5. Write the new regression equation. 6. How would you characterize the magnitud
ID: 3203826 • Letter: 5
Question
5. Write the new regression equation.
6. How would you characterize the magnitude of the obtained R 2 value? Provide a rationale for your answer.
7. How much variance in months to RN to BSN program completion is explained by knowing the student’s enrollment age?
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. HomoscedasticityExplanation / Answer
5. The simple linear regression equation is represented as: ycap=bx+a, where, a is y intercept, b is slope, y is dependent variable, and x is independent variable.
Given, b=-2.9 and a=16.25
Therefore, the new regression equation is as follows:
y=-2.9x+16.25 (ans)
6. According to Cohen, a R^2 values are categorized as follows:
0.02-small, 0.15-moderate, 0.26 or higher-large.
Therefore, a R^2 value of 0.407 is considered to have large effect.
7. The R^2 value multiplied with 100 gives the percent amount of variation in Y explained by variation in X. Here, around 40.7% variation in months to RN to BSN program is explained by knowing the student's enrollment age.
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