation coefficients include Pearson’s correlation coefficient and Spearman’s correlation coefficient.

We have hired qualified statisticians who can offer the best Statistics assignment writing assistance and guide you properly by solving complicated problems and provide Correlation and Regression assignment help.

### Correlation and Regression are not the Same

There are a few differences between Correlation and regression that are explained in our Correlation and Regression case study assignment help as follows:

• Correlation is a statistical measure that determines the association or relationship of two quantities. Regression describes the manner an independent variable is related numerically to a dependent variable.
• Correlation represents the linear relationship between the two variables. Regression, on the other hand, is used to estimate a variable and for fitting the best line on the basis of some other variable.
• There is not any difference between independent and dependent variables in correlation; the correlation between y and x is similar to x and y. However, the regression of x on y is different from y on x.
• Correlations say about the strength of the relationship between variables. Regression indicates the impact of the change in the unit of an independent variable on a dependent variable.
• Correlation targets to find a numerical value, which expresses the relationship between the two variables. In regression, the objective is predicting the values of a random variable depending on the value of a fixed variable.

### Uses of Correlation and Regression

Correlation and regression are commonly used techniques used to investigate the relationship between the two methods. The objective of correlation analysis is seeing whether two variables differ and for quantifying the strength between the relationship of two variables. Regression explains the relationship in the equation form.

For instance, students who take English as well as a Maths test, correlation is used for deciding whether students who are good at English are good at Maths too. Regression is used to decide whether English marks may be used for predicting marks in Maths.

Our Correlation and Regression essay writers online have explained their three important uses as follows:

• Used for testing hypotheses regarding the cause and effect relationships. In this experiment, the value of x-variable is determined and notice whether a variation in X results in variation in Y.
• See whether there is a relationship between two variables without a cause and effect relationship. Here, no variables are determined, both are variable naturally.
• The third important use is estimating the value of a variable that corresponds to the value of another variable.

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