Note: Please clearly label your answers submission sheet by section (Parts 1-4) and question number so it is straightforward to grade.  Part 4:   True/False/Uncertain with explanation (30 points, 5 each) In this section, answer whether the statement is True, False or Uncertain and explain why. Note: assume that any statements written in italics are true, these are just setting up relevant scenarios. You must explain your answer. Explanation determines the grade!!   Imagine the economy is at potential Y*. With contractionary monetary policy (decreasing M), the Fed can permanently reduce the price level, by accepting a temporary recession (Y

  Which of the following supported Philip II in his creation of a disciplined army and new military technology?  

A researcher wants to know if the number of practice quizzes that a student completes has any impact on their exam grade and sets up an experiment to learn more. Write a null hypothesis for the researcher.

____________ is when children play with similar toys but have no interaction.                

  Which of the following accurately describes Confucius’s teaching?  

  Which of the following most accurately describes the Mahayana School of Buddhism?  

What is the term for ear wax?

  Which of the following was a defining architectural feature of the polis?  

  Which of the following is a second-generation society?  

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; 1 point). 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)_________ (2 points) the number of trees the maximum depth of each tree learning rate dropout rate the number of splits in each tree