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.

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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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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?
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?
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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?
A statistical program is recommended. Consider the following data for two variables, x and y. 24 26 30 22 35 40 y 13 22 33 34 41 35 (a) Develop an estimated regression equation for the data of the form ŷ = b, + b,x. (Round b, to one decimal place and b, to three decimal places.) (b) Use the results from part (a) to test for a significant relationship between x and y. Use a = 0.05. Find the value of the test statistic. (Round your answer to two decimal places.) F = Find the p-value. (Round your answer to three decimal places.) p-value = Is the relationship between x and y significant? O Yes, the relationship is significant. O No, the relationship is not significant. (c) Develop a scatter diagram for the data. y 45 y 45 y 45 y 45 T 40 40- 40 40 35 35- 35 35 30 30 30 30 25 25 25 25 20 20 20 20 15 15 15 15 10 10 10 10 5. 5. 5. 5 20 25 30 35 40 45 20 25 30 35 40 45 20 25 30 35 40 45 20 25 30 35 40 45 Does the scatter diagram suggest an estimated regression equation of the form ý = b, + b,x…
KidsFeet Regression Line y-Intercept The KidsFeet dataframe contains data collected on 39 fourth grade students in Ann Arbor, MI, in October 1997. Two of the measurements taken on the children were the length in centimeters, (length), and width in centimeters, (width), of their longest foot. This data could be used to answer the following Research Question: How is the width of a fourth-grade student's foot related to the length? Which of the following is the y-intercept of the regression line? ## ## Simple Linear Regression## ## Correlation coefficient r = 0.6411 ## ## Equation of Regression Line:## ## length = 9.817 + 1.658 * width ## ## Residual Standard Error: s = 1.025 ## R^2 (unadjusted): R^2 = 0.411 ( ) 1.0248 ( ) 1.6576 ( ) 22.8153 ( ) 0.411 ( ) 9.8172 ( )0.6411
Simple linear regression model helps to adjust out the effects of confounding factors in a statistical analysis. A. TrueB. FalseC. None of the above choices
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 99% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $818. The equation of the regression line is y = 1.451x - 40.698. Wages (state), x Wages (federal), y 713 760 785 805 851 870 921 926 947 985 1,002 1,045 1,091 1,145 1,185 1,239 1,281 1,307 1,320 1,400 Construct and interpret a 99% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $818. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. We can be 99% confident that when the average weekly wages of state government employees is $818, the average weekly wages of federal government employees will be between $ and…

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