New York University Wk 6 Linear Regression Discussion IMPORTANT NOTE REGARDING WORD LIMIT REQUIREMENTS: Please note that each and every assignment has its

New York University Wk 6 Linear Regression Discussion IMPORTANT NOTE REGARDING WORD LIMIT REQUIREMENTS:

Please note that each and every assignment has its own word limit.

Linear regression is a type of predictive analysis that uses one or more predictor variables to predict cases’ scores on an outcome variable, which uses a linear equation to assess the relationship between two variables. There are two types of linear regression such as multiple linear regression and single linear regression. Multiple linear regressions are predictions that are based on multiple predictor variables being combined to predict an outcome variable and simple linear regressions are predictions that are based on the “Y” factor is predicted from a single predictor variable (Corty, 2016).

A strength of linear regression is the ability to predict the outcome of a variable by using a regression line for example, if a person has X levels of bipolar disorder, what will be their level of bipolar after 8 weeks of medical intervention; the outcome is the Y variable. When the regression line is calculated the sloped will show the relationship between Y and X. For instance, if the slope is positive, then there is a direct positive relationship where when one variable increases then the other variable will also increase, but if the slope is negative, then when X is increased, the outcome Y will decrease, this is an inverse relationship.

A peer-reviewed study that used linear regression in its analysis is called “improving the prediction of total surgical procedure time using linear regression modeling,” by Edelman et al. The model was used to improve the accuracy of prediction for the total prediction time (TPT) based on estimated surgeon-controlled time (eSCT) and other variables, such as types of operation, eSCT, American Society of Anesthesiologist, and type of anesthesia used, etc. The outcomes demonstrated that using the linear regression model improved the accuracy of predicting TPT and other variables compared to using a fixed ratio model. Additionally, the article’s findings suggested that because of the improved accuracy through the linear regression’s “planning and sequencing algorithm, [it] may enable an increase in the utilization of ORs, leading to significant financial and productivity-related benefits.” There was no challenge in me understanding the linear regression results (Edelman et al., 2017).

Reference

Corty, E. (2016). Using and interpreting statistics: A practical text for the behavioral, social, and health sciences (3rd ed.). New York, NY: Macmillan Learning.

Edelman, E. R., van Kuijk, S., Hamaekers, A., de Korte, M., van Merode, G. G., & Buhre, W. (2017). Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling. Frontiers in medicine, (4)85. https://doi.org/10.3389/fmed.2017.00085

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