(Select all that apply) The includes which interventions for SIADH?

Ultrasound is better at heating deep tissue with a high collagen content. Which of the following tissues contain high levels of collagen? 1. tendons and ligaments 2. bones 3. skin 4. cartilage 5. All of the above

A child weighs 33 lb and has an order for cephalexin 100 mg/kg/day PO divided q8h.Available: Cephalexin 250 mg per 5 mL.How many milliliters will you administer per dose?

Calculate the dosage and indicate the number of milliliters you will administer. Round to the nearest tenth. Order: ketorolac 25 mg IM q6h PRN for pain.  Available:

Calculate the dosage and indicate the number of milliliters you will administer. Round to the nearest tenth. Order: Tobramycin 50 mg IM q8h Available:

Calculate the dosage and indicate the number of milliliters you will administer. Round to the nearest tenth. Order: dexamethasone 9 mg IV daily for 4 days Available:

Order: Penicillin V Potassium 125 mg PO q6h for a child weighing 36 lb. The recommended dose PO for a child is 25-75 mg/kg/day divided q6-8hr. Available: Penicillin V Potassium oral solution 250 mg per 5 mL Determine if the dose ordered is safe. If the dose is safe, calculate the number of mL the nurse should administer.  Is the dose safe, type yes or no . The nurse should administer mL.

A client weighs 140.8 lb. The recommended initial dose of diltiazem is 0.25 mg/kg as a bolus. How many milliliters is the initial dose for this client? 

Midterm Exam 2 – Open Book Section – Part 2 Instructions The R/Python Jupyter Notebook files include the questions, the empty code chunk sections for your code, and the text blocks for your responses. Answer the questions below by completing the R/Python Jupyter Notebook file. You may make slight adjustments to get the file to convert but otherwise keep the formatting the same. Once you’ve finished answering the questions, submit the PDF file of the exam to Gradescope. Please use the corresponding Gradescope link for the submission. The Gradescope link expires after 10 minutes, so make sure you submit PDF to Gradescope within 10 minutes! Resubmission within the 10-minute window is allowed. Data Analysis (60 points) Data Set online_course_completion.csv

Q2 Logistic Regression model (Use trainData for this question) (30 points) ( 5 points) a) Create a logistic regression model using Completed as the response variable and the following predicitng variables:  Hours_Studied, Age, Region. Call it *model1*. Display the summary. Using “model1”, interpret the coefficients of the following predictors below with respect to BOTH the log-odds of turnover and the odds of completed. 1) Hours_Studied 2) RegionWest (5 points)  b) Using the “trainData” dataset, create a logistic regression model using Completed as response variable and all variables in “trainData” as predictors (call it model2) and display the summary of model2. Using the model coefficients, how would you compute the predicted probability of completion for a 30-year-old male from the South enrolled in the free course who studied 10 hours and submitted 5 assignments? (10 points) c) This dataset is without replications. Aggregate the response data to convert it into binary data with replications. (Use categorical variables only)Fit the following logistic models:i) Using aggregated data. Call it model.agg. Display the summaryii) Using the non-aggregated data. Call it model.withoutagg. Display the summary. (Use the same categorical variables as in model.agg) What is the difference between the coefficients of both the models? ExplainHow are the null and residual deviance different for both models? Explain (3 points) d) Perform a test for overall regression of the logistic regression “model.agg”, using a significance level of alpha=0.05. Does the overall regression have explanatory power? Provide interpretation of the test. (3 points) e) Perform the goodness of fit test on model.agg using deviance and Pearson residuals and interpret the results. (4 points) f) Evaluate the predicting power of the “model.withoutagg” in Q2c using cross validation.