Please import the resilience dataset into R by the R command below. resilience <- read.csv("https://raw.githubusercontent.com/njlytal/MATH315/main/resilience.csv") This is survey data collected from 1,350 students across 25 medical schools in the United States as part of a study examining the life of students and residents in healthcare professions. Variable names and the descriptions of the dataset are as follows: 1. dreem: Perception of the educational environment was assessed via the DREEM questionnaire; possible scores range from 0 to 200, with higher scores representing a more positive perception about the educational environment. Sample questions include "I feel I am being well prepared for my profession" and "The atmosphere motivates me as a learner". 2. train: The first two years of medical school are focused on basic science education (pre-clinical curriculum) and the last two years consist of rotations in clinical settings (clinical curriculum). After medical school, students undergo residency training in which they work as practicing physicians under the supervision of a senior clinician (residence training). 3. age: age in years 4. sex: either female or male 5. res: resilience level, either VeryHigh, High, ModHigh, ModLow, Low, or VeryLow. 6. bdi: Measure of the depression symptoms. The BDI questionnaire was used to assess depressive symptoms. Possible scores vary from 0 to 63, with higher scores indicating either more numerous or more severe depressive symptoms. First, reorder the levels of the variables called res and train by running the R commands below: resilience$res <- factor(resilience $res, levels=c("VeryLow", "Low", "ModLow", "ModHigh", "High", "VeryHigh")) resilience$train <- factor(resilienceStrain, levels = c("pre-clinical", "clinical", "residency")) Next, please fit a multiple linear regression model in R to estimate the association between training level (train) and perception of the educational environment (dreem), after adjusting for age, sex, resilience level (res), and BDI score (bdi). After you fit the model, please report the predicted linear regression model for perception of the educational environment by filling the blanks below (Note: please keep four decimal places as shown in the R output): hat(y) = *trainclinical- 11.9195*resModHigh + 19.5385*res High +27.8120*resVeryHigh - 1.0004*bdi *trainresidency - 0.4642*age + 0.4398*sexmale + 1.7925*resLow +5.7776*resModLow + We can conclude that when accounting for the other factors, the training level is significantly associated with the perception of the educational environment (p <0.01); on average, individuals with higher training levels tended to have more (hint: positive/negative) perceptions of their educational environment after adjusting for age, sex, resilience level (res), and BDI score (bdi). The proportion of the variance of the educational environment perception explained by the multiple linear regression model is (please keep four decimal places).

Please import the resilience dataset into R by the R command below. resilience <- read.csv("https://raw.githubusercontent.com/njlytal/MATH315/main/resilience.csv") This is survey data collected from 1,350 students across 25 medical schools in the United States as part of a study examining the life of students and residents in healthcare professions. Variable names and the descriptions of the dataset are as follows: 1. dreem: Perception of the educational environment was assessed via the DREEM questionnaire; possible scores range from 0 to 200, with higher scores representing a more positive perception about the educational environment. Sample questions include "I feel I am being well prepared for my profession" and "The atmosphere motivates me as a learner". 2. train: The first two years of medical school are focused on basic science education (pre-clinical curriculum) and the last two years consist of rotations in clinical settings (clinical curriculum). After medical school, students undergo residency training in which they work as practicing physicians under the supervision of a senior clinician (residence training). 3. age: age in years 4. sex: either female or male 5. res: resilience level, either VeryHigh, High, ModHigh, ModLow, Low, or VeryLow. 6. bdi: Measure of the depression symptoms. The BDI questionnaire was used to assess depressive symptoms. Possible scores vary from 0 to 63, with higher scores indicating either more numerous or more severe depressive symptoms. First, reorder the levels of the variables called res and train by running the R commands below: resilience$res <- factor(resilience $res, levels=c("VeryLow", "Low", "ModLow", "ModHigh", "High", "VeryHigh")) resilience$train <- factor(resilienceStrain, levels = c("pre-clinical", "clinical", "residency")) Next, please fit a multiple linear regression model in R to estimate the association between training level (train) and perception of the educational environment (dreem), after adjusting for age, sex, resilience level (res), and BDI score (bdi). After you fit the model, please report the predicted linear regression model for perception of the educational environment by filling the blanks below (Note: please keep four decimal places as shown in the R output): hat(y) = trainclinical- 11.9195resModHigh + 19.5385res High +27.8120resVeryHigh - 1.0004bdi trainresidency - 0.4642age + 0.4398sexmale + 1.7925resLow +5.7776resModLow + We can conclude that when accounting for the other factors, the training level is significantly associated with the perception of the educational environment (p <0.01); on average, individuals with higher training levels tended to have more (hint: positive/negative) perceptions of their educational environment after adjusting for age, sex, resilience level (res), and BDI score (bdi). The proportion of the variance of the educational environment perception explained by the multiple linear regression model is (please keep four decimal places).

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

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 2AGP

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