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!

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!

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter24: Forecasting Models

Section24.8: Multiple Regression

Problem 9P

See similar textbooks

Similar questions

Suppose you have to select one project partner from a set of four classmates, who have different GPAs. Assume you do not know any student’s GPA in advance but can get to know it after you have picked a student from that group (a) Suppose you pick one of the four students at random and accept that student as your project partner. What is the probability that your partner is the one with the highest GPA? (b) Suppose you decide to reject the first student and to then accept the next student if and only if that student has a higher GPA. Note that you MUST have a partner, so if the first three are rejected by you, then you have to accept the fourth student. What is the probability that your partner will be the one with the highest GPA.
In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535
Solve the problem using STEPWISE Method. Remember to use the step by step procedures (Step 1-7)
I need the answer as soon as possible
Suppose you are working as a data scientist for an online retailer, and you are tasked with optimizing the pricing strategy for a new product. The product is currently priced at $50, but you want to determine whether a higher or lower price would result in greater profit. You estimate that the demand for the product can be modeled using a normal distribution with a mean of 100 units per day and a standard deviation of 20 units per day. The cost of producing each unit is $30, and the retailer's profit is the difference between the revenue and the cost. The retailer incurs a fixed cost of $1000 per day for operating expenses, regardless of the number of units sold. Use simulation to answer to the question.
You decide to run a simpler model to predict churn, using only the variables tenure (in months) and TotalCharges (in US$). The output is given below. The AIC of this model is 4727.6 (in contrast to the AIC of 4240 for the full model). On the basis of this which model would be expected to give superior predictive performance? Actual ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 2.471e-01 5.360e-02 4.611 4.01e-06 *** ## tenure < 2e-16 *** -1.124e-01 5.816e-03 -19.334 ## TotalCharges 8.236e-04 5.618e-05 14.660 < 2e-16 *** ## No --- ## Signif. codes: 0 ## Yes Yes ## Null deviance: 5701.5 on 4921 ## Residual deviance: 4721.6 on 4919 ## AIC: 4727.6 515 345 ## (Dispersion parameter for binomial family taken to be 1) ## Predicted ***** No 795 3267 0.001 Confusion Matrix (Training) **** Actual 0.01 Yes No degrees of freedom degrees of freedom Yes The simpler model (with just tenure and TotalCharges) The full model (with all variables) 0.05 0.1 220 145 Predicted No 339…
Draw an ER model based on following scenario.A travel agency has different bus stations at different location. Each station has a unique name, locationand buses. Each bus has a unique bus number, color and type. The bus takes routes between stations.Each route has a name, arrival time and departure time. A single bus can take routes to different stations.Some buses may not take any route in some days however a station must have a route each day. Eachbus contains seats. Each seat has unique number, row and seat type. There are three different types ofseats as first class, standard and economy. Passengers can make reservations. Passenger has id number,full name, age, gender and address. Each reservation will have a unique reservation number, fare, dateand time. Each reservation can have any numbers of seats. The reservation is dependent on passenger.
1.) We want to build a model to predict the weight (in Ibs) of a car. This prediction will be based on multiple features of the car, such as: "Number of Cylinders", "Miles Per Gallon", "Production Year", etc. To train the model, we are given 1000 examples of cars along with the feature values and class for each car. What technique could we use in this case? Multiple Linear Regression Simple Linear Regression O K-Means Clustering K-Nearest Neighbors
In a database describing 100 examples of printer failures, 75 are hardware failures and 25 are driver failures. Of the hardware failures, 15 had Windows. Of the driver failures, 15 had Windows. If the probability of a driver failure is 25/100, the probability that the system where a failure occurred was Windows is 30/100 and the probability of a failure in the Windows system given it has been caused by the driver is 15/25, what is the probability that a failure has been caused by a driver knowing that the system is Windows?
Electronic Spreadsheet Applications Compare What-If Analysis using Trial and Error and Goal Seek to the given scenario: Let's say a student is enrolled in an online class at a learning institution for a semester. His overall average grade stands at 43% in the course (Term Grade is 45%, Midterm Grade is 65%, Class Participation is 62% and Final Exam is 0%). Unfortunately, he missed his Final Exam and was given 0%. However, he has the opportunity to redo his Final Exam and needs at least an overall average of 60% to pass the course. How can you use Trial and Error and Goal Seek to find out what is the lowest grade he needs on the Final Exam to pass the class? Which method worked best for you and why?
Medical records show a sample population of 1000 people, of those 1000 people, 98% do not have a terminal illness and 2% do have a terminal illness. A Health Insurance company would like try out a new cheaper test for terminal illness. Their results show that 98% of the people that do have a terminal illness test positive, while 1% of the people who do not have a terminal illness test positive for one. A corporation known as Ken’s Kids is concerned about patients that are slipping through the cracks with this new medical testing. If the new medical testing is adopted, what % of the people will be misdiagnosed as not having a terminal illness, but really have one? Assuming a population of 200 million people, how many people that have a terminal illness, given this new testing will never know that they do? (Please show all work , and have a legend for symbols).
Depending on the cause of death, racial/ethnic and gender disparities exist on years of potential life lost in the United States. Years of Potential Life Lost (YPLL) can be defined as the number of years lost when death occurs before one’s life expectancy. For this assignment, life expectancy will be set for everyone at 75. For YPLL, a death of a very young person is weighted more than a death of a very old person (Seabert, McKenzie, & Pinger, 2022). AS Instructions: Investigate one cause of death (infant mortality, homicide, suicide, unintentional injuries (such as car accidents, drowning, falling, etc.), pregnancy complications, COVID-19, etc.) in the United States and research the YPLL for racial/ethnic groups and genders. Summarize your findings in your paper and discuss how calculating YPLL can change the way we think about that particular cause of death differently. Provide factual documentation that reinforces your findings from at least three peer reviewed sources…