Correlation and RegressionCreate a new document that contains your solutions to the problems.Please use statcrunch.com account (provided), Also looking for someone who can do all my projects and term paper for this class for the next four weeksCorrelation and RegressionA survey was carried out to find the salaries for technicians working in maintenanceat Atlanta International Airport. The number of years spent in education wasrecorded as was their yearly salary. Below in the table are the results for this survey.Education (years) Income ($ ,000s)8 2012 2810 2720 5516 4518 6013 2912 2516 3714 32a) Describe in words and statistical terms the relationship between the number ofyears in education and corresponding salary earned. Discuss the results in terms ofthis sample and the population in general. (50 points)b) If William has 16 years in education what would be the expected yearly salary?(25 points)c) If John only has 5 years of education what salary would he expect on average?(25 points)

Correlation and Regression

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Regression describes a statistical technique that helps to measure the association between a dependent variable and independent variable (s). Given dependent and independent variables, regression measures the direction and rate of change of one variable to the other. Therefore, through regression, it is possible to predict the value of dependent variable using the independent variables; the predictor and criterion variables help in determining the cause and effect association. On the other hand, correlation measures the closeness of the association between variables. It indicates the degree of association between variables (Jain, 2009).

The two statistical methods are similar in that they measure relationship aspects amid variables. Regression measures the direction of an association between variables while correlation measures the degree of association between variables (Jain, 2009). Besides, the two are similar in that they make use of available variables; without the use of these variables, the statistical methods cannot come up with the relationship aspects between variables.

On the other hand, there are some differences between the two statistical approaches. In regression, there must exist an independent variable and dependent variable (s) while in determining correlation, there is no need of having dependent and independent variables; the analysis does not involve the use of fixed and dependent variables. Correlation does not assume cause and effect association between two variables always. Although variables may high correlation, it does not imply one variable is the effect while the other is the effect. However, regression always expresses cause and effect association amid two variables (Jain, 2009). Besides, regression helps in making prediction while correlation does not aid in making any prediction between variables. Therefore, regression analysis will be preferred to correlation in researches involving cause and effect relationships.

References

Jain, T.R. (2009). Quantitative Method for MBA. New York: FK Publications.