What is the main assumption of the ratio-to-moving-averages method?
The seasonal component is additive.
The ratio-to-moving-averages method assumes that the seasonal component is additive, meaning that the seasonal effects are added to the trend component to generate the time series data. This assumption is crucial when decomposing time series data into its components.
Similar Questions
For a monthly data, the link relative for any month is given by:
The following frequency distribution is of which type?
Class and Frequency Table
Class / वर्ग Frequency / आवृत्ति 0-5 4 0-10 7 0-15 11 0-20 16 0-25 23 Compute the standard deviation of the following numbers: 5, 6, 4, and 2
The last period's sales forecast was Rs 100 lakhs and demand was Rs 90 lakhs. What is the simple exponential smoothing sales forecast (in Rs lakhs) with alpha of 0.4 for the next period?
A physical instructor claims that the mean weight of students in school is greater than 82 kg with standard deviation 20. If a sample of size 81 students is selected with mean weight of 90. The test statistic equals to
Monthly sales data (in units) shows the following trend-adjusted ratios for three months: January (1.1), February (0.9), and March (1.0). What is the average seasonal index for this quarter?
Important Questions from Statistical Averages
For a monthly data, the link relative for any month is given by:
The following frequency distribution is of which type?
Class and Frequency Table
Class / वर्ग Frequency / आवृत्ति 0-5 4 0-10 7 0-15 11 0-20 16 0-25 23 Compute the standard deviation of the following numbers: 5, 6, 4, and 2
The last period's sales forecast was Rs 100 lakhs and demand was Rs 90 lakhs. What is the simple exponential smoothing sales forecast (in Rs lakhs) with alpha of 0.4 for the next period?
Two continuous random variables X and Y are related as
Y = 2X + 3
Let \(\sigma_X^2\) and \(\sigma_Y^2\) denote the variances of X and Y, respectively. The variances are related as
