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- Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? Is there an indication of a seasonal pattern? b. Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. c. Compute the quarterly forecasts for next year.Use the following returns for X and Y. Returns Year X Y 1. 21.8% 26.4% 2. -16.8 -3.8 3. 9.8 28.4 4. 19.8 -14.6 5. 4.8. 32.4 a. Calculate the average returns for X and Y. (Do not round intermediate calculations and enter your answers as a percent rounded to 2 decimal places, e.g., 32.16.) b. Calculate the variances for X and Y. (Do not round intermediate calculations and round your answers to 6 decimal places, e.g., 32.161616.) c. Calculate the standard deviations for X and Y. (Do not round intermediate calculations and enter your answers as a percent rounded to 2 decimal places, e.g., 32.16.)A linear regression model is Units = 4,004 – 0.659×Week. For week 46, what is the forecast for the number of units? Round your answer to the nearest whole number. units
- A regression line has an intercept value of 174.71 and slope value of 25.22. What is the predicted value (the y-value) in the 100th period (the x-value is 100)? Round to two decimal places.Use the following returns for X and Y. Year 12345 Returns X 21.6% -16.6 9.6 19.2 4.6 Y 25.8% -3.6 27.8 -14.2 31.8 a. Calculate the average returns for X and Y. Note: Do not round intermediate calculations and enter your answers as a percent rounded to 2 decimal places, e.g., 32.16. a. Average return b. Variance c. Standard deviation b. Calculate the variances for X and Y. Note: Do not round intermediate calculations and round your answer to 6 decimal places, e.g., .161616. c. Calculate the standard deviations for X and Y. Note: Do not round intermediate calculations and enter your answers as a percent rounded to 2 decimal places, e.g., 32.16. X % % % %Calculate the standard deviation of this scenario Outcome 1: Recession. Probability = 40% . Return = 7.38%. Outcome 1: Recovery. Probability = 60%. Return = 17.27 %. Answer in % terms w/o % sign and to 4 decimal places (1.2345)
- Boom Nasional Berhad, find the standard deviation. PROBABILITY RETURN 0.35 8% 0.20 3% 0.20 20% 025 15%The following table illustrates the regression results from regressing monthly IBM excess returns on monthly S&P 500 excess returns. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations Intercept Excess Market 0.517 0.268 ANOVA O 0.264 Regression Residual Total O 0.422 0.517 0.268 0.264 0.080 180.000 df 1.000 178.000 179.000 Coefficients 0.001 1.120 SS MS 0.422 0.422 1.153 0.006 1.575 What is the estimated correlation between excess IBM and market returns? F 65.085 Standard Error t Stat P-value 0.006 0.237 0.813 0.139 8.068 0.000 Significance F 0.000Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2. 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. Compute the quarterly forecasts for next year based on the model you developed in part (b). Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, … t = 12 for quarter 4 in year 3. Compute the quarterly forecasts for next year based on the model you developed in part (d). Is the model you developed in part (b) or the model you developed in part (d) more effective? Justify your answer.
- Find the APR using the formula APR= 2nr/n+1 when n= 36 and r= 8%The returns on share A follow the market model with coefficients CA = 0.01, BA = 1.25. If at time t, K MT = 0.02 and the actual return on share A is 0.025, calculate EAt (the error term). 2. %3D %3D1)Find the forecast for 2012 using the time series trend method. Period Demand 2007 200 2008 150 2009 125 2010 100 2011 50 2) Find the forecast for all possible periods N=3, using the data and the moving average method.