On the Data tab of the Trend Analysis dialog box, specify the data for the analysis, specify the time scale, and select the type of model.
In Y variable, enter a column of numeric data that were collected at regular intervals and recorded in time order. If your data are in multiple columns (for example, you have data for each year in a separate column), you must stack the data into a single column.
In this worksheet, Sales contains the number of computers that are sold each month.
C1 |
---|
Sales |
195000 |
213330 |
208005 |
249000 |
237040 |
(Optional) Enter a column to label the x-axis with values, such as dates. If you don't enter a column, Minitab labels each time period with an integer starting at 1.
The data fits a line, which indicates that the rate of change is uniform over time. The model is Y_{t} = β_{0} + (β_{1} * t) + e_{t}. In this model, β_{1} represents the average change from one period to the next.
The data have a curvature, which indicates that the rate of change varies over time. The model is Y_{t} = β_{0}+ β_{1} * t + (β_{2}* t^{2}) + e_{t}.
The data have a steep curvature, which indicates that the rate of change varies more quickly over time. For example, a savings account might exhibit exponential growth. The model is Y_{t} = β_{0} + (β_{1}^{t}) + e_{t}.
The data has an S-shape, which indicates that the direction of the change varies over time. The model is Y_{t} = (10^{a}) / (β_{0} + β_{1} * β_{2}^{t}).