In business schools it is common to teach statistics courses using Microsoft Excel, due to its wide accessibility and the familiarity of business students with the software. There is a large debate regarding this practice, but at this point the reality is clear: the figure that I am familiar with is about 50% of basic stat courses in b-schools use Excel and 50% use statistical software such as Minitab or JMP. Another trend is moving from offline software to “cloud computing” — Software such as www.statcrunch.com offer basic stat functions in an online, collaborative, social-networky style. Following the popularity of … Continue reading Google Spreadsheets for teaching probability?
In my recent book Practical Time Series Forecasting: A Practical Guide, I included an example of using Microsoft Excel’s moving average plot to suppress monthly seasonality. This is done by creating a line plot of the series over time and then Add Trendline > Moving Average (see my post about suppressing seasonality). The purpose of adding the moving average trendline to a time plot is to better see a trend in the data, by suppressing seasonality. A moving average with window width w means averaging across each set of w consecutive values. For visualizing a time series, we typically use a centered moving average … Continue reading Moving Average chart in Excel: what is plotted?
Visualizing a time series is an essential step in exploring its behavior. Statisticians think of a time series as a combination of four components: trend, seasonality, level and noise. All real-world series contain a level and noise, but not necessarily a trend and/or seasonality. It is important to determine whether trend and/or seasonality exist in a series in order to choose appropriate models and methods for descriptive or forecasting purposes. Hence, looking at a time plot, typical questions include: is there a trend? if so, what type of function can approximate it? (linear, exponential, etc.) is the trend fixed throughout the period … Continue reading Visualizing time series: suppressing one pattern to enhance another pattern