I need help with these questions 1. is the factor interaction significant?2. are the main effects significant?3. does the covariate significantly influence the DV? ### Tests of Between-Subjects Effects**Dependent Variable:** rincom2| Source | Type III Sum of Squares | df | Mean Square | F | Sig. ||------------------|-------------------------|-----|-------------|---------|-------|| **Corrected Model** | 2740.349 | 10 | 274.035 | 19.551 | .000 || **Intercept** | 4462.685 | 1 | 4462.685 | 318.397 | .000 || **hrs2** | 332.343 | 1 | 332.343 | 23.712 | .000 || **sex** | 251.952 | 1 | 251.952 | 17.976 | .000 || **degree** | 1306.097 | 4 | 326.524 | 23.296 | .000 || **sex * degree** | 77.795 | 4 | 19.449 | 1.388 | .237 || **Error** | 9460.866 | 675 | 14.016 | | || **Total** | 150407.000 | 686 | | | || **Corrected Total** | 12201.214 | 685 | | | |### ExplanationThis table presents the results from an ANOVA test that evaluates the effects of several independent variables on the dependent variable "rincom2".- **Corrected Model**: This row aggregates the overall effects of the model. The significant F-value (.000) suggests a statistically significant model. - **Intercept**: This row represents the constant in the model, which is highly significant with a p-value of .000.- **hrs2**: This variable has a significant effect on the dependent variable with F=23.712, p=.000.- **sex**: This variable also significantly affects the dependent variable with F=17.976, p=.000.- **degree**: Across multiple levels (df=4), this variable significantly influences the dependent variable, F=23.296, p=.000.- **sex * degree**:

Answered: I need help with these questions 1.…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

I need help with these questions

1. is the factor interaction significant?

2. are the main effects significant?

3. does the covariate significantly influence the DV?

### Tests of Between-Subjects Effects

**Dependent Variable:** rincom2

| Source | Type III Sum of Squares | df | Mean Square | F | Sig. |
|------------------|-------------------------|-----|-------------|---------|-------|
| **Corrected Model** | 2740.349 | 10 | 274.035 | 19.551 | .000 |
| **Intercept** | 4462.685 | 1 | 4462.685 | 318.397 | .000 |
| **hrs2** | 332.343 | 1 | 332.343 | 23.712 | .000 |
| **sex** | 251.952 | 1 | 251.952 | 17.976 | .000 |
| **degree** | 1306.097 | 4 | 326.524 | 23.296 | .000 |
| **sex * degree** | 77.795 | 4 | 19.449 | 1.388 | .237 |
| **Error** | 9460.866 | 675 | 14.016 | | |
| **Total** | 150407.000 | 686 | | | |
| **Corrected Total** | 12201.214 | 685 | | | |

### Explanation

This table presents the results from an ANOVA test that evaluates the effects of several independent variables on the dependent variable "rincom2".

- **Corrected Model**: This row aggregates the overall effects of the model. The significant F-value (.000) suggests a statistically significant model.

- **Intercept**: This row represents the constant in the model, which is highly significant with a p-value of .000.

- **hrs2**: This variable has a significant effect on the dependent variable with F=23.712, p=.000.

- **sex**: This variable also significantly affects the dependent variable with F=17.976, p=.000.

- **degree**: Across multiple levels (df=4), this variable significantly influences the dependent variable, F=23.296, p=.000.

- **sex * degree**:

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

Step 1: Part 1

From the given ANOVA table it is evident that the p-value (Sig.) for the interaction effect (sex*degree) is 0.237 which is less than 0.05 (significance level). Therefore, the null hypothesis of no interaction effect is not rejected. We can conclude that the factor interaction is not significant.