How would the force between the Earth and Sun change if the distance between them changed by a factor of `d`? Please read and follow my input directions below.   Input Instructions: Only type a numerical value rounded to the nearest whole number that represents by what factor the force will change in the given scenario.  For example, if the force would double, you would simply input the value 2.  Do not include any decimals, letters, words, E, or scientific notation in your answer.  You must match my answer exactly for credit. If something prevents you from typing in your answer here, just manually write your answer following the same rounding directions into the bonus section at the end of the exam. 

Refer to the given diagram, which shows consumption schedules for economies A and B. We can say that the

Why doesn’t the Fed want to drive nominal interest rates below zero in response to a financial crisis and recession?

In an economy, the government wants to increase aggregate demand by $50 billion at each price level to increase real GDP and reduce unemployment. If the MPS is 0.4, then it could increase government spending by

The multiplier is

With a marginal propensity to save of 0.4, the marginal propensity to consume will be

Which of the following fiscal policy changes would be the most contractionary?

True or False: Patients can be court-orders (given Jarvis Medication Orders) for Electroconvulsive Therapy (ECT).

Decision Tree (DF) for classification selects a feature and threshold to split feature space in each node that minimize impurity measures. Please select the two impurity measures of DT for classification:_____________ 

Q27. Bonus Question (5 points) The above figure is for the gradient boosting algorithm for regression. Step 1. A new decision tree (DT) is trained with feature X and label r (i.e., residual) to predict the residual. Step 2. The predicted residual in Step 1 is multiplied by the learning rate and is added to the prior predicted The learning rate is between 0 and 1 for slow learning to avoid overfitting. Step 3. The residual is updated by subtracting the new DT in Step 1 multiplied by the learning rate. Step 4. The final predicted Y in the gradient boosting is the additive function of DTs multiplied by the learning       rate in each stage. Overall, gradient boosting is a (1) _____________ (a. parallel learning, b. sequential learning; 2 points). In addition, a new decision tree in each stage is created based on the information from the prior trees to improve performance. Based on the algorithm, which one is not a hyperparameter for gradient boosting? (2)_________ (3 points) the number of trees the maximum depth of each tree learning rate dropout rate the number of splits in each tree