Assume you first collected a quarterly sales data (in millions of dollars) over a four-year period from the first quarter 2016 to the fourth quarter 2019 then computed (the normalised) seasonal index for each quarter using ratio-to-moving-average method, and finally fitted a linear trend model based on the deseasonalised sales data and a time period (X), coded as 0, 1, ......, n. If the estimated coefficients of the intercept and X (time period) are 153.93 and 6.01, respectively, what is the seasonally unadjusted forecast value of sales (in millions of dollars) for the first quarter of 2020? Round your final answer to two decimal places. Assume the (normalised) seasonal indices for the quarterly sales data are 1.04, 1.02, 0.96 and 0.98 for quarters 1, 2, 3 and 4, respectively.

Assume you collected a quarterly sales data (in millions of dollars) over a four-year period from the first quarter 2016 to the fourth quarter 2019, and computed the seasonal index for each quarter. If the (normalised) seasonal index for the third quarter is 0.96, then we can interpret it as follows. a. In the third quarter, sales are 4% below the annual average. b. There is a 96% difference in sales between the third quarter and the fourth quarter. c. In the third quarter, sales are 96% below the annual average. d. There is a 4% difference in sales between the first quarter and the third quarter.

Assume you collected a quarterly sales data (in millions of dollars) over a four-year period from the first quarter 2016 to the fourth quarter 2019, and computed the seasonal index for each quarter. If (actual) sales were 221 in the fourth quarter of 2018 and the (normalised) seasonal index for the fourth quarter was 0.98, what is the deseasonalised sales value (in millions of dollars) in the fourth quarter of 2018? Round your answer to two decimal places.

In ratio to moving average method for seasonal indices, the ratio of an observed value to the moving average measure the influence of _________. a. both trend and cyclical variation b. both seasonal and irregular variation c. cyclical variation d. trend

What is the purpose of the seasonal adjustment technique in time series analysis?Review LaterTo remove trend from the time seriesTo remove seasonality from the time seriesTo remove noise from the time seriesTo smooth out the time series data

A retail company collects sales data at its stores on a daily basis. The shops open 7 days per week. They have found by plotting the data that sale figures on Saturday and Sunday are higher than other days of the week. If they would like to fit a time series regression model with trend and seasonality components, how many seasonal dummies do they need to create? [Answer as an integer]