Below are the quarterly gross domestic product (GDP) data of country X from 1980 to 1989....

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Below are the quarterly gross domestic product (GDP) data of country X from 1980 to 1989. Create a SAS Studio dataset/SAS Forecast Studio project for the analytics tasks below. Quarter 1980Q1 1980Q2 1980Q3 1980Q4 1981Q1 1981Q2 1981Q3 1981Q4 1982Q1 1982Q2 1982Q3 1982Q4 1983Q1 1983Q2 1983Q3 1983Q4 1984Q1 1984Q2 1984Q3 1984Q4 GDP 2650.1 2643.9 2705.3 2832.9 2953.5 2993.0 3079.6 3096.3 3092.9 3146.2 3164.2 3195.1 3254.9 3367.1 3450.9 3547.3 3666.9 3754.6 3818.2 3869.1 Quarter 1985Q1 1985Q2 1985Q3 1985Q4 1986Q1 1986Q2 1986Q3 198604 1987Q1 1987Q2 1987Q3 1987Q4 1988Q1 1988Q2 1988Q3 1988Q4 1989Q1 1989Q2 1989Q3 1989Q4 GDP 3940.0 3997.5 4076.9 4140.5 4215.7 4232.0 4290.2 4336.6 4408.3 4494.9 4573.5 4683.0 4752.4 4857.2 4947.3 5044.6 5139.9 5218.5 5277.3 5340.4 (a) Determine the trend of the series by applying linear regression for curve fitting (b) Evaluate whether there are any seasonal effects in this time series by creating a new quarterly variable as the independent variable of a linear regression. (c) Combine the trend and seasonal variables that you have used in (a) and (b) to construct a joint linear regression model for the GDP. Comment on whether the findings regarding the trend and seasonal effects in (a) and (b) are still relevant in this joint effect model. Below are the quarterly gross domestic product (GDP) data of country X from 1980 to 1989. Create a SAS Studio dataset/SAS Forecast Studio project for the analytics tasks below. Quarter 1980Q1 1980Q2 1980Q3 1980Q4 1981Q1 1981Q2 1981Q3 1981Q4 1982Q1 1982Q2 1982Q3 1982Q4 1983Q1 1983Q2 1983Q3 1983Q4 1984Q1 1984Q2 1984Q3 1984Q4 GDP 2650.1 2643.9 2705.3 2832.9 2953.5 2993.0 3079.6 3096.3 3092.9 3146.2 3164.2 3195.1 3254.9 3367.1 3450.9 3547.3 3666.9 3754.6 3818.2 3869.1 Quarter 1985Q1 1985Q2 1985Q3 1985Q4 1986Q1 1986Q2 1986Q3 198604 1987Q1 1987Q2 1987Q3 1987Q4 1988Q1 1988Q2 1988Q3 1988Q4 1989Q1 1989Q2 1989Q3 1989Q4 GDP 3940.0 3997.5 4076.9 4140.5 4215.7 4232.0 4290.2 4336.6 4408.3 4494.9 4573.5 4683.0 4752.4 4857.2 4947.3 5044.6 5139.9 5218.5 5277.3 5340.4 (a) Determine the trend of the series by applying linear regression for curve fitting (b) Evaluate whether there are any seasonal effects in this time series by creating a new quarterly variable as the independent variable of a linear regression. (c) Combine the trend and seasonal variables that you have used in (a) and (b) to construct a joint linear regression model for the GDP. Comment on whether the findings regarding the trend and seasonal effects in (a) and (b) are still relevant in this joint effect model.

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Determine the trend of the series by applying linear regression for curve fitting Year Quarter GDP 1... View the full answer