Correlation and Regression

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.

Correlation and Regression

Correlation and Regression

Use the Education Resources Information Center (ERIC) (Links to an external site.) to find an
article that meets the following criteria.
Peer-reviewed.
Full text available on ERIC.
Contains the words “Correlation” or “Regression.”
Download the full-text article and write a four-paragraph (maximum) critique of the article. Follow
the guidelines in the Writing Article Critiques page.

Do you believe the results? Why or why not?
Reporting results Hint: Generally, descriptive and inferential statistics are reported to two decimal
places, i.e., t = 0.12, r = 0.12, etc. The exception is probability which is typically reported to four
decimal places, i.e., p = 0.1234.
State the reported Correlation and/or Regression Equation in two ways:
If Correlation only, calculate or report the value of the Correlation Coefficient and Coefficient of
Determination; explain what these statistics mean to the reader.
If Regression equation only, report the equation and associated units; also, calculate or report a
predicted value; explain what this value means to the reader.
State the definitions and units of measure of all of the variables reported.
Are the assumptions required to correctly use either the Correlation statistic and/or the
Regression equation reported and/or valid?
Can the reported mean of your article data variables be treated as a value from a population
having a normal distribution? Why or why not?
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required critique elements.
This criterion is linked to a Learning OutcomeWriting Skill
Sentences are clear, and wording is unambiguous. Correct word choice, correct spelling, and
correct grammar. Writing style can still be conversational rather than formal

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