I need help with this question 1. can you conclude homogeneity of variance- covariance?2. what statistic is most appropriate of the multivariate results? ### Box's Test of Equality of Covariance Matrices#### Data- **Box's M:** 22.491- **F:** 1.060- **df1 (Degrees of Freedom 1):** 21- **df2 (Degrees of Freedom 2):** 801321.764- **Sig. (Significance Value):** .384#### ExplanationBox's Test assesses the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. A significance value (Sig.) greater than 0.05 typically indicates that there is no statistically significant difference among the covariance matrices of the groups, implying equality. Here, with a Sig. value of .384, the null hypothesis likely holds, suggesting the matrices are equal across groups.

Box's M test is generally used in MANOVA - Multivariate Analysis of Variance.It is deployed to check…

Answered: 1. can you conclude homogeneity of…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Related questions

Question

I need help with this question

1. can you conclude homogeneity of variance- covariance?

2. what statistic is most appropriate of the multivariate results?

### Box's Test of Equality of Covariance Matrices

#### Data

- **Box's M:** 22.491
- **F:** 1.060
- **df1 (Degrees of Freedom 1):** 21
- **df2 (Degrees of Freedom 2):** 801321.764
- **Sig. (Significance Value):** .384

#### Explanation

Box's Test assesses the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. A significance value (Sig.) greater than 0.05 typically indicates that there is no statistically significant difference among the covariance matrices of the groups, implying equality. Here, with a Sig. value of .384, the null hypothesis likely holds, suggesting the matrices are equal across groups.

Definition Definition Measure of how two random variables change together. Covariance indicates the joint variability or the directional relationship between two variables. When two variables change in the same direction (i.e., if they either increase or decrease together), they have a positive covariance. When the change is in opposite directions (i.e., if one increases and the other decreases), the two variables have a a negative covariance.

Expert Solution