Enter your data for Trend Analysis (Data tab)

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.

Enter your data

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

Time scale labels

(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.

Type of model

Under Type of model, specify the type of model that matches the trend in your data. To choose between these four models:
  • Graph the data using a time series plot. Then, compare your plot to the following figures to determine the correct type of model.
  • You can fit the four models and compare the measures of accuracy (MAPE, MAD, and MSD). Choose the model with the smallest accuracy measures.
Linear

The data fits a line, which indicates that the rate of change is uniform over time. The model is Yt = β0 + (β1 * t) + et. In this model, β1 represents the average change from one period to the next.

Quadratic

The data have a curvature, which indicates that the rate of change varies over time. The model is Yt = β0+ β1 * t + (β2* t2) + et.

Exponential growth

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 Yt = β0 + (β1t) + et.

S-curve (Pearl-Reed logistic)

The data has an S-shape, which indicates that the direction of the change varies over time. The model is Yt = (10a) / (β0 + β1 * β2t).

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