What is the predicted length of stay for a 50-year-old male with no complicating diagnosis and no comorbidity (i.e., that also means no heart disease, diabetes, hypertension, obesity, peritonitis, or cancer), who had a 1-day pre-operative stay, and there were no complications during the surgery?Interpret the estimated parameters for pre-operative LOS and the presence of complications.What strikes you about (the values of) the estimated model parameters? What phenomenon may explain some of these puzzling results? To be able to better manage the length of stay (LOS) of patients undergoing laparoscopic appendectomy, clinical researchers built apredictive (regression) model. The estimated model parameters are summarized in the table below:ВSEp-valueIntercept7.5420.760<.001Pre-operative LOS0.9410.066<.001Presence of complications- 3.9490.573<.001Complicating diagnosis-0.863 0.234<.001Gender (Female vs. Male)-0.160 0.230 0.487Age0.0240.0070.001Presence of comorbidities0.7400.3460.033Heart disease0.2370.8710.786Diabetes-1.8610.9720.057Hypertension1.0530.5630.064Obesity-0.9110.9540.341-0.6490.856 0.449PeritonitisCancer- 1.998 1.480 0.1781. What is the predicted length of stay for a 50-year-old male with no complicating diagnosis and no comorbidity (i.e., that also means noheart disease, diabetes, hypertension, obesity, peritonitis, or cancer), who had a 1-day pre-operative stay, and there were nocomplications during the surgery?2. Interpret the estimated parameters for pre-operative LOS and the presence of complications.3. What strikes you about (the values of) the estimated model parameters? What phenomenon may explain some of these puzzlingresults?Variable

Given the output of the regression as shown below1. Age =50no presence of complications, thus the…

To be able to better manage the length of stay (LOS) of patients undergoing laparoscopic appendectomy, clinical researchers built a predictive (regression) model. The estimated model parameters are summarized in the table below: Variable Intercept Pre-operative LOS Presence of complications Complicating diagnosis Gender (Female vs. Male) Age Presence of comorbidities Heart disease Diabetes Hypertension Obesity Peritonitis B 7.542 0.941 SE p-value 0.760 <.001 0.066 <.001 -3.949 0.573 <.001 -0.863 0.234 <.001 -0.160 0.230 0.487 0.024 0.007 0.001 0.740 0.346 0.033 0.237 0.871 0.786 -1.861 0.972 0.057 1.053 0.563 0.064 -0.911 0.954 0.341 -0.649 0.856 0.449 -1.998 1.480 0.178 Cancer 1. What is the predicted length of stay for a 50-year-old male with no complicating diagnosis and no comorbidity (i.e., that also means no heart disease, diabetes, hypertension, obesity, peritonitis, or cancer), who had a 1-day pre-operative stay, and there were no complications during the surgery? 2. Interpret the estimated parameters for pre-operative LOS and the presence of complications. 3. What strikes you about (the values of) the estimated model parameters? What phenomenon may explain some of these puzzling results?

To be able to better manage the length of stay (LOS) of patients undergoing laparoscopic appendectomy, clinical researchers built a predictive (regression) model. The estimated model parameters are summarized in the table below: Variable Intercept Pre-operative LOS Presence of complications Complicating diagnosis Gender (Female vs. Male) Age Presence of comorbidities Heart disease Diabetes Hypertension Obesity Peritonitis B 7.542 0.941 SE p-value 0.760 <.001 0.066 <.001 -3.949 0.573 <.001 -0.863 0.234 <.001 -0.160 0.230 0.487 0.024 0.007 0.001 0.740 0.346 0.033 0.237 0.871 0.786 -1.861 0.972 0.057 1.053 0.563 0.064 -0.911 0.954 0.341 -0.649 0.856 0.449 -1.998 1.480 0.178 Cancer 1. What is the predicted length of stay for a 50-year-old male with no complicating diagnosis and no comorbidity (i.e., that also means no heart disease, diabetes, hypertension, obesity, peritonitis, or cancer), who had a 1-day pre-operative stay, and there were no complications during the surgery? 2. Interpret the estimated parameters for pre-operative LOS and the presence of complications. 3. What strikes you about (the values of) the estimated model parameters? What phenomenon may explain some of these puzzling results?

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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