Submit your answers to the following questions using the ANOVA source table below. The table depicts a two-way ANOVA in which gender has two groups (male and female), marital status has three groups (married, single never married, divorced), and the means refer to happiness scores (n = 100):

What is/are the independent variable(s)? What is/are the dependent variable(s)?
What would be an appropriate null hypothesis? Alternate hypothesis?
What are the degrees of freedom for 1) gender, 2) marital status, 3) interaction between gender and marital status, and 4) error or within variance?
Calculate the mean square for 1) gender, 2) marital status, 3) interaction between gender and marital status, and 4) error or within variance.
Calculate the F ratio for 1) gender, 2) marital status, and 3) interaction between gender and marital status.
Identify the criterion Fs at alpha = .05 for 1) gender, 2) marital status, and 3) interaction between gender and marital status.
If alpha is set at .05, what conclusions can you make?
Source Sum of Squares (degrees of freedom [df]) Mean Square Fobt. Fcrit.
Gender 68.15 ? ? ? ?
Marital Status 127.37 ? ? ? ?
Gender * Marital Status (A x B) 41.90 ? ? ? ?
Error (Within) 864.82 ? ? NA NA
Total 1102.24 99 NA NA NA

To answer the questions using the ANOVA source table, let's go step by step:

1) The independent variable(s) in this study are gender and marital status. Gender has two groups (male and female), while marital status has three groups (married, single never married, divorced).

2) The dependent variable in this study is the happiness scores.

3) The null hypothesis for each factor can be stated as follows:
- For gender: There is no difference in happiness scores between males and females.
- For marital status: There is no difference in happiness scores between married, single never married, and divorced individuals.
- For the interaction between gender and marital status: The effect of gender on happiness scores is the same across all marital status groups.

The alternative hypothesis would be the opposite of the null hypothesis for each factor.

4) The degrees of freedom for the factors can be found in the ANOVA source table:
- Gender: df = 1 (two groups minus one)
- Marital Status: df = 2 (three groups minus one)
- Interaction between gender and marital status: df = 2 (two groups minus one)
- Error or within variance: df = 93 (total df - sum of df for all factors)

5) The mean square for each factor can be calculated by dividing the sum of squares (SS) by the respective degrees of freedom (df):
- Mean Square for Gender = SS for Gender / df for Gender
- Mean Square for Marital Status = SS for Marital Status / df for Marital Status
- Mean Square for Interaction between Gender and Marital Status = SS for Interaction / df for Interaction
- Mean Square for Error or within variance = SS for Error / df for Error

6) The F ratio for each factor can be calculated by dividing the mean square of the factor by the mean square for error (within variance):
- F ratio for Gender = Mean Square for Gender / Mean Square for Error
- F ratio for Marital Status = Mean Square for Marital Status / Mean Square for Error
- F ratio for Interaction between Gender and Marital Status = Mean Square for Interaction / Mean Square for Error

7) The criterion Fs at alpha = .05 can be obtained from an F-distribution table or calculated using statistical software. Let's assume the Fcrit is 4.00 for this example.

8) If alpha is set at .05, we can make conclusions based on the F ratios and Fcrit:
- For gender: If the calculated F ratio is greater than the Fcrit (4.00), we reject the null hypothesis and conclude that there is a significant difference in happiness scores between males and females.
- For marital status: If the calculated F ratio is greater than the Fcrit (4.00), we reject the null hypothesis and conclude that there is a significant difference in happiness scores between the marital status groups.
- For the interaction between gender and marital status: If the calculated F ratio is greater than the Fcrit (4.00), we reject the null hypothesis and conclude that the effect of gender on happiness scores differs across the marital status groups.

Note: Since the table is incomplete (missing values for degrees of freedom, mean square, Fobt, Fcrit), the specific conclusions and final statistical decisions cannot be determined without the complete information.