Answer:
B. It gives equal weight to all values in the computation.
Step-by-step explanation:
Moving averages are a commonly used technique in time series analysis, which involves studying data that is collected over time. A moving average is a statistical measure that is used to smooth a series of data points by taking the average of a set of consecutive data points. There are several different types of moving averages, but one of the most common is the simple moving average, which is calculated by taking the sum of a set of data points and dividing it by the number of points in the set.
One of the key characteristics of moving averages is that they give greater weight to more recent data. This means that more recent data points in the series will have a larger impact on the moving average than older data points. For example, if we are using a simple moving average with a window size of three, the most recent data point in the series will be given a weight of 1, the second most recent data point will be given a weight of 0.5, and the third most recent data point will be given a weight of 0.33. This means that the more recent data points will have a greater impact on the moving average than the older data points. Therefore, statement B, "It gives equal weight to all values in the computation," is not true.