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# Working with Correlated Variables

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Determine whether the variable hours worked per week by an individual are correlated to the variable of the individual's satisfaction with his or her job. Be sure to use the proper df and show all the steps of the calculations. Also include an explanation of how the critical value was determined. Be sure to interpret the results in light of the hypothesis that the number of hours worked is related to the satisfaction of how an individual feels about his or her job.

Please view the data in the attached file.

Add two extreme outliers of your choice to each of the variables (i.e., for the number of hours worked and satisfaction with one's job, add two values that are much higher than the highest value you obtained). Recalculate the correlation coefficient and determine whether the variable hours worked per week is still correlated with the variable of satisfaction with the job. Be sure to use the proper df and show all the steps for the calculations, including the determination of the critical value. Interpret the new results in light of the hypothesis. Also, interpret the effect of outliers on correlation coefficients.

Imagine you have the same data (mean, standard deviation, etc.) that you originally collected, except you have a much larger sample size of one thousand. Explain what happens to the magnitude of the correlation, and why that would be important if there are outliers in the data.

https://brainmass.com/statistics/descriptive-statistics/working-with-correlated-variables-550639

#### Solution Summary

This solution is comprised of a detailed explanation of correlation regression analysis. This solution mainly discussed how the correlation regression analysis will be performed on the question. The solution provides the description of correlation analysis excluding outliers.

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## Test whether age is a variable between education and hours worked

However, here is the question:

Provide your findings of this analysis as you would in a research report.
Be specific in your analysis - start from the larger portion of the analysis and move to specifics. Restate the RQ; provide descriptive analysis (not needed unless you consider there is a key point here); ANOVA (fit); correlation analysis (could variables co-vary excessively); R-Square; coefficients - the key is to answer the question: is age a spurious variable and influence the relationship between education and hours worked?

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