In order to forecast the stock price of Google, a financial analyst used two differentforecasting methods: an ARIMA method, and a GARCH method. The results aresummarized in the following table:ARIMA MethodYear Actual Value Forecast Value Year20212021202220222023202310011011598105118GARCH MethodActual Value Forecast Value100110115For each method compute the following accuracy measures:1. Mean square error (MSE)2. Root-Mean-Squared Error (RMSE).Which forecasting method would you recommend? Why?99104116

The order to forecast the stock price of Google, a financial analyst uses to different forecasting…

Answered: In order to forecast the stock price of…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The Management Department recently purchased a smallcopier for faculty use. Although the workload of the office staff has improved somewhat, the secretaries are still mak-ing too many trips to the Dean’s office when the depart-mental copier is out of service. Sylvia, the departmental secretary, has been keeping track of failure rates and ser-vice times. With a mean time between failures of 100 hours and a mean time to repair of 24 hours, how much of thetime is the new copier available for faculty use?
A veterinarian wants to perform a study at a local animal shelter where 200 dogs need training on behavior modification. The purpose of the study would be to determine if using a firm voice during training for 10 minutes improves a dog's learning behavior compared to not speaking during training for 10 minutes. Part A: Describe an appropriate design for the study. Part B: The hypotheses for this study are as follows: Ho: There is no difference in the mean improvement of dogs during behavior modification for either treatment. Ha: The mean improvement of dogs during behavior modification is greater for the firm voice treatment. The shelters will allow trainers to use a firm voice during training if the null hypothesis is rejected. What are the possible Type I and II errors? Describe the consequences of each in the context of this study and discuss which type you think is more serious.n
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In output data analysis, autocorrelation and initialization bias are the potential issues to overcome. Select one: True False
Suppose researchers at an abdominal transplant clinic are concerned about the rate of graft loss due to diabetes status prior to receiving a donor kidney. Research has shown that gender discordance, or receiving a gender from a donor of an opposite gender may increase the risk of both exposure and outcome after transplant. Assume the following tables represent the stratified analysis of the potential confounding variable. (9 points) Gender Discordance Graft Failure No Graft Failure Total Diabetes II 23 10 33 No Diabetes II 4 44 48 Total 27 54 81 Gender Concordance Graft Failure No Graft Failure Total Diabetes II 9 34 43 No Diabetes II 12 87 99 Total 21 121 142 A) Calculate the stratum specific estimates for the odds ratios in each strata. B) Observe the difference in the odds ratios. Based on observation alone, what are we likely to conclude regarding the relationship between outcome and exposure…
Name the forecasting methods
The dataset HollywoodMovies - 2e contains information on 970 movies that came out of Hollywood between 2007 and 2013. One of the variables is the budget (in millions of dollars) to make the movie. What would be the relevant parameter and statistic for this problem (think -- is this categorical or quantitative)? O u: The mean budget of all Hollywood Movies made between 2007 and 2013. O p: The average proportion of budgets of Hollywood Movies made between 2007 and 2013. T: The mean budget of all Hollywood Movies made between 2007 and 2013. Op: The proportion of Hollywood Movies made between 2007 and 2013.
HDI revisited The United Nations DevelopmentProgramme (UNDP) uses the Human Development Index (HDI) in an attempt to summarize in one number the prog-ress in health, education, and economics of a country. The number of phone subscribers per 100 people is positivelyassociated with economic progress in a country. Can thenumber of phone subscribers be used to predict the HDI?Here is a scatterplot of HDI against phone subscribers:a) Explain why fitting a linear model to these data mightbe misleading.b) If you fit a linear model to the data, what do you think ascatterplot of residuals vs. predicted HDI will look like?
A research firm regularly conducts customer satisfaction surveys for an amusement park. A survey of 200 randomly selected park visitors on a weekday in May concluded the average age of visitors to the park was 39 years. When the survey was repeated with 200 randomly selected park visitors on a Saturday in May, the average age of visitors to the park was determined to be 17 years. Does the information represents a quantitative or qualitative variable? What is the age of the visitor? Does the visitor have a season pass?
A psychology student conducted a study on using a chief executive officer's facial structure to predict a firm's financial performance. The facial width-to-height ratio (WHR) for each in a sample of 55 CEOs at publicly traded firms was determined. The sample resulted in x=1.74 and s=2.02. The student wants to predict the financial performance of a firm based on the value of the true mean facial WHR of CEOs. The student wants to use the value of μ=2.3. Do you recommend he use this value? Conduct a test of hypothesis for μ to help you answer the question. Specify all the elements of the test, including H0, Ha, test statistic, p-value, and your conclusion. Test at α=0.10. Find the p-value. p-value=enter your response here (
The questions below are based on a dataset containing the characteristics of 5,407households. In particular, we will use the following variables:- size: home size, measured in square feet.- hhinc: annual household net income, measured in euros.- owner: dummy variable equal to 1 if the living space is owned, and 0 if the livingspace is rented.- hhsize1: dummy variable equal to 1 if the number of household’s members is 1 or2, and 0 otherwise.- hhsize2: dummy variable equal to 1 if the number of household’s members is 3 or4, and 0 otherwise.- hhsize3: dummy variable equal to 1 if the number of household’s members is 5 orabove 5, and 0 otherwise.- edu: education level of household’s members. We conduct a simple regression of size on hhinc, now using robust standard errors.The regression output is reported in Table 2. Why are the estimated coefficients inTable 2 equal to the estimated coefficients in Table 1? Do the conclusions on thestatistical significance at 5% of the coefficient of…

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