Which chart is typically best for showing the distribution of a numeric variable like order revenue?

The health care provider ordered Diazepam (Valium) 8.5 mg Oral Solution q8h.  Pharmacy has available 25 mg per 5mL (5mg/mL).  How many milliliters will the nurse administer per dose? (round answer to the nearest tenth, if applicable) [BLANK-1]

A university administrator argues:“Over the past five years, departments that increased their use of online instruction have seen higher student enrollment. Additionally, surveys indicate that a majority of students prefer flexible learning options. Since our department has recently expanded its online offerings, we can expect our enrollment to increase as well. Therefore, increasing online instruction is a reliable way to boost enrollment.” Read and analyzing this passage, selecting ALL correct, applicable answers

In churn prediction, if churners are rare and you always predict ‘no churn’, accuracy will likely be:

Scenario C: Decision Trees and EnsemblesYou train a decision tree classifier for churn with different maximum depths.You observe the following test performance: Depth 2: Accuracy 0.78, Recall(churn) 0.30 Depth 6: Accuracy 0.82, Recall(churn) 0.40 Depth 20: Accuracy 0.80, Recall(churn) 0.28 Which depth most strongly suggests overfitting?

Scenario D: Revenue Prediction (Regression)A business predicts weekly revenue using features like ad_spend, number_of_customers, and average_discount.Two models are evaluated on a held-out test set: Model A: R² = 0.62, RMSE = 18,000 Model B: R² = 0.58, RMSE = 16,000 Lower RMSE is better. Higher R² is better.If the business cares about minimizing typical dollar error in forecasts, they should prefer:

Scenario B: Customer Churn Classification A subscription business wants to predict whether a customer will churn (cancel) next month. Target: churn (1 = churned, 0 = stayed). The business cares more about catching likely churners than about occasionally flagging a loyal customer. Which metric best aligns with the goal of catching churners?

You want to standardize the variables in df[features] before running a distance-based clustering method. Which code does that correctly?

Suppose this line is run:labels = KMeans(n_clusters=3, random_state=0, n_init=10).fit_predict(X_scaled)What is stored in labels?

According to G. E. Moore, there is no difference between ethical and natural properties.