BST612 Multiple Regression Analysis Paper Editing Services

BST612 Multiple Regression Analysis Assignments Solution

BST612 Multiple Regression Analysis Paper Editing Services


1. The variables have constant means.
2. The variables are independent of each other.
3. The variables are normally distributed.
4. Null hypothesis: Has total number parameters constant
5. Alternative hypothesis: Not all its parameters are constant

From the multiple linear regression results, it is observed that student sleep latency and student sleep length correlates strongly with the student academic communication performance at -0.562 and 0.870 correlation values which are greater than 3 showing strong correlation (Misawa, 2012). On the other hand, student’s sleep irregularity does not contribute much to student’s academic performances at a correlation value of 0.120 which is less than 3 indicating weak correlation. Moreover, tolerance value is less than 1 indicating that there is no multi-colinearity again; the variance inflation factor indicates that there is no multi-co linearity. This proves the assumption that the variables in the model are independent of each other. Moreover, the correlation coefficient of the model indicates that the predictor variables contribute about 79.8% of the student’s academic performance (DeBerard, 2004).

(F = 40.884, df 1= 3 df 2= 31 p- value < 0.001) the p-value in the model is less than 0.001 indicating that the model is significant in predicting how different sleepsbehaviors affect the student’s academic performance (Sullivan, 2012). Student sleep length contributes greatly to the student’s academic performance, (Lund, 2010), with a beta coefficient of a standard value of 0.768 while student sleep irregularity does not greatly contribute to the prediction of the outcome with a beta coefficient of 0.055 (Akers, 2017).  Sleep onset latency and sleep length plays a unique role in predicting the how sleep behaviors affect student’s academic performance while sleep irregularity does not from the significant results of the predictor variables in the model (Gomes, 2011).


1. Misawa, T., Nakamura, K., &Imada, M. (2012). Ab initio evidence for strong correlation associated with Mott proximity in iron-based superconductors. Physical review letters108(17), 177007
2. DeBerard, M. S., Spielmans, G., &Julka, D. (2004). Predictors of academic achievement and retention among college freshmen: A longitudinal study. College student journal38(1), 66-80.
3. Sullivan, G. M., &Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of graduate medical education4(3), 279-282.
4. Lund, H. G., Reider, B. D., Whiting, A. B., & Prichard, J. R. (2010). Sleep patterns and predictors of disturbed sleep in a large population of college students. Journal of adolescent health46(2), 124-132.
5. Akers, R. (2017). Social learning and social structure: A general theory of crime and deviance. Routledge.
6. Gomes, A. A., Tavares, J., & de Azevedo, M. H. P. (2011). Sleep and academicperformance managementin undergraduates: a multi-measure, multi-predictor approach. Chronobiology International28(9), 786-801.
7. Elbadrawy, A., Studham, R. S., &Karypis, G. (2015, March). Collaborative multi-regression models for predicting students' performance in course activities. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 103-107). ACM.
8. Duan, L., Niu, D., &Gu, Z. (2008, December). Long and medium term power load forecasting with multi-level recursive regression analysis. In Intelligent Information Technology Application, 2008. IITA'08. Second International Symposium on (Vol. 1, pp. 514-518). IEEE