Load the carsmall data set.
The variable Model_Year contains data for the year a car was manufactured, and the variable Cylinders contains data for the number of engine cylinders in the car. The Acceleration and Displacement variables contain data for car acceleration and displacement.
Use the table function to create a table of factor values from the data in Model_Year and Cylinders.
Create a matrix of response variables from Acceleration and Displacement.
Perform a two-way MANOVA using the factor values in tbl and the response variables in y.
maov =
2-way manova
Y1,Y2 ~ 1 + Year + Cylinders
Source DF TestStatistic Value F DFNumerator DFDenominator pValue
_________ __ _____________ ________ ______ ___________ _____________ __________
Year 2 pillai 0.084893 2.1056 4 190 0.081708
Cylinders 2 pillai 0.94174 42.27 4 190 2.5049e-25
Error 95
Total 99
Properties, Methods
maov is a two-way manova object that contains the results of the two-way MANOVA. The output displays the formula for the MANOVA model and a MANOVA table. In the formula, the car acceleration and displacement are represented by the variables Y1 and Y2, respectively. The MANOVA table contains a small p-value corresponding to the Cylinders term in the MANOVA model. The small p-value indicates that, at the 95% confidence level, enough evidence exists to conclude that Cylinders has a statistically significant effect on the mean response vector. Year has a p-value larger than 0.05, which indicates that not enough evidence exists to conclude that Year has a statistically significant effect on the mean response vector at the 95% confidence level.
Use the barttest function to determine the dimension of the space spanned by the mean response vectors corresponding to the factor Year.
The output shows that the mean response vectors corresponding to Year span a point, indicating that they are not statistically different from each other. This result is consistent with the large p-value for Year.