Chapter 16: Using Formulas for Statistical Analysis
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The alpha value, shown in cell H2 on Figure 16.6, is the weight given to the most recent
data point, or 30% in this example. The remaining 70% weight is applied to the rest of the
data points. The second most recent is weighted 30% of the remaining 70% (21%), the third
most recent is weighted 30% of 70% of 70% (14.7%), and so on.
The prior week’s value is multiplied by the alpha value, and that is added to the remaining
percentage multiplied by the prior forecast. The prior forecast already has all of the previ-
ous calculations built into it.
The further away a demand value gets, the less it impacts the exponential smoothing
forecast. In other words, last week’s number is more important than the week before last.
Figure 16.7 shows a chart of the demand, the moving average, and the exponential forecast.
Note how the exponential forecast responds to changes in demand more quickly than the
moving average.
FIGURE 16.7
Demand of a product visualized
Using Functions to Create Descriptive Statistics
Descriptive statistics allow you to present data in quantitative summaries that are simple to
understand. When you sum data, count data, and average data, you’re producing descrip-
tive statistics. In this section, we’ll take a look at some of the functions that you can use
to profile a data set and enable comparisons that can be used in other analyses.