Whichof the following statements correctly describe iterated and direct multi period forecasting, respectively? (Check all that apply.)A. In direct multi-period forecasts, regression coefficients are used directly to make forecasts h periods into the future.B. In direct multi-period forecasts, regression coefficients are used directly to make forecasts for the horizon h.C. In iterated multi-period forecasts, one-step ahead forecasts are used as intermediary steps to make forecasts for the horizon h.D. In iterated multi-period forecasts, regression coefficients are used directly to make forecasts h periods into the future. The DF-GLS test for a unit root has higher power than the Dickey-Fuller test.Why should you use a more powerful test?O A. A more powerful test improves the analyst's ability to distinguish between a unit AR root and a root less than 1.O B. A more powerful test is more likely to reject the null hypothesis when the null hypothesis is false.OC. A more powerful test is more efficient.O D. Both (a) and (b) are true.

“Since you have asked multiple questions, we will solve the first question for you. If you want any…

Answered: Which of the following statements…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

LINEAR REGRESSION 2) During the last three months, on 10 occasions a sound equipment store appeared in television commercials to promote your sales. The managers want to investigate whether there is a relationship between the number of commercials broadcast on the weekend and the sales in the following week according to the table (img1) A.Find the coefficient of determination. comment on it B. Find the equation of the regression line and calculate the estimated data for each value of the independent variable. C. Determine the residual variance, the standard error of estimate, and the explained variance. comment them
Plzzz explain
Is economic growth in Asia related to Europe? A linear regression model was constructed to predict the growth in Asian GDP on the basis of growth in European GDP during the years 2000-2020:Response variable: Growth in Asian GDP (%)R2 = 0.265 Term Estimate Intercept 2.19 growth in European GDP (%) 0.61 (a) What is the equation of the regression line? A. growth in Asian GDP = 0.61 + 2.19(growth in European GDP) B. growth in European GDP = 2.19 + 0.61(growth in Asian GDP) C. growth in European GDP = 0.61 + 2.19(growth in Asian GDP) D. growth in Asian GDP = 2.19 + 0.61(growth in European GDP) (b) What is the meaning of the slope? (Growth in European GDP/ Growth in Asian GDP) is predicted to increase by (0.61% / 2.19%) for each additional 1% growth in (Asian GDP/ European GDP).(c) What is the meaning of the intercept? (Growth in European GDP/ Growth in Asian GDP) is predicted to equal (0.61% / 2.19%) when European GDP Growth= 0% / Asian GDP Growth= 0%).
The regression results obtained for the models: Model A: Balance Model B: Balance = = Model C: Balance = Variable Intercept are summarized in the following table. Time Prime Timex Prime 60 +61Prime + ε 60 +61 Time + 62 Prime + 63 Time × Prime + ɛ 60 +61 Prime + 6₂Time x Prime + ε, Model A 88,020 (t = 77.89) N/A -18,000 (t -11.26) N/A = 1,532,480,000 Model B 90,269 (t = 24.35) -148 (t H -0.64) -28,493 (t -5.36) 662 (t 2.03) = 0.7254 0.7198 F • 1,369,126,091 Model C 88,020 (t N/A -26,244 (t = -6.66) 514 (t = 2.27) 0.7547 0.7388 81.19) SSE R² Adjusted R² Note: The values of relevant test statistics are shown in parentheses beside the estimated coefficients. Using Model B, which of the following is the alternative hypothesis for testing the significance of Time? 1,381,128, 299 0.7526 0.7421
The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers): A.What would be the typical linear regression equation for the number of sales? B.Make the sales forecast for period 15 of next year. C. Make the sales forecast for period 17 of next year. D. What is the standard error for the data?
Plzz explain..
c. What are the 2 main pieces of information you get from the linear regression model? Should I go back to the data set and recalculate slope and y intercept if I already have determined the equation of the trend line?
help!
Question in comment
The city wants to further develop the model to include the weather conditions of rainy, cloudy, or sunny. Ice Rink Attendance Temperature (°F) Weather Condition 140 45 Sunny 110 40 Cloudy 135 50 Cloudy 120 40 Cloudy 165 45 Sunny 180 45 Sunny 210 50 Sunny 190 45 Cloudy 170 40 Cloudy 130 45 Rainy 140 45 Cloudy 80 35 Rainy 120 45 Cloudy 100 45 Rainy What is the regression equation? Use the equation to forecast ice rink attendance if the temperature is 38°F and sunny. Should you keep weather condition in the model?
D. Calculate a linear regression using Stabley. Write the equation below. E. What is the correlation coefficient? f. What is the slope of your LSRL? Interpret the slope using context. G. What is the Y- intercept of your LSRL? Interpret the Y-intercept using context
Show step by step on EXCEL. I am having a hard time on this.

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS