Select the most accurate statement regarding RiskOptimizer. It provides the same functionality as the optimization engine included with Excel (i.e., Solver). O It generates results that show the sensitivity of simulation outputs to changes in inputs. O It helps an analyst identify the probability distribution that is the best fit of frequency data. O It can be used to find the values of cells in an Excel sheet that maximize the average profit in a simulation of company operations. O It is a data visualization application that is included with @Risk.

Select the most accurate statement regarding RiskOptimizer. It provides the same functionality as the optimization engine included with Excel (i.e., Solver). O It generates results that show the sensitivity of simulation outputs to changes in inputs. O It helps an analyst identify the probability distribution that is the best fit of frequency data. O It can be used to find the values of cells in an Excel sheet that maximize the average profit in a simulation of company operations. O It is a data visualization application that is included with @Risk.

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter23: Simulation With The Excel Add-in @risk

Section: Chapter Questions

Problem 3RP

See similar textbooks

Similar questions

Correct answer will be upvoted else Multiple Downvoted. Computer science. You are given an integer n (n>1). Your assignment is to find a succession of integers a1,a2,… ,ak with the end goal that: every simulated intelligence is completely more prominent than 1; a1⋅a2⋅… ⋅ak=n (I. e. the result of this grouping is n); ai+1 is separable by simulated intelligence for every I from 1 to k−1; k is the most extreme conceivable (I. e. the length of this grouping is the greatest conceivable). In case there are a few such groupings, any of them is adequate. It tends to be demonstrated that somewhere around one substantial grouping consistently exists for any integer n>1. You need to answer t autonomous experiments. Input The primary line of the input contains one integer t (1≤t≤5000) — the number of experiments. Then, at that point, t experiments follow. The main line of the experiment contains one integer n (2≤n≤1010). It is ensured that the amount of n…
A police academy has just brought in a batch of new recruits. All recruits are given an aptitude test and a fitness test. Suppose a researcher wants to know if there is a significant difference in aptitude scores based on a recruits’ fitness level. Recruits are ranked on a scale of 1 – 3 for fitness (1 = lowest fitness category, 3 = highest fitness category). Using “ApScore” as your dependent variable and “FitGroup” as your independent variable, conduct a One-Way, Between Subjects, ANOVA, at α = 0.05, to see if there is a significant difference on the aptitude test between fitness groups. Identify the correct values for dfbetween and dfwithin A. dfbetween = 7 dfwithin = 7 B. dfbetween = 13 dfwithin = 2 C. dfbetween = 2 dfwithin = 13 D. dfbetween = 2 dfwithin = 2
Subject: Machine Learning Question Number 5 : What are Ensemble Algorithms? Write an algorithm for BOOSTING method. Assume that two individuals offer to sell you their predictive models M1 and M2. The confusion matrices produced by each model are as follows. What is the accuracy of each model? Assuming that precision is of paramount importance in your application, which of the two models would you buy? Why?
The Magellan mutual fund has beaten the Standard andPoor’s 500 during 11 of the last 13 years. People use this asan argument that you can “beat the market.” Here’s anotherway to look at it that shows that Magellan’s beating themarket 11 out of 13 times is not unusual. Consider 50 mutualfunds, each of which has a 50% chance of beating the marketduring a given year. Use simulation to estimate theprobability that over a 13-year period the “best” of the 50mutual funds will beat the market for at least 11 out of 13years. This probability turns out to exceed 40%, whichmeans that the best mutual fund’s beating the market 11 outof 13 years is not an unusual occurrence!
In the context of evolutionary computing the goal function is known as the fitnessfunction and the problem is to maximize it. The typical formulation has to be changedin a simple way.min f (x) = − max[− f (x)] (4.9)Another requirement is that the goal function is positive.Phenotype evolution treats x as a phenotype and the goal function as the fitnessfunction. The typical framework for the method is as follows:
Which statements are true about LASSO linear regression? Group of answer choices has embedded variable selection by shrinking the coefficient of some variables to exactly zero. has one hyper-parameter lambda (The regularization coefficient) which needs to be tuned if there are multiple correlated predictors lasso will select all of them adds the L2 norm of the coefficients as penalty to the loss function to penalize larger coefficients
Select what you think is correct (multiple options are possible)? A) Logistic regression is a parametric classification algorithm and decision tree B) Logistic regression is based on a linear combination of parameters as is decision tree C) Logistic regression is based on a linear combination of parameters and a link function called sigmoid D) Decision trees tend to have high bias and low variance that random forests fix E) Decision trees use unsupervised learning
Fill in the bubbles for ALL correct choices: there may be more than one correct choice, but there is always at least one correct choice. Which of the following are true for the k-nearest neighbor (k-NN) algorithm? A: k-NN can be used for both classification and regression. B: As k increases, the bias usually increases. C: The decision boundary looks smoother with smaller values of k. D: As k increases, the variance usually increases.
IN PYTHON Each group is endowed with Rs. 100 crores. Identify a client profile and sets expectations for the client. The investment starting date is 1st July 2022 (buy all stocks in the portfolio) and ending date is 1st July 2023 (sell all stocks in the portfolio). Use your own logic in identifying the right stocks for forming a portfolio and apply different strategies learnt in the classes. Clearly explain why you are selecting a particular stock in your portfolio. As an alternative method, use generative AI for selecting stock and redo the various optimization strategies. How to implement this in R/python? Please provide the code for this exercise..
Hey guys can I get help with the following question: Function 6 Write a function to return the number of false negatives (as an integer) of a logistic regression model Given the training features (X_train), training labels (y_train), testing features (X_test) and testing labels (y_test) ### START FUNCTION 6 def log_reg_fn(X_train, y_train, X_test, y_test): # YOUR CODE HERE ### END FUNCTION 6 log_reg_fn(X_train_c, y_train_c, X_test_c, y_test_c)
Neurons compute? The activation function and the linear function, which is represented by the equation z = Wx + b, are both calculated by neurons. Both a linear function, represented by the equation z = Wx + b, and an activation function are computed by neurons. When g is present in a neuron, the input x is scaled linearly (Wx + b). Before using the result in an activation function, neurons perform a calculation to get the mean of all of their features.
In this section we create the model object and define the loss function and optimizer. For the loss function use nn.CrossEntropyLoss() instead of the mean-squared loss done in class. Use the same optimizer as in class. learning_rate = 0.001 num_epochs = 5 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #this is to use gpu if available # **create your neural net model here, call the variable 'model'** # ** complete following lines of code to define loss function and optimizer #criterion = **complete this** #optimizer = **complete this**