8. What was the correlation between the actual y values and the predicted y valu

Question

8. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

9. Write your interpretation of the results as you would in an APA-formatted journal.

10. Given the results of your analyses, would you use the calculated regression equation to predict future students ’ program completion time by using enrollment age as x ? Provide a rationale for your answer.

RESEARCH DESIGNS APPROPRIATE FOR SIMPLE LINEAR REGRESSION Research designs that may utilize simple linear regression include any associational design (Gliner et al., 2009). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent vari- able submitted to simple linear regression must be measured as continuous, at the inter- val or ratio level. STATISTICAL FORMULA AND ASSUMPTIONS Use of simple linear regression involves the following assumptions (Zar, 2010): 1. Normal distribution of the dependent ly) variable 2. Linear relationship between x and y 3. Independent observations 4. No (or little) multicollinearity 5. Homoscedasticity

Explanation / Answer

Result:

8. What was the correlation between the actual y values and the predicted y values using the new regression equation in the example?

Calculated R=-0.638 ( slope of regression is negative)

9. Write your interpretation of the results as you would in an APA-formatted journal.

The correlation between Number of degrees and Months to completion was significant, r(18) = .638, p < .05, supporting the claim that as Number of degrees decreases the Months to completion decreases.

10. Given the results of your analyses, would you use the calculated regression equation to predict future students ’ program completion time by using enrollment age as x ? Provide a rationale for your answer.

We can use the calculated regression equation to predict future students ’ program completion time by using enrollment age as x provided the future x value is in the range of given x values.

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